From 69e646ddeac1919f88cff631925e276c1ab4b6ef Mon Sep 17 00:00:00 2001 From: exdysa <91800957+exdysa@users.noreply.github.com> Date: Sun, 21 Dec 2025 16:24:07 -0500 Subject: [PATCH] ~migrate autodoc --- docs/index.html | 7 - docs/search.js | 46 - docs/zodiac.html | 486 ---- docs/zodiac/graph.html | 1057 --------- docs/zodiac/providers.html | 246 -- docs/zodiac/providers/constants.html | 2633 --------------------- docs/zodiac/providers/pools.html | 1319 ----------- docs/zodiac/providers/proto_class.html | 277 --- docs/zodiac/providers/registry_entry.html | 907 ------- docs/zodiac/streams.html | 251 -- docs/zodiac/streams/class_stream.html | 549 ----- docs/zodiac/streams/media_stream.html | 470 ---- docs/zodiac/streams/model_stream.html | 625 ----- docs/zodiac/streams/plot_stream.html | 340 --- docs/zodiac/streams/task_stream.html | 712 ------ docs/zodiac/streams/token_stream.html | 503 ---- docs/zodiac/toga.html | 246 -- docs/zodiac/toga/app.html | 2239 ------------------ docs/zodiac/toga/interface.html | 513 ---- docs/zodiac/toga/palette.html | 1308 ---------- docs/zodiac/toga/signatures.html | 1179 --------- pyproject.toml | 1 - 22 files changed, 15914 deletions(-) delete mode 100644 docs/index.html delete mode 100644 docs/search.js delete mode 100644 docs/zodiac.html delete mode 100644 docs/zodiac/graph.html delete mode 100644 docs/zodiac/providers.html delete mode 100644 docs/zodiac/providers/constants.html delete mode 100644 docs/zodiac/providers/pools.html delete mode 100644 docs/zodiac/providers/proto_class.html delete mode 100644 docs/zodiac/providers/registry_entry.html delete mode 100644 docs/zodiac/streams.html delete mode 100644 docs/zodiac/streams/class_stream.html delete mode 100644 docs/zodiac/streams/media_stream.html delete mode 100644 docs/zodiac/streams/model_stream.html delete mode 100644 docs/zodiac/streams/plot_stream.html delete mode 100644 docs/zodiac/streams/task_stream.html delete mode 100644 docs/zodiac/streams/token_stream.html delete mode 100644 docs/zodiac/toga.html delete mode 100644 docs/zodiac/toga/app.html delete mode 100644 docs/zodiac/toga/interface.html delete mode 100644 docs/zodiac/toga/palette.html delete mode 100644 docs/zodiac/toga/signatures.html diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index a754836..0000000 --- a/docs/index.html +++ /dev/null @@ -1,7 +0,0 @@ - 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Parse launch arguments (mostly turning down/disconnecting loud dependency packages)

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\n\n

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Create instance of graph processor & initialize objectieves for tracing paths

\n\n
Parameters
\n\n
    \n
  • nx_graph: Preassembled graph of models to substitute, default uses nx.MultiDiGraph()
    • \n
\n\n

========================================================

\n\n

GIVEN

\n\n

A : The list of VALID CONVERSIONS contains all of Zodiac's supported generative modalities

\n\n

B : The graph is populated directly from the contents of the list in A

\n\n

Thus: All possible node start and end points listed in A are included in graph B.

\n\n

Therefore : It is impossible to call a node that does not exist.

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Generate graph of coordinate pairs from valid conversions

\n\n

Model libraries are auto-detected from cache loading

\n\n
Parameters
\n\n
    \n
  • registry_data: Registry function or method of calling registry, defaults to
    • \n
\n\n
Returns
\n\n
\n

Graph modeling all current ML/AI tasks appended with model data

\n
\n\n

========================================================

\n\n

GIVEN

\n\n

A : The set of all models M on the executing system

\n\n

B : P is the randomly distributed set of start and end points required to graph M

\n\n

Thus: Because of the randomness of B, the set P is unlikely to construct a complete graph attached all available points.

\n\n

Therefore : While we can trust a node exists, we CANNOT trust the system has an edge to reach it

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Find a valid path from current state (mode_in) to designated state (mode_out)

\n\n
Parameters
\n\n
    \n
  • mode_in: Input prompt type or starting state/states
    • \n
  • mode_out: The user-selected ending-state
    • \n
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Populate models list for text fields\nCheck if model has been adjusted, if so adjust list\n1.0 weight bottom, <1.0 weight top

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Determine entry edge, determine index, then adjust weight

\n\n
Parameters
\n\n
    \n
  • edge_number: Text pattern from models class attribute to identify the model by
    • \n
  • mode_in: The conversion type, representing a source graph node
    • \n
  • mode_out: The target type, , representing a source graph node
    • \n
\n\n
Raises
\n\n
    \n
  • ValueError: No models fit the request
    • \n
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Create operating instructions from user input\nTrace the next hop along the path, collect all compatible models\nSet current model based on weight and next available

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Perform network test to ensure a host server is running

\n\n
Parameters
\n\n
    \n
  • api_name: Type of host API
    • \n
  • api_url: The (default) configuration data for that API
    • \n
\n\n
Returns
\n\n
\n

Whether the server is up or not

\n
\n", "signature": "(api_name: str, api_url: str) -> bool:", "funcdef": "def"}, "zodiac.providers.constants.has_api": {"fullname": "zodiac.providers.constants.has_api", "modulename": "zodiac.providers.constants", "qualname": "has_api", "kind": "function", "doc": "

Check available modules, try to import dynamically.

\n\n

True for successful import, else False

\n\n
Parameters
\n\n
    \n
  • api_name: Constant name for API
    • \n
  • _data: filled by config decorator, ignore, defaults to None
    • \n
\n\n
Returns
\n\n
\n

Package availability or check_host for servers; boolean result

\n
\n", "signature": "(api_name: str, data: dict = None) -> bool:", "funcdef": "def"}, "zodiac.providers.constants.show_all_docstring": {"fullname": "zodiac.providers.constants.show_all_docstring", "modulename": "zodiac.providers.constants", "qualname": "show_all_docstring", "kind": "variable", "doc": "

\n", "default_value": "':param _show_all(): Show all POSSIBLE API types of a given class'"}, "zodiac.providers.constants.show_available_docstring": {"fullname": "zodiac.providers.constants.show_available_docstring", "modulename": "zodiac.providers.constants", "qualname": "show_available_docstring", "kind": "variable", "doc": "

\n", "default_value": "':param _show_available(): Show all AVAILABLE API types of a given class'"}, "zodiac.providers.constants.check_type_docstring": {"fullname": "zodiac.providers.constants.check_type_docstring", "modulename": "zodiac.providers.constants", "qualname": "check_type_docstring", "kind": "variable", "doc": "

\n", "default_value": "':param _check_type: Check for a SINGLE API availability'"}, "zodiac.providers.constants.base_enum_docstring": {"fullname": "zodiac.providers.constants.base_enum_docstring", "modulename": "zodiac.providers.constants", "qualname": "base_enum_docstring", "kind": "variable", "doc": "

\n", "default_value": "':param _show_all(): Show all POSSIBLE API types of a given class:param _show_available(): Show all AVAILABLE API types of a given class:param _check_type: Check for a SINGLE API availability'"}, "zodiac.providers.constants.BaseEnum": {"fullname": "zodiac.providers.constants.BaseEnum", "modulename": "zodiac.providers.constants", "qualname": "BaseEnum", "kind": "class", "doc": "

\n", "bases": "enum.Enum"}, "zodiac.providers.constants.BaseEnum.show_all": {"fullname": "zodiac.providers.constants.BaseEnum.show_all", "modulename": "zodiac.providers.constants", "qualname": "BaseEnum.show_all", "kind": "function", "doc": "

Show all POSSIBLE API types of a given class

\n", "signature": "(cls) -> List:", "funcdef": "def"}, "zodiac.providers.constants.BaseEnum.show_available": {"fullname": "zodiac.providers.constants.BaseEnum.show_available", "modulename": "zodiac.providers.constants", "qualname": "BaseEnum.show_available", "kind": "function", "doc": "

Show all AVAILABLE API types of a given class

\n", "signature": "(cls) -> bool:", "funcdef": "def"}, "zodiac.providers.constants.BaseEnum.check_type": {"fullname": "zodiac.providers.constants.BaseEnum.check_type", "modulename": "zodiac.providers.constants", "qualname": "BaseEnum.check_type", "kind": "function", "doc": "

Check for a SINGLE API availability

\n", "signature": "(cls, type_name: str) -> bool:", "funcdef": "def"}, "zodiac.providers.constants.CueType": {"fullname": "zodiac.providers.constants.CueType", "modulename": "zodiac.providers.constants", "qualname": "CueType", "kind": "class", "doc": "

\n", "bases": "BaseEnum"}, "zodiac.providers.constants.CueType.HUB": {"fullname": "zodiac.providers.constants.CueType.HUB", "modulename": "zodiac.providers.constants", "qualname": "CueType.HUB", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.HUB: (True, 'HUB')>"}, "zodiac.providers.constants.CueType.KAGGLE": {"fullname": "zodiac.providers.constants.CueType.KAGGLE", "modulename": "zodiac.providers.constants", "qualname": "CueType.KAGGLE", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.KAGGLE: (False, 'KAGGLE')>"}, "zodiac.providers.constants.CueType.LLAMAFILE": {"fullname": "zodiac.providers.constants.CueType.LLAMAFILE", "modulename": "zodiac.providers.constants", "qualname": "CueType.LLAMAFILE", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.LLAMAFILE: (False, 'LLAMAFILE')>"}, "zodiac.providers.constants.CueType.LM_STUDIO": {"fullname": "zodiac.providers.constants.CueType.LM_STUDIO", "modulename": "zodiac.providers.constants", "qualname": "CueType.LM_STUDIO", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.LM_STUDIO: (False, 'LM_STUDIO')>"}, "zodiac.providers.constants.CueType.MLX_AUDIO": {"fullname": "zodiac.providers.constants.CueType.MLX_AUDIO", "modulename": "zodiac.providers.constants", "qualname": "CueType.MLX_AUDIO", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.MLX_AUDIO: (True, 'MLX_AUDIO')>"}, "zodiac.providers.constants.CueType.OLLAMA": {"fullname": "zodiac.providers.constants.CueType.OLLAMA", "modulename": "zodiac.providers.constants", "qualname": "CueType.OLLAMA", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.OLLAMA: (True, 'OLLAMA')>"}, "zodiac.providers.constants.CueType.VLLM": {"fullname": "zodiac.providers.constants.CueType.VLLM", "modulename": "zodiac.providers.constants", "qualname": "CueType.VLLM", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<CueType.VLLM: (False, 'VLLM')>"}, "zodiac.providers.constants.example_str": {"fullname": "zodiac.providers.constants.example_str", "modulename": "zodiac.providers.constants", "qualname": "example_str", "kind": "variable", "doc": "

\n", "default_value": "('function_name', 'import.function_name')"}, "zodiac.providers.constants.PkgType": {"fullname": "zodiac.providers.constants.PkgType", "modulename": "zodiac.providers.constants", "qualname": "PkgType", "kind": "class", "doc": "

Package dependency constants\nCollected info from hub model tags and dependencies\n

\n", "bases": "BaseEnum"}, "zodiac.providers.constants.PkgType.AUDIOGEN": {"fullname": "zodiac.providers.constants.PkgType.AUDIOGEN", "modulename": "zodiac.providers.constants", "qualname": "PkgType.AUDIOGEN", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])>"}, "zodiac.providers.constants.PkgType.BAGEL": {"fullname": "zodiac.providers.constants.PkgType.BAGEL", "modulename": "zodiac.providers.constants", "qualname": "PkgType.BAGEL", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>"}, "zodiac.providers.constants.PkgType.BITNET": {"fullname": "zodiac.providers.constants.PkgType.BITNET", "modulename": "zodiac.providers.constants", "qualname": "PkgType.BITNET", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.BITNET: (False, 'BITNET', ['microsoft/BitNet'])>"}, "zodiac.providers.constants.PkgType.BITSANDBYTES": {"fullname": "zodiac.providers.constants.PkgType.BITSANDBYTES", "modulename": "zodiac.providers.constants", "qualname": "PkgType.BITSANDBYTES", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.BITSANDBYTES: (False, 'BITSANDBYTES', [])>"}, "zodiac.providers.constants.PkgType.DFLOAT11": {"fullname": "zodiac.providers.constants.PkgType.DFLOAT11", "modulename": "zodiac.providers.constants", "qualname": "PkgType.DFLOAT11", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.DFLOAT11: (False, 'DFLOAT11', ['LeanModels/DFloat11'])>"}, "zodiac.providers.constants.PkgType.DIFFUSERS": {"fullname": "zodiac.providers.constants.PkgType.DIFFUSERS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.DIFFUSERS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])>"}, "zodiac.providers.constants.PkgType.EXLLAMAV2": {"fullname": "zodiac.providers.constants.PkgType.EXLLAMAV2", "modulename": "zodiac.providers.constants", "qualname": "PkgType.EXLLAMAV2", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.EXLLAMAV2: (False, 'EXLLAMAV2', [])>"}, "zodiac.providers.constants.PkgType.F_LITE": {"fullname": "zodiac.providers.constants.PkgType.F_LITE", "modulename": "zodiac.providers.constants", "qualname": "PkgType.F_LITE", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.F_LITE: (False, 'F_LITE', ['fal-ai/f-lite'])>"}, "zodiac.providers.constants.PkgType.HIDIFFUSION": {"fullname": "zodiac.providers.constants.PkgType.HIDIFFUSION", "modulename": "zodiac.providers.constants", "qualname": "PkgType.HIDIFFUSION", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])>"}, "zodiac.providers.constants.PkgType.IMAGE_GEN_AUX": {"fullname": "zodiac.providers.constants.PkgType.IMAGE_GEN_AUX", "modulename": "zodiac.providers.constants", "qualname": "PkgType.IMAGE_GEN_AUX", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.IMAGE_GEN_AUX: (False, 'IMAGE_GEN_AUX', ['huggingface/image_gen_aux'])>"}, "zodiac.providers.constants.PkgType.JAX": {"fullname": "zodiac.providers.constants.PkgType.JAX", "modulename": "zodiac.providers.constants", "qualname": "PkgType.JAX", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.JAX: (True, 'JAX', [])>"}, "zodiac.providers.constants.PkgType.KERAS": {"fullname": "zodiac.providers.constants.PkgType.KERAS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.KERAS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.KERAS: (False, 'KERAS', [])>"}, "zodiac.providers.constants.PkgType.LLAMA": {"fullname": "zodiac.providers.constants.PkgType.LLAMA", "modulename": "zodiac.providers.constants", "qualname": "PkgType.LLAMA", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.LLAMA: (True, 'LLAMA_CPP', [])>"}, "zodiac.providers.constants.PkgType.LUMINA_MGPT": {"fullname": "zodiac.providers.constants.PkgType.LUMINA_MGPT", "modulename": "zodiac.providers.constants", "qualname": "PkgType.LUMINA_MGPT", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.LUMINA_MGPT: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT'])>"}, "zodiac.providers.constants.PkgType.LUMINA_MGPT2": {"fullname": "zodiac.providers.constants.PkgType.LUMINA_MGPT2", "modulename": "zodiac.providers.constants", "qualname": "PkgType.LUMINA_MGPT2", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.LUMINA_MGPT2: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT-2.0'])>"}, "zodiac.providers.constants.PkgType.MFLUX": {"fullname": "zodiac.providers.constants.PkgType.MFLUX", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MFLUX", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MFLUX: (True, 'MFLUX', [])>"}, "zodiac.providers.constants.PkgType.MLX_AUDIO": {"fullname": "zodiac.providers.constants.PkgType.MLX_AUDIO", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MLX_AUDIO", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])>"}, "zodiac.providers.constants.PkgType.MLX_CHROMA": {"fullname": "zodiac.providers.constants.PkgType.MLX_CHROMA", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MLX_CHROMA", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MLX_CHROMA: (False, 'CHROMA', ['exdysa/jack813-mlx-chroma'])>"}, "zodiac.providers.constants.PkgType.MLX_LM": {"fullname": "zodiac.providers.constants.PkgType.MLX_LM", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MLX_LM", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MLX_LM: (True, 'MLX_LM', [])>"}, "zodiac.providers.constants.PkgType.MLX_VLM": {"fullname": "zodiac.providers.constants.PkgType.MLX_VLM", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MLX_VLM", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MLX_VLM: (True, 'MLX_VLM', [])>"}, "zodiac.providers.constants.PkgType.MLX": {"fullname": "zodiac.providers.constants.PkgType.MLX", "modulename": "zodiac.providers.constants", "qualname": "PkgType.MLX", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.MLX: (True, 'MLX', [])>"}, "zodiac.providers.constants.PkgType.ONNX": {"fullname": "zodiac.providers.constants.PkgType.ONNX", "modulename": "zodiac.providers.constants", "qualname": "PkgType.ONNX", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.ONNX: (True, 'ONNX', ['ONNX'])>"}, "zodiac.providers.constants.PkgType.ORPHEUS_TTS": {"fullname": "zodiac.providers.constants.PkgType.ORPHEUS_TTS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.ORPHEUS_TTS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.ORPHEUS_TTS: (False, 'ORPHEUS_TTS', ['canopyai/Orpheus-TTS'])>"}, "zodiac.providers.constants.PkgType.OUTETTS": {"fullname": "zodiac.providers.constants.PkgType.OUTETTS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.OUTETTS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.OUTETTS: (False, 'OUTETTS', ['edwko/OuteTTS'])>"}, "zodiac.providers.constants.PkgType.PARLER_TTS": {"fullname": "zodiac.providers.constants.PkgType.PARLER_TTS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.PARLER_TTS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])>"}, "zodiac.providers.constants.PkgType.PLEIAS": {"fullname": "zodiac.providers.constants.PkgType.PLEIAS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.PLEIAS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.PLEIAS: (False, 'PLEIAS', ['exdysa/Pleias-Pleias-RAG-Library'])>"}, "zodiac.providers.constants.PkgType.SENTENCE_TRANSFORMERS": {"fullname": "zodiac.providers.constants.PkgType.SENTENCE_TRANSFORMERS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.SENTENCE_TRANSFORMERS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])>"}, "zodiac.providers.constants.PkgType.SHOW_O": {"fullname": "zodiac.providers.constants.PkgType.SHOW_O", "modulename": "zodiac.providers.constants", "qualname": "PkgType.SHOW_O", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.SHOW_O: (False, 'SHOW_O', ['showlab/show-o'])>"}, "zodiac.providers.constants.PkgType.SPANDREL_EXTRA_ARCHES": {"fullname": "zodiac.providers.constants.PkgType.SPANDREL_EXTRA_ARCHES", "modulename": "zodiac.providers.constants", "qualname": "PkgType.SPANDREL_EXTRA_ARCHES", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.SPANDREL_EXTRA_ARCHES: (False, 'SPANDREL_EXTRA_ARCHES', [])>"}, "zodiac.providers.constants.PkgType.SPANDREL": {"fullname": "zodiac.providers.constants.PkgType.SPANDREL", "modulename": "zodiac.providers.constants", "qualname": "PkgType.SPANDREL", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.SPANDREL: (False, 'SPANDREL', [])>"}, "zodiac.providers.constants.PkgType.SVDQUANT": {"fullname": "zodiac.providers.constants.PkgType.SVDQUANT", "modulename": "zodiac.providers.constants", "qualname": "PkgType.SVDQUANT", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.SVDQUANT: (False, 'NUNCHAKU', ['mit-han-lab/nunchaku'])>"}, "zodiac.providers.constants.PkgType.TENSORFLOW": {"fullname": "zodiac.providers.constants.PkgType.TENSORFLOW", "modulename": "zodiac.providers.constants", "qualname": "PkgType.TENSORFLOW", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.TENSORFLOW: (False, 'TENSORFLOW', [])>"}, "zodiac.providers.constants.PkgType.TORCH": {"fullname": "zodiac.providers.constants.PkgType.TORCH", "modulename": "zodiac.providers.constants", "qualname": "PkgType.TORCH", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.TORCH: (True, 'TORCH', [])>"}, "zodiac.providers.constants.PkgType.TORCHAUDIO": {"fullname": "zodiac.providers.constants.PkgType.TORCHAUDIO", "modulename": "zodiac.providers.constants", "qualname": "PkgType.TORCHAUDIO", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.TORCHAUDIO: (True, 'TORCHAUDIO', [])>"}, "zodiac.providers.constants.PkgType.TORCHVISION": {"fullname": "zodiac.providers.constants.PkgType.TORCHVISION", "modulename": "zodiac.providers.constants", "qualname": "PkgType.TORCHVISION", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.TORCHVISION: (True, 'TORCHVISION', [])>"}, "zodiac.providers.constants.PkgType.TRANSFORMERS": {"fullname": "zodiac.providers.constants.PkgType.TRANSFORMERS", "modulename": "zodiac.providers.constants", "qualname": "PkgType.TRANSFORMERS", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])>"}, "zodiac.providers.constants.PkgType.VLLM": {"fullname": "zodiac.providers.constants.PkgType.VLLM", "modulename": "zodiac.providers.constants", "qualname": "PkgType.VLLM", "kind": "variable", "doc": "

\n", "annotation": ": tuple", "default_value": "<PkgType.VLLM: (False, 'VLLM', [])>"}, "zodiac.providers.constants.ChipType": {"fullname": "zodiac.providers.constants.ChipType", "modulename": "zodiac.providers.constants", "qualname": "ChipType", "kind": "class", "doc": "

\n", "bases": "enum.Enum"}, "zodiac.providers.constants.ChipType.initialize_device": {"fullname": "zodiac.providers.constants.ChipType.initialize_device", "modulename": "zodiac.providers.constants", "qualname": "ChipType.initialize_device", "kind": "function", "doc": "

\n", "signature": "(cls) -> None:", "funcdef": "def"}, "zodiac.providers.constants.ChipType.CUDA": {"fullname": "zodiac.providers.constants.ChipType.CUDA", "modulename": "zodiac.providers.constants", "qualname": "ChipType.CUDA", "kind": "variable", "doc": "

\n", "default_value": "(False, 'CUDA', [<PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>, <PkgType.BITSANDBYTES: (False, 'BITSANDBYTES', [])>, <PkgType.DFLOAT11: (False, 'DFLOAT11', ['LeanModels/DFloat11'])>, <PkgType.EXLLAMAV2: (False, 'EXLLAMAV2', [])>, <PkgType.F_LITE: (False, 'F_LITE', ['fal-ai/f-lite'])>, <PkgType.LUMINA_MGPT: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT'])>, <PkgType.ORPHEUS_TTS: (False, 'ORPHEUS_TTS', ['canopyai/Orpheus-TTS'])>, <PkgType.OUTETTS: (False, 'OUTETTS', ['edwko/OuteTTS'])>, <PkgType.VLLM: (False, 'VLLM', [])>])"}, "zodiac.providers.constants.ChipType.MPS": {"fullname": "zodiac.providers.constants.ChipType.MPS", "modulename": "zodiac.providers.constants", "qualname": "ChipType.MPS", "kind": "variable", "doc": "

\n", "default_value": "(True, 'MPS', [<PkgType.MFLUX: (True, 'MFLUX', [])>, <PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])>, <PkgType.MLX_LM: (True, 'MLX_LM', [])>, <PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>])"}, "zodiac.providers.constants.ChipType.XPU": {"fullname": "zodiac.providers.constants.ChipType.XPU", "modulename": "zodiac.providers.constants", "qualname": "ChipType.XPU", "kind": "variable", "doc": "

\n", "default_value": "(False, 'XPU', [])"}, "zodiac.providers.constants.ChipType.MTIA": {"fullname": "zodiac.providers.constants.ChipType.MTIA", "modulename": "zodiac.providers.constants", "qualname": "ChipType.MTIA", "kind": "variable", "doc": "

\n", "default_value": "(False, 'MTIA', [])"}, "zodiac.providers.constants.ChipType.CPU": {"fullname": "zodiac.providers.constants.ChipType.CPU", "modulename": "zodiac.providers.constants", "qualname": "ChipType.CPU", "kind": "variable", "doc": "

\n", "default_value": "(True, 'CPU', [<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])>, <PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])>, <PkgType.LLAMA: (True, 'LLAMA_CPP', [])>, <PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])>, <PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])>, <PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])>, <PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])>, <PkgType.TORCH: (True, 'TORCH', [])>])"}, "zodiac.providers.constants.GenTypeC": {"fullname": "zodiac.providers.constants.GenTypeC", "modulename": "zodiac.providers.constants", "qualname": "GenTypeC", "kind": "class", "doc": "

Generative inference types in C-dimensional order

\n\n

Comprehensiveness, sorted from 'most involved' to 'least involved'

\n\n

The terms define 'artistic' and ambiguous operations

\n\n
Parameters
\n\n
    \n
  • clone: Copying identity, voice, exact mirror
    • \n
  • sync: Tone, tempo, color, quality, genre, scale, mood
    • \n
  • translate: A range of comprehensible approximations
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "zodiac.providers.constants.GenTypeC.clone": {"fullname": "zodiac.providers.constants.GenTypeC.clone", "modulename": "zodiac.providers.constants", "qualname": "GenTypeC.clone", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)]", "default_value": "None"}, "zodiac.providers.constants.GenTypeC.sync": {"fullname": "zodiac.providers.constants.GenTypeC.sync", "modulename": "zodiac.providers.constants", "qualname": "GenTypeC.sync", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)]", "default_value": "None"}, "zodiac.providers.constants.GenTypeC.translate": {"fullname": "zodiac.providers.constants.GenTypeC.translate", "modulename": "zodiac.providers.constants", "qualname": "GenTypeC.translate", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)]", "default_value": "None"}, "zodiac.providers.constants.GenTypeCText": {"fullname": "zodiac.providers.constants.GenTypeCText", "modulename": "zodiac.providers.constants", "qualname": "GenTypeCText", "kind": "class", "doc": "

Generative inference types in C-dimensional order for text operations

\n\n

Comprehensiveness, sorted from 'most involved' to 'least involved'

\n\n

The terms define 'concrete' and more rigid operations

\n\n
Parameters
\n\n
    \n
  • research: Quoting, paraphrasing, and deriving from sources
    • \n
  • chain_of_thought: A performance of processing step-by-step (similar to reasoning)
    • \n
  • question_answer: Basic, straightforward responses
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "zodiac.providers.constants.GenTypeCText.research": {"fullname": "zodiac.providers.constants.GenTypeCText.research", "modulename": "zodiac.providers.constants", "qualname": "GenTypeCText.research", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))]", "default_value": "None"}, "zodiac.providers.constants.GenTypeCText.chain_of_thought": {"fullname": "zodiac.providers.constants.GenTypeCText.chain_of_thought", "modulename": "zodiac.providers.constants", "qualname": "GenTypeCText.chain_of_thought", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))]", "default_value": "None"}, "zodiac.providers.constants.GenTypeCText.question_answer": {"fullname": "zodiac.providers.constants.GenTypeCText.question_answer", "modulename": "zodiac.providers.constants", "qualname": "GenTypeCText.question_answer", "kind": "variable", "doc": "

\n", "annotation": ": Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))]", "default_value": "None"}, "zodiac.providers.constants.GenTypeE": {"fullname": "zodiac.providers.constants.GenTypeE", "modulename": "zodiac.providers.constants", "qualname": "GenTypeE", "kind": "class", "doc": "

Generative inference operation types in E-dimensional order

\n\n

Equivalence, lists sorted from 'highly-similar' to 'loosely correlated.'\"

\n\n
Parameters
\n\n
    \n
  • universal: Affecting all conversions
    • \n
  • text: Text-only conversions
    • \n
\n\n

multimedia generation

\n\n
Y-axis: Detail (Most involved to least involved)\n\u2502\n\u2502                             clone\n\u2502                 sync\n\u2502 translate\n\u2502\n+\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500> X-axis: Equivalence (Loosely correlated to highly similar)\n
\n\n

text generation

\n\n
Y-axis: Detail (Most involved to least involved)\n\u2502\n\u2502                           research\n\u2502             chain-of-thought\n\u2502 question/answer\n\u2502\n+\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500> X-axis:  Equivalence (Loosely correlated to highly similar)\n
\n\n

This is essentially the translation operation of C types, and the mapping of them to E

\n\n

An abstract generalization of the set of all multimodal generative synthesis processes

\n\n

The sum of each coordinate pair reflects effective compute use

\n\n

In this way, both C types and their similarity are translatable, but not 1:1 identical

\n\n

Text is allowed to perform all 6 core operations. Other media perform only 3.

\n", "bases": "pydantic.main.BaseModel"}, "zodiac.providers.constants.GenTypeE.universal": {"fullname": "zodiac.providers.constants.GenTypeE.universal", "modulename": "zodiac.providers.constants", "qualname": "GenTypeE.universal", "kind": "variable", "doc": "

\n", "annotation": ": zodiac.providers.constants.GenTypeC", "default_value": "GenTypeC(clone=None, sync=None, translate=None)"}, "zodiac.providers.constants.GenTypeE.text": {"fullname": "zodiac.providers.constants.GenTypeE.text", "modulename": "zodiac.providers.constants", "qualname": "GenTypeE.text", "kind": "variable", "doc": "

\n", "annotation": ": zodiac.providers.constants.GenTypeCText", "default_value": "GenTypeCText(research=None, chain_of_thought=None, question_answer=None)"}, "zodiac.providers.constants.VALID_CONVERSIONS": {"fullname": "zodiac.providers.constants.VALID_CONVERSIONS", "modulename": "zodiac.providers.constants", "qualname": "VALID_CONVERSIONS", "kind": "variable", "doc": "

\n", "default_value": "['text', 'image', 'music', 'speech', 'audio', 'video', '3d', 'vector_graphic', 'upscale_image']"}, "zodiac.providers.constants.VALID_JUNCTIONS": {"fullname": "zodiac.providers.constants.VALID_JUNCTIONS", "modulename": "zodiac.providers.constants", "qualname": "VALID_JUNCTIONS", "kind": "variable", "doc": "

\n", "default_value": "['']"}, "zodiac.providers.constants.tasks": {"fullname": "zodiac.providers.constants.tasks", "modulename": "zodiac.providers.constants", "qualname": "tasks", "kind": "variable", "doc": "

\n", "default_value": "['audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'image-to-text', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'text2text-generation', 'token-classification', 'translation', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']"}, "zodiac.providers.constants.VALID_TASKS": {"fullname": "zodiac.providers.constants.VALID_TASKS", "modulename": "zodiac.providers.constants", "qualname": "VALID_TASKS", "kind": "variable", "doc": "

\n", "default_value": "{<CueType.VLLM: (False, 'VLLM')>: {('text', 'text'): ['text'], ('image', 'text'): ['vision']}, <CueType.OLLAMA: (True, 'OLLAMA')>: {('text', 'text'): ['mllama'], ('image', 'text'): ['llava', 'vllm']}, <CueType.LLAMAFILE: (False, 'LLAMAFILE')>: {('text', 'text'): ['text']}, <CueType.LM_STUDIO: (False, 'LM_STUDIO')>: {('text', 'text'): ['llm']}, <CueType.HUB: (True, 'HUB')>: {('image', 'image'): ['image-to-image', 'inpaint', 'inpainting', 'depth-to-image', 'i2i', 'image-to-image', 'any-to-any'], ('text', 'image'): ['kolors', 'kolorspipeline', 'image-generation', 'any-to-any', 'text-to-image', 'chromapipeline'], ('image', 'text'): ['image-classification', 'image-to-text', 'image-text-to-text', 'visual-question-answering', 'image-captioning', 'image-segmentation', 'depth-estimation', 'image-feature-extraction', 'mask-generation', 'object-detection', 'visual-question-answering', 'keypoint-detection', 'vllm', 'vqa', 'vision', 'zero-shot-object-detection', 'zero-shot-image-classification', 'timm', 'zero-shot image classification', 'any-to-any', 'vidore'], ('image', 'video'): ['image-to-video', 'i2v', 'reference-to-video', 'refernce-to-video'], ('video', 'text'): ['video-classification'], ('text', 'video'): ['video generation', 't2v', 'text-to-video', 'HunyuanVideoPipeline'], ('text', 'text'): ['any-to-any', 'named entity recognition', 'entity typing', 'relation classification', 'question answering', 'fill-mask', 'chat', 'conversational', 'text-generation', 'causal-lm', 'text2text-generation', 'document-question-answering', 'feature-extraction', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'token-classification', 'translation', 'zero-shot-classification', 'translation_xx_to_yy', 't2t', 'chatglm', 'exbert'], ('text', 'audio'): ['text-to-audio', 't2a', 'any-to-any', 'text-to-audio', ' musicldmpipeline', 'musicgen', 'audiocraft', 'audiogen', 'AudioLDMPipeline', 'AudioLDM2Pipeline'], ('audio', 'text'): ['zero-shot-audio-classification', 'audio-classification', 'a2t', 'audio-text-to-text', 'any-to-any'], ('text', 'speech'): ['text-to-speech', 'tts', 'any-to-any', 'annotation'], ('speech', 'text'): ['speech-to-text', 'speech', 'speech-translation', 'speech-summarization', 'automatic-speech-recognition', 'dictation', 'stt', 'any-to-any', 'hf-asr-leaderboard']}, <CueType.KAGGLE: (False, 'KAGGLE')>: {('text', 'text'): ['text']}}"}, "zodiac.providers.pools": {"fullname": "zodiac.providers.pools", "modulename": "zodiac.providers.pools", "kind": "module", "doc": "

Feed models to RegistryEntry class

\n"}, "zodiac.providers.pools.nfo": {"fullname": "zodiac.providers.pools.nfo", "modulename": "zodiac.providers.pools", "qualname": "nfo", "kind": "function", "doc": "

Prints the values to a stream, or to sys.stdout by default.

\n\n

sep\n string inserted between values, default a space.\nend\n string appended after the last value, default a newline.\nfile\n a file-like object (stream); defaults to the current sys.stdout.\nflush\n whether to forcibly flush the stream.

\n", "signature": "(*args, sep=' ', end='\\n', file=None, flush=False):", "funcdef": "def"}, "zodiac.providers.pools.MODE_DATA": {"fullname": "zodiac.providers.pools.MODE_DATA", "modulename": "zodiac.providers.pools", "qualname": "MODE_DATA", "kind": "variable", "doc": "

\n", "default_value": "<nnll.mir.json_cache.JSONCache object>"}, "zodiac.providers.pools.add_mode_types": {"fullname": "zodiac.providers.pools.add_mode_types", "modulename": "zodiac.providers.pools", "qualname": "add_mode_types", "kind": "function", "doc": "

Add mode\u2011related metadata for a given MIR tag.

\n\n
Parameters
\n\n
    \n
  • mir_tag: List of tag components that identify a model in the MIR database.
    • \n
  • data: Dictionary containing mode entries; defaults to None.\n:returns: Mapping with keys extracted from data for the fused tag.
    • \n
\n", "signature": "(\tmir_tag: list[str],\tdata: dict | None = None) -> dict[str, list[str] | str]:", "funcdef": "async def"}, "zodiac.providers.pools.add_pkg_types": {"fullname": "zodiac.providers.pools.add_pkg_types", "modulename": "zodiac.providers.pools", "qualname": "add_pkg_types", "kind": "function", "doc": "

Augment package data with additional entries based on pipeline class and mode.

\n\n
Parameters
\n\n
    \n
  • pkg_data: Existing package mapping where keys are indices and values are package specs.
    • \n
  • mode: The pipeline mode extracted from MIR metadata.\n:returns: Updated pkg_data with GPU-specific packages
    • \n
\n", "signature": "(\tpkg_data: dict,\tmode: str,\tmir_tag: list[str]) -> dict[int | str, typing.Any]:", "funcdef": "async def"}, "zodiac.providers.pools.generate_entry": {"fullname": "zodiac.providers.pools.generate_entry", "modulename": "zodiac.providers.pools", "qualname": "generate_entry", "kind": "function", "doc": "

Create a registry entry dictionary from MIR information.

\n\n
Parameters
\n\n
    \n
  • mir_tag: The hierarchical tag identifying a model in the MIR database.
    • \n
  • mir_db: The MIR database instance providing access to stored metadata.
    • \n
  • model_tags: Additional tags to attach to the model; defaults to None.
    • \n
  • pkg_data: Existing package data; defaults to None.\n:returns: Mapping of values for registry construction.
    • \n
\n", "signature": "(\tmir_tag: List[str],\tmir_db: dict,\tmodel_tags: list[str] | None = None,\tpkg_data: dict | None = None) -> dict[str, list[str] | str]:", "funcdef": "async def"}, "zodiac.providers.pools.hub_pool": {"fullname": "zodiac.providers.pools.hub_pool", "modulename": "zodiac.providers.pools", "qualname": "hub_pool", "kind": "function", "doc": "

Build a registry of models from the local Huggingface Hub cache

\n\n
Parameters
\n\n
    \n
  • mir_db: An existing instance of the MIR database
    • \n
  • api_data: Dictionary of service data pertaining to providers
    • \n
  • entries: Previous registry entries to append
    • \n
\n\n
Returns
\n\n
\n

A list of RegistryEntry elements, or None

\n
\n", "signature": "(\tmir_db: Callable,\tapi_data: Dict[str, Any],\tentries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.ollama_pool": {"fullname": "zodiac.providers.pools.ollama_pool", "modulename": "zodiac.providers.pools", "qualname": "ollama_pool", "kind": "function", "doc": "

Build a registry of models from local Ollama service

\n\n
Parameters
\n\n
    \n
  • mir_db: An existing instance of the MIR database
    • \n
  • api_data: Dictionary of service data pertaining to providers
    • \n
  • entries: Previous registry entries to append
    • \n
\n\n
Returns
\n\n
\n

A list of RegistryEntry elements, or None

\n
\n", "signature": "(\tmir_db: Callable,\tapi_data: Dict[str, Any],\tentries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.vllm_pool": {"fullname": "zodiac.providers.pools.vllm_pool", "modulename": "zodiac.providers.pools", "qualname": "vllm_pool", "kind": "function", "doc": "

Build a registry of models from local VLLM service

\n\n
Parameters
\n\n
    \n
  • mir_db: An existing instance of the MIR database
    • \n
  • api_data: Dictionary of service data pertaining to providers
    • \n
  • entries: Previous registry entries to append
    • \n
\n\n
Returns
\n\n
\n

A list of RegistryEntry elements, or None

\n
\n", "signature": "(\tmir_db: Callable,\tapi_data: Dict[str, Any],\tentries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.llamafile_pool": {"fullname": "zodiac.providers.pools.llamafile_pool", "modulename": "zodiac.providers.pools", "qualname": "llamafile_pool", "kind": "function", "doc": "

Build a registry of models from a local Llamafile server

\n\n
Parameters
\n\n
    \n
  • mir_db: An existing instance of the MIR database
    • \n
  • api_data: Dictionary of service data pertaining to providers
    • \n
  • entries: Previous registry entries to append
    • \n
\n\n
Returns
\n\n
\n

A list of RegistryEntry elements, or None

\n
\n", "signature": "(\tmir_db: Callable,\tapi_data: Dict[str, Any],\tentries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.lm_studio_pool": {"fullname": "zodiac.providers.pools.lm_studio_pool", "modulename": "zodiac.providers.pools", "qualname": "lm_studio_pool", "kind": "function", "doc": "

Build a registry of models from local LM Studio service

\n\n
Parameters
\n\n
    \n
  • mir_db: An existing instance of the MIR database
    • \n
  • api_data: Dictionary of service data pertaining to providers
    • \n
  • entries: Previous registry entries to append
    • \n
\n\n
Returns
\n\n
\n

A list of RegistryEntry elements, or None

\n
\n", "signature": "(\tmir_db: Callable,\tapi_data: Dict[str, Any],\tentries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.register_models": {"fullname": "zodiac.providers.pools.register_models", "modulename": "zodiac.providers.pools", "qualname": "register_models", "kind": "function", "doc": "

Retrieve models from ollama server, local huggingface hub cache, local lmstudio cache & vllm.

\n\n
Parameters
\n\n
    \n
  • data: Testing - Override for API CueType data dictionary\n\u6211\u5011\u4e0d\u61c9\u8a72\u7e7c\u7e8c\u70baLMStudio\u7de8\u78bc\u3002 \u6b61\u8fce\u8ca2\u737b\u8005\u4f86\u6539\u9032\u5b83\u3002 LMStudio is not OSS, but contributions are welcome.
    • \n
\n", "signature": "(\tdata: Optional[Dict[str, Any]] = None) -> list[zodiac.providers.registry_entry.RegistryEntry] | None:", "funcdef": "async def"}, "zodiac.providers.pools.generate_pool": {"fullname": "zodiac.providers.pools.generate_pool", "modulename": "zodiac.providers.pools", "qualname": "generate_pool", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "zodiac.providers.proto_class": {"fullname": "zodiac.providers.proto_class", "modulename": "zodiac.providers.proto_class", "kind": "module", "doc": "

\n"}, "zodiac.providers.registry_entry": {"fullname": "zodiac.providers.registry_entry", "modulename": "zodiac.providers.registry_entry", "kind": "module", "doc": "

Register model types

\n"}, "zodiac.providers.registry_entry.RegistryEntry": {"fullname": "zodiac.providers.registry_entry.RegistryEntry", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry", "kind": "class", "doc": "

Validate Hub / Ollama / LMStudio model input

\n", "bases": "pydantic.main.BaseModel"}, "zodiac.providers.registry_entry.RegistryEntry.cuetype": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.cuetype", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.cuetype", "kind": "variable", "doc": "

\n", "annotation": ": zodiac.providers.constants.CueType", "default_value": "PydanticUndefined"}, "zodiac.providers.registry_entry.RegistryEntry.model": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.model", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.model", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.providers.registry_entry.RegistryEntry.size": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.size", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.size", "kind": "variable", "doc": "

\n", "annotation": ": int", "default_value": "PydanticUndefined"}, "zodiac.providers.registry_entry.RegistryEntry.tags": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.tags", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.tags", "kind": "variable", "doc": "

\n", "annotation": ": List[str]", "default_value": "PydanticUndefined"}, "zodiac.providers.registry_entry.RegistryEntry.timestamp": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.timestamp", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.timestamp", "kind": "variable", "doc": "

\n", "annotation": ": int", "default_value": "PydanticUndefined"}, "zodiac.providers.registry_entry.RegistryEntry.mode": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.mode", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.mode", "kind": "variable", "doc": "

\n", "annotation": ": str | None", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.api_kwargs": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.api_kwargs", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.api_kwargs", "kind": "variable", "doc": "

\n", "annotation": ": Optional[dict]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.mir": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.mir", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.mir", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[str]]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.bundle": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.bundle", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.bundle", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[List[str]]]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.model_family": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.model_family", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.model_family", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[str]]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.modules": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.modules", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.modules", "kind": "variable", "doc": "

\n", "annotation": ": Optional[dict[str, dict]]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.package": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.package", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.package", "kind": "variable", "doc": "

\n", "annotation": ": Union[zodiac.providers.constants.PkgType, zodiac.providers.constants.CueType, NoneType]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.path": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.path", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.path", "kind": "variable", "doc": "

\n", "annotation": ": Union[str, pathlib._local.Path, List[Union[str, pathlib._local.Path]], NoneType]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.pipe": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.pipe", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.pipe", "kind": "variable", "doc": "

\n", "annotation": ": Optional[dict[str, Union[List[List[str]], List[str], str]]]", "default_value": "(None,)"}, "zodiac.providers.registry_entry.RegistryEntry.tasks": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.tasks", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.tasks", "kind": "variable", "doc": "

\n", "annotation": ": Optional[List[Union[str, List[str]]]]", "default_value": "(None,)"}, "zodiac.providers.registry_entry.RegistryEntry.tokenizer": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.tokenizer", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.tokenizer", "kind": "variable", "doc": "

\n", "annotation": ": Optional[pathlib._local.Path]", "default_value": "None"}, "zodiac.providers.registry_entry.RegistryEntry.available_tasks": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.available_tasks", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.available_tasks", "kind": "variable", "doc": "

Filter tag tasks into edge coordinates for graphing

\n", "annotation": ": List[Tuple]"}, "zodiac.providers.registry_entry.RegistryEntry.create_entry": {"fullname": "zodiac.providers.registry_entry.RegistryEntry.create_entry", "modulename": "zodiac.providers.registry_entry", "qualname": "RegistryEntry.create_entry", "kind": "function", "doc": "

API specific data to call models

\n\n
Parameters
\n\n
    \n
  • cuetype: Provider to trigger loading
    • \n
  • model: Cache location for model
    • \n
  • size: File size (usually in bytes)
    • \n
  • tags: List of available machine tasks for model
    • \n
  • api_kwargs: Localhost server defaults, defaults to None
    • \n
  • keys: List of available data buckets inside the MIR tree, defaults to None
    • \n
  • mir: MIR information, defaults to None
    • \n
  • model_family: Compatibility information for the model, defaults to None
    • \n
  • modules: List of packages that can support the model, defaults to None
    • \n
  • path: Location of the model on disk, defaults to None
    • \n
  • pipe: List of components to build the execution for the model, defaults to None
    • \n
  • package: Package name and availability, defaults to None
    • \n
  • tasks: Available methods to run the model
    • \n
  • timestamp: Download time of model, defaults to None
    • \n
  • tokenizer: Tokenizer configuration location, defaults to None
    • \n
\n\n
Returns
\n\n
\n

An instance of RegistryEntry with the provided values

\n
\n", "signature": "(\tcls,\tcuetype: zodiac.providers.constants.CueType,\tmodel: str,\tsize: int,\ttags: List[str],\tapi_kwargs: dict = None,\tmir: Optional[List[str]] = None,\tbundle: Optional[List[List[str]]] = None,\tmode: str | None = None,\tmodel_family: Optional[List[str]] = None,\tmodules: Optional[dict[str, dict]] = None,\tpackage: Union[zodiac.providers.constants.PkgType, zodiac.providers.constants.CueType, NoneType] = None,\tpath: Union[str, pathlib._local.Path, List[Union[str, pathlib._local.Path]], NoneType] = None,\tpipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = None,\ttasks: Optional[List[Union[str, List[str]]]] = None,\ttimestamp: Optional[int] = None,\ttokenizer=typing.Optional[str]):", "funcdef": "def"}, "zodiac.streams": {"fullname": "zodiac.streams", "modulename": "zodiac.streams", "kind": "module", "doc": "

\n"}, "zodiac.streams.class_stream": {"fullname": "zodiac.streams.class_stream", "modulename": "zodiac.streams.class_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.class_stream.ancestor_data": {"fullname": "zodiac.streams.class_stream.ancestor_data", "modulename": "zodiac.streams.class_stream", "qualname": "ancestor_data", "kind": "function", "doc": "

Trace lineage of a model for the specified field

\n\n
Parameters
\n\n
    \n
  • registry_entry: RegistryEntry for the model that needs to be traced
    • \n
  • field_name: The name of the database field containing the data sought
    • \n
\n\n
Returns
\n\n
\n

A generator populated with matching data fields

\n
\n", "signature": "(\tmir_tag_or_registry_entry: zodiac.providers.registry_entry.RegistryEntry | list,\tfield_name: str = 'pkg') -> Generator:", "funcdef": "async def"}, "zodiac.streams.class_stream.best_package": {"fullname": "zodiac.streams.class_stream.best_package", "modulename": "zodiac.streams.class_stream", "qualname": "best_package", "kind": "function", "doc": "

Identify the best package based on model data and package sets.

\n\n
Parameters
\n\n
    \n
  • mir_db_pkg: Dictionary containing package data to match
    • \n
  • ready_pkg_types: List of priority package processors to evaluate
    • \n
\n\n
Returns
\n\n
\n

Tuple containing (class name, package type) if match found, otherwise None

\n
\n", "signature": "(\tpkg_data: zodiac.providers.registry_entry.RegistryEntry | dict[int | str, typing.Any],\tready_list: list[tuple[zodiac.providers.constants.ChipType]] = [(True, 'MPS', [<PkgType.MFLUX: (True, 'MFLUX', [])>, <PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])>, <PkgType.MLX_LM: (True, 'MLX_LM', [])>, <PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>]), (True, 'CPU', [<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])>, <PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])>, <PkgType.LLAMA: (True, 'LLAMA_CPP', [])>, <PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])>, <PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])>, <PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])>, <PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])>, <PkgType.TORCH: (True, 'TORCH', [])>])]) -> tuple[str]:", "funcdef": "async def"}, "zodiac.streams.class_stream.find_package": {"fullname": "zodiac.streams.class_stream.find_package", "modulename": "zodiac.streams.class_stream", "qualname": "find_package", "kind": "function", "doc": "

Look up class and package in MIR from RegistryEntry.

\n\n
Parameters
\n\n
    \n
  • entry: A RegistryEntry object containing MIR (Model Identifier Resource) details.
    • \n
\n\n
Returns
\n\n
\n

A tuple containing the class name of the package and its type if found; otherwise, None.

\n
\n\n
Raises
\n\n
    \n
  • AttributeError: If PkgType or ChipType classes are not properly defined.
    • \n
\n", "signature": "(\tentry: zodiac.providers.registry_entry.RegistryEntry = None,\tmir_entry: list[str] | None = None) -> Tuple[str]:", "funcdef": "async def"}, "zodiac.streams.class_stream.stage_class": {"fullname": "zodiac.streams.class_stream.stage_class", "modulename": "zodiac.streams.class_stream", "qualname": "stage_class", "kind": "function", "doc": "

Returns a tuple of data for each sub-class of a module

\n\n
Parameters
\n\n
    \n
  • class_obj: The class item to inspect.\nex:('diffusers', 'models.autoencoders.autoencoder_kl', 'AutoencoderKL', ),
    • \n
\n", "signature": "(class_object: Callable) -> List[Tuple[Union[str, Callable]]]:", "funcdef": "async def"}, "zodiac.streams.media_stream": {"fullname": "zodiac.streams.media_stream", "modulename": "zodiac.streams.media_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.media_stream.AudioMachine": {"fullname": "zodiac.streams.media_stream.AudioMachine", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine", "kind": "class", "doc": "

\n"}, "zodiac.streams.media_stream.AudioMachine.audio_stream": {"fullname": "zodiac.streams.media_stream.AudioMachine.audio_stream", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine.audio_stream", "kind": "variable", "doc": "

\n", "default_value": "[0]"}, "zodiac.streams.media_stream.AudioMachine.frequency": {"fullname": "zodiac.streams.media_stream.AudioMachine.frequency", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine.frequency", "kind": "variable", "doc": "

\n", "default_value": "0"}, "zodiac.streams.media_stream.AudioMachine.duration": {"fullname": "zodiac.streams.media_stream.AudioMachine.duration", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine.duration", "kind": "variable", "doc": "

\n", "annotation": ": float", "default_value": "3.0"}, "zodiac.streams.media_stream.AudioMachine.precision": {"fullname": "zodiac.streams.media_stream.AudioMachine.precision", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine.precision", "kind": "variable", "doc": "

\n", "default_value": "0.0"}, "zodiac.streams.media_stream.AudioMachine.sample_length": {"fullname": "zodiac.streams.media_stream.AudioMachine.sample_length", "modulename": "zodiac.streams.media_stream", "qualname": "AudioMachine.sample_length", "kind": "variable", "doc": "

\n", "default_value": "0.0"}, "zodiac.streams.media_stream.record_audio": {"fullname": "zodiac.streams.media_stream.record_audio", "modulename": "zodiac.streams.media_stream", "qualname": "record_audio", "kind": "function", "doc": "

Get audio from mic

\n", "signature": "(self, frequency: int = 16000) -> None:", "funcdef": "async def"}, "zodiac.streams.media_stream.play_audio": {"fullname": "zodiac.streams.media_stream.play_audio", "modulename": "zodiac.streams.media_stream", "qualname": "play_audio", "kind": "function", "doc": "

Playback audio recordings

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.streams.media_stream.erase_audio": {"fullname": "zodiac.streams.media_stream.erase_audio", "modulename": "zodiac.streams.media_stream", "qualname": "erase_audio", "kind": "function", "doc": "

Clear audio graph and recording

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.streams.model_stream": {"fullname": "zodiac.streams.model_stream", "modulename": "zodiac.streams.model_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.model_stream.nfo": {"fullname": "zodiac.streams.model_stream.nfo", "modulename": "zodiac.streams.model_stream", "qualname": "nfo", "kind": "function", "doc": "

Prints the values to a stream, or to sys.stdout by default.

\n\n

sep\n string inserted between values, default a space.\nend\n string appended after the last value, default a newline.\nfile\n a file-like object (stream); defaults to the current sys.stdout.\nflush\n whether to forcibly flush the stream.

\n", "signature": "(*args, sep=' ', end='\\n', file=None, flush=False):", "funcdef": "def"}, "zodiac.streams.model_stream.ModelStream": {"fullname": "zodiac.streams.model_stream.ModelStream", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream", "kind": "class", "doc": "

A base class for data sources, providing an implementation of data\nnotifications.

\n", "bases": "toga.sources.base.Source"}, "zodiac.streams.model_stream.ModelStream.model_graph": {"fullname": "zodiac.streams.model_stream.ModelStream.model_graph", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.model_graph", "kind": "function", "doc": "

Build an intent graph from models using the IntentProcessor class

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.streams.model_stream.ModelStream.show_edges": {"fullname": "zodiac.streams.model_stream.ModelStream.show_edges", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.show_edges", "kind": "function", "doc": "

Retrieve and sort edges from the intent graph.

\n\n
Parameters
\n\n
    \n
  • target: If True, sorts based on the second element of each edge pair; defaults to False.
    • \n
\n\n
Returns
\n\n
\n

A sorted list of unique elements from the edge pairs.

\n
\n", "signature": "(self, target: bool = False) -> List[str]:", "funcdef": "async def"}, "zodiac.streams.model_stream.ModelStream.trace_models": {"fullname": "zodiac.streams.model_stream.ModelStream.trace_models", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.trace_models", "kind": "function", "doc": "

Trace model path through input to output mode, then updates the internal model list..

\n\n
Parameters
\n\n
    \n
  • mode_in: The input mode for tracing.
    • \n
  • mode_out: The output mode for tracing.
    • \n
\n\n
Returns
\n\n
\n

A list of traced models.

\n
\n", "signature": "(self, mode_in: str, mode_out: str) -> List[Tuple[str, int]]:", "funcdef": "async def"}, "zodiac.streams.model_stream.ModelStream.chart_path": {"fullname": "zodiac.streams.model_stream.ModelStream.chart_path", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.chart_path", "kind": "function", "doc": "

Return hop names of current path

\n\n
Returns
\n\n
\n

List of [x,y,z] node names along the chosen path

\n
\n", "signature": "(self) -> List[str]:", "funcdef": "async def"}, "zodiac.streams.model_stream.ModelStream.index": {"fullname": "zodiac.streams.model_stream.ModelStream.index", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.index", "kind": "function", "doc": "

\n", "signature": "(self, entry):", "funcdef": "def"}, "zodiac.streams.model_stream.ModelStream.clear": {"fullname": "zodiac.streams.model_stream.ModelStream.clear", "modulename": "zodiac.streams.model_stream", "qualname": "ModelStream.clear", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "async def"}, "zodiac.streams.plot_stream": {"fullname": "zodiac.streams.plot_stream", "modulename": "zodiac.streams.plot_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.plot_stream.main": {"fullname": "zodiac.streams.plot_stream.main", "modulename": "zodiac.streams.plot_stream", "qualname": "main", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "async def"}, "zodiac.streams.task_stream": {"fullname": "zodiac.streams.task_stream", "modulename": "zodiac.streams.task_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.task_stream.nfo": {"fullname": "zodiac.streams.task_stream.nfo", "modulename": "zodiac.streams.task_stream", "qualname": "nfo", "kind": "function", "doc": "

Prints the values to a stream, or to sys.stdout by default.

\n\n

sep\n string inserted between values, default a space.\nend\n string appended after the last value, default a newline.\nfile\n a file-like object (stream); defaults to the current sys.stdout.\nflush\n whether to forcibly flush the stream.

\n", "signature": "(*args, sep=' ', end='\\n', file=None, flush=False):", "funcdef": "def"}, "zodiac.streams.task_stream.flatten_map": {"fullname": "zodiac.streams.task_stream.flatten_map", "modulename": "zodiac.streams.task_stream", "qualname": "flatten_map", "kind": "function", "doc": "

\n", "signature": "(nested: List[str], unpack: str):", "funcdef": "def"}, "zodiac.streams.task_stream.TaskStream": {"fullname": "zodiac.streams.task_stream.TaskStream", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream", "kind": "class", "doc": "

A base class for data sources, providing an implementation of data\nnotifications.

\n", "bases": "toga.sources.base.Source"}, "zodiac.streams.task_stream.TaskStream.basic_tasks": {"fullname": "zodiac.streams.task_stream.TaskStream.basic_tasks", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.basic_tasks", "kind": "variable", "doc": "

\n"}, "zodiac.streams.task_stream.TaskStream.exclusive_tasks": {"fullname": "zodiac.streams.task_stream.TaskStream.exclusive_tasks", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.exclusive_tasks", "kind": "variable", "doc": "

\n"}, "zodiac.streams.task_stream.TaskStream.all_tasks": {"fullname": "zodiac.streams.task_stream.TaskStream.all_tasks", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.all_tasks", "kind": "variable", "doc": "

\n"}, "zodiac.streams.task_stream.TaskStream.set_filter_type": {"fullname": "zodiac.streams.task_stream.TaskStream.set_filter_type", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.set_filter_type", "kind": "function", "doc": "

Filter class items by modality

\n\n
Parameters
\n\n
    \n
  • mode_in: Input modality operation, defaults to \"image\"
    • \n
  • mode_out: Output modality operation, defaults to \"image
    • \n
\n", "signature": "(self, mode_in: str = 'image', mode_out: str = 'image') -> None:", "funcdef": "async def"}, "zodiac.streams.task_stream.TaskStream.filter_tasks": {"fullname": "zodiac.streams.task_stream.TaskStream.filter_tasks", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.filter_tasks", "kind": "function", "doc": "

Processes preformatted task data by removing specified prefixes and keywords, then adds valid data to task_data.

\n\n
Parameters
\n\n
    \n
  • preformatted_task_data: A list of strings to be processed.
    • \n
  • snip_words: A list of prefixes or suffixes to be removed from each pipe.
    • \n
\n\n
Returns
\n\n
\n

A sorted list of unique task_data entries after processing.

\n
\n", "signature": "(\tself,\tregistry_entry: zodiac.providers.registry_entry.RegistryEntry) -> List[str]:", "funcdef": "async def"}, "zodiac.streams.task_stream.TaskStream.index": {"fullname": "zodiac.streams.task_stream.TaskStream.index", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.index", "kind": "function", "doc": "

\n", "signature": "(self, entry):", "funcdef": "def"}, "zodiac.streams.task_stream.TaskStream.clear": {"fullname": "zodiac.streams.task_stream.TaskStream.clear", "modulename": "zodiac.streams.task_stream", "qualname": "TaskStream.clear", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "async def"}, "zodiac.streams.token_stream": {"fullname": "zodiac.streams.token_stream", "modulename": "zodiac.streams.token_stream", "kind": "module", "doc": "

\n"}, "zodiac.streams.token_stream.TokenStream": {"fullname": "zodiac.streams.token_stream.TokenStream", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream", "kind": "class", "doc": "

A base class for data sources, providing an implementation of data\nnotifications.

\n", "bases": "toga.sources.base.Source"}, "zodiac.streams.token_stream.TokenStream.tokenizer": {"fullname": "zodiac.streams.token_stream.TokenStream.tokenizer", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream.tokenizer", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "zodiac.streams.token_stream.TokenStream.message": {"fullname": "zodiac.streams.token_stream.TokenStream.message", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream.message", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "zodiac.streams.token_stream.TokenStream.tokenizer_args": {"fullname": "zodiac.streams.token_stream.TokenStream.tokenizer_args", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream.tokenizer_args", "kind": "variable", "doc": "

\n"}, "zodiac.streams.token_stream.TokenStream.set_tokenizer": {"fullname": "zodiac.streams.token_stream.TokenStream.set_tokenizer", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream.set_tokenizer", "kind": "function", "doc": "

Pass message to model routine

\n\n
Parameters
\n\n
    \n
  • model: Path to model
    • \n
  • message: Text to encode
    • \n
\n\n
Returns
\n\n
\n

Token embeddings for the model

\n
\n", "signature": "(\tself,\tregistry_entry: zodiac.providers.registry_entry.RegistryEntry) -> Callable:", "funcdef": "async def"}, "zodiac.streams.token_stream.TokenStream.token_count": {"fullname": "zodiac.streams.token_stream.TokenStream.token_count", "modulename": "zodiac.streams.token_stream", "qualname": "TokenStream.token_count", "kind": "function", "doc": "

Return token count of message based on model

\n\n
Parameters
\n\n
    \n
  • model: Model path to lookup tokenizer for
    • \n
  • message: Message to tokenize
    • \n
\n\n
Returns
\n\n
\n

int Number of tokens needed to represent message

\n
\n", "signature": "(self, message: str) -> Callable:", "funcdef": "async def"}, "zodiac.toga": {"fullname": "zodiac.toga", "modulename": "zodiac.toga", "kind": "module", "doc": "

\n"}, "zodiac.toga.app": {"fullname": "zodiac.toga.app", "modulename": "zodiac.toga.app", "kind": "module", "doc": "

\n"}, "zodiac.toga.app.OS_NAME": {"fullname": "zodiac.toga.app.OS_NAME", "modulename": "zodiac.toga.app", "qualname": "OS_NAME", "kind": "function", "doc": "

Returns the system/OS name, e.g. 'Linux', 'Windows' or 'Java'.

\n\n

An empty string is returned if the value cannot be determined.

\n", "signature": "():", "funcdef": "def"}, "zodiac.toga.app.Interface": {"fullname": "zodiac.toga.app.Interface", "modulename": "zodiac.toga.app", "qualname": "Interface", "kind": "class", "doc": "

\n", "bases": "toga.app.App"}, "zodiac.toga.app.Interface.formatted_units": {"fullname": "zodiac.toga.app.Interface.formatted_units", "modulename": "zodiac.toga.app", "qualname": "Interface.formatted_units", "kind": "variable", "doc": "

\n", "default_value": "[' \u2756 chr', ' \u27d0 tok', ' " sec ']"}, "zodiac.toga.app.Interface.bg_graph": {"fullname": "zodiac.toga.app.Interface.bg_graph", "modulename": "zodiac.toga.app", "qualname": "Interface.bg_graph", "kind": "variable", "doc": "

\n", "default_value": "'#070708'"}, "zodiac.toga.app.Interface.bg_text": {"fullname": "zodiac.toga.app.Interface.bg_text", "modulename": "zodiac.toga.app", "qualname": "Interface.bg_text", "kind": "variable", "doc": "

\n", "default_value": "'#1B1B1B'"}, "zodiac.toga.app.Interface.bg": {"fullname": "zodiac.toga.app.Interface.bg", "modulename": "zodiac.toga.app", "qualname": "Interface.bg", "kind": "variable", "doc": "

\n", "default_value": "'#1B1B1B'"}, "zodiac.toga.app.Interface.bg_static": {"fullname": "zodiac.toga.app.Interface.bg_static", "modulename": "zodiac.toga.app", "qualname": "Interface.bg_static", "kind": "variable", "doc": "

\n", "default_value": "'#5D5E62'"}, "zodiac.toga.app.Interface.activity": {"fullname": "zodiac.toga.app.Interface.activity", "modulename": "zodiac.toga.app", "qualname": "Interface.activity", "kind": "variable", "doc": "

\n", "default_value": "'#8122C4'"}, "zodiac.toga.app.Interface.static": {"fullname": "zodiac.toga.app.Interface.static", "modulename": "zodiac.toga.app", "qualname": "Interface.static", "kind": "variable", "doc": "

\n", "default_value": "Pack(color=rgb(114, 115, 120))"}, "zodiac.toga.app.Interface.fg_static": {"fullname": "zodiac.toga.app.Interface.fg_static", "modulename": "zodiac.toga.app", "qualname": "Interface.fg_static", "kind": "variable", "doc": "

\n", "default_value": "Pack(color=rgb(141, 142, 148))"}, "zodiac.toga.app.Interface.scroll_buffer": {"fullname": "zodiac.toga.app.Interface.scroll_buffer", "modulename": "zodiac.toga.app", "qualname": "Interface.scroll_buffer", "kind": "variable", "doc": "

\n", "default_value": "5000"}, "zodiac.toga.app.Interface.graph_disabled": {"fullname": "zodiac.toga.app.Interface.graph_disabled", "modulename": "zodiac.toga.app", "qualname": "Interface.graph_disabled", "kind": "variable", "doc": "

\n", "default_value": "'http://localhost'"}, "zodiac.toga.app.Interface.graph_server": {"fullname": "zodiac.toga.app.Interface.graph_server", "modulename": "zodiac.toga.app", "qualname": "Interface.graph_server", "kind": "variable", "doc": "

\n", "default_value": "'http://127.0.0.1:8188'"}, "zodiac.toga.app.Interface.status_info": {"fullname": "zodiac.toga.app.Interface.status_info", "modulename": "zodiac.toga.app", "qualname": "Interface.status_info", "kind": "variable", "doc": "

\n", "default_value": "('Connecting...', 'Server?', 'Ready.', 'Done.', 'No File.', 'Read Failed.', 'Attached.', 'Copied.')"}, "zodiac.toga.app.Interface.ticker": {"fullname": "zodiac.toga.app.Interface.ticker", "modulename": "zodiac.toga.app", "qualname": "Interface.ticker", "kind": "function", "doc": "

Process and synthesize input data based on selected model.

\n\n
Parameters
\n\n
    \n

  • widget: The UI widget that triggered this action, typically used for state management.

    • \n

  • external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False

    • \n
\n", "signature": "(\tself,\twidget: Callable,\texternal: bool = False,\t**kwargs) -> toga.widgets.base.Widget:", "funcdef": "async def"}, "zodiac.toga.app.Interface.stream_text": {"fullname": "zodiac.toga.app.Interface.stream_text", "modulename": "zodiac.toga.app", "qualname": "Interface.stream_text", "kind": "function", "doc": "

\n", "signature": "(self, prompts, context_data, predictor_data):", "funcdef": "async def"}, "zodiac.toga.app.Interface.generate_media": {"fullname": "zodiac.toga.app.Interface.generate_media", "modulename": "zodiac.toga.app", "qualname": "Interface.generate_media", "kind": "function", "doc": "

\n", "signature": "(self, prompts, registry_entry) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.halt": {"fullname": "zodiac.toga.app.Interface.halt", "modulename": "zodiac.toga.app", "qualname": "Interface.halt", "kind": "function", "doc": "

Stop processing prompt

\n\n
Parameters
\n\n
    \n
  • widget: The calling widget object
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.empty_prompt": {"fullname": "zodiac.toga.app.Interface.empty_prompt", "modulename": "zodiac.toga.app", "qualname": "Interface.empty_prompt", "kind": "function", "doc": "

Clears the prompt input area.

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.copy_reply": {"fullname": "zodiac.toga.app.Interface.copy_reply", "modulename": "zodiac.toga.app", "qualname": "Interface.copy_reply", "kind": "function", "doc": "

Push the reply into the clipboard

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.attach_file": {"fullname": "zodiac.toga.app.Interface.attach_file", "modulename": "zodiac.toga.app", "qualname": "Interface.attach_file", "kind": "function", "doc": "

Attaches a file's contents to the prompt area.

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.reset_position": {"fullname": "zodiac.toga.app.Interface.reset_position", "modulename": "zodiac.toga.app", "qualname": "Interface.reset_position", "kind": "function", "doc": "

Scrolls text panel to bottom after content update.

\n\n
Parameters
\n\n
    \n
  • widget: text panel widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.on_select_handler": {"fullname": "zodiac.toga.app.Interface.on_select_handler", "modulename": "zodiac.toga.app", "qualname": "Interface.on_select_handler", "kind": "function", "doc": "

React to input/output choice

\n\n
Parameters
\n\n
    \n
  • widget: The widget that triggered the event.
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.model_graph": {"fullname": "zodiac.toga.app.Interface.model_graph", "modulename": "zodiac.toga.app", "qualname": "Interface.model_graph", "kind": "function", "doc": "

Builds the model graph.

\n", "signature": "(self):", "funcdef": "async def"}, "zodiac.toga.app.Interface.token_estimate": {"fullname": "zodiac.toga.app.Interface.token_estimate", "modulename": "zodiac.toga.app", "qualname": "Interface.token_estimate", "kind": "function", "doc": "

Updates character and token count based on user input.

\n\n
Parameters
\n\n
    \n
  • widget: Input widget providing text
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.populate_in_types": {"fullname": "zodiac.toga.app.Interface.populate_in_types", "modulename": "zodiac.toga.app", "qualname": "Interface.populate_in_types", "kind": "function", "doc": "

Builds the input types selection.

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.populate_out_types": {"fullname": "zodiac.toga.app.Interface.populate_out_types", "modulename": "zodiac.toga.app", "qualname": "Interface.populate_out_types", "kind": "function", "doc": "

Builds the output types selection.

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.populate_model_stack": {"fullname": "zodiac.toga.app.Interface.populate_model_stack", "modulename": "zodiac.toga.app", "qualname": "Interface.populate_model_stack", "kind": "function", "doc": "

Builds the model stack selection dropdown.

\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.populate_task_stack": {"fullname": "zodiac.toga.app.Interface.populate_task_stack", "modulename": "zodiac.toga.app", "qualname": "Interface.populate_task_stack", "kind": "function", "doc": "

Builds the task stack selection dropdown.

\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.switch_tabs": {"fullname": "zodiac.toga.app.Interface.switch_tabs", "modulename": "zodiac.toga.app", "qualname": "Interface.switch_tabs", "kind": "function", "doc": "

Switches between text and graph tabs.

\n\n
Parameters
\n\n
    \n
  • widget: The triggering widget (optional), defaults to None
    • \n
\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.app.Interface.ping_server": {"fullname": "zodiac.toga.app.Interface.ping_server", "modulename": "zodiac.toga.app", "qualname": "Interface.ping_server", "kind": "function", "doc": "

\n", "signature": "(\tself,\twidget: toga.widgets.base.Widget,\t**kwargs) -> toga.widgets.base.Widget:", "funcdef": "async def"}, "zodiac.toga.app.Interface.active_server": {"fullname": "zodiac.toga.app.Interface.active_server", "modulename": "zodiac.toga.app", "qualname": "Interface.active_server", "kind": "function", "doc": "

\n", "signature": "(self, enabled: bool = True):", "funcdef": "async def"}, "zodiac.toga.app.Interface.initialize_inputs": {"fullname": "zodiac.toga.app.Interface.initialize_inputs", "modulename": "zodiac.toga.app", "qualname": "Interface.initialize_inputs", "kind": "function", "doc": "

Initializes UI elements for input handling.

\n", "signature": "(self):", "funcdef": "def"}, "zodiac.toga.app.Interface.initialize_static": {"fullname": "zodiac.toga.app.Interface.initialize_static", "modulename": "zodiac.toga.app", "qualname": "Interface.initialize_static", "kind": "function", "doc": "

Create the main input fields

\n", "signature": "(self) -> None:", "funcdef": "def"}, "zodiac.toga.app.Interface.initialize_layout": {"fullname": "zodiac.toga.app.Interface.initialize_layout", "modulename": "zodiac.toga.app", "qualname": "Interface.initialize_layout", "kind": "function", "doc": "

Create the layout of the application.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "zodiac.toga.app.Interface.startup": {"fullname": "zodiac.toga.app.Interface.startup", "modulename": "zodiac.toga.app", "qualname": "Interface.startup", "kind": "function", "doc": "

Startup Logic. Initialize widgets and layout, then asynchronous tasks for populating datagets

\n", "signature": "(self) -> None:", "funcdef": "def"}, "zodiac.toga.app.main": {"fullname": "zodiac.toga.app.main", "modulename": "zodiac.toga.app", "qualname": "main", "kind": "function", "doc": "

The entry point for the application.

\n", "signature": "(url: str = 'http://127.0.0.1:8188'):", "funcdef": "def"}, "zodiac.toga.interface": {"fullname": "zodiac.toga.interface", "modulename": "zodiac.toga.interface", "kind": "module", "doc": "

\n"}, "zodiac.toga.palette": {"fullname": "zodiac.toga.palette", "modulename": "zodiac.toga.palette", "kind": "module", "doc": "

\n"}, "zodiac.toga.palette.CommandPalette": {"fullname": "zodiac.toga.palette.CommandPalette", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette", "kind": "class", "doc": "

\n"}, "zodiac.toga.palette.CommandPalette.ticker": {"fullname": "zodiac.toga.palette.CommandPalette.ticker", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.ticker", "kind": "function", "doc": "

Process and synthesize input data based on selected model.

\n\n
Parameters
\n\n
    \n

  • widget: The UI widget that triggered this action, typically used for state management.

    • \n

  • external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False

    • \n
\n", "signature": "(\tself,\twidget: Callable,\texternal: bool = False,\t**kwargs) -> toga.widgets.base.Widget:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.stream_text": {"fullname": "zodiac.toga.palette.CommandPalette.stream_text", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.stream_text", "kind": "function", "doc": "

\n", "signature": "(self, prompts, context_data, predictor_data):", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.generate_media": {"fullname": "zodiac.toga.palette.CommandPalette.generate_media", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.generate_media", "kind": "function", "doc": "

\n", "signature": "(self, prompts, registry_entry) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.halt": {"fullname": "zodiac.toga.palette.CommandPalette.halt", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.halt", "kind": "function", "doc": "

Stop processing prompt

\n\n
Parameters
\n\n
    \n
  • widget: The calling widget object
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.empty_prompt": {"fullname": "zodiac.toga.palette.CommandPalette.empty_prompt", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.empty_prompt", "kind": "function", "doc": "

Clears the prompt input area.

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.copy_reply": {"fullname": "zodiac.toga.palette.CommandPalette.copy_reply", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.copy_reply", "kind": "function", "doc": "

Push the reply into the clipboard

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.attach_file": {"fullname": "zodiac.toga.palette.CommandPalette.attach_file", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.attach_file", "kind": "function", "doc": "

Attaches a file's contents to the prompt area.

\n\n
Parameters
\n\n
    \n
  • widget: Triggering widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.reset_position": {"fullname": "zodiac.toga.palette.CommandPalette.reset_position", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.reset_position", "kind": "function", "doc": "

Scrolls text panel to bottom after content update.

\n\n
Parameters
\n\n
    \n
  • widget: text panel widget
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.on_select_handler": {"fullname": "zodiac.toga.palette.CommandPalette.on_select_handler", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.on_select_handler", "kind": "function", "doc": "

React to input/output choice

\n\n
Parameters
\n\n
    \n
  • widget: The widget that triggered the event.
    • \n
\n", "signature": "(self, widget, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.model_graph": {"fullname": "zodiac.toga.palette.CommandPalette.model_graph", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.model_graph", "kind": "function", "doc": "

Builds the model graph.

\n", "signature": "(self):", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.populate_in_types": {"fullname": "zodiac.toga.palette.CommandPalette.populate_in_types", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.populate_in_types", "kind": "function", "doc": "

Builds the input types selection.

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.populate_out_types": {"fullname": "zodiac.toga.palette.CommandPalette.populate_out_types", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.populate_out_types", "kind": "function", "doc": "

Builds the output types selection.

\n", "signature": "(self) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.populate_model_stack": {"fullname": "zodiac.toga.palette.CommandPalette.populate_model_stack", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.populate_model_stack", "kind": "function", "doc": "

Builds the model stack selection dropdown.

\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.populate_task_stack": {"fullname": "zodiac.toga.palette.CommandPalette.populate_task_stack", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.populate_task_stack", "kind": "function", "doc": "

Builds the task stack selection dropdown.

\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.switch_tabs": {"fullname": "zodiac.toga.palette.CommandPalette.switch_tabs", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.switch_tabs", "kind": "function", "doc": "

Switches between text and graph tabs.

\n\n
Parameters
\n\n
    \n
  • widget: The triggering widget (optional), defaults to None
    • \n
\n", "signature": "(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.ping_server": {"fullname": "zodiac.toga.palette.CommandPalette.ping_server", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.ping_server", "kind": "function", "doc": "

\n", "signature": "(\tself,\twidget: toga.widgets.base.Widget,\t**kwargs) -> toga.widgets.base.Widget:", "funcdef": "async def"}, "zodiac.toga.palette.CommandPalette.active_server": {"fullname": "zodiac.toga.palette.CommandPalette.active_server", "modulename": "zodiac.toga.palette", "qualname": "CommandPalette.active_server", "kind": "function", "doc": "

\n", "signature": "(self, enabled: bool = True):", "funcdef": "async def"}, "zodiac.toga.signatures": {"fullname": "zodiac.toga.signatures", "modulename": "zodiac.toga.signatures", "kind": "module", "doc": "

\n"}, "zodiac.toga.signatures.StreamActivity": {"fullname": "zodiac.toga.signatures.StreamActivity", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity", "kind": "class", "doc": "

Provides customizable status message streaming for DSPy programs.

\n\n

This class serves as a base for creating custom status message providers. Users can subclass\nand override its methods to define specific status messages for different stages of program execution,\neach method must return a string.

\n\n

Example:

\n\n
\n
class MyStatusMessageProvider(StatusMessageProvider):\n    def lm_start_status_message(self, instance, inputs):\n        return f"Calling LM with inputs {inputs}..."\n\n    def module_end_status_message(self, outputs):\n        return f"Module finished with output: {outputs}!"\n\nprogram = dspy.streamify(dspy.Predict("q->a"), status_message_provider=MyStatusMessageProvider())\n
\n
\n", "bases": "dspy.streaming.messages.StatusMessageProvider"}, "zodiac.toga.signatures.StreamActivity.lm_start_status_message": {"fullname": "zodiac.toga.signatures.StreamActivity.lm_start_status_message", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity.lm_start_status_message", "kind": "function", "doc": "

Status message before a dspy.LM is called.

\n", "signature": "(self, instance, inputs):", "funcdef": "def"}, "zodiac.toga.signatures.StreamActivity.module_start_status_message": {"fullname": "zodiac.toga.signatures.StreamActivity.module_start_status_message", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity.module_start_status_message", "kind": "function", "doc": "

Status message before a dspy.Module or dspy.Predict is called.

\n", "signature": "(self, instance, inputs):", "funcdef": "def"}, "zodiac.toga.signatures.StreamActivity.lm_end_status_message": {"fullname": "zodiac.toga.signatures.StreamActivity.lm_end_status_message", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity.lm_end_status_message", "kind": "function", "doc": "

Status message after a dspy.LM is called.

\n", "signature": "(self, outputs):", "funcdef": "def"}, "zodiac.toga.signatures.StreamActivity.tool_start_status_message": {"fullname": "zodiac.toga.signatures.StreamActivity.tool_start_status_message", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity.tool_start_status_message", "kind": "function", "doc": "

Status message before a dspy.Tool is called.

\n", "signature": "(self, instance, inputs):", "funcdef": "def"}, "zodiac.toga.signatures.StreamActivity.tool_end_status_message": {"fullname": "zodiac.toga.signatures.StreamActivity.tool_end_status_message", "modulename": "zodiac.toga.signatures", "qualname": "StreamActivity.tool_end_status_message", "kind": "function", "doc": "

Status message after a dspy.Tool is called.

\n", "signature": "(self, outputs):", "funcdef": "def"}, "zodiac.toga.signatures.QATask": {"fullname": "zodiac.toga.signatures.QATask", "modulename": "zodiac.toga.signatures", "qualname": "QATask", "kind": "class", "doc": "

Reply with short responses within 60-90 word/10k character code limits

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.QATask.question": {"fullname": "zodiac.toga.signatures.QATask.question", "modulename": "zodiac.toga.signatures", "qualname": "QATask.question", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.QATask.answer": {"fullname": "zodiac.toga.signatures.QATask.answer", "modulename": "zodiac.toga.signatures", "qualname": "QATask.answer", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.TARGET_LANGUAGE": {"fullname": "zodiac.toga.signatures.TARGET_LANGUAGE", "modulename": "zodiac.toga.signatures", "qualname": "TARGET_LANGUAGE", "kind": "variable", "doc": "

\n", "default_value": "'English'"}, "zodiac.toga.signatures.TranslateTask": {"fullname": "zodiac.toga.signatures.TranslateTask", "modulename": "zodiac.toga.signatures", "qualname": "TranslateTask", "kind": "class", "doc": "

Given the fields message, produce the fields translation.

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.TranslateTask.message": {"fullname": "zodiac.toga.signatures.TranslateTask.message", "modulename": "zodiac.toga.signatures", "qualname": "TranslateTask.message", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.image.Image | dspy.adapters.types.audio.Audio | str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.TranslateTask.translation": {"fullname": "zodiac.toga.signatures.TranslateTask.translation", "modulename": "zodiac.toga.signatures", "qualname": "TranslateTask.translation", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.image.Image | dspy.adapters.types.audio.Audio | str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.VisionTask": {"fullname": "zodiac.toga.signatures.VisionTask", "modulename": "zodiac.toga.signatures", "qualname": "VisionTask", "kind": "class", "doc": "

Describe the image in detail.

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.VisionTask.image": {"fullname": "zodiac.toga.signatures.VisionTask.image", "modulename": "zodiac.toga.signatures", "qualname": "VisionTask.image", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.image.Image", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.VisionTask.description": {"fullname": "zodiac.toga.signatures.VisionTask.description", "modulename": "zodiac.toga.signatures", "qualname": "VisionTask.description", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.TranscribeTask": {"fullname": "zodiac.toga.signatures.TranscribeTask", "modulename": "zodiac.toga.signatures", "qualname": "TranscribeTask", "kind": "class", "doc": "

Transcribe spoken words into text

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.TranscribeTask.message": {"fullname": "zodiac.toga.signatures.TranscribeTask.message", "modulename": "zodiac.toga.signatures", "qualname": "TranscribeTask.message", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.audio.Audio", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.TranscribeTask.answer": {"fullname": "zodiac.toga.signatures.TranscribeTask.answer", "modulename": "zodiac.toga.signatures", "qualname": "TranscribeTask.answer", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.GenerativeImageTask": {"fullname": "zodiac.toga.signatures.GenerativeImageTask", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeImageTask", "kind": "class", "doc": "

Given the fields message, produce the fields image.

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.GenerativeImageTask.message": {"fullname": "zodiac.toga.signatures.GenerativeImageTask.message", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeImageTask.message", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.GenerativeImageTask.image": {"fullname": "zodiac.toga.signatures.GenerativeImageTask.image", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeImageTask.image", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.image.Image", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.GenerativeAudioTask": {"fullname": "zodiac.toga.signatures.GenerativeAudioTask", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeAudioTask", "kind": "class", "doc": "

Given the fields message, produce the fields audio.

\n", "bases": "dspy.signatures.signature.Signature"}, "zodiac.toga.signatures.GenerativeAudioTask.message": {"fullname": "zodiac.toga.signatures.GenerativeAudioTask.message", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeAudioTask.message", "kind": "variable", "doc": "

\n", "annotation": ": str", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.GenerativeAudioTask.audio": {"fullname": "zodiac.toga.signatures.GenerativeAudioTask.audio", "modulename": "zodiac.toga.signatures", "qualname": "GenerativeAudioTask.audio", "kind": "variable", "doc": "

\n", "annotation": ": dspy.adapters.types.audio.Audio", "default_value": "PydanticUndefined"}, "zodiac.toga.signatures.QuestionAnswer": {"fullname": "zodiac.toga.signatures.QuestionAnswer", "modulename": "zodiac.toga.signatures", "qualname": "QuestionAnswer", "kind": "class", "doc": "

\n", "bases": "dspy.primitives.module.Module"}, "zodiac.toga.signatures.QuestionAnswer.predict": {"fullname": "zodiac.toga.signatures.QuestionAnswer.predict", "modulename": "zodiac.toga.signatures", "qualname": "QuestionAnswer.predict", "kind": "variable", "doc": "

\n"}, "zodiac.toga.signatures.QuestionAnswer.forward": {"fullname": "zodiac.toga.signatures.QuestionAnswer.forward", "modulename": "zodiac.toga.signatures", "qualname": "QuestionAnswer.forward", "kind": "function", "doc": "

\n", "signature": "(self, question, **kwargs):", "funcdef": "def"}, "zodiac.toga.signatures.Predictor": {"fullname": "zodiac.toga.signatures.Predictor", "modulename": "zodiac.toga.signatures", "qualname": "Predictor", "kind": "class", "doc": "

\n", "bases": "dspy.primitives.module.Module"}, "zodiac.toga.signatures.Predictor.program": {"fullname": "zodiac.toga.signatures.Predictor.program", "modulename": "zodiac.toga.signatures", "qualname": "Predictor.program", "kind": "variable", "doc": "

\n"}, "zodiac.toga.signatures.ready_predictor": {"fullname": "zodiac.toga.signatures.ready_predictor", "modulename": "zodiac.toga.signatures", "qualname": "ready_predictor", "kind": "function", "doc": "

\n", "signature": "(\tregistry_entry: zodiac.providers.registry_entry.RegistryEntry,\tasync_stream: bool = True,\tdspy_stream: bool = True,\tmax_workers: int = 8,\tcache: bool = True):", "funcdef": "async def"}}, "docInfo": {"zodiac": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "zodiac.start_trace": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 7, "bases": 0, "doc": 3}, "zodiac.set_env": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 3}, "zodiac.main": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 29}, "zodiac.graph": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.nfo": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 64, "bases": 0, "doc": 55}, "zodiac.graph.IntentProcessor": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 64, "bases": 0, "doc": 115}, "zodiac.graph.IntentProcessor.intent_graph": {"qualname": 3, "fullname": 5, "annotation": 5, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.coord_path": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.registry_entries": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.models": {"qualname": 2, "fullname": 4, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.weight_idx": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.graph.IntentProcessor.calc_graph": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 38, "bases": 0, "doc": 149}, "zodiac.graph.IntentProcessor.set_path": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 36, "bases": 0, "doc": 47}, "zodiac.graph.IntentProcessor.set_registry_entries": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 27}, "zodiac.graph.IntentProcessor.edit_weight": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 47, "bases": 0, "doc": 81}, "zodiac.graph.IntentProcessor.pull_path_entries": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 60, "bases": 0, "doc": 28}, "zodiac.providers": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.MIR_DB": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 9, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CUETYPE_PATH_NAMED": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 6, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CUETYPE_CONFIG": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.TEMPLATE_CONFIG": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.VERSIONS_DATA": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.VERSIONS_CONFIG": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 103, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.check_host": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 31, "bases": 0, "doc": 58}, "zodiac.providers.constants.has_api": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 37, "bases": 0, "doc": 69}, "zodiac.providers.constants.show_all_docstring": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 17, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.show_available_docstring": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 17, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.check_type_docstring": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 13, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.base_enum_docstring": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 37, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.BaseEnum": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 3}, "zodiac.providers.constants.BaseEnum.show_all": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 11}, "zodiac.providers.constants.BaseEnum.show_available": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 11}, "zodiac.providers.constants.BaseEnum.check_type": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 8}, "zodiac.providers.constants.CueType": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 1, "doc": 3}, "zodiac.providers.constants.CueType.HUB": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.KAGGLE": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.LLAMAFILE": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.LM_STUDIO": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 12, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.MLX_AUDIO": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 12, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.OLLAMA": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.CueType.VLLM": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 10, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.example_str": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 1, "doc": 15}, "zodiac.providers.constants.PkgType.AUDIOGEN": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 17, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.BAGEL": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 16, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.BITNET": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 15, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.BITSANDBYTES": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.DFLOAT11": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 15, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.DIFFUSERS": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.EXLLAMAV2": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.F_LITE": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 19, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.HIDIFFUSION": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 16, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.IMAGE_GEN_AUX": {"qualname": 4, "fullname": 7, "annotation": 2, "default_value": 21, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.JAX": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.KERAS": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.LLAMA": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 12, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.LUMINA_MGPT": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 19, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.LUMINA_MGPT2": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 21, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MFLUX": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MLX_AUDIO": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 13, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MLX_CHROMA": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MLX_LM": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 13, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MLX_VLM": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 13, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.MLX": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.ONNX": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 15, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.ORPHEUS_TTS": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.OUTETTS": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 15, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.PARLER_TTS": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.PLEIAS": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.SENTENCE_TRANSFORMERS": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 13, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.SHOW_O": {"qualname": 3, "fullname": 6, "annotation": 2, "default_value": 18, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.SPANDREL_EXTRA_ARCHES": {"qualname": 4, "fullname": 7, "annotation": 2, "default_value": 15, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.SPANDREL": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.SVDQUANT": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 17, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.TENSORFLOW": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.TORCH": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.TORCHAUDIO": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.TORCHVISION": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.TRANSFORMERS": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.PkgType.VLLM": {"qualname": 2, "fullname": 5, "annotation": 2, "default_value": 11, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 3}, "zodiac.providers.constants.ChipType.initialize_device": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType.CUDA": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 125, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType.MPS": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 53, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType.XPU": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 7, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType.MTIA": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 7, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.ChipType.CPU": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 101, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.GenTypeC": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 76}, "zodiac.providers.constants.GenTypeC.clone": {"qualname": 2, "fullname": 5, "annotation": 10, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.GenTypeC.sync": {"qualname": 2, "fullname": 5, "annotation": 10, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.GenTypeC.translate": {"qualname": 2, "fullname": 5, "annotation": 10, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.GenTypeCText": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 88}, "zodiac.providers.constants.GenTypeCText.research": {"qualname": 2, "fullname": 5, "annotation": 20, "default_value": 1, "signature": 0, "bases": 0, "doc": 3}, "zodiac.providers.constants.GenTypeCText.chain_of_thought": {"qualname": 4, 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{"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"zodiac.providers.constants.GenTypeE": {"tf": 1.7320508075688772}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"zodiac.providers.pools.generate_entry": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "q": {"docs": {"zodiac.toga.signatures.StreamActivity": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"zodiac.providers.constants.GenTypeC": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"zodiac.toga.signatures.StreamActivity": {"tf": 2.449489742783178}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"zodiac.providers.constants.GenTypeCText": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"zodiac.providers.constants.GenTypeCText": {"tf": 1}}, "df": 1, "/": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"zodiac.providers.constants.GenTypeE": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {"zodiac.providers.constants.GenTypeE": {"tf": 1.4142135623730951}, "zodiac.streams.model_stream.ModelStream.chart_path": {"tf": 1}}, "df": 2}, "x": {"docs": {"zodiac.providers.constants.GenTypeE": {"tf": 1.4142135623730951}, "zodiac.streams.model_stream.ModelStream.chart_path": {"tf": 1}}, "df": 2}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {"zodiac.providers.pools.add_mode_types": {"tf": 1}, "zodiac.providers.pools.add_pkg_types": {"tf": 1}, "zodiac.providers.registry_entry.RegistryEntry.create_entry": {"tf": 1}}, "df": 3}, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"zodiac.streams.task_stream.TaskStream.filter_tasks": {"tf": 1}}, "df": 1}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"zodiac.providers.registry_entry.RegistryEntry.create_entry": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {"zodiac.streams.class_stream.stage_class": {"tf": 1}}, "df": 1}}, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {"zodiac.toga.app.OS_NAME": {"tf": 1}}, "df": 1}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; - - // mirrored in build-search-index.js (part 1) - // Also split on html tags. this is a cheap heuristic, but good enough. - elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); - - let searchIndex; - if (docs._isPrebuiltIndex) { - console.info("using precompiled search index"); - searchIndex = elasticlunr.Index.load(docs); - } else { - console.time("building search index"); - // mirrored in build-search-index.js (part 2) - searchIndex = elasticlunr(function () { - this.pipeline.remove(elasticlunr.stemmer); - this.pipeline.remove(elasticlunr.stopWordFilter); - this.addField("qualname"); - this.addField("fullname"); - this.addField("annotation"); - this.addField("default_value"); - this.addField("signature"); - this.addField("bases"); - this.addField("doc"); - this.setRef("fullname"); - }); - for (let doc of docs) { - searchIndex.addDoc(doc); - } - console.timeEnd("building search index"); - } - - return (term) => searchIndex.search(term, { - fields: { - qualname: {boost: 4}, - fullname: {boost: 2}, - annotation: {boost: 2}, - default_value: {boost: 2}, - signature: {boost: 2}, - bases: {boost: 2}, - doc: {boost: 1}, - }, - expand: true - }); -})(); \ No newline at end of file diff --git a/docs/zodiac.html b/docs/zodiac.html deleted file mode 100644 index e5b5651..0000000 --- a/docs/zodiac.html +++ /dev/null @@ -1,486 +0,0 @@ - - - - - - - zodiac API documentation - - - - - - - - - -
-
-

-zodiac

- - - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0  */ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4import sys
-  5import os
-  6import argparse
-  7import multiprocessing as mp
-  8
-  9mp.set_start_method("spawn", force=True)
- 10sys.path.append(os.getcwd())
- 11# import platform
- 12
- 13# if platform.system == "darwin":
- 14#     import multiprocessing as mp
- 15
- 16#     mp.set_start_method("fork", force=True)
- 17
- 18
- 19def start_trace():
- 20    from viztracer import VizTracer
- 21    from datetime import datetime
- 22
- 23    assembled_path = os.path.join("log", f".nnll{datetime.now().strftime('%Y%m%d')}_trace.json")
- 24    os.makedirs("log", exist_ok=True)
- 25    tracer = VizTracer()
- 26    tracer.start()
- 27    return tracer, assembled_path
- 28
- 29
- 30def set_env(args: argparse.ArgumentParser):
- 31    os.environ["TELEMETRY"] = "False"
- 32    os.environ["TOKENIZERS_PARALLELISM"] = "false"
- 33    try:
- 34        import huggingface_hub
- 35    except (ImportError, ModuleNotFoundError, Exception):  # pylint: disable=broad-exception-caught
- 36        pass
- 37    else:
- 38        huggingface_hub.constants.HF_HUB_DISABLE_TELEMETRY = 1  # privacy
- 39        huggingface_hub.constants.HF_HUB_DISABLE_IMPLICIT_TOKEN = 1
- 40        huggingface_hub.constants.HF_XET_HIGH_PERFORMANCE = int(args.net or args.diag)  # download methods (if online)
- 41        huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = int(args.net or args.diag)
- 42        huggingface_hub.constants.HF_HUB_DISABLE_PROGRESS_BARS = int(not args.diag)  # superficial/diagnostic
- 43        huggingface_hub.constants.HF_HUB_OFFLINE = int(not args.net or not args.diag)  # -net = True -> hub offline = False/0
- 44        os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"
- 45        os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"
- 46        os.environ["HF_XET_HIGH_PERFORMANCE"] = str(int(args.net or args.diag))
- 47        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = str(int(args.net or args.diag))
- 48        os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = str(not args.diag)
- 49        os.environ["HF_HUB_OFFLINE"] = str(int(not args.net or not args.diag))
- 50
- 51        os.environ["DISABLE_HF_TOKENIZER_DOWNLOAD"] = str(not args.net or not args.diag)  # litellm
- 52        # huggingface_hub.constants.HF_HUB_VERBOSITY
- 53
- 54    try:
- 55        import litellm
- 56    except (ImportError, ModuleNotFoundError, Exception):  # pylint: disable=broad-exception-caught
- 57        pass
- 58    else:
- 59        litellm.disable_token_counter = False
- 60        litellm.disable_streaming_logging = True
- 61        litellm.turn_off_message_logging = True
- 62        litellm.suppress_debug_info = False
- 63        litellm.json_logs = False  # type: ignore
- 64        litellm.disable_end_user_cost_tracking = True
- 65        litellm.telemetry = False
- 66        litellm.disable_hf_tokenizer_download = not args.net  # -net = True -> disable download = False/0
- 67        os.environ["DISABLE_END_USER_COST_TRACKING"] = "True"
- 68    return True
- 69
- 70
- 71def main() -> None:
- 72    """Parse launch arguments (mostly turning down/disconnecting loud dependency packages)\n
- 73    :param args: Launch arguments from command line
- 74    """
- 75
- 76    parser = argparse.ArgumentParser(description="Multimodal generative media sequencer")
- 77    parser.add_argument("-n", "--net", action="store_true", help="Allow network access (for downloading requirements)")
- 78    parser.add_argument("-t", "--trace", action="store_true", help="Enable trace logs (generated in log folder)")
- 79
- 80    parser.add_argument("-d", "--diag", action="store_true", help="Process using diagnostic settings")
- 81
- 82    args = parser.parse_args()
- 83
- 84    parser = argparse.ArgumentParser(description="Multimodal generative media sequencer")
- 85    parser.add_argument("-n", "--net", action="store_true", help="Allow network access (for downloading requirements)")
- 86    parser.add_argument("-t", "--trace", action="store_true", help="Enable trace logs (generated in log folder)")
- 87
- 88    parser.add_argument("-d", "--diag", action="store_true", help="Process using diagnostic settings")
- 89
- 90    args = parser.parse_args()
- 91
- 92    env_ready = set_env(args)
- 93    if env_ready:
- 94        tracer, assembled_path = start_trace() if args.trace else None, None
- 95        return tracer, assembled_path
- 96
- 97
- 98if __name__ == "__main__":
- 99    tracer, assembled_path = main()
-100    if tracer and hasattr(tracer.stop):
-101        tracer.stop()
-102        tracer.save(output_file=assembled_path)
-
- - -
-
- -
- - def - start_trace(): - - - -
- - 
20def start_trace():
-21    from viztracer import VizTracer
-22    from datetime import datetime
-23
-24    assembled_path = os.path.join("log", f".nnll{datetime.now().strftime('%Y%m%d')}_trace.json")
-25    os.makedirs("log", exist_ok=True)
-26    tracer = VizTracer()
-27    tracer.start()
-28    return tracer, assembled_path
-
- - - - -
-
- -
- - def - set_env(args: argparse.ArgumentParser): - - - -
- - 
31def set_env(args: argparse.ArgumentParser):
-32    os.environ["TELEMETRY"] = "False"
-33    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-34    try:
-35        import huggingface_hub
-36    except (ImportError, ModuleNotFoundError, Exception):  # pylint: disable=broad-exception-caught
-37        pass
-38    else:
-39        huggingface_hub.constants.HF_HUB_DISABLE_TELEMETRY = 1  # privacy
-40        huggingface_hub.constants.HF_HUB_DISABLE_IMPLICIT_TOKEN = 1
-41        huggingface_hub.constants.HF_XET_HIGH_PERFORMANCE = int(args.net or args.diag)  # download methods (if online)
-42        huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = int(args.net or args.diag)
-43        huggingface_hub.constants.HF_HUB_DISABLE_PROGRESS_BARS = int(not args.diag)  # superficial/diagnostic
-44        huggingface_hub.constants.HF_HUB_OFFLINE = int(not args.net or not args.diag)  # -net = True -> hub offline = False/0
-45        os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1"
-46        os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"
-47        os.environ["HF_XET_HIGH_PERFORMANCE"] = str(int(args.net or args.diag))
-48        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = str(int(args.net or args.diag))
-49        os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = str(not args.diag)
-50        os.environ["HF_HUB_OFFLINE"] = str(int(not args.net or not args.diag))
-51
-52        os.environ["DISABLE_HF_TOKENIZER_DOWNLOAD"] = str(not args.net or not args.diag)  # litellm
-53        # huggingface_hub.constants.HF_HUB_VERBOSITY
-54
-55    try:
-56        import litellm
-57    except (ImportError, ModuleNotFoundError, Exception):  # pylint: disable=broad-exception-caught
-58        pass
-59    else:
-60        litellm.disable_token_counter = False
-61        litellm.disable_streaming_logging = True
-62        litellm.turn_off_message_logging = True
-63        litellm.suppress_debug_info = False
-64        litellm.json_logs = False  # type: ignore
-65        litellm.disable_end_user_cost_tracking = True
-66        litellm.telemetry = False
-67        litellm.disable_hf_tokenizer_download = not args.net  # -net = True -> disable download = False/0
-68        os.environ["DISABLE_END_USER_COST_TRACKING"] = "True"
-69    return True
-
- - - - -
-
- -
- - def - main() -> None: - - - -
- - 
72def main() -> None:
-73    """Parse launch arguments (mostly turning down/disconnecting loud dependency packages)\n
-74    :param args: Launch arguments from command line
-75    """
-76
-77    parser = argparse.ArgumentParser(description="Multimodal generative media sequencer")
-78    parser.add_argument("-n", "--net", action="store_true", help="Allow network access (for downloading requirements)")
-79    parser.add_argument("-t", "--trace", action="store_true", help="Enable trace logs (generated in log folder)")
-80
-81    parser.add_argument("-d", "--diag", action="store_true", help="Process using diagnostic settings")
-82
-83    args = parser.parse_args()
-84
-85    parser = argparse.ArgumentParser(description="Multimodal generative media sequencer")
-86    parser.add_argument("-n", "--net", action="store_true", help="Allow network access (for downloading requirements)")
-87    parser.add_argument("-t", "--trace", action="store_true", help="Enable trace logs (generated in log folder)")
-88
-89    parser.add_argument("-d", "--diag", action="store_true", help="Process using diagnostic settings")
-90
-91    args = parser.parse_args()
-92
-93    env_ready = set_env(args)
-94    if env_ready:
-95        tracer, assembled_path = start_trace() if args.trace else None, None
-96        return tracer, assembled_path
-
- - -

Parse launch arguments (mostly turning down/disconnecting loud dependency packages)

- -
Parameters
- -
    -
  • args: Launch arguments from command line
    • -
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/graph.html b/docs/zodiac/graph.html deleted file mode 100644 index bfe4311..0000000 --- a/docs/zodiac/graph.html +++ /dev/null @@ -1,1057 +0,0 @@ - - - - - - - zodiac.graph API documentation - - - - - - - - - -
-
-

-zodiac.graph

- - - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4
-  5import sys
-  6import os
-  7import networkx as nx
-  8from typing import Optional
-  9from nnll.monitor.file import dbug, dbuq
- 10from zodiac.providers.pools import register_models  # leaving here for mocking
- 11
- 12sys.path.append(os.getcwd())
- 13nfo = print
- 14
- 15
- 16class IntentProcessor:
- 17    intent_graph: Optional[dict[nx.Graph]] = None
- 18    coord_path: Optional[list[str]] = None
- 19    registry_entries: Optional[list[dict[dict]]] = None
- 20    models: Optional[list[tuple[str]]] = None
- 21    weight_idx: Optional[list[str]] = None
- 22    # additional_model_names: dict = None
- 23
- 24    def __init__(self, intent_graph: nx.MultiDiGraph = nx.MultiDiGraph()) -> None:
- 25        """
- 26        Create instance of graph processor & initialize objectieves for tracing paths\n
- 27        :param nx_graph:Preassembled graph of models to substitute, default uses nx.MultiDiGraph()
- 28
- 29        ========================================================\n
- 30        ### GIVEN\n
- 31        A : The list of `VALID CONVERSIONS` contains all of Zodiac's supported generative modalities\n
- 32        B : The graph is populated directly from the contents of the list in A\n
- 33        Thus: All possible node start and end points listed in A are included in graph B.\n
- 34        Therefore : It is impossible to call a node that does not exist.\n
- 35        """
- 36        from zodiac.providers.constants import VALID_CONVERSIONS
- 37
- 38        self.intent_graph = intent_graph
- 39        self.intent_graph.add_nodes_from(VALID_CONVERSIONS)
- 40
- 41    async def calc_graph(self, registry_entries: Optional[list] = None) -> None:
- 42        """Generate graph of coordinate pairs from valid conversions\n
- 43        Model libraries are auto-detected from cache loading\n
- 44        :param registry_data: Registry function or method of calling registry, defaults to
- 45        :return: Graph modeling all current ML/AI tasks appended with model data
- 46
- 47        ========================================================\n
- 48        ### GIVEN\n
- 49        A : The set of all models M on the executing system\n
- 50        B : P is the randomly distributed set of start and end points required to graph M\n
- 51        Thus: Because of the randomness of B, the set P is unlikely to construct a complete graph attached all available points.\n
- 52        Therefore : While we can trust a node exists, we **CANNOT** trust the system has an edge to reach it\n
- 53        """
- 54        # import asyncio
- 55
- 56        if not registry_entries:
- 57            registry_entries = await register_models()
- 58        nfo("Building graph...")
- 59
- 60        if registry_entries is None:
- 61            nfo("Registry error, graph attributes not applied.")
- 62        elif len(self.intent_graph.edges) > 0:
- 63            nfo("Edges already calculated")
- 64            return self.intent_graph
- 65        else:
- 66            for model in registry_entries:
- 67                try:
- 68                    self.intent_graph.add_edges_from(model.available_tasks, entry=model, weight=1.0)
- 69                except AttributeError as error_log:
- 70                    dbug(error_log)
- 71                    nfo("Error: Registry initialized but not populated with data. Graph could not create edges.")
- 72
- 73        nfo("Complete {self.intent_graph}")
- 74        return self.intent_graph
- 75
- 76    def set_path(self, mode_in: str, mode_out: str) -> None:
- 77        """Find a valid path from current state (mode_in) to designated state (mode_out)\n
- 78        :param mode_in: Input prompt type or starting state/states
- 79        :type mode_in: str
- 80        :param mode_out: The user-selected ending-state
- 81        :type mode_out: str
- 82        """
- 83
- 84        if nx.has_path(self.intent_graph, mode_in, mode_out):  # Ensure path exists (otherwise 'bidirectional' may loop infinitely)
- 85            # Self loops in the multidirected graph complete themselves
- 86            # In practice, this means often the same model can be used to compute prompt input and response output
- 87            # Unfortunately, this doesn't always work in all modalities, ex. Image to Image.
- 88            # This condition is meant to solve the case of non-text self-loop edge being an incomplete transformation
- 89
- 90            if mode_in == mode_out and mode_in != "text":  # Its not a great solution, but it works for the moment
- 91                orig_mode_out = mode_out
- 92                mode_out = "text"
- 93                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 94                self.coord_path.append(orig_mode_out)
- 95            else:
- 96                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 97                if len(self.coord_path) == 1:
- 98                    self.coord_path.append(mode_out)  # this behaviour likely to change in future
- 99
-100        else:
-101            nfo("No Path available...\n")
-102
-103    def set_registry_entries(self) -> None:
-104        """Populate models list for text fields
-105        Check if model has been adjusted, if so adjust list
-106        1.0 weight bottom, <1.0 weight top"""
-107
-108        try:
-109            self.registry_entries = self.pull_path_entries(self.intent_graph, self.coord_path)
-110        except KeyError as error_log:
-111            dbug(error_log)
-112            return ["", ""]
-113        idx = 0
-114        self.models = []
-115
-116        if self.registry_entries:
-117            for edge, registry in enumerate(self.registry_entries):
-118                model = registry["entry"].model
-119                dbuq(f"node {edge}")
-120                adj_model = (os.path.basename(model), edge)
-121                self.models.append(adj_model)
-122            self.weight_idx = self.weight_idx or []
-123            for model in self.weight_idx:
-124                if model in self.models:
-125                    self.models.remove(model)
-126                    adj_model = (f"*{model[0]}", model[1])
-127                    self.models.insert(idx, adj_model)
-128                    idx += 1
-129
-130    def edit_weight(self, edge_number: str, mode_in: str, mode_out: str) -> None:
-131        """Determine entry edge, determine index, then adjust weight\n
-132        :param edge_number: Text pattern from `models` class attribute to identify the model by
-133        :param mode_in: The conversion type, representing a source graph node
-134        :param mode_out: The target type, , representing a source graph node
-135        :raises ValueError: No models fit the request
-136        """
-137
-138        self.weight_idx = self.weight_idx or []
-139
-140        try:
-141            if not nx.has_path(self.intent_graph, mode_in, mode_out):
-142                raise KeyError()
-143            model = self.intent_graph[mode_in][mode_out][edge_number]["entry"].model
-144        except KeyError as error_log:
-145            nfo(
-146                f"Failed to adjust weight of '{edge_number}' within registry contents \
-147                '{self.intent_graph} {mode_in} {mode_out}'. Model or registry entry not found. "
-148            )
-149            dbug(error_log)
-150            return self.set_registry_entries()
-151
-152        weight = self.intent_graph[mode_in][mode_out][edge_number]["weight"]
-153        item = (os.path.basename(model), edge_number)
-154        nfo(f" model : {model}  weight: {weight} ")
-155
-156        if weight < 1.0:
-157            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight + 0.1, 1)
-158            self.models = [((f"*{os.path.basename(model)}", edge_number))]
-159            if item in self.weight_idx:
-160                self.weight_idx.remove(item)
-161        else:
-162            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight - 0.1, 1)
-163            self.weight_idx.append(item)
-164        self.set_registry_entries()
-165
-166    def pull_path_entries(self, nx_graph: nx.Graph, traced_path: list[tuple]) -> None:
-167        """Create operating instructions from user input
-168        Trace the next hop along the path, collect all compatible models
-169        Set current model based on weight and next available"""
-170
-171        registry_entries = []
-172        if traced_path is not None and nx.has_path(nx_graph, traced_path[0], traced_path[1]):
-173            registry_entries = [  # ruff : noqa
-174                nx_graph[traced_path[index]][traced_path[index + 1]][hop]  #
-175                for index in range(len(traced_path) - 1)  #
-176                for hop in nx_graph[traced_path[index]][traced_path[index + 1]]  #
-177            ]
-178        return registry_entries
-
- - -
-
-
- - def - nfo(*args, sep=' ', end='\n', file=None, flush=False): - - -
- - -

Prints the values to a stream, or to sys.stdout by default.

- -

sep - string inserted between values, default a space. -end - string appended after the last value, default a newline. -file - a file-like object (stream); defaults to the current sys.stdout. -flush - whether to forcibly flush the stream.

-
- - -
-
- -
- - class - IntentProcessor: - - - -
- - 
 17class IntentProcessor:
- 18    intent_graph: Optional[dict[nx.Graph]] = None
- 19    coord_path: Optional[list[str]] = None
- 20    registry_entries: Optional[list[dict[dict]]] = None
- 21    models: Optional[list[tuple[str]]] = None
- 22    weight_idx: Optional[list[str]] = None
- 23    # additional_model_names: dict = None
- 24
- 25    def __init__(self, intent_graph: nx.MultiDiGraph = nx.MultiDiGraph()) -> None:
- 26        """
- 27        Create instance of graph processor & initialize objectieves for tracing paths\n
- 28        :param nx_graph:Preassembled graph of models to substitute, default uses nx.MultiDiGraph()
- 29
- 30        ========================================================\n
- 31        ### GIVEN\n
- 32        A : The list of `VALID CONVERSIONS` contains all of Zodiac's supported generative modalities\n
- 33        B : The graph is populated directly from the contents of the list in A\n
- 34        Thus: All possible node start and end points listed in A are included in graph B.\n
- 35        Therefore : It is impossible to call a node that does not exist.\n
- 36        """
- 37        from zodiac.providers.constants import VALID_CONVERSIONS
- 38
- 39        self.intent_graph = intent_graph
- 40        self.intent_graph.add_nodes_from(VALID_CONVERSIONS)
- 41
- 42    async def calc_graph(self, registry_entries: Optional[list] = None) -> None:
- 43        """Generate graph of coordinate pairs from valid conversions\n
- 44        Model libraries are auto-detected from cache loading\n
- 45        :param registry_data: Registry function or method of calling registry, defaults to
- 46        :return: Graph modeling all current ML/AI tasks appended with model data
- 47
- 48        ========================================================\n
- 49        ### GIVEN\n
- 50        A : The set of all models M on the executing system\n
- 51        B : P is the randomly distributed set of start and end points required to graph M\n
- 52        Thus: Because of the randomness of B, the set P is unlikely to construct a complete graph attached all available points.\n
- 53        Therefore : While we can trust a node exists, we **CANNOT** trust the system has an edge to reach it\n
- 54        """
- 55        # import asyncio
- 56
- 57        if not registry_entries:
- 58            registry_entries = await register_models()
- 59        nfo("Building graph...")
- 60
- 61        if registry_entries is None:
- 62            nfo("Registry error, graph attributes not applied.")
- 63        elif len(self.intent_graph.edges) > 0:
- 64            nfo("Edges already calculated")
- 65            return self.intent_graph
- 66        else:
- 67            for model in registry_entries:
- 68                try:
- 69                    self.intent_graph.add_edges_from(model.available_tasks, entry=model, weight=1.0)
- 70                except AttributeError as error_log:
- 71                    dbug(error_log)
- 72                    nfo("Error: Registry initialized but not populated with data. Graph could not create edges.")
- 73
- 74        nfo("Complete {self.intent_graph}")
- 75        return self.intent_graph
- 76
- 77    def set_path(self, mode_in: str, mode_out: str) -> None:
- 78        """Find a valid path from current state (mode_in) to designated state (mode_out)\n
- 79        :param mode_in: Input prompt type or starting state/states
- 80        :type mode_in: str
- 81        :param mode_out: The user-selected ending-state
- 82        :type mode_out: str
- 83        """
- 84
- 85        if nx.has_path(self.intent_graph, mode_in, mode_out):  # Ensure path exists (otherwise 'bidirectional' may loop infinitely)
- 86            # Self loops in the multidirected graph complete themselves
- 87            # In practice, this means often the same model can be used to compute prompt input and response output
- 88            # Unfortunately, this doesn't always work in all modalities, ex. Image to Image.
- 89            # This condition is meant to solve the case of non-text self-loop edge being an incomplete transformation
- 90
- 91            if mode_in == mode_out and mode_in != "text":  # Its not a great solution, but it works for the moment
- 92                orig_mode_out = mode_out
- 93                mode_out = "text"
- 94                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 95                self.coord_path.append(orig_mode_out)
- 96            else:
- 97                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 98                if len(self.coord_path) == 1:
- 99                    self.coord_path.append(mode_out)  # this behaviour likely to change in future
-100
-101        else:
-102            nfo("No Path available...\n")
-103
-104    def set_registry_entries(self) -> None:
-105        """Populate models list for text fields
-106        Check if model has been adjusted, if so adjust list
-107        1.0 weight bottom, <1.0 weight top"""
-108
-109        try:
-110            self.registry_entries = self.pull_path_entries(self.intent_graph, self.coord_path)
-111        except KeyError as error_log:
-112            dbug(error_log)
-113            return ["", ""]
-114        idx = 0
-115        self.models = []
-116
-117        if self.registry_entries:
-118            for edge, registry in enumerate(self.registry_entries):
-119                model = registry["entry"].model
-120                dbuq(f"node {edge}")
-121                adj_model = (os.path.basename(model), edge)
-122                self.models.append(adj_model)
-123            self.weight_idx = self.weight_idx or []
-124            for model in self.weight_idx:
-125                if model in self.models:
-126                    self.models.remove(model)
-127                    adj_model = (f"*{model[0]}", model[1])
-128                    self.models.insert(idx, adj_model)
-129                    idx += 1
-130
-131    def edit_weight(self, edge_number: str, mode_in: str, mode_out: str) -> None:
-132        """Determine entry edge, determine index, then adjust weight\n
-133        :param edge_number: Text pattern from `models` class attribute to identify the model by
-134        :param mode_in: The conversion type, representing a source graph node
-135        :param mode_out: The target type, , representing a source graph node
-136        :raises ValueError: No models fit the request
-137        """
-138
-139        self.weight_idx = self.weight_idx or []
-140
-141        try:
-142            if not nx.has_path(self.intent_graph, mode_in, mode_out):
-143                raise KeyError()
-144            model = self.intent_graph[mode_in][mode_out][edge_number]["entry"].model
-145        except KeyError as error_log:
-146            nfo(
-147                f"Failed to adjust weight of '{edge_number}' within registry contents \
-148                '{self.intent_graph} {mode_in} {mode_out}'. Model or registry entry not found. "
-149            )
-150            dbug(error_log)
-151            return self.set_registry_entries()
-152
-153        weight = self.intent_graph[mode_in][mode_out][edge_number]["weight"]
-154        item = (os.path.basename(model), edge_number)
-155        nfo(f" model : {model}  weight: {weight} ")
-156
-157        if weight < 1.0:
-158            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight + 0.1, 1)
-159            self.models = [((f"*{os.path.basename(model)}", edge_number))]
-160            if item in self.weight_idx:
-161                self.weight_idx.remove(item)
-162        else:
-163            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight - 0.1, 1)
-164            self.weight_idx.append(item)
-165        self.set_registry_entries()
-166
-167    def pull_path_entries(self, nx_graph: nx.Graph, traced_path: list[tuple]) -> None:
-168        """Create operating instructions from user input
-169        Trace the next hop along the path, collect all compatible models
-170        Set current model based on weight and next available"""
-171
-172        registry_entries = []
-173        if traced_path is not None and nx.has_path(nx_graph, traced_path[0], traced_path[1]):
-174            registry_entries = [  # ruff : noqa
-175                nx_graph[traced_path[index]][traced_path[index + 1]][hop]  #
-176                for index in range(len(traced_path) - 1)  #
-177                for hop in nx_graph[traced_path[index]][traced_path[index + 1]]  #
-178            ]
-179        return registry_entries
-
- - - - -
- -
- - IntentProcessor( intent_graph: networkx.classes.multidigraph.MultiDiGraph = <networkx.classes.multidigraph.MultiDiGraph object>) - - - -
- - 
25    def __init__(self, intent_graph: nx.MultiDiGraph = nx.MultiDiGraph()) -> None:
-26        """
-27        Create instance of graph processor & initialize objectieves for tracing paths\n
-28        :param nx_graph:Preassembled graph of models to substitute, default uses nx.MultiDiGraph()
-29
-30        ========================================================\n
-31        ### GIVEN\n
-32        A : The list of `VALID CONVERSIONS` contains all of Zodiac's supported generative modalities\n
-33        B : The graph is populated directly from the contents of the list in A\n
-34        Thus: All possible node start and end points listed in A are included in graph B.\n
-35        Therefore : It is impossible to call a node that does not exist.\n
-36        """
-37        from zodiac.providers.constants import VALID_CONVERSIONS
-38
-39        self.intent_graph = intent_graph
-40        self.intent_graph.add_nodes_from(VALID_CONVERSIONS)
-
- - -

Create instance of graph processor & initialize objectieves for tracing paths

- -
Parameters
- -
    -
  • nx_graph: Preassembled graph of models to substitute, default uses nx.MultiDiGraph()
    • -
- -

========================================================

- -

GIVEN

- -

A : The list of VALID CONVERSIONS contains all of Zodiac's supported generative modalities

- -

B : The graph is populated directly from the contents of the list in A

- -

Thus: All possible node start and end points listed in A are included in graph B.

- -

Therefore : It is impossible to call a node that does not exist.

-
- - -
-
-
- intent_graph: Optional[dict[networkx.classes.graph.Graph]] = -None - - -
- - - - -
-
-
- coord_path: Optional[list[str]] = -None - - -
- - - - -
-
-
- registry_entries: Optional[list[dict[dict]]] = -None - - -
- - - - -
-
-
- models: Optional[list[tuple[str]]] = -None - - -
- - - - -
-
-
- weight_idx: Optional[list[str]] = -None - - -
- - - - -
-
- -
- - async def - calc_graph(self, registry_entries: Optional[list] = None) -> None: - - - -
- - 
42    async def calc_graph(self, registry_entries: Optional[list] = None) -> None:
-43        """Generate graph of coordinate pairs from valid conversions\n
-44        Model libraries are auto-detected from cache loading\n
-45        :param registry_data: Registry function or method of calling registry, defaults to
-46        :return: Graph modeling all current ML/AI tasks appended with model data
-47
-48        ========================================================\n
-49        ### GIVEN\n
-50        A : The set of all models M on the executing system\n
-51        B : P is the randomly distributed set of start and end points required to graph M\n
-52        Thus: Because of the randomness of B, the set P is unlikely to construct a complete graph attached all available points.\n
-53        Therefore : While we can trust a node exists, we **CANNOT** trust the system has an edge to reach it\n
-54        """
-55        # import asyncio
-56
-57        if not registry_entries:
-58            registry_entries = await register_models()
-59        nfo("Building graph...")
-60
-61        if registry_entries is None:
-62            nfo("Registry error, graph attributes not applied.")
-63        elif len(self.intent_graph.edges) > 0:
-64            nfo("Edges already calculated")
-65            return self.intent_graph
-66        else:
-67            for model in registry_entries:
-68                try:
-69                    self.intent_graph.add_edges_from(model.available_tasks, entry=model, weight=1.0)
-70                except AttributeError as error_log:
-71                    dbug(error_log)
-72                    nfo("Error: Registry initialized but not populated with data. Graph could not create edges.")
-73
-74        nfo("Complete {self.intent_graph}")
-75        return self.intent_graph
-
- - -

Generate graph of coordinate pairs from valid conversions

- -

Model libraries are auto-detected from cache loading

- -
Parameters
- -
    -
  • registry_data: Registry function or method of calling registry, defaults to
    • -
- -
Returns
- -
-

Graph modeling all current ML/AI tasks appended with model data

-
- -

========================================================

- -

GIVEN

- -

A : The set of all models M on the executing system

- -

B : P is the randomly distributed set of start and end points required to graph M

- -

Thus: Because of the randomness of B, the set P is unlikely to construct a complete graph attached all available points.

- -

Therefore : While we can trust a node exists, we CANNOT trust the system has an edge to reach it

-
- - -
-
- -
- - def - set_path(self, mode_in: str, mode_out: str) -> None: - - - -
- - 
 77    def set_path(self, mode_in: str, mode_out: str) -> None:
- 78        """Find a valid path from current state (mode_in) to designated state (mode_out)\n
- 79        :param mode_in: Input prompt type or starting state/states
- 80        :type mode_in: str
- 81        :param mode_out: The user-selected ending-state
- 82        :type mode_out: str
- 83        """
- 84
- 85        if nx.has_path(self.intent_graph, mode_in, mode_out):  # Ensure path exists (otherwise 'bidirectional' may loop infinitely)
- 86            # Self loops in the multidirected graph complete themselves
- 87            # In practice, this means often the same model can be used to compute prompt input and response output
- 88            # Unfortunately, this doesn't always work in all modalities, ex. Image to Image.
- 89            # This condition is meant to solve the case of non-text self-loop edge being an incomplete transformation
- 90
- 91            if mode_in == mode_out and mode_in != "text":  # Its not a great solution, but it works for the moment
- 92                orig_mode_out = mode_out
- 93                mode_out = "text"
- 94                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 95                self.coord_path.append(orig_mode_out)
- 96            else:
- 97                self.coord_path = nx.bidirectional_shortest_path(self.intent_graph, mode_in, mode_out)
- 98                if len(self.coord_path) == 1:
- 99                    self.coord_path.append(mode_out)  # this behaviour likely to change in future
-100
-101        else:
-102            nfo("No Path available...\n")
-
- - -

Find a valid path from current state (mode_in) to designated state (mode_out)

- -
Parameters
- -
    -
  • mode_in: Input prompt type or starting state/states
    • -
  • mode_out: The user-selected ending-state
    • -
-
- - -
-
- -
- - def - set_registry_entries(self) -> None: - - - -
- - 
104    def set_registry_entries(self) -> None:
-105        """Populate models list for text fields
-106        Check if model has been adjusted, if so adjust list
-107        1.0 weight bottom, <1.0 weight top"""
-108
-109        try:
-110            self.registry_entries = self.pull_path_entries(self.intent_graph, self.coord_path)
-111        except KeyError as error_log:
-112            dbug(error_log)
-113            return ["", ""]
-114        idx = 0
-115        self.models = []
-116
-117        if self.registry_entries:
-118            for edge, registry in enumerate(self.registry_entries):
-119                model = registry["entry"].model
-120                dbuq(f"node {edge}")
-121                adj_model = (os.path.basename(model), edge)
-122                self.models.append(adj_model)
-123            self.weight_idx = self.weight_idx or []
-124            for model in self.weight_idx:
-125                if model in self.models:
-126                    self.models.remove(model)
-127                    adj_model = (f"*{model[0]}", model[1])
-128                    self.models.insert(idx, adj_model)
-129                    idx += 1
-
- - -

Populate models list for text fields -Check if model has been adjusted, if so adjust list -1.0 weight bottom, <1.0 weight top

-
- - -
-
- -
- - def - edit_weight(self, edge_number: str, mode_in: str, mode_out: str) -> None: - - - -
- - 
131    def edit_weight(self, edge_number: str, mode_in: str, mode_out: str) -> None:
-132        """Determine entry edge, determine index, then adjust weight\n
-133        :param edge_number: Text pattern from `models` class attribute to identify the model by
-134        :param mode_in: The conversion type, representing a source graph node
-135        :param mode_out: The target type, , representing a source graph node
-136        :raises ValueError: No models fit the request
-137        """
-138
-139        self.weight_idx = self.weight_idx or []
-140
-141        try:
-142            if not nx.has_path(self.intent_graph, mode_in, mode_out):
-143                raise KeyError()
-144            model = self.intent_graph[mode_in][mode_out][edge_number]["entry"].model
-145        except KeyError as error_log:
-146            nfo(
-147                f"Failed to adjust weight of '{edge_number}' within registry contents \
-148                '{self.intent_graph} {mode_in} {mode_out}'. Model or registry entry not found. "
-149            )
-150            dbug(error_log)
-151            return self.set_registry_entries()
-152
-153        weight = self.intent_graph[mode_in][mode_out][edge_number]["weight"]
-154        item = (os.path.basename(model), edge_number)
-155        nfo(f" model : {model}  weight: {weight} ")
-156
-157        if weight < 1.0:
-158            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight + 0.1, 1)
-159            self.models = [((f"*{os.path.basename(model)}", edge_number))]
-160            if item in self.weight_idx:
-161                self.weight_idx.remove(item)
-162        else:
-163            self.intent_graph[mode_in][mode_out][edge_number]["weight"] = round(weight - 0.1, 1)
-164            self.weight_idx.append(item)
-165        self.set_registry_entries()
-
- - -

Determine entry edge, determine index, then adjust weight

- -
Parameters
- -
    -
  • edge_number: Text pattern from models class attribute to identify the model by
    • -
  • mode_in: The conversion type, representing a source graph node
    • -
  • mode_out: The target type, , representing a source graph node
    • -
- -
Raises
- -
    -
  • ValueError: No models fit the request
    • -
-
- - -
-
- -
- - def - pull_path_entries( self, nx_graph: networkx.classes.graph.Graph, traced_path: list[tuple]) -> None: - - - -
- - 
167    def pull_path_entries(self, nx_graph: nx.Graph, traced_path: list[tuple]) -> None:
-168        """Create operating instructions from user input
-169        Trace the next hop along the path, collect all compatible models
-170        Set current model based on weight and next available"""
-171
-172        registry_entries = []
-173        if traced_path is not None and nx.has_path(nx_graph, traced_path[0], traced_path[1]):
-174            registry_entries = [  # ruff : noqa
-175                nx_graph[traced_path[index]][traced_path[index + 1]][hop]  #
-176                for index in range(len(traced_path) - 1)  #
-177                for hop in nx_graph[traced_path[index]][traced_path[index + 1]]  #
-178            ]
-179        return registry_entries
-
- - -

Create operating instructions from user input -Trace the next hop along the path, collect all compatible models -Set current model based on weight and next available

-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/providers.html b/docs/zodiac/providers.html deleted file mode 100644 index 46060b2..0000000 --- a/docs/zodiac/providers.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - zodiac.providers API documentation - - - - - - - - - -
-
-

-zodiac.providers

- - - - - - - 
1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0  */ -->
-2#  # # <!-- // /*  d a r k s h a p e s */ -->
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/providers/constants.html b/docs/zodiac/providers/constants.html deleted file mode 100644 index 6382b09..0000000 --- a/docs/zodiac/providers/constants.html +++ /dev/null @@ -1,2633 +0,0 @@ - - - - - - - zodiac.providers.constants API documentation - - - - - - - - - -
-
-

-zodiac.providers.constants

- - - - - - - 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
-  2# <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4
-  5# pylint:disable=no-name-in-module
-  6
-  7import os
-  8from enum import Enum
-  9from typing import Annotated, Callable, List, Optional, Union
- 10
- 11from nnll.configure.init_gpu import first_available
- 12from nnll.mir.json_cache import TEMPLATE_PATH_NAMED, VERSIONS_PATH_NAMED, JSONCache
- 13from nnll.mir.maid import MIRDatabase
- 14from nnll.monitor.file import dbuq
- 15from pydantic import BaseModel, Field
- 16from transformers.pipelines import PIPELINE_REGISTRY
- 17
- 18MIR_DB = MIRDatabase()
- 19CUETYPE_PATH_NAMED = os.path.join(os.path.dirname(__file__), "cuetype.json")
- 20CUETYPE_CONFIG = JSONCache(CUETYPE_PATH_NAMED)
- 21TEMPLATE_CONFIG = JSONCache(TEMPLATE_PATH_NAMED)
- 22VERSIONS_DATA = JSONCache(VERSIONS_PATH_NAMED)
- 23VERSIONS_DATA._load_cache()
- 24VERSIONS_CONFIG = VERSIONS_DATA._cache
- 25
- 26
- 27def check_host(api_name: str, api_url: str) -> bool:
- 28    """Perform network test to ensure a host server is running\n
- 29    :param api_name: Type of host API
- 30    :param api_url: The (default) configuration data for that API
- 31    :return: Whether the server is up or not
- 32    """
- 33
- 34    from json.decoder import JSONDecodeError
- 35
- 36    import httpcore
- 37    import httpx
- 38    import requests
- 39    from openai import APIConnectionError, APIStatusError, APITimeoutError  # , JSONDecodeError,
- 40    from urllib3.exceptions import MaxRetryError, NewConnectionError
- 41
- 42    if api_name == "LM_STUDIO":
- 43        from lmstudio import APIConnectionError, APIStatusError, APITimeoutError, JSONDecodeError
- 44    try:
- 45        dbuq(api_url)
- 46        request = requests.get(api_url, timeout=(1, 1))
- 47        if request is not None:
- 48            dbuq(vars(request))
- 49            if hasattr(request, "status_code"):
- 50                status = request.status_code
- 51                dbuq(status)
- 52            if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
- 53                dbuq(f"Available {api_name}")
- 54                return True
- 55            elif hasattr(request, "json"):
- 56                status = request.json()
- 57                if status.get("result") == "OK":
- 58                    dbuq(f"Available {api_name}")
- 59                    return True
- 60            request.raise_for_status()
- 61            requests.HTTPError()
- 62    except (
- 63        APIConnectionError,
- 64        APITimeoutError,
- 65        APIStatusError,
- 66        requests.exceptions.InvalidURL,
- 67        requests.exceptions.ConnectionError,
- 68        requests.adapters.ConnectionError,
- 69        requests.HTTPError,
- 70        httpcore.ConnectError,
- 71        httpx.ConnectError,
- 72        ConnectionRefusedError,
- 73        MaxRetryError,
- 74        NewConnectionError,
- 75        TimeoutError,
- 76        JSONDecodeError,
- 77        OSError,
- 78        RuntimeError,
- 79        ConnectionError,
- 80    ) as error_log:
- 81        dbuq(error_log)
- 82    return False
- 83
- 84
- 85@CUETYPE_CONFIG.decorator
- 86def has_api(api_name: str, data: dict = None) -> bool:
- 87    """Check available modules, try to import dynamically.\n
- 88    True for successful import, else False\n
- 89
- 90    :param api_name: Constant name for API
- 91    :param _data: filled by config decorator, ignore, defaults to None
- 92    :return: Package availability or `check_host` for servers; boolean result
- 93    :rtype: bool
- 94    """
- 95    from importlib import import_module
- 96    from json.decoder import JSONDecodeError
- 97
- 98    hosted_apis = ["OLLAMA", "LM_STUDIO", "LLAMAFILE", "VLLM"]  # , "CORTEX" ] #became jan
- 99    try:
-100        api_data = data.get(api_name, {"module": api_name.lower()})  # pylint: disable=unsubscriptable-object
-101    except JSONDecodeError as error_log:
-102        dbuq(error_log)
-103        return False
-104    try:
-105        module = import_module(api_data.get("module"))
-106        if module:
-107            if api_name not in hosted_apis:
-108                return True
-109            else:
-110                dbuq(api_data.get("api_url"))
-111                url = api_data.get("api_url")
-112                if url:
-113                    return check_host(api_name, url)
-114    except (UnboundLocalError, ImportError, ModuleNotFoundError, JSONDecodeError) as error_log:
-115        dbuq(error_log)
-116    dbuq("|Ignorable| Source unavailable:", f"{api_name}")
-117    return False
-118
-119
-120show_all_docstring = ":param _show_all(): Show all POSSIBLE API types of a given class"
-121show_available_docstring = ":param _show_available(): Show all AVAILABLE API types of a given class"
-122check_type_docstring = ":param _check_type: Check for a SINGLE API availability"
-123
-124base_enum_docstring = f"""{show_all_docstring}{show_available_docstring}{check_type_docstring}"""
-125
-126
-127class BaseEnum(Enum):
-128    f"""{base_enum_docstring}"""
-129
-130    @classmethod
-131    def show_all(cls) -> List:
-132        """Show all POSSIBLE API types of a given class"""
-133        return [x for x, y in cls.__members__.items()]
-134
-135    @classmethod
-136    def show_available(cls) -> bool:
-137        """Show all AVAILABLE API types of a given class"""
-138        return [library.value[1] for library in list(cls) if library.value[0] is True]
-139
-140    @classmethod
-141    def check_type(cls, type_name: str) -> bool:
-142        """Check for a SINGLE API availability"""
-143        type_name = type_name.upper()
-144        return has_api(type_name)
-145
-146
-147class CueType(BaseEnum):
-148    f"""Model Provider constants\n
-149    Caches and servers\n
-150    <NAME: (Availability, IMPORT_NAME)>{base_enum_docstring}
-151    {base_enum_docstring}"""
-152
-153    # Dfferentiation of boolean conditions
-154    # GIVEN : The state of all provider modules & servers are marked at launch
-155
-156    HUB: tuple = (has_api("HUB"), "HUB")
-157    KAGGLE: tuple = (has_api("KAGGLE"), "KAGGLE")
-158    LLAMAFILE: tuple = (has_api("LLAMAFILE"), "LLAMAFILE")
-159    LM_STUDIO: tuple = (has_api("LM_STUDIO"), "LM_STUDIO")
-160    MLX_AUDIO: tuple = (has_api("MLX_AUDIO"), "MLX_AUDIO")
-161    OLLAMA: tuple = (has_api("OLLAMA"), "OLLAMA")
-162    VLLM: tuple = (has_api("VLLM"), "VLLM")
-163
-164
-165example_str = ("function_name", "import.function_name")
-166
-167
-168class PkgType(BaseEnum):
-169    """Package dependency constants
-170    Collected info from hub model tags and dependencies
-171    <NAME: (Availability, IMPORT_NAME, [Github repositories*]
-172    *if applicable, otherwise IMPORT_NAME is pip package
-173    NOTE: NAME is colloquial and does not always match IMPORT_NAME>"""
-174
-175    AUDIOGEN: tuple = (has_api("AUDIOCRAFT"), "AUDIOCRAFT", ["exdysa/facebookresearch-audiocraft-revamp"])  # this fork supports mps
-176    BAGEL: tuple = (has_api("BAGEL"), "BAGEL", ["bytedance-seed/BAGEL"])
-177    BITNET: tuple = (has_api("BITNET"), "BITNET", ["microsoft/BitNet"])
-178    BITSANDBYTES: tuple = (has_api("BITSANDBYTES"), "BITSANDBYTES", [])  # bitsandbytes-foundation/bitsandbytes
-179    DFLOAT11: tuple = (has_api("DFLOAT11"), "DFLOAT11", ["LeanModels/DFloat11"])
-180    DIFFUSERS: tuple = (has_api("DIFFUSERS"), "DIFFUSERS", [])
-181    EXLLAMAV2: tuple = (has_api("EXLLAMAV2"), "EXLLAMAV2", [])  # turboderp-org/exllamav2
-182    F_LITE: tuple = (has_api("F_LITE"), "F_LITE", ["fal-ai/f-lite"])
-183    HIDIFFUSION: tuple = (has_api("HIDIFFUSION"), "HIDIFFUSION", ["megvii-research/HiDiffusion"])
-184    IMAGE_GEN_AUX: tuple = (has_api("IMAGE_GEN_AUX"), "IMAGE_GEN_AUX", ["huggingface/image_gen_aux"])
-185    JAX: tuple = (has_api("JAX"), "JAX", [])
-186    KERAS: tuple = (has_api("KERAS"), "KERAS", [])
-187    LLAMA: tuple = (has_api("LLAMA_CPP"), "LLAMA_CPP", [])
-188    LUMINA_MGPT: tuple = (has_api("INFERENCE_SOLVER"), "INFERENCE_SOLVER", ["Alpha-VLLM/Lumina-mGPT"])
-189    LUMINA_MGPT2: tuple = (has_api("INFERENCE_SOLVER"), "INFERENCE_SOLVER", ["Alpha-VLLM/Lumina-mGPT-2.0"])
-190    MFLUX: tuple = (has_api("MFLUX"), "MFLUX", [])  # "filipstrand/mflux"
-191    MLX_AUDIO: tuple = (CueType.check_type("MLX_AUDIO"), "MLX_AUDIO", [])  # Blaizzy/mlx-audio
-192    MLX_CHROMA: tuple = (has_api("CHROMA"), "CHROMA", ["exdysa/jack813-mlx-chroma"])
-193    MLX_LM: tuple = (has_api("MLX_LM"), "MLX_LM", [])  # "ml-explore/mlx-lm"
-194    MLX_VLM: tuple = (has_api("MLX_VLM"), "MLX_VLM", [])  # Blaizzy/mlx-vlm
-195    MLX: tuple = (has_api("MLX_LM"), "MLX", [])
-196    ONNX: tuple = (has_api("ONNX"), "ONNX", ["ONNX"])
-197    ORPHEUS_TTS: tuple = (has_api("ORPHEUS_TTS"), "ORPHEUS_TTS", ["canopyai/Orpheus-TTS"])
-198    OUTETTS: tuple = (has_api("OUTETTS"), "OUTETTS", ["edwko/OuteTTS"])
-199    PARLER_TTS: tuple = (has_api("PARLER_TTS"), "PARLER_TTS", ["huggingface/parler-tts"])
-200    PLEIAS: tuple = (has_api("PLEIAS"), "PLEIAS", ["exdysa/Pleias-Pleias-RAG-Library"])  # bypasses vllm for macos to avoid requiring gcc/AVIX
-201    SENTENCE_TRANSFORMERS: tuple = (has_api("SENTENCE_TRANSFORMERS"), "SENTENCE_TRANSFORMERS", [])  # UKPLab/sentence-transformers
-202    SHOW_O: tuple = (has_api("SHOW_O"), "SHOW_O", ["showlab/show-o"])
-203    SPANDREL_EXTRA_ARCHES: tuple = (has_api("SPANDREL_EXTRA_ARCHES"), "SPANDREL_EXTRA_ARCHES", [])
-204    SPANDREL: tuple = (has_api("SPANDREL"), "SPANDREL", [])
-205    SVDQUANT: tuple = (has_api("NUNCHAKU"), "NUNCHAKU", ["mit-han-lab/nunchaku"])
-206    TENSORFLOW: tuple = (has_api("TENSORFLOW"), "TENSORFLOW", [])
-207    TORCH: tuple = (has_api("TORCH"), "TORCH", [])  # Possible that torch is NOT needed (mlx_lm, or some other unforeseen future )
-208    TORCHAUDIO: tuple = (has_api("TORCHAUDIO"), "TORCHAUDIO", [])
-209    TORCHVISION: tuple = (has_api("TORCHVISION"), "TORCHVISION", [])
-210    TRANSFORMERS: tuple = (has_api("TRANSFORMERS"), "TRANSFORMERS", [])
-211    VLLM: tuple = (CueType.check_type("VLLM"), "VLLM", [])
-212
-213
-214class ChipType(Enum):
-215    f"""Device constants\n
-216    CUDA, MPS, XPU, MTIA [Supported PkgTypes]\n
-217    {base_enum_docstring}"""
-218
-219    def __call__(cls):
-220        cls.initialie_device()
-221
-222    @classmethod
-223    def initialize_device(cls) -> None:
-224        chip_types = [
-225            (
-226                "CUDA",
-227                [
-228                    PkgType.BAGEL,
-229                    PkgType.BITSANDBYTES,
-230                    PkgType.DFLOAT11,
-231                    PkgType.EXLLAMAV2,
-232                    PkgType.F_LITE,
-233                    PkgType.LUMINA_MGPT,
-234                    PkgType.ORPHEUS_TTS,
-235                    PkgType.OUTETTS,
-236                    PkgType.VLLM,
-237                ],
-238            ),
-239            ("MPS", [PkgType.MFLUX, PkgType.MLX_AUDIO, PkgType.MLX_LM, PkgType.BAGEL]),
-240            ("XPU", []),
-241            ("MTIA", []),
-242        ]
-243        cls._device = first_available(assign=True, init=True, clean=True)  # pylint:disable=no-member, protected-access
-244        if hasattr(cls._device, "type"):
-245            gpu = cls._device.type
-246        else:
-247            gpu = ""
-248        for name, pkg_type in chip_types:
-249            setattr(cls, name, (name.lower() in gpu, name, pkg_type))
-250        setattr(
-251            cls,
-252            "CPU",
-253            (
-254                True,
-255                "CPU",
-256                [
-257                    PkgType.AUDIOGEN,
-258                    PkgType.PARLER_TTS,
-259                    PkgType.LLAMA,
-260                    PkgType.HIDIFFUSION,
-261                    PkgType.SENTENCE_TRANSFORMERS,
-262                    PkgType.DIFFUSERS,
-263                    PkgType.TRANSFORMERS,
-264                    PkgType.TORCH,
-265                ],
-266            ),
-267        )
-268
-269    @classmethod
-270    def _show_all(cls) -> List[str]:
-271        """Show all POSSIBLE processor types"""
-272        atypes = [atype for atype in cls.__dict__ if "_" not in atype]
-273        return atypes
-274
-275    @classmethod
-276    def _show_ready(cls, api_name: Optional[str] = None) -> Union[List[str], bool]:
-277        """Show all READY devices.\n
-278        If api_name is provided, checks if the specific API is ready.
-279        :param api_name: Boolean check for the specific API by name, defaults to None
-280        :return: `bool` or list of ready devices
-281        """
-282        atypes = cls._show_all()
-283        if api_name:
-284            return api_name.upper() in [x for x in atypes if getattr(cls, x)[0]]
-285        return [getattr(cls, x) for x in atypes if getattr(cls, x)[0] is True]
-286
-287    @classmethod
-288    def _show_pkgs(cls) -> Union[List[PkgType], str]:
-289        """Return compatible PkgTypes for all available chipsets\n
-290        If no chipsets are detected, returns onlyCPU compatibility options\n
-291        :return: `PkgType`s for the available processors including CPU
-292        """
-293        pkg_names = getattr(cls, "CPU")[-1]
-294        atypes = cls._show_ready()
-295        available = [pkg[1] for pkg in atypes if pkg[0] is True]
-296        priority = next(iter(available), "CPU") if available else "CPU"
-297        if priority not in [pkg[1] for pkg in cls._show_all() if not pkg[2]] and priority != "CPU":
-298            pkg_names = atypes[0][2] + pkg_names
-299        return pkg_names
-300
-301
-302ChipType.initialize_device()
-303
-304
-305# class PipeType(Enum):
-306#     MFLUX: tuple = (ChipType._show_ready("mps"), PkgType.check_type("MFLUX"), {"mir_tag": "flux"})  # pylint:disable=protected-access
-307# MFLUX: tuple = ("MPS" in ChipType._show_ready("mps"), PkgType.MFLUX, {"mir_tag": "flux"})  # pylint:disable=protected-access
-308
-309
-310# Experimental way to abstract/declarify complex task names
-311class GenTypeC(BaseModel):
-312    """
-313    Generative inference types in ***C***-dimensional order\n
-314    ***Comprehensiveness***, sorted from 'most involved' to 'least involved'\n
-315    The terms define 'artistic' and ambiguous operations\n
-316
-317    :param clone: Copying identity, voice, exact mirror
-318    :param sync: Tone, tempo, color, quality, genre, scale, mood
-319    :param translate: A range of comprehensible approximations\n
-320    """
-321
-322    clone: Annotated[Callable | None, Field(default=None)]
-323    sync: Annotated[Callable | None, Field(default=None)]
-324    translate: Annotated[Callable | None, Field(default=None)]
-325
-326
-327class GenTypeCText(BaseModel):
-328    """
-329    Generative inference types in ***C***-dimensional order for text operations\n
-330    ***Comprehensiveness***, sorted from 'most involved' to 'least involved'\n
-331    The terms define 'concrete' and more rigid operations\n
-332
-333    :param research: Quoting, paraphrasing, and deriving from sources
-334    :param chain_of_thought: A performance of processing step-by-step (similar to `reasoning`)
-335    :param question_answer: Basic, straightforward responses\n
-336    """
-337
-338    research: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-339    chain_of_thought: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-340    question_answer: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-341
-342
-343class GenTypeE(BaseModel):
-344    """
-345    Generative inference operation types in ***E***-dimensional order \n
-346    ***Equivalence***, lists sorted from 'highly-similar' to 'loosely correlated.'"\n
-347    :param universal: Affecting all conversions
-348    :param text: Text-only conversions\n
-349
-350    ***multimedia generation***
-351    ```
-352    Y-axis: Detail (Most involved to least involved)
-353    │
-354    │                             clone
-355    │                 sync
-356    │ translate
-357    │
-358    +───────────────────────────────────────> X-axis: Equivalence (Loosely correlated to highly similar)
-359    ```
-360    ***text generation***
-361    ```
-362    Y-axis: Detail (Most involved to least involved)
-363    │
-364    │                           research
-365    │             chain-of-thought
-366    │ question/answer
-367    │
-368    +───────────────────────────────────────> X-axis:  Equivalence (Loosely correlated to highly similar)
-369    ```
-370
-371    This is essentially the translation operation of C types, and the mapping of them to E \n
-372
-373    An abstract generalization of the set of all multimodal generative synthesis processes\n
-374    The sum of each coordinate pair reflects effective compute use\n
-375    In this way, both C types and their similarity are translatable, but not 1:1 identical\n
-376    Text is allowed to perform all 6 core operations. Other media perform only 3.\n
-377    """
-378
-379    # note: `sync` may have better terms, such as 'harmonize' or 'attune'. `sync` was chosen because it is shorter
-380
-381    universal: GenTypeC = GenTypeC(clone=None, sync=None, translate=None)
-382    text: GenTypeCText = GenTypeCText(research=None, chain_of_thought=None, question_answer=None)
-383
-384
-385# Here, a case could be made that tasks could be determined by filters, rather than graphing
-386# This is valid but, it offers no guarantees for difficult logic conditions that can be easily verified by graph algorithms
-387# Using graphs also allows us to offload the logic elsewhere
-388
-389VALID_CONVERSIONS = ["text", "image", "music", "speech", "audio", "video", "3d", "vector_graphic", "upscale_image"]
-390VALID_JUNCTIONS = [""]
-391
-392# note : decide on a way to keep paired tuples and sets together inside config dict
-393
-394tasks = PIPELINE_REGISTRY.get_supported_tasks() + ["translation_XX_to_YY"]
-395
-396VALID_TASKS = {  # normalized to lowercase
-397    CueType.VLLM: {
-398        ("text", "text"): ["text"],
-399        ("image", "text"): ["vision"],
-400    },
-401    CueType.OLLAMA: {
-402        ("text", "text"): ["mllama"],
-403        ("image", "text"): ["llava", "vllm"],
-404    },
-405    CueType.LLAMAFILE: {
-406        ("text", "text"): ["text"],
-407    },
-408    CueType.LM_STUDIO: {
-409        # ("image", "text"): [("vision", True)],
-410        ("text", "text"): ["llm"],
-411    },
-412    CueType.HUB: {
-413        ("image", "image"): [
-414            "image-to-image",
-415            "inpaint",
-416            "inpainting",
-417            "depth-to-image",
-418            "i2i",
-419            "image-to-image",
-420            "any-to-any",
-421        ],
-422        ("text", "image"): ["kolors", "kolorspipeline", "image-generation", "any-to-any", "text-to-image", "chromapipeline"],
-423        ("image", "text"): [
-424            "image-classification",
-425            "image-to-text",
-426            "image-text-to-text",
-427            "visual-question-answering",
-428            "image-captioning",
-429            "image-segmentation",
-430            "depth-estimation",
-431            "image-feature-extraction",
-432            "mask-generation",
-433            "object-detection",
-434            "visual-question-answering",
-435            "keypoint-detection",
-436            "vllm",
-437            "vqa",
-438            "vision",
-439            "zero-shot-object-detection",
-440            "zero-shot-image-classification",
-441            "timm",
-442            "zero-shot image classification",
-443            "any-to-any",
-444            "vidore",
-445        ],
-446        ("image", "video"): ["image-to-video", "i2v", "reference-to-video", "refernce-to-video"],
-447        ("video", "text"): ["video-classification"],
-448        ("text", "video"): ["video generation", "t2v", "text-to-video", "HunyuanVideoPipeline"],
-449        ("text", "text"): [
-450            "any-to-any",
-451            "named entity recognition",
-452            "entity typing",
-453            "relation classification",
-454            "question answering",
-455            "fill-mask",
-456            "chat",
-457            "conversational",
-458            "text-generation",
-459            "causal-lm",
-460            "text2text-generation",
-461            "document-question-answering",
-462            "feature-extraction",
-463            "question-answering",
-464            "sentiment-analysis",
-465            "summarization",
-466            "table-question-answering",
-467            "text-classification",
-468            "token-classification",
-469            "translation",
-470            "zero-shot-classification",
-471            "translation_xx_to_yy",
-472            "t2t",
-473            "chatglm",
-474            "exbert",
-475        ],
-476        ("text", "audio"): ["text-to-audio", "t2a", "any-to-any", "text-to-audio", " musicldmpipeline", "musicgen", "audiocraft", "audiogen", "AudioLDMPipeline", "AudioLDM2Pipeline"],
-477        ("audio", "text"): ["zero-shot-audio-classification", "audio-classification", "a2t", "audio-text-to-text", "any-to-any"],
-478        ("text", "speech"): [
-479            "text-to-speech",
-480            "tts",
-481            "any-to-any",
-482            "annotation",
-483        ],
-484        ("speech", "text"): [
-485            "speech-to-text",
-486            "speech",
-487            "speech-translation",
-488            "speech-summarization",
-489            "automatic-speech-recognition",
-490            "dictation",
-491            "stt",
-492            "any-to-any",
-493            "hf-asr-leaderboard",
-494        ],
-495    },
-496    CueType.KAGGLE: {
-497        ("text", "text"): ["text"],
-498    },
-499    # CueType.CORTEX: {
-500    #     ("text", "text"): ["text"],
-501    # },
-502}
-
- - -
-
-
- MIR_DB = -<nnll.mir.maid.MIRDatabase object> - - -
- - - - -
-
-
- CUETYPE_PATH_NAMED = -'/Users/unauthorized/Documents/GitHub/cursor/darkshapes/zodiac/zodiac/providers/cuetype.json' - - -
- - - - -
-
-
- CUETYPE_CONFIG = -<nnll.mir.json_cache.JSONCache object> - - -
- - - - -
-
-
- TEMPLATE_CONFIG = -<nnll.mir.json_cache.JSONCache object> - - -
- - - - -
-
-
- VERSIONS_DATA = -<nnll.mir.json_cache.JSONCache object> - - -
- - - - -
-
-
- VERSIONS_CONFIG = - - {'semantic': ['-?\\d+[bBmMkK]', '-?v\\d+', '(?<=\\d)[.-](?=\\d)', '-prior$', '-diffusers$', '-large$', '-medium$'], 'suffixes': ['-\\d{1,2}[bBmMkK]', '-\\d[1-9][bBmMkK]', '-v\\d{1,2}', '-\\d{3,}$', '-\\d{4,}.*', '-\\d{4,}[px].*'], 'ignore': ['-xt$', '-box$', '-preview$', '-base.*', '-Tiny$', '-full$', '-mini.*', '-multimodal.*', '-instruct.*']} - - -
- - - - -
-
- -
- - def - check_host(api_name: str, api_url: str) -> bool: - - - -
- - 
28def check_host(api_name: str, api_url: str) -> bool:
-29    """Perform network test to ensure a host server is running\n
-30    :param api_name: Type of host API
-31    :param api_url: The (default) configuration data for that API
-32    :return: Whether the server is up or not
-33    """
-34
-35    from json.decoder import JSONDecodeError
-36
-37    import httpcore
-38    import httpx
-39    import requests
-40    from openai import APIConnectionError, APIStatusError, APITimeoutError  # , JSONDecodeError,
-41    from urllib3.exceptions import MaxRetryError, NewConnectionError
-42
-43    if api_name == "LM_STUDIO":
-44        from lmstudio import APIConnectionError, APIStatusError, APITimeoutError, JSONDecodeError
-45    try:
-46        dbuq(api_url)
-47        request = requests.get(api_url, timeout=(1, 1))
-48        if request is not None:
-49            dbuq(vars(request))
-50            if hasattr(request, "status_code"):
-51                status = request.status_code
-52                dbuq(status)
-53            if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-54                dbuq(f"Available {api_name}")
-55                return True
-56            elif hasattr(request, "json"):
-57                status = request.json()
-58                if status.get("result") == "OK":
-59                    dbuq(f"Available {api_name}")
-60                    return True
-61            request.raise_for_status()
-62            requests.HTTPError()
-63    except (
-64        APIConnectionError,
-65        APITimeoutError,
-66        APIStatusError,
-67        requests.exceptions.InvalidURL,
-68        requests.exceptions.ConnectionError,
-69        requests.adapters.ConnectionError,
-70        requests.HTTPError,
-71        httpcore.ConnectError,
-72        httpx.ConnectError,
-73        ConnectionRefusedError,
-74        MaxRetryError,
-75        NewConnectionError,
-76        TimeoutError,
-77        JSONDecodeError,
-78        OSError,
-79        RuntimeError,
-80        ConnectionError,
-81    ) as error_log:
-82        dbuq(error_log)
-83    return False
-
- - -

Perform network test to ensure a host server is running

- -
Parameters
- -
    -
  • api_name: Type of host API
    • -
  • api_url: The (default) configuration data for that API
    • -
- -
Returns
- -
-

Whether the server is up or not

-
-
- - -
-
- -
-
@CUETYPE_CONFIG.decorator
- - def - has_api(api_name: str, data: dict = None) -> bool: - - - -
- - 
 86@CUETYPE_CONFIG.decorator
- 87def has_api(api_name: str, data: dict = None) -> bool:
- 88    """Check available modules, try to import dynamically.\n
- 89    True for successful import, else False\n
- 90
- 91    :param api_name: Constant name for API
- 92    :param _data: filled by config decorator, ignore, defaults to None
- 93    :return: Package availability or `check_host` for servers; boolean result
- 94    :rtype: bool
- 95    """
- 96    from importlib import import_module
- 97    from json.decoder import JSONDecodeError
- 98
- 99    hosted_apis = ["OLLAMA", "LM_STUDIO", "LLAMAFILE", "VLLM"]  # , "CORTEX" ] #became jan
-100    try:
-101        api_data = data.get(api_name, {"module": api_name.lower()})  # pylint: disable=unsubscriptable-object
-102    except JSONDecodeError as error_log:
-103        dbuq(error_log)
-104        return False
-105    try:
-106        module = import_module(api_data.get("module"))
-107        if module:
-108            if api_name not in hosted_apis:
-109                return True
-110            else:
-111                dbuq(api_data.get("api_url"))
-112                url = api_data.get("api_url")
-113                if url:
-114                    return check_host(api_name, url)
-115    except (UnboundLocalError, ImportError, ModuleNotFoundError, JSONDecodeError) as error_log:
-116        dbuq(error_log)
-117    dbuq("|Ignorable| Source unavailable:", f"{api_name}")
-118    return False
-
- - -

Check available modules, try to import dynamically.

- -

True for successful import, else False

- -
Parameters
- -
    -
  • api_name: Constant name for API
    • -
  • _data: filled by config decorator, ignore, defaults to None
    • -
- -
Returns
- -
-

Package availability or check_host for servers; boolean result

-
-
- - -
-
-
- show_all_docstring = -':param _show_all(): Show all POSSIBLE API types of a given class' - - -
- - - - -
-
-
- show_available_docstring = -':param _show_available(): Show all AVAILABLE API types of a given class' - - -
- - - - -
-
-
- check_type_docstring = -':param _check_type: Check for a SINGLE API availability' - - -
- - - - -
-
-
- base_enum_docstring = - - ':param _show_all(): Show all POSSIBLE API types of a given class:param _show_available(): Show all AVAILABLE API types of a given class:param _check_type: Check for a SINGLE API availability' - - -
- - - - -
-
- -
- - class - BaseEnum(enum.Enum): - - - -
- - 
128class BaseEnum(Enum):
-129    f"""{base_enum_docstring}"""
-130
-131    @classmethod
-132    def show_all(cls) -> List:
-133        """Show all POSSIBLE API types of a given class"""
-134        return [x for x, y in cls.__members__.items()]
-135
-136    @classmethod
-137    def show_available(cls) -> bool:
-138        """Show all AVAILABLE API types of a given class"""
-139        return [library.value[1] for library in list(cls) if library.value[0] is True]
-140
-141    @classmethod
-142    def check_type(cls, type_name: str) -> bool:
-143        """Check for a SINGLE API availability"""
-144        type_name = type_name.upper()
-145        return has_api(type_name)
-
- - - - -
- -
-
@classmethod
- - def - show_all(cls) -> List: - - - -
- - 
131    @classmethod
-132    def show_all(cls) -> List:
-133        """Show all POSSIBLE API types of a given class"""
-134        return [x for x, y in cls.__members__.items()]
-
- - -

Show all POSSIBLE API types of a given class

-
- - -
-
- -
-
@classmethod
- - def - show_available(cls) -> bool: - - - -
- - 
136    @classmethod
-137    def show_available(cls) -> bool:
-138        """Show all AVAILABLE API types of a given class"""
-139        return [library.value[1] for library in list(cls) if library.value[0] is True]
-
- - -

Show all AVAILABLE API types of a given class

-
- - -
-
- -
-
@classmethod
- - def - check_type(cls, type_name: str) -> bool: - - - -
- - 
141    @classmethod
-142    def check_type(cls, type_name: str) -> bool:
-143        """Check for a SINGLE API availability"""
-144        type_name = type_name.upper()
-145        return has_api(type_name)
-
- - -

Check for a SINGLE API availability

-
- - -
-
-
- -
- - class - CueType(BaseEnum): - - - -
- - 
148class CueType(BaseEnum):
-149    f"""Model Provider constants\n
-150    Caches and servers\n
-151    <NAME: (Availability, IMPORT_NAME)>{base_enum_docstring}
-152    {base_enum_docstring}"""
-153
-154    # Dfferentiation of boolean conditions
-155    # GIVEN : The state of all provider modules & servers are marked at launch
-156
-157    HUB: tuple = (has_api("HUB"), "HUB")
-158    KAGGLE: tuple = (has_api("KAGGLE"), "KAGGLE")
-159    LLAMAFILE: tuple = (has_api("LLAMAFILE"), "LLAMAFILE")
-160    LM_STUDIO: tuple = (has_api("LM_STUDIO"), "LM_STUDIO")
-161    MLX_AUDIO: tuple = (has_api("MLX_AUDIO"), "MLX_AUDIO")
-162    OLLAMA: tuple = (has_api("OLLAMA"), "OLLAMA")
-163    VLLM: tuple = (has_api("VLLM"), "VLLM")
-
- - - - -
-
- HUB: tuple = -<CueType.HUB: (True, 'HUB')> - - -
- - - - -
-
-
- KAGGLE: tuple = -<CueType.KAGGLE: (False, 'KAGGLE')> - - -
- - - - -
-
-
- LLAMAFILE: tuple = -<CueType.LLAMAFILE: (False, 'LLAMAFILE')> - - -
- - - - -
-
-
- LM_STUDIO: tuple = -<CueType.LM_STUDIO: (False, 'LM_STUDIO')> - - -
- - - - -
-
-
- MLX_AUDIO: tuple = -<CueType.MLX_AUDIO: (True, 'MLX_AUDIO')> - - -
- - - - -
-
-
- OLLAMA: tuple = -<CueType.OLLAMA: (True, 'OLLAMA')> - - -
- - - - -
-
-
- VLLM: tuple = -<CueType.VLLM: (False, 'VLLM')> - - -
- - - - -
-
-
Inherited Members
-
- -
-
-
-
-
- example_str = -('function_name', 'import.function_name') - - -
- - - - -
-
- -
- - class - PkgType(BaseEnum): - - - -
- - 
169class PkgType(BaseEnum):
-170    """Package dependency constants
-171    Collected info from hub model tags and dependencies
-172    <NAME: (Availability, IMPORT_NAME, [Github repositories*]
-173    *if applicable, otherwise IMPORT_NAME is pip package
-174    NOTE: NAME is colloquial and does not always match IMPORT_NAME>"""
-175
-176    AUDIOGEN: tuple = (has_api("AUDIOCRAFT"), "AUDIOCRAFT", ["exdysa/facebookresearch-audiocraft-revamp"])  # this fork supports mps
-177    BAGEL: tuple = (has_api("BAGEL"), "BAGEL", ["bytedance-seed/BAGEL"])
-178    BITNET: tuple = (has_api("BITNET"), "BITNET", ["microsoft/BitNet"])
-179    BITSANDBYTES: tuple = (has_api("BITSANDBYTES"), "BITSANDBYTES", [])  # bitsandbytes-foundation/bitsandbytes
-180    DFLOAT11: tuple = (has_api("DFLOAT11"), "DFLOAT11", ["LeanModels/DFloat11"])
-181    DIFFUSERS: tuple = (has_api("DIFFUSERS"), "DIFFUSERS", [])
-182    EXLLAMAV2: tuple = (has_api("EXLLAMAV2"), "EXLLAMAV2", [])  # turboderp-org/exllamav2
-183    F_LITE: tuple = (has_api("F_LITE"), "F_LITE", ["fal-ai/f-lite"])
-184    HIDIFFUSION: tuple = (has_api("HIDIFFUSION"), "HIDIFFUSION", ["megvii-research/HiDiffusion"])
-185    IMAGE_GEN_AUX: tuple = (has_api("IMAGE_GEN_AUX"), "IMAGE_GEN_AUX", ["huggingface/image_gen_aux"])
-186    JAX: tuple = (has_api("JAX"), "JAX", [])
-187    KERAS: tuple = (has_api("KERAS"), "KERAS", [])
-188    LLAMA: tuple = (has_api("LLAMA_CPP"), "LLAMA_CPP", [])
-189    LUMINA_MGPT: tuple = (has_api("INFERENCE_SOLVER"), "INFERENCE_SOLVER", ["Alpha-VLLM/Lumina-mGPT"])
-190    LUMINA_MGPT2: tuple = (has_api("INFERENCE_SOLVER"), "INFERENCE_SOLVER", ["Alpha-VLLM/Lumina-mGPT-2.0"])
-191    MFLUX: tuple = (has_api("MFLUX"), "MFLUX", [])  # "filipstrand/mflux"
-192    MLX_AUDIO: tuple = (CueType.check_type("MLX_AUDIO"), "MLX_AUDIO", [])  # Blaizzy/mlx-audio
-193    MLX_CHROMA: tuple = (has_api("CHROMA"), "CHROMA", ["exdysa/jack813-mlx-chroma"])
-194    MLX_LM: tuple = (has_api("MLX_LM"), "MLX_LM", [])  # "ml-explore/mlx-lm"
-195    MLX_VLM: tuple = (has_api("MLX_VLM"), "MLX_VLM", [])  # Blaizzy/mlx-vlm
-196    MLX: tuple = (has_api("MLX_LM"), "MLX", [])
-197    ONNX: tuple = (has_api("ONNX"), "ONNX", ["ONNX"])
-198    ORPHEUS_TTS: tuple = (has_api("ORPHEUS_TTS"), "ORPHEUS_TTS", ["canopyai/Orpheus-TTS"])
-199    OUTETTS: tuple = (has_api("OUTETTS"), "OUTETTS", ["edwko/OuteTTS"])
-200    PARLER_TTS: tuple = (has_api("PARLER_TTS"), "PARLER_TTS", ["huggingface/parler-tts"])
-201    PLEIAS: tuple = (has_api("PLEIAS"), "PLEIAS", ["exdysa/Pleias-Pleias-RAG-Library"])  # bypasses vllm for macos to avoid requiring gcc/AVIX
-202    SENTENCE_TRANSFORMERS: tuple = (has_api("SENTENCE_TRANSFORMERS"), "SENTENCE_TRANSFORMERS", [])  # UKPLab/sentence-transformers
-203    SHOW_O: tuple = (has_api("SHOW_O"), "SHOW_O", ["showlab/show-o"])
-204    SPANDREL_EXTRA_ARCHES: tuple = (has_api("SPANDREL_EXTRA_ARCHES"), "SPANDREL_EXTRA_ARCHES", [])
-205    SPANDREL: tuple = (has_api("SPANDREL"), "SPANDREL", [])
-206    SVDQUANT: tuple = (has_api("NUNCHAKU"), "NUNCHAKU", ["mit-han-lab/nunchaku"])
-207    TENSORFLOW: tuple = (has_api("TENSORFLOW"), "TENSORFLOW", [])
-208    TORCH: tuple = (has_api("TORCH"), "TORCH", [])  # Possible that torch is NOT needed (mlx_lm, or some other unforeseen future )
-209    TORCHAUDIO: tuple = (has_api("TORCHAUDIO"), "TORCHAUDIO", [])
-210    TORCHVISION: tuple = (has_api("TORCHVISION"), "TORCHVISION", [])
-211    TRANSFORMERS: tuple = (has_api("TRANSFORMERS"), "TRANSFORMERS", [])
-212    VLLM: tuple = (CueType.check_type("VLLM"), "VLLM", [])
-
- - -

Package dependency constants -Collected info from hub model tags and dependencies -

-
- - -
-
- AUDIOGEN: tuple = -<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])> - - -
- - - - -
-
-
- BAGEL: tuple = -<PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])> - - -
- - - - -
-
-
- BITNET: tuple = -<PkgType.BITNET: (False, 'BITNET', ['microsoft/BitNet'])> - - -
- - - - -
-
-
- BITSANDBYTES: tuple = -<PkgType.BITSANDBYTES: (False, 'BITSANDBYTES', [])> - - -
- - - - -
-
-
- DFLOAT11: tuple = -<PkgType.DFLOAT11: (False, 'DFLOAT11', ['LeanModels/DFloat11'])> - - -
- - - - -
-
-
- DIFFUSERS: tuple = -<PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])> - - -
- - - - -
-
-
- EXLLAMAV2: tuple = -<PkgType.EXLLAMAV2: (False, 'EXLLAMAV2', [])> - - -
- - - - -
-
-
- F_LITE: tuple = -<PkgType.F_LITE: (False, 'F_LITE', ['fal-ai/f-lite'])> - - -
- - - - -
-
-
- HIDIFFUSION: tuple = -<PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])> - - -
- - - - -
-
-
- IMAGE_GEN_AUX: tuple = -<PkgType.IMAGE_GEN_AUX: (False, 'IMAGE_GEN_AUX', ['huggingface/image_gen_aux'])> - - -
- - - - -
-
-
- JAX: tuple = -<PkgType.JAX: (True, 'JAX', [])> - - -
- - - - -
-
-
- KERAS: tuple = -<PkgType.KERAS: (False, 'KERAS', [])> - - -
- - - - -
-
-
- LLAMA: tuple = -<PkgType.LLAMA: (True, 'LLAMA_CPP', [])> - - -
- - - - -
-
-
- LUMINA_MGPT: tuple = -<PkgType.LUMINA_MGPT: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT'])> - - -
- - - - -
-
-
- LUMINA_MGPT2: tuple = -<PkgType.LUMINA_MGPT2: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT-2.0'])> - - -
- - - - -
-
-
- MFLUX: tuple = -<PkgType.MFLUX: (True, 'MFLUX', [])> - - -
- - - - -
-
-
- MLX_AUDIO: tuple = -<PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])> - - -
- - - - -
-
-
- MLX_CHROMA: tuple = -<PkgType.MLX_CHROMA: (False, 'CHROMA', ['exdysa/jack813-mlx-chroma'])> - - -
- - - - -
-
-
- MLX_LM: tuple = -<PkgType.MLX_LM: (True, 'MLX_LM', [])> - - -
- - - - -
-
-
- MLX_VLM: tuple = -<PkgType.MLX_VLM: (True, 'MLX_VLM', [])> - - -
- - - - -
-
-
- MLX: tuple = -<PkgType.MLX: (True, 'MLX', [])> - - -
- - - - -
-
-
- ONNX: tuple = -<PkgType.ONNX: (True, 'ONNX', ['ONNX'])> - - -
- - - - -
-
-
- ORPHEUS_TTS: tuple = -<PkgType.ORPHEUS_TTS: (False, 'ORPHEUS_TTS', ['canopyai/Orpheus-TTS'])> - - -
- - - - -
-
-
- OUTETTS: tuple = -<PkgType.OUTETTS: (False, 'OUTETTS', ['edwko/OuteTTS'])> - - -
- - - - -
-
-
- PARLER_TTS: tuple = -<PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])> - - -
- - - - -
-
-
- PLEIAS: tuple = -<PkgType.PLEIAS: (False, 'PLEIAS', ['exdysa/Pleias-Pleias-RAG-Library'])> - - -
- - - - -
-
-
- SENTENCE_TRANSFORMERS: tuple = -<PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])> - - -
- - - - -
-
-
- SHOW_O: tuple = -<PkgType.SHOW_O: (False, 'SHOW_O', ['showlab/show-o'])> - - -
- - - - -
-
-
- SPANDREL_EXTRA_ARCHES: tuple = -<PkgType.SPANDREL_EXTRA_ARCHES: (False, 'SPANDREL_EXTRA_ARCHES', [])> - - -
- - - - -
-
-
- SPANDREL: tuple = -<PkgType.SPANDREL: (False, 'SPANDREL', [])> - - -
- - - - -
-
-
- SVDQUANT: tuple = -<PkgType.SVDQUANT: (False, 'NUNCHAKU', ['mit-han-lab/nunchaku'])> - - -
- - - - -
-
-
- TENSORFLOW: tuple = -<PkgType.TENSORFLOW: (False, 'TENSORFLOW', [])> - - -
- - - - -
-
-
- TORCH: tuple = -<PkgType.TORCH: (True, 'TORCH', [])> - - -
- - - - -
-
-
- TORCHAUDIO: tuple = -<PkgType.TORCHAUDIO: (True, 'TORCHAUDIO', [])> - - -
- - - - -
-
-
- TORCHVISION: tuple = -<PkgType.TORCHVISION: (True, 'TORCHVISION', [])> - - -
- - - - -
-
-
- TRANSFORMERS: tuple = -<PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])> - - -
- - - - -
-
-
- VLLM: tuple = -<PkgType.VLLM: (False, 'VLLM', [])> - - -
- - - - -
-
-
Inherited Members
-
- -
-
-
-
- -
- - class - ChipType(enum.Enum): - - - -
- - 
215class ChipType(Enum):
-216    f"""Device constants\n
-217    CUDA, MPS, XPU, MTIA [Supported PkgTypes]\n
-218    {base_enum_docstring}"""
-219
-220    def __call__(cls):
-221        cls.initialie_device()
-222
-223    @classmethod
-224    def initialize_device(cls) -> None:
-225        chip_types = [
-226            (
-227                "CUDA",
-228                [
-229                    PkgType.BAGEL,
-230                    PkgType.BITSANDBYTES,
-231                    PkgType.DFLOAT11,
-232                    PkgType.EXLLAMAV2,
-233                    PkgType.F_LITE,
-234                    PkgType.LUMINA_MGPT,
-235                    PkgType.ORPHEUS_TTS,
-236                    PkgType.OUTETTS,
-237                    PkgType.VLLM,
-238                ],
-239            ),
-240            ("MPS", [PkgType.MFLUX, PkgType.MLX_AUDIO, PkgType.MLX_LM, PkgType.BAGEL]),
-241            ("XPU", []),
-242            ("MTIA", []),
-243        ]
-244        cls._device = first_available(assign=True, init=True, clean=True)  # pylint:disable=no-member, protected-access
-245        if hasattr(cls._device, "type"):
-246            gpu = cls._device.type
-247        else:
-248            gpu = ""
-249        for name, pkg_type in chip_types:
-250            setattr(cls, name, (name.lower() in gpu, name, pkg_type))
-251        setattr(
-252            cls,
-253            "CPU",
-254            (
-255                True,
-256                "CPU",
-257                [
-258                    PkgType.AUDIOGEN,
-259                    PkgType.PARLER_TTS,
-260                    PkgType.LLAMA,
-261                    PkgType.HIDIFFUSION,
-262                    PkgType.SENTENCE_TRANSFORMERS,
-263                    PkgType.DIFFUSERS,
-264                    PkgType.TRANSFORMERS,
-265                    PkgType.TORCH,
-266                ],
-267            ),
-268        )
-269
-270    @classmethod
-271    def _show_all(cls) -> List[str]:
-272        """Show all POSSIBLE processor types"""
-273        atypes = [atype for atype in cls.__dict__ if "_" not in atype]
-274        return atypes
-275
-276    @classmethod
-277    def _show_ready(cls, api_name: Optional[str] = None) -> Union[List[str], bool]:
-278        """Show all READY devices.\n
-279        If api_name is provided, checks if the specific API is ready.
-280        :param api_name: Boolean check for the specific API by name, defaults to None
-281        :return: `bool` or list of ready devices
-282        """
-283        atypes = cls._show_all()
-284        if api_name:
-285            return api_name.upper() in [x for x in atypes if getattr(cls, x)[0]]
-286        return [getattr(cls, x) for x in atypes if getattr(cls, x)[0] is True]
-287
-288    @classmethod
-289    def _show_pkgs(cls) -> Union[List[PkgType], str]:
-290        """Return compatible PkgTypes for all available chipsets\n
-291        If no chipsets are detected, returns onlyCPU compatibility options\n
-292        :return: `PkgType`s for the available processors including CPU
-293        """
-294        pkg_names = getattr(cls, "CPU")[-1]
-295        atypes = cls._show_ready()
-296        available = [pkg[1] for pkg in atypes if pkg[0] is True]
-297        priority = next(iter(available), "CPU") if available else "CPU"
-298        if priority not in [pkg[1] for pkg in cls._show_all() if not pkg[2]] and priority != "CPU":
-299            pkg_names = atypes[0][2] + pkg_names
-300        return pkg_names
-
- - - - -
- -
-
@classmethod
- - def - initialize_device(cls) -> None: - - - -
- - 
223    @classmethod
-224    def initialize_device(cls) -> None:
-225        chip_types = [
-226            (
-227                "CUDA",
-228                [
-229                    PkgType.BAGEL,
-230                    PkgType.BITSANDBYTES,
-231                    PkgType.DFLOAT11,
-232                    PkgType.EXLLAMAV2,
-233                    PkgType.F_LITE,
-234                    PkgType.LUMINA_MGPT,
-235                    PkgType.ORPHEUS_TTS,
-236                    PkgType.OUTETTS,
-237                    PkgType.VLLM,
-238                ],
-239            ),
-240            ("MPS", [PkgType.MFLUX, PkgType.MLX_AUDIO, PkgType.MLX_LM, PkgType.BAGEL]),
-241            ("XPU", []),
-242            ("MTIA", []),
-243        ]
-244        cls._device = first_available(assign=True, init=True, clean=True)  # pylint:disable=no-member, protected-access
-245        if hasattr(cls._device, "type"):
-246            gpu = cls._device.type
-247        else:
-248            gpu = ""
-249        for name, pkg_type in chip_types:
-250            setattr(cls, name, (name.lower() in gpu, name, pkg_type))
-251        setattr(
-252            cls,
-253            "CPU",
-254            (
-255                True,
-256                "CPU",
-257                [
-258                    PkgType.AUDIOGEN,
-259                    PkgType.PARLER_TTS,
-260                    PkgType.LLAMA,
-261                    PkgType.HIDIFFUSION,
-262                    PkgType.SENTENCE_TRANSFORMERS,
-263                    PkgType.DIFFUSERS,
-264                    PkgType.TRANSFORMERS,
-265                    PkgType.TORCH,
-266                ],
-267            ),
-268        )
-
- - - - -
-
-
- CUDA = - - (False, 'CUDA', [<PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>, <PkgType.BITSANDBYTES: (False, 'BITSANDBYTES', [])>, <PkgType.DFLOAT11: (False, 'DFLOAT11', ['LeanModels/DFloat11'])>, <PkgType.EXLLAMAV2: (False, 'EXLLAMAV2', [])>, <PkgType.F_LITE: (False, 'F_LITE', ['fal-ai/f-lite'])>, <PkgType.LUMINA_MGPT: (False, 'INFERENCE_SOLVER', ['Alpha-VLLM/Lumina-mGPT'])>, <PkgType.ORPHEUS_TTS: (False, 'ORPHEUS_TTS', ['canopyai/Orpheus-TTS'])>, <PkgType.OUTETTS: (False, 'OUTETTS', ['edwko/OuteTTS'])>, <PkgType.VLLM: (False, 'VLLM', [])>]) - - -
- - - - -
-
-
- MPS = - - (True, 'MPS', [<PkgType.MFLUX: (True, 'MFLUX', [])>, <PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])>, <PkgType.MLX_LM: (True, 'MLX_LM', [])>, <PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>]) - - -
- - - - -
-
-
- XPU = -(False, 'XPU', []) - - -
- - - - -
-
-
- MTIA = -(False, 'MTIA', []) - - -
- - - - -
-
-
- CPU = - - (True, 'CPU', [<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])>, <PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])>, <PkgType.LLAMA: (True, 'LLAMA_CPP', [])>, <PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])>, <PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])>, <PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])>, <PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])>, <PkgType.TORCH: (True, 'TORCH', [])>]) - - -
- - - - -
-
-
- -
- - class - GenTypeC(pydantic.main.BaseModel): - - - -
- - 
312class GenTypeC(BaseModel):
-313    """
-314    Generative inference types in ***C***-dimensional order\n
-315    ***Comprehensiveness***, sorted from 'most involved' to 'least involved'\n
-316    The terms define 'artistic' and ambiguous operations\n
-317
-318    :param clone: Copying identity, voice, exact mirror
-319    :param sync: Tone, tempo, color, quality, genre, scale, mood
-320    :param translate: A range of comprehensible approximations\n
-321    """
-322
-323    clone: Annotated[Callable | None, Field(default=None)]
-324    sync: Annotated[Callable | None, Field(default=None)]
-325    translate: Annotated[Callable | None, Field(default=None)]
-
- - -

Generative inference types in C-dimensional order

- -

Comprehensiveness, sorted from 'most involved' to 'least involved'

- -

The terms define 'artistic' and ambiguous operations

- -
Parameters
- -
    -
  • clone: Copying identity, voice, exact mirror
    • -
  • sync: Tone, tempo, color, quality, genre, scale, mood
    • -
  • translate: A range of comprehensible approximations
    • -
-
- - -
-
- clone: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)] = -None - - -
- - - - -
-
-
- sync: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)] = -None - - -
- - - - -
-
-
- translate: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None)] = -None - - -
- - - - -
-
-
- -
- - class - GenTypeCText(pydantic.main.BaseModel): - - - -
- - 
328class GenTypeCText(BaseModel):
-329    """
-330    Generative inference types in ***C***-dimensional order for text operations\n
-331    ***Comprehensiveness***, sorted from 'most involved' to 'least involved'\n
-332    The terms define 'concrete' and more rigid operations\n
-333
-334    :param research: Quoting, paraphrasing, and deriving from sources
-335    :param chain_of_thought: A performance of processing step-by-step (similar to `reasoning`)
-336    :param question_answer: Basic, straightforward responses\n
-337    """
-338
-339    research: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-340    chain_of_thought: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-341    question_answer: Annotated[Optional[Callable | None], Field(default=None, examples=example_str)]
-
- - -

Generative inference types in C-dimensional order for text operations

- -

Comprehensiveness, sorted from 'most involved' to 'least involved'

- -

The terms define 'concrete' and more rigid operations

- -
Parameters
- -
    -
  • research: Quoting, paraphrasing, and deriving from sources
    • -
  • chain_of_thought: A performance of processing step-by-step (similar to reasoning)
    • -
  • question_answer: Basic, straightforward responses
    • -
-
- - -
-
- research: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))] = -None - - -
- - - - -
-
-
- chain_of_thought: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))] = -None - - -
- - - - -
-
-
- question_answer: Annotated[Optional[Callable], FieldInfo(annotation=NoneType, required=False, default=None, examples=('function_name', 'import.function_name'))] = -None - - -
- - - - -
-
-
- -
- - class - GenTypeE(pydantic.main.BaseModel): - - - -
- - 
344class GenTypeE(BaseModel):
-345    """
-346    Generative inference operation types in ***E***-dimensional order \n
-347    ***Equivalence***, lists sorted from 'highly-similar' to 'loosely correlated.'"\n
-348    :param universal: Affecting all conversions
-349    :param text: Text-only conversions\n
-350
-351    ***multimedia generation***
-352    ```
-353    Y-axis: Detail (Most involved to least involved)
-354    │
-355    │                             clone
-356    │                 sync
-357    │ translate
-358    │
-359    +───────────────────────────────────────> X-axis: Equivalence (Loosely correlated to highly similar)
-360    ```
-361    ***text generation***
-362    ```
-363    Y-axis: Detail (Most involved to least involved)
-364    │
-365    │                           research
-366    │             chain-of-thought
-367    │ question/answer
-368    │
-369    +───────────────────────────────────────> X-axis:  Equivalence (Loosely correlated to highly similar)
-370    ```
-371
-372    This is essentially the translation operation of C types, and the mapping of them to E \n
-373
-374    An abstract generalization of the set of all multimodal generative synthesis processes\n
-375    The sum of each coordinate pair reflects effective compute use\n
-376    In this way, both C types and their similarity are translatable, but not 1:1 identical\n
-377    Text is allowed to perform all 6 core operations. Other media perform only 3.\n
-378    """
-379
-380    # note: `sync` may have better terms, such as 'harmonize' or 'attune'. `sync` was chosen because it is shorter
-381
-382    universal: GenTypeC = GenTypeC(clone=None, sync=None, translate=None)
-383    text: GenTypeCText = GenTypeCText(research=None, chain_of_thought=None, question_answer=None)
-
- - -

Generative inference operation types in E-dimensional order

- -

Equivalence, lists sorted from 'highly-similar' to 'loosely correlated.'"

- -
Parameters
- -
    -
  • universal: Affecting all conversions
    • -
  • text: Text-only conversions
    • -
- -

multimedia generation

- -
Y-axis: Detail (Most involved to least involved)
-│
-│                             clone
-│                 sync
-│ translate
-│
-+───────────────────────────────────────> X-axis: Equivalence (Loosely correlated to highly similar)
-
- -

text generation

- -
Y-axis: Detail (Most involved to least involved)
-│
-│                           research
-│             chain-of-thought
-│ question/answer
-│
-+───────────────────────────────────────> X-axis:  Equivalence (Loosely correlated to highly similar)
-
- -

This is essentially the translation operation of C types, and the mapping of them to E

- -

An abstract generalization of the set of all multimodal generative synthesis processes

- -

The sum of each coordinate pair reflects effective compute use

- -

In this way, both C types and their similarity are translatable, but not 1:1 identical

- -

Text is allowed to perform all 6 core operations. Other media perform only 3.

-
- - -
-
- universal: GenTypeC = -GenTypeC(clone=None, sync=None, translate=None) - - -
- - - - -
-
-
- text: GenTypeCText = -GenTypeCText(research=None, chain_of_thought=None, question_answer=None) - - -
- - - - -
-
-
-
- VALID_CONVERSIONS = -['text', 'image', 'music', 'speech', 'audio', 'video', '3d', 'vector_graphic', 'upscale_image'] - - -
- - - - -
-
-
- VALID_JUNCTIONS = -[''] - - -
- - - - -
-
-
- tasks = - - ['audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'image-to-text', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'text2text-generation', 'token-classification', 'translation', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY'] - - -
- - - - -
-
-
- VALID_TASKS = - - {<CueType.VLLM: (False, 'VLLM')>: {('text', 'text'): ['text'], ('image', 'text'): ['vision']}, <CueType.OLLAMA: (True, 'OLLAMA')>: {('text', 'text'): ['mllama'], ('image', 'text'): ['llava', 'vllm']}, <CueType.LLAMAFILE: (False, 'LLAMAFILE')>: {('text', 'text'): ['text']}, <CueType.LM_STUDIO: (False, 'LM_STUDIO')>: {('text', 'text'): ['llm']}, <CueType.HUB: (True, 'HUB')>: {('image', 'image'): ['image-to-image', 'inpaint', 'inpainting', 'depth-to-image', 'i2i', 'image-to-image', 'any-to-any'], ('text', 'image'): ['kolors', 'kolorspipeline', 'image-generation', 'any-to-any', 'text-to-image', 'chromapipeline'], ('image', 'text'): ['image-classification', 'image-to-text', 'image-text-to-text', 'visual-question-answering', 'image-captioning', 'image-segmentation', 'depth-estimation', 'image-feature-extraction', 'mask-generation', 'object-detection', 'visual-question-answering', 'keypoint-detection', 'vllm', 'vqa', 'vision', 'zero-shot-object-detection', 'zero-shot-image-classification', 'timm', 'zero-shot image classification', 'any-to-any', 'vidore'], ('image', 'video'): ['image-to-video', 'i2v', 'reference-to-video', 'refernce-to-video'], ('video', 'text'): ['video-classification'], ('text', 'video'): ['video generation', 't2v', 'text-to-video', 'HunyuanVideoPipeline'], ('text', 'text'): ['any-to-any', 'named entity recognition', 'entity typing', 'relation classification', 'question answering', 'fill-mask', 'chat', 'conversational', 'text-generation', 'causal-lm', 'text2text-generation', 'document-question-answering', 'feature-extraction', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'token-classification', 'translation', 'zero-shot-classification', 'translation_xx_to_yy', 't2t', 'chatglm', 'exbert'], ('text', 'audio'): ['text-to-audio', 't2a', 'any-to-any', 'text-to-audio', ' musicldmpipeline', 'musicgen', 'audiocraft', 'audiogen', 'AudioLDMPipeline', 'AudioLDM2Pipeline'], ('audio', 'text'): ['zero-shot-audio-classification', 'audio-classification', 'a2t', 'audio-text-to-text', 'any-to-any'], ('text', 'speech'): ['text-to-speech', 'tts', 'any-to-any', 'annotation'], ('speech', 'text'): ['speech-to-text', 'speech', 'speech-translation', 'speech-summarization', 'automatic-speech-recognition', 'dictation', 'stt', 'any-to-any', 'hf-asr-leaderboard']}, <CueType.KAGGLE: (False, 'KAGGLE')>: {('text', 'text'): ['text']}} - - -
- - - - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/providers/pools.html b/docs/zodiac/providers/pools.html deleted file mode 100644 index e135195..0000000 --- a/docs/zodiac/providers/pools.html +++ /dev/null @@ -1,1319 +0,0 @@ - - - - - - - zodiac.providers.pools API documentation - - - - - - - - - -
-
-

-zodiac.providers.pools

- -

Feed models to RegistryEntry class

-
- - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0  */ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4"""Feed models to RegistryEntry class"""
-  5
-  6# pylint:disable=protected-access, no-member
-  7from typing import Any, Callable, Dict, List, Optional
-  8
-  9from nnll.model_detect.identity import ModelIdentity
- 10from nnll.mir.json_cache import MODES_PATH_NAMED, JSONCache
- 11from zodiac.providers.constants import CUETYPE_CONFIG, MIR_DB, CueType, PkgType, VALID_TASKS
- 12from zodiac.providers.registry_entry import RegistryEntry
- 13from nnll.monitor.file import dbuq
- 14
- 15nfo = print
- 16
- 17MODE_DATA = JSONCache(MODES_PATH_NAMED)
- 18
- 19
- 20@MODE_DATA.decorator
- 21async def add_mode_types(mir_tag: list[str], data: dict | None = None) -> dict[str, list[str] | str]:
- 22    """Add mode‑related metadata for a given MIR tag.\n
- 23    :param mir_tag: List of tag components that identify a model in the MIR database.
- 24    :param data: Dictionary containing mode entries; defaults to ``None``.
- 25    :returns: Mapping with keys extracted from ``data`` for the fused tag."""
- 26
- 27    fused_tag = mir_tag
- 28    mir_details = {
- 29        "mode": data.get(fused_tag, {}).get("pipeline"),
- 30        "pkg_type": data.get(fused_tag, {}).get("library"),
- 31        "tags": data.get(fused_tag, {}).get("tags"),
- 32    }
- 33    return mir_details
- 34
- 35
- 36async def add_pkg_types(pkg_data: dict, mode: str, mir_tag: list[str]) -> dict[int | str, Any]:
- 37    """Augment package data with additional entries based on pipeline class and mode.\n
- 38    :param pkg_data: Existing package mapping where keys are indices and values are package specs.
- 39    :param mode: The pipeline mode extracted from MIR metadata.
- 40    :returns: Updated ``pkg_data`` with GPU-specific packages"""
- 41    package_name = pkg_data.get(next(iter(pkg_data)))
- 42    class_name = package_name.get(next(iter(package_name)))
- 43    class_name = list(class_name)[0]
- 44    # if (class_name == "FluxPipeline" or "flux1" in mir_tag[0]) and PkgType.MFLUX.value[0]:
- 45    #     alias = "schnell" if "schnell" in mir_tag[0] else "dev"
- 46    #     class_data = {
- 47    #         f"{PkgType.MFLUX.value[1].lower()}": "flux.flux.Flux1",
- 48    #     }
- 49    # pkg_data.setdefault(str(len(pkg_data)), class_data)
- 50    if class_name == "ChromaPipeline" and PkgType.MLX_CHROMA.value[0]:
- 51        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_CHROMA.value[1].lower(): "ChromaPipeline"})
- 52    if mode in VALID_TASKS[CueType.HUB][("text", "text")] and PkgType.MLX_LM.value[0]:
- 53        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_LM.value[1].lower(): "load"})
- 54    if mode in VALID_TASKS[CueType.HUB][("image", "text")] and PkgType.MLX_VLM.value[0]:
- 55        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_LM.value[1].lower(): "load"})
- 56    return pkg_data
- 57
- 58
- 59async def generate_entry(mir_tag: List[str], mir_db: dict, model_tags: list[str] | None = None, pkg_data: dict | None = None) -> dict[str, list[str] | str]:
- 60    """Create a registry entry dictionary from MIR information.\n
- 61    :param mir_tag: The hierarchical tag identifying a model in the MIR database.
- 62    :param mir_db: The MIR database instance providing access to stored metadata.
- 63    :param model_tags:  Additional tags to attach to the model; defaults to ``None``.
- 64    :param pkg_data: Existing package data; defaults to ``None``.
- 65    :returns: Mapping of values for registry construction."""
- 66    from zodiac.streams.class_stream import ancestor_data
- 67
- 68    fused_tag = ".".join(mir_tag)
- 69    mir_info = mir_db.database.get(mir_tag[0], {}).get(mir_tag[1])
- 70    modalities = await add_mode_types(fused_tag)
- 71    mode_data = modalities.get("mode")
- 72    if tags := modalities.get("tags", []):
- 73        model_tags = tags if not model_tags else model_tags + tags
- 74    if not mir_info:
- 75        mir_info = {}
- 76    else:
- 77        pkg_data = mir_info.get("pkg")
- 78        if pkg_data:
- 79            pkg_data: dict = await add_pkg_types(pkg_data, mode_data, mir_tag)
- 80    pipe_data = mir_info.get("pipe_names")
- 81    if not pipe_data:
- 82        pipe_data: list[dict] = await ancestor_data(mir_tag, field_name="pipe_names")
- 83        pipe_data: dict | None = next(iter(pipe_data), {})
- 84    task_data = mir_info.get("tasks")
- 85    if not task_data:
- 86        task_data: list[dict] = await ancestor_data(mir_tag, field_name="tasks")
- 87    entry_data = {
- 88        "mir": mir_tag,
- 89        "tasks": task_data,
- 90        "modules": pkg_data,
- 91        "pipe": pipe_data,
- 92        "mode": mode_data,
- 93        "tags": model_tags,
- 94    }
- 95    return entry_data
- 96
- 97
- 98async def hub_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
- 99    """Build a registry of models from the local Huggingface Hub cache\n
-100    :param mir_db: An existing instance of the MIR database
-101    :param api_data: Dictionary of service data pertaining to providers
-102    :param entries: Previous registry entries to append
-103    :return: A list of RegistryEntry elements, or None"""
-104
-105    from huggingface_hub import CacheNotFound, repocard, scan_cache_dir
-106    from huggingface_hub.errors import EntryNotFoundError, LocalEntryNotFoundError, OfflineModeIsEnabled
-107    from requests import HTTPError
-108
-109    entry_data = {}
-110
-111    async def generate_cache_data() -> Any:
-112        try:
-113            cache_dir = scan_cache_dir()
-114        except CacheNotFound:
-115            nfo("Cache error")
-116            yield None, None
-117        else:
-118            for repo in cache_dir.repos:
-119                try:
-120                    card = repocard.RepoCard.load(repo.repo_id)
-121                except (LocalEntryNotFoundError, EntryNotFoundError, HTTPError, OfflineModeIsEnabled) as error_log:
-122                    nfo(f"Pooling error: '{error_log}'")
-123                    yield None, None
-124                yield repo, card
-125
-126    async for repo, card in generate_cache_data():
-127        model_id = ModelIdentity()
-128        if repo:
-129            tags = []
-130            base_model = None
-131            tokenizer = None
-132            pkg_type = None
-133            mir_tags = None
-134            card_data = getattr(card, "data", None)
-135            if card_data:
-136                base_model = card_data.get("base_model")
-137                if isinstance(base_model, str):
-138                    base_model = [base_model]
-139                pipeline_tag = card_data.get("pipeline_tag")
-140                if tags:
-141                    tags.append(pipeline_tag)
-142                else:
-143                    tags = [pipeline_tag]
-144                if pkg_name := card_data.get("library_name"):
-145                    if hasattr(PkgType, pkg_name := pkg_name.replace("-", "_").upper()):
-146                        pkg_type = getattr(PkgType, pkg_name)
-147            tokenizer = await model_id.get_cache_path(file_name="tokenizer.json", repo_obj=repo)
-148            mir_tags = await model_id.label_model(
-149                repo_id=repo.repo_id,
-150                base_model=base_model if isinstance(base_model, str) or base_model is None else base_model[0],
-151                cue_type=CueType.HUB.value[1],
-152            )
-153            tags = card_data.get("tags", []) if card_data else None
-154            if mir_tags:
-155                if isinstance(mir_tags, list) and isinstance(mir_tags[0], list):
-156                    mir_bundle = mir_tags if len(mir_tags) > 1 else None
-157                    dbuq(mir_tags)
-158                    mir_tag = next(iter(mir_id for mir_id in mir_tags if any(arch for arch in [".dit.", "unet"] if arch in mir_id[0])), mir_tags[0])
-159                else:
-160                    mir_bundle = None
-161                    mir_tag = mir_tags
-162                base_model = base_model.append(mir_tag[0]) if base_model else [mir_tag[0]]
-163                entry_data = await generate_entry(mir_tag=mir_tag, mir_db=mir_db, model_tags=tags)
-164                entry_data.setdefault("bundle", mir_bundle)
-165            else:
-166                mir_tags = [[]]
-167                entry_data = {
-168                    "tags": [],
-169                }
-170            nfo(mir_tags if mir_tags[0] else f"mir tag not found for {repo.repo_id}")
-171            entry = RegistryEntry.create_entry(
-172                model=repo.repo_id,
-173                size=repo.size_on_disk,
-174                cuetype=CueType.HUB,
-175                package=pkg_type,
-176                model_family=base_model if base_model else [""],
-177                path=str(repo.repo_path),
-178                api_kwargs=api_data[CueType.HUB.value[1]],  # api_data based on package_name (diffusers/mlx_audio)
-179                timestamp=int(repo.last_modified),
-180                tokenizer=tokenizer,
-181                **entry_data,
-182            )
-183            entries.append(entry)
-184    return entries
-185
-186
-187async def ollama_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-188    """Build a registry of models from local Ollama service\n
-189    :param mir_db: An existing instance of the MIR database
-190    :param api_data: Dictionary of service data pertaining to providers
-191    :param entries: Previous registry entries to append
-192    :return: A list of RegistryEntry elements, or None"""
-193
-194    async def generate_cache_data() -> Any:
-195        from ollama import ListResponse, show, list as ollama_list
-196
-197        cache_dir: ListResponse = ollama_list()
-198        if cache_dir:
-199            for model in cache_dir.models:
-200                gguf_data = show(model.model)
-201                yield model, gguf_data
-202
-203    entry_data = {}
-204    entries = [] if not entries else entries
-205    config = api_data[CueType.OLLAMA.value[1]]
-206    async for model, gguf_data in generate_cache_data():
-207        model_id = ModelIdentity()
-208        base_model = gguf_data.modelinfo.get("general.architecture")
-209        gguf_data = (gguf_data.modelfile,)
-210        if hasattr(model, "family") and model.details.family != base_model:
-211            base_model = model.details.family
-212        if mir_tag := await model_id.label_model(repo_id=model.model, base_model=base_model, cue_type=CueType.OLLAMA.value[1], repo_obj=gguf_data):
-213            entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, model_tags=[model.details.family])
-214        nfo(f"no tag for {model.model}") if not mir_tag else nfo(f"{mir_tag}")
-215        entry = RegistryEntry.create_entry(
-216            model=f"{api_data[CueType.OLLAMA.value[1]].get('prefix')}{model.model}",
-217            size=model.size.real,
-218            model_family=[base_model],
-219            cuetype=CueType.OLLAMA,
-220            package=PkgType.LLAMA,
-221            api_kwargs=config["api_kwargs"],
-222            timestamp=int(model.modified_at.timestamp()),
-223            tokenizer=None,
-224            **entry_data,
-225        )
-226        entries.append(entry)
-227    return entries
-228
-229
-230async def vllm_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-231    """Build a registry of models from local VLLM service\n
-232    :param mir_db: An existing instance of the MIR database
-233    :param api_data: Dictionary of service data pertaining to providers
-234    :param entries: Previous registry entries to append
-235    :return: A list of RegistryEntry elements, or None"""
-236
-237    from openai import OpenAI
-238
-239    entry_data = {}
-240    entries = [] if not entries else entries
-241    config = api_data[CueType.VLLM.value[1]]
-242    cache_dir = OpenAI(base_url=api_data["VLLM"]["api_kwargs"]["api_base"], api_key=api_data["VLLM"]["api_kwargs"]["api_key"])
-243    for model in cache_dir.models.list().data:
-244        model_id = ModelIdentity()
-245        id_name = model["data"].get("id")
-246        mir_tag = None
-247        if id_name:
-248            if mir_tag := await model_id.label_model(repo_id=id_name, base_model=None, cue_type=CueType.VLLM.value[1]):
-249                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=["text"])
-250        entry = RegistryEntry.create_entry(
-251            model=f"{api_data[CueType.VLLM.value[1]].get('prefix')}{id_name}",
-252            size=model._data.size_bytes,
-253            cuetype=CueType.VLLM,
-254            api_kwargs={**config["api_kwargs"]},
-255            timestamp=int(model.modified_at.timestamp()),
-256            **entry_data,
-257        )
-258        entries.append(entry)
-259    return entries
-260
-261
-262async def llamafile_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-263    """Build a registry of models from a local Llamafile server\n
-264    :param mir_db: An existing instance of the MIR database
-265    :param api_data: Dictionary of service data pertaining to providers
-266    :param entries: Previous registry entries to append
-267    :return: A list of RegistryEntry elements, or None"""
-268    from openai import OpenAI
-269
-270    entry_data = {}
-271    mir_tag = None
-272    entries = [] if not entries else entries
-273    cache_dir: OpenAI = OpenAI(base_url=api_data["LLAMAFILE"]["api_kwargs"]["api_base"], api_key="sk-no-key-required")
-274    config = api_data[CueType.LLAMAFILE.value[1]]
-275    for model in cache_dir.models.list().data:
-276        model_id = ModelIdentity()
-277        if hasattr(model, id):
-278            if mir_tag := await model_id.label_model(model.id, CueType.LLAMAFILE.value[1]):
-279                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=["text"])
-280        entry = RegistryEntry.create_entry(
-281            model=f"{api_data[CueType.LLAMAFILE.value[1]].get('prefix')}{model.id}",
-282            size=0,
-283            cuetype=CueType.LLAMAFILE,
-284            api_kwargs=config["api_kwargs"],
-285            timestamp=int(model.created),
-286            **entry_data,
-287        )
-288        entries.append(entry)
-289    return entries
-290
-291
-292async def lm_studio_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-293    """Build a registry of models from local LM Studio service\n
-294    :param mir_db: An existing instance of the MIR database
-295    :param api_data: Dictionary of service data pertaining to providers
-296    :param entries: Previous registry entries to append
-297    :return: A list of RegistryEntry elements, or None"""
-298
-299    from lmstudio import LMStudioClient
-300
-301    entry_data = {}
-302    entries = [] if not entries else entries
-303    client = LMStudioClient()
-304    models = client.list_models()
-305    config = api_data[CueType.LM_STUDIO.value[1]]
-306    for model in models:
-307        model_id = ModelIdentity()
-308        tags = []
-309        if hasattr(model, "vision"):
-310            tags.extend(["vision", model.vision])
-311        if hasattr(model, "tool_use"):
-312            tags.append(["tool", model.tool_use])
-313        mir_tag = None
-314        if hasattr(model, "architecture"):
-315            if mir_tag := await model_id.label_model(model.architecture, None, CueType.LM_STUDIO.value[1]):
-316                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=tags)
-317        entry = RegistryEntry.create_entry(
-318            model=f"{api_data[CueType.LM_STUDIO.value[1]].get('prefix')}{model.model_key}",
-319            size=model._data.size_bytes,
-320            cuetype=CueType.LM_STUDIO,
-321            api_kwargs={**config["api_kwargs"]},
-322            timestamp=int(model.modified_at.timestamp()),
-323            **entry_data,
-324        )
-325        entries.append(entry)
-326    return entries
-327
-328
-329async def register_models(data: Optional[Dict[str, Any]] = None) -> list[RegistryEntry] | None:
-330    """Retrieve models from ollama server, local huggingface hub cache, local lmstudio cache & vllm.\n
-331    :param: data: Testing -  Override for API CueType data dictionary
-332    我們不應該繼續為LMStudio編碼。 歡迎貢獻者來改進它。 LMStudio is not OSS, but contributions are welcome."""
-333
-334    @CUETYPE_CONFIG.decorator
-335    async def read_cuetype(data: Optional[Dict[str, Any]] = None) -> dict:
-336        return data
-337
-338    if not data:
-339        data = await read_cuetype()
-340
-341    async def entry_generator():
-342        entry_map = {
-343            CueType.HUB.value: hub_pool,
-344            CueType.OLLAMA.value: ollama_pool,
-345            CueType.LLAMAFILE.value: llamafile_pool,
-346            CueType.VLLM.value: vllm_pool,
-347            CueType.LM_STUDIO.value: lm_studio_pool,
-348        }
-349        for cue_type, pool in entry_map.items():
-350            yield cue_type, pool
-351
-352    entries = []
-353    async for cue_type, provider in entry_generator():
-354        if cue_type[0]:
-355            api_data = data
-356            entries = await provider(api_data=api_data, mir_db=MIR_DB, entries=entries)
-357
-358    return sorted(entries, key=lambda x: x.timestamp, reverse=True)
-359
-360
-361def generate_pool():
-362    import asyncio
-363    from time import perf_counter
-364    from nnll.monitor.console import nfo
-365
-366    start = perf_counter()
-367    models = asyncio.run(register_models())
-368    nfo(f'Complete. Time: {perf_counter() - start}" Registered: {len(models)}')
-369    return models
-370
-371
-372if __name__ == "__main__":
-373    import asyncio
-374    from nnll.monitor.file import dbuq
-375
-376    models = asyncio.run(register_models())
-377    dbuq(models)
-378    from pprint import pprint
-379
-380    pprint(models)
-
- - -
-
-
- - def - nfo(*args, sep=' ', end='\n', file=None, flush=False): - - -
- - -

Prints the values to a stream, or to sys.stdout by default.

- -

sep - string inserted between values, default a space. -end - string appended after the last value, default a newline. -file - a file-like object (stream); defaults to the current sys.stdout. -flush - whether to forcibly flush the stream.

-
- - -
-
-
- MODE_DATA = -<nnll.mir.json_cache.JSONCache object> - - -
- - - - -
-
- -
-
@MODE_DATA.decorator
- - async def - add_mode_types( mir_tag: list[str], data: dict | None = None) -> dict[str, list[str] | str]: - - - -
- - 
21@MODE_DATA.decorator
-22async def add_mode_types(mir_tag: list[str], data: dict | None = None) -> dict[str, list[str] | str]:
-23    """Add mode‑related metadata for a given MIR tag.\n
-24    :param mir_tag: List of tag components that identify a model in the MIR database.
-25    :param data: Dictionary containing mode entries; defaults to ``None``.
-26    :returns: Mapping with keys extracted from ``data`` for the fused tag."""
-27
-28    fused_tag = mir_tag
-29    mir_details = {
-30        "mode": data.get(fused_tag, {}).get("pipeline"),
-31        "pkg_type": data.get(fused_tag, {}).get("library"),
-32        "tags": data.get(fused_tag, {}).get("tags"),
-33    }
-34    return mir_details
-
- - -

Add mode‑related metadata for a given MIR tag.

- -
Parameters
- -
    -
  • mir_tag: List of tag components that identify a model in the MIR database.
    • -
  • data: Dictionary containing mode entries; defaults to None. -:returns: Mapping with keys extracted from data for the fused tag.
    • -
-
- - -
-
- -
- - async def - add_pkg_types( pkg_data: dict, mode: str, mir_tag: list[str]) -> dict[int | str, typing.Any]: - - - -
- - 
37async def add_pkg_types(pkg_data: dict, mode: str, mir_tag: list[str]) -> dict[int | str, Any]:
-38    """Augment package data with additional entries based on pipeline class and mode.\n
-39    :param pkg_data: Existing package mapping where keys are indices and values are package specs.
-40    :param mode: The pipeline mode extracted from MIR metadata.
-41    :returns: Updated ``pkg_data`` with GPU-specific packages"""
-42    package_name = pkg_data.get(next(iter(pkg_data)))
-43    class_name = package_name.get(next(iter(package_name)))
-44    class_name = list(class_name)[0]
-45    # if (class_name == "FluxPipeline" or "flux1" in mir_tag[0]) and PkgType.MFLUX.value[0]:
-46    #     alias = "schnell" if "schnell" in mir_tag[0] else "dev"
-47    #     class_data = {
-48    #         f"{PkgType.MFLUX.value[1].lower()}": "flux.flux.Flux1",
-49    #     }
-50    # pkg_data.setdefault(str(len(pkg_data)), class_data)
-51    if class_name == "ChromaPipeline" and PkgType.MLX_CHROMA.value[0]:
-52        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_CHROMA.value[1].lower(): "ChromaPipeline"})
-53    if mode in VALID_TASKS[CueType.HUB][("text", "text")] and PkgType.MLX_LM.value[0]:
-54        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_LM.value[1].lower(): "load"})
-55    if mode in VALID_TASKS[CueType.HUB][("image", "text")] and PkgType.MLX_VLM.value[0]:
-56        pkg_data.setdefault(str(len(pkg_data)), {PkgType.MLX_LM.value[1].lower(): "load"})
-57    return pkg_data
-
- - -

Augment package data with additional entries based on pipeline class and mode.

- -
Parameters
- -
    -
  • pkg_data: Existing package mapping where keys are indices and values are package specs.
    • -
  • mode: The pipeline mode extracted from MIR metadata. -:returns: Updated pkg_data with GPU-specific packages
    • -
-
- - -
-
- -
- - async def - generate_entry( mir_tag: List[str], mir_db: dict, model_tags: list[str] | None = None, pkg_data: dict | None = None) -> dict[str, list[str] | str]: - - - -
- - 
60async def generate_entry(mir_tag: List[str], mir_db: dict, model_tags: list[str] | None = None, pkg_data: dict | None = None) -> dict[str, list[str] | str]:
-61    """Create a registry entry dictionary from MIR information.\n
-62    :param mir_tag: The hierarchical tag identifying a model in the MIR database.
-63    :param mir_db: The MIR database instance providing access to stored metadata.
-64    :param model_tags:  Additional tags to attach to the model; defaults to ``None``.
-65    :param pkg_data: Existing package data; defaults to ``None``.
-66    :returns: Mapping of values for registry construction."""
-67    from zodiac.streams.class_stream import ancestor_data
-68
-69    fused_tag = ".".join(mir_tag)
-70    mir_info = mir_db.database.get(mir_tag[0], {}).get(mir_tag[1])
-71    modalities = await add_mode_types(fused_tag)
-72    mode_data = modalities.get("mode")
-73    if tags := modalities.get("tags", []):
-74        model_tags = tags if not model_tags else model_tags + tags
-75    if not mir_info:
-76        mir_info = {}
-77    else:
-78        pkg_data = mir_info.get("pkg")
-79        if pkg_data:
-80            pkg_data: dict = await add_pkg_types(pkg_data, mode_data, mir_tag)
-81    pipe_data = mir_info.get("pipe_names")
-82    if not pipe_data:
-83        pipe_data: list[dict] = await ancestor_data(mir_tag, field_name="pipe_names")
-84        pipe_data: dict | None = next(iter(pipe_data), {})
-85    task_data = mir_info.get("tasks")
-86    if not task_data:
-87        task_data: list[dict] = await ancestor_data(mir_tag, field_name="tasks")
-88    entry_data = {
-89        "mir": mir_tag,
-90        "tasks": task_data,
-91        "modules": pkg_data,
-92        "pipe": pipe_data,
-93        "mode": mode_data,
-94        "tags": model_tags,
-95    }
-96    return entry_data
-
- - -

Create a registry entry dictionary from MIR information.

- -
Parameters
- -
    -
  • mir_tag: The hierarchical tag identifying a model in the MIR database.
    • -
  • mir_db: The MIR database instance providing access to stored metadata.
    • -
  • model_tags: Additional tags to attach to the model; defaults to None.
    • -
  • pkg_data: Existing package data; defaults to None. -:returns: Mapping of values for registry construction.
    • -
-
- - -
-
- -
- - async def - hub_pool( mir_db: Callable, api_data: Dict[str, Any], entries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
 99async def hub_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-100    """Build a registry of models from the local Huggingface Hub cache\n
-101    :param mir_db: An existing instance of the MIR database
-102    :param api_data: Dictionary of service data pertaining to providers
-103    :param entries: Previous registry entries to append
-104    :return: A list of RegistryEntry elements, or None"""
-105
-106    from huggingface_hub import CacheNotFound, repocard, scan_cache_dir
-107    from huggingface_hub.errors import EntryNotFoundError, LocalEntryNotFoundError, OfflineModeIsEnabled
-108    from requests import HTTPError
-109
-110    entry_data = {}
-111
-112    async def generate_cache_data() -> Any:
-113        try:
-114            cache_dir = scan_cache_dir()
-115        except CacheNotFound:
-116            nfo("Cache error")
-117            yield None, None
-118        else:
-119            for repo in cache_dir.repos:
-120                try:
-121                    card = repocard.RepoCard.load(repo.repo_id)
-122                except (LocalEntryNotFoundError, EntryNotFoundError, HTTPError, OfflineModeIsEnabled) as error_log:
-123                    nfo(f"Pooling error: '{error_log}'")
-124                    yield None, None
-125                yield repo, card
-126
-127    async for repo, card in generate_cache_data():
-128        model_id = ModelIdentity()
-129        if repo:
-130            tags = []
-131            base_model = None
-132            tokenizer = None
-133            pkg_type = None
-134            mir_tags = None
-135            card_data = getattr(card, "data", None)
-136            if card_data:
-137                base_model = card_data.get("base_model")
-138                if isinstance(base_model, str):
-139                    base_model = [base_model]
-140                pipeline_tag = card_data.get("pipeline_tag")
-141                if tags:
-142                    tags.append(pipeline_tag)
-143                else:
-144                    tags = [pipeline_tag]
-145                if pkg_name := card_data.get("library_name"):
-146                    if hasattr(PkgType, pkg_name := pkg_name.replace("-", "_").upper()):
-147                        pkg_type = getattr(PkgType, pkg_name)
-148            tokenizer = await model_id.get_cache_path(file_name="tokenizer.json", repo_obj=repo)
-149            mir_tags = await model_id.label_model(
-150                repo_id=repo.repo_id,
-151                base_model=base_model if isinstance(base_model, str) or base_model is None else base_model[0],
-152                cue_type=CueType.HUB.value[1],
-153            )
-154            tags = card_data.get("tags", []) if card_data else None
-155            if mir_tags:
-156                if isinstance(mir_tags, list) and isinstance(mir_tags[0], list):
-157                    mir_bundle = mir_tags if len(mir_tags) > 1 else None
-158                    dbuq(mir_tags)
-159                    mir_tag = next(iter(mir_id for mir_id in mir_tags if any(arch for arch in [".dit.", "unet"] if arch in mir_id[0])), mir_tags[0])
-160                else:
-161                    mir_bundle = None
-162                    mir_tag = mir_tags
-163                base_model = base_model.append(mir_tag[0]) if base_model else [mir_tag[0]]
-164                entry_data = await generate_entry(mir_tag=mir_tag, mir_db=mir_db, model_tags=tags)
-165                entry_data.setdefault("bundle", mir_bundle)
-166            else:
-167                mir_tags = [[]]
-168                entry_data = {
-169                    "tags": [],
-170                }
-171            nfo(mir_tags if mir_tags[0] else f"mir tag not found for {repo.repo_id}")
-172            entry = RegistryEntry.create_entry(
-173                model=repo.repo_id,
-174                size=repo.size_on_disk,
-175                cuetype=CueType.HUB,
-176                package=pkg_type,
-177                model_family=base_model if base_model else [""],
-178                path=str(repo.repo_path),
-179                api_kwargs=api_data[CueType.HUB.value[1]],  # api_data based on package_name (diffusers/mlx_audio)
-180                timestamp=int(repo.last_modified),
-181                tokenizer=tokenizer,
-182                **entry_data,
-183            )
-184            entries.append(entry)
-185    return entries
-
- - -

Build a registry of models from the local Huggingface Hub cache

- -
Parameters
- -
    -
  • mir_db: An existing instance of the MIR database
    • -
  • api_data: Dictionary of service data pertaining to providers
    • -
  • entries: Previous registry entries to append
    • -
- -
Returns
- -
-

A list of RegistryEntry elements, or None

-
-
- - -
-
- -
- - async def - ollama_pool( mir_db: Callable, api_data: Dict[str, Any], entries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
188async def ollama_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-189    """Build a registry of models from local Ollama service\n
-190    :param mir_db: An existing instance of the MIR database
-191    :param api_data: Dictionary of service data pertaining to providers
-192    :param entries: Previous registry entries to append
-193    :return: A list of RegistryEntry elements, or None"""
-194
-195    async def generate_cache_data() -> Any:
-196        from ollama import ListResponse, show, list as ollama_list
-197
-198        cache_dir: ListResponse = ollama_list()
-199        if cache_dir:
-200            for model in cache_dir.models:
-201                gguf_data = show(model.model)
-202                yield model, gguf_data
-203
-204    entry_data = {}
-205    entries = [] if not entries else entries
-206    config = api_data[CueType.OLLAMA.value[1]]
-207    async for model, gguf_data in generate_cache_data():
-208        model_id = ModelIdentity()
-209        base_model = gguf_data.modelinfo.get("general.architecture")
-210        gguf_data = (gguf_data.modelfile,)
-211        if hasattr(model, "family") and model.details.family != base_model:
-212            base_model = model.details.family
-213        if mir_tag := await model_id.label_model(repo_id=model.model, base_model=base_model, cue_type=CueType.OLLAMA.value[1], repo_obj=gguf_data):
-214            entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, model_tags=[model.details.family])
-215        nfo(f"no tag for {model.model}") if not mir_tag else nfo(f"{mir_tag}")
-216        entry = RegistryEntry.create_entry(
-217            model=f"{api_data[CueType.OLLAMA.value[1]].get('prefix')}{model.model}",
-218            size=model.size.real,
-219            model_family=[base_model],
-220            cuetype=CueType.OLLAMA,
-221            package=PkgType.LLAMA,
-222            api_kwargs=config["api_kwargs"],
-223            timestamp=int(model.modified_at.timestamp()),
-224            tokenizer=None,
-225            **entry_data,
-226        )
-227        entries.append(entry)
-228    return entries
-
- - -

Build a registry of models from local Ollama service

- -
Parameters
- -
    -
  • mir_db: An existing instance of the MIR database
    • -
  • api_data: Dictionary of service data pertaining to providers
    • -
  • entries: Previous registry entries to append
    • -
- -
Returns
- -
-

A list of RegistryEntry elements, or None

-
-
- - -
-
- -
- - async def - vllm_pool( mir_db: Callable, api_data: Dict[str, Any], entries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
231async def vllm_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-232    """Build a registry of models from local VLLM service\n
-233    :param mir_db: An existing instance of the MIR database
-234    :param api_data: Dictionary of service data pertaining to providers
-235    :param entries: Previous registry entries to append
-236    :return: A list of RegistryEntry elements, or None"""
-237
-238    from openai import OpenAI
-239
-240    entry_data = {}
-241    entries = [] if not entries else entries
-242    config = api_data[CueType.VLLM.value[1]]
-243    cache_dir = OpenAI(base_url=api_data["VLLM"]["api_kwargs"]["api_base"], api_key=api_data["VLLM"]["api_kwargs"]["api_key"])
-244    for model in cache_dir.models.list().data:
-245        model_id = ModelIdentity()
-246        id_name = model["data"].get("id")
-247        mir_tag = None
-248        if id_name:
-249            if mir_tag := await model_id.label_model(repo_id=id_name, base_model=None, cue_type=CueType.VLLM.value[1]):
-250                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=["text"])
-251        entry = RegistryEntry.create_entry(
-252            model=f"{api_data[CueType.VLLM.value[1]].get('prefix')}{id_name}",
-253            size=model._data.size_bytes,
-254            cuetype=CueType.VLLM,
-255            api_kwargs={**config["api_kwargs"]},
-256            timestamp=int(model.modified_at.timestamp()),
-257            **entry_data,
-258        )
-259        entries.append(entry)
-260    return entries
-
- - -

Build a registry of models from local VLLM service

- -
Parameters
- -
    -
  • mir_db: An existing instance of the MIR database
    • -
  • api_data: Dictionary of service data pertaining to providers
    • -
  • entries: Previous registry entries to append
    • -
- -
Returns
- -
-

A list of RegistryEntry elements, or None

-
-
- - -
-
- -
- - async def - llamafile_pool( mir_db: Callable, api_data: Dict[str, Any], entries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
263async def llamafile_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-264    """Build a registry of models from a local Llamafile server\n
-265    :param mir_db: An existing instance of the MIR database
-266    :param api_data: Dictionary of service data pertaining to providers
-267    :param entries: Previous registry entries to append
-268    :return: A list of RegistryEntry elements, or None"""
-269    from openai import OpenAI
-270
-271    entry_data = {}
-272    mir_tag = None
-273    entries = [] if not entries else entries
-274    cache_dir: OpenAI = OpenAI(base_url=api_data["LLAMAFILE"]["api_kwargs"]["api_base"], api_key="sk-no-key-required")
-275    config = api_data[CueType.LLAMAFILE.value[1]]
-276    for model in cache_dir.models.list().data:
-277        model_id = ModelIdentity()
-278        if hasattr(model, id):
-279            if mir_tag := await model_id.label_model(model.id, CueType.LLAMAFILE.value[1]):
-280                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=["text"])
-281        entry = RegistryEntry.create_entry(
-282            model=f"{api_data[CueType.LLAMAFILE.value[1]].get('prefix')}{model.id}",
-283            size=0,
-284            cuetype=CueType.LLAMAFILE,
-285            api_kwargs=config["api_kwargs"],
-286            timestamp=int(model.created),
-287            **entry_data,
-288        )
-289        entries.append(entry)
-290    return entries
-
- - -

Build a registry of models from a local Llamafile server

- -
Parameters
- -
    -
  • mir_db: An existing instance of the MIR database
    • -
  • api_data: Dictionary of service data pertaining to providers
    • -
  • entries: Previous registry entries to append
    • -
- -
Returns
- -
-

A list of RegistryEntry elements, or None

-
-
- - -
-
- -
- - async def - lm_studio_pool( mir_db: Callable, api_data: Dict[str, Any], entries: List[zodiac.providers.registry_entry.RegistryEntry]) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
293async def lm_studio_pool(mir_db: Callable, api_data: Dict[str, Any], entries: List[RegistryEntry]) -> list[RegistryEntry] | None:
-294    """Build a registry of models from local LM Studio service\n
-295    :param mir_db: An existing instance of the MIR database
-296    :param api_data: Dictionary of service data pertaining to providers
-297    :param entries: Previous registry entries to append
-298    :return: A list of RegistryEntry elements, or None"""
-299
-300    from lmstudio import LMStudioClient
-301
-302    entry_data = {}
-303    entries = [] if not entries else entries
-304    client = LMStudioClient()
-305    models = client.list_models()
-306    config = api_data[CueType.LM_STUDIO.value[1]]
-307    for model in models:
-308        model_id = ModelIdentity()
-309        tags = []
-310        if hasattr(model, "vision"):
-311            tags.extend(["vision", model.vision])
-312        if hasattr(model, "tool_use"):
-313            tags.append(["tool", model.tool_use])
-314        mir_tag = None
-315        if hasattr(model, "architecture"):
-316            if mir_tag := await model_id.label_model(model.architecture, None, CueType.LM_STUDIO.value[1]):
-317                entry_data = await generate_entry(mir_tag=mir_tag[0], mir_db=mir_db, tags=tags)
-318        entry = RegistryEntry.create_entry(
-319            model=f"{api_data[CueType.LM_STUDIO.value[1]].get('prefix')}{model.model_key}",
-320            size=model._data.size_bytes,
-321            cuetype=CueType.LM_STUDIO,
-322            api_kwargs={**config["api_kwargs"]},
-323            timestamp=int(model.modified_at.timestamp()),
-324            **entry_data,
-325        )
-326        entries.append(entry)
-327    return entries
-
- - -

Build a registry of models from local LM Studio service

- -
Parameters
- -
    -
  • mir_db: An existing instance of the MIR database
    • -
  • api_data: Dictionary of service data pertaining to providers
    • -
  • entries: Previous registry entries to append
    • -
- -
Returns
- -
-

A list of RegistryEntry elements, or None

-
-
- - -
-
- -
- - async def - register_models( data: Optional[Dict[str, Any]] = None) -> list[zodiac.providers.registry_entry.RegistryEntry] | None: - - - -
- - 
330async def register_models(data: Optional[Dict[str, Any]] = None) -> list[RegistryEntry] | None:
-331    """Retrieve models from ollama server, local huggingface hub cache, local lmstudio cache & vllm.\n
-332    :param: data: Testing -  Override for API CueType data dictionary
-333    我們不應該繼續為LMStudio編碼。 歡迎貢獻者來改進它。 LMStudio is not OSS, but contributions are welcome."""
-334
-335    @CUETYPE_CONFIG.decorator
-336    async def read_cuetype(data: Optional[Dict[str, Any]] = None) -> dict:
-337        return data
-338
-339    if not data:
-340        data = await read_cuetype()
-341
-342    async def entry_generator():
-343        entry_map = {
-344            CueType.HUB.value: hub_pool,
-345            CueType.OLLAMA.value: ollama_pool,
-346            CueType.LLAMAFILE.value: llamafile_pool,
-347            CueType.VLLM.value: vllm_pool,
-348            CueType.LM_STUDIO.value: lm_studio_pool,
-349        }
-350        for cue_type, pool in entry_map.items():
-351            yield cue_type, pool
-352
-353    entries = []
-354    async for cue_type, provider in entry_generator():
-355        if cue_type[0]:
-356            api_data = data
-357            entries = await provider(api_data=api_data, mir_db=MIR_DB, entries=entries)
-358
-359    return sorted(entries, key=lambda x: x.timestamp, reverse=True)
-
- - -

Retrieve models from ollama server, local huggingface hub cache, local lmstudio cache & vllm.

- -
Parameters
- -
    -
  • data: Testing - Override for API CueType data dictionary -我們不應該繼續為LMStudio編碼。 歡迎貢獻者來改進它。 LMStudio is not OSS, but contributions are welcome.
    • -
-
- - -
-
- -
- - def - generate_pool(): - - - -
- - 
362def generate_pool():
-363    import asyncio
-364    from time import perf_counter
-365    from nnll.monitor.console import nfo
-366
-367    start = perf_counter()
-368    models = asyncio.run(register_models())
-369    nfo(f'Complete. Time: {perf_counter() - start}" Registered: {len(models)}')
-370    return models
-
- - - - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/providers/proto_class.html b/docs/zodiac/providers/proto_class.html deleted file mode 100644 index 00c9ce3..0000000 --- a/docs/zodiac/providers/proto_class.html +++ /dev/null @@ -1,277 +0,0 @@ - - - - - - - zodiac.providers.proto_class API documentation - - - - - - - - - -
-
-

-zodiac.providers.proto_class

- - - - - - - 
 1# # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
- 2# # <!-- // /*  d a r k s h a p e s */ -->
- 3
- 4
- 5# from pydantic import BaseModel, computed_field
- 6# from zodiac.providers.constants import PkgType
- 7
- 8# DiffusersType:
- 9#     num_inference_steps:
-10#     guidance_scale:
-11#     denoising_end:
-12#     output_type:
-13#     callback_steps:
-14#     negative_prompt:
-15#     prompt:
-16#     prompt_embeds:
-17#     negative_prompt_embeds:
-18
-19#     "output_type": "latent",
-20#     "safety_checker": false,
-21#     "width": 1024,
-22#     "height": 1024
-23
-24# DefaultEntry(BaseModel)
-25
-26
-27# class
-28#     steps:
-29#     guidance:
-30#     aspect:
-31#     width:
-32#     height: str
-33
-34# PkgType
-35
-36# class FormulaEntry(BaseModel):
-37#     packages
-38#     device_type
-39#     provider
-40#     parameters
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/providers/registry_entry.html b/docs/zodiac/providers/registry_entry.html deleted file mode 100644 index 84e3c1b..0000000 --- a/docs/zodiac/providers/registry_entry.html +++ /dev/null @@ -1,907 +0,0 @@ - - - - - - - zodiac.providers.registry_entry API documentation - - - - - - - - - -
-
-

-zodiac.providers.registry_entry

- -

Register model types

-
- - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0  */ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4"""Register model types"""
-  5
-  6# pylint: disable=line-too-long, import-outside-toplevel, protected-access, unsubscriptable-object
-  7
-  8from pathlib import Path
-  9from typing import List, Optional, Tuple, Union, Iterable
- 10
- 11from nnll.monitor.file import dbuq
- 12from pydantic import BaseModel, computed_field
- 13
- 14from zodiac.providers.constants import VALID_CONVERSIONS, VALID_TASKS, CueType, PkgType
- 15
- 16
- 17class RegistryEntry(BaseModel):
- 18    """Validate Hub / Ollama / LMStudio model input"""
- 19
- 20    cuetype: CueType
- 21
- 22    model: str
- 23    size: int
- 24    tags: List[str]
- 25    timestamp: int
- 26    mode: str | None = None
- 27    api_kwargs: Optional[dict] = None
- 28    mir: Optional[List[str]] = None
- 29    bundle: Optional[List[List[str]]] = None
- 30    model_family: Optional[List[str]] = None
- 31    modules: Optional[dict[str, dict]] = None
- 32    package: Optional[Union[PkgType, CueType]] = None
- 33    path: Optional[Union[str, Path, List[Union[str, Path]]]] = None
- 34    pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = (None,)
- 35    tasks: Optional[List[Union[str, List[str]]]] = (None,)
- 36    tokenizer: Optional[Path] = None
- 37
- 38    @computed_field
- 39    @property
- 40    def available_tasks(self) -> List[Tuple]:
- 41        """Filter tag tasks into edge coordinates for graphing"""
- 42        # This is a best effort at parsing tags; it is not perfect, and there is room for improvement
- 43        # particularly: Tokenizers, being locatable here, should be assigned to their model entry
- 44        # the logistics of how this occurs have been difficult to implement
- 45        # additionally, tag recognition of tasks needs cleaner, which requires practical testing to solve
- 46        import re
- 47
- 48        default_task = None
- 49        processed_tasks = []
- 50        dbuq(self.model)
- 51        if self.cuetype in [x for x in list(CueType) if x != CueType.HUB]:  # Literal list of CueType, must use list()
- 52            default_task = ("text", "text")  # usually these are txt gen libraries
- 53        elif self.cuetype == CueType.HUB:
- 54            dbuq(self.cuetype)  # pair tags from the hub such 'x-to-y' such as 'text-to-text' etc
- 55            pattern = re.compile(r"(\w+)-to-(\w+)")
- 56            for tag in [*self.tags, self.mode]:
- 57                if tag:
- 58                    match = pattern.search(tag.lower())
- 59                    if match and all(group in VALID_CONVERSIONS for group in match.groups()) and (match.group(1), match.group(2)) not in processed_tasks:
- 60                        processed_tasks.append((match.group(1), match.group(2)))
- 61        for tag in self.tags:  # when pair-tagged elements are not available, potential to duplicate HUB tags here
- 62            for (graph_src, graph_dest), tags in VALID_TASKS[self.cuetype].items():
- 63                if tag.lower() in tags and (graph_src, graph_dest) not in processed_tasks:
- 64                    processed_tasks.append((graph_src, graph_dest))
- 65        if default_task and default_task not in processed_tasks:
- 66            processed_tasks.append(default_task)
- 67        return processed_tasks
- 68
- 69    @classmethod
- 70    def create_entry(
- 71        cls,
- 72        cuetype: CueType,
- 73        model: str,
- 74        size: int,
- 75        tags: List[str],
- 76        api_kwargs: dict = None,
- 77        mir: Optional[List[str]] = None,
- 78        bundle: Optional[List[List[str]]] = None,
- 79        mode: str | None = None,
- 80        model_family: Optional[List[str]] = None,
- 81        modules: Optional[dict[str, dict]] = None,
- 82        package: Optional[Union[PkgType, CueType]] = None,
- 83        path: Optional[Union[str, Path, List[Union[str, Path]]]] = None,
- 84        pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = None,
- 85        tasks: Optional[List[Union[str, List[str]]]] = None,
- 86        timestamp: Optional[int] = None,
- 87        tokenizer=Optional[str],
- 88    ):
- 89        """API specific data to call models\n
- 90        :param cuetype: Provider to trigger loading
- 91        :param model:Cache location for model
- 92        :param size: File size (usually in bytes)
- 93        :param tags: List of available machine tasks for model
- 94        :param api_kwargs: Localhost server defaults, defaults to None
- 95        :param keys: List of available data buckets inside the MIR tree, defaults to None
- 96        :param mir: MIR information, defaults to None
- 97        :param model_family: Compatibility information for the model, defaults to None
- 98        :param modules: List of packages that can support the model, defaults to None
- 99        :param path: Location of the model on disk, defaults to None
-100        :param pipe: List of components to build the execution for the model, defaults to None
-101        :param package: Package name and availability, defaults to None
-102        :param tasks: Available methods to run the model
-103        :param timestamp: Download time of model, defaults to None
-104        :param tokenizer: Tokenizer configuration location, defaults to None
-105        :return: An instance of RegistryEntry with the provided values
-106        """
-107        from datetime import datetime
-108
-109        entry = cls(
-110            api_kwargs=api_kwargs,
-111            bundle=bundle,
-112            cuetype=cuetype,
-113            mir=mir,
-114            mode=mode,
-115            model_family=model_family,
-116            model=model,
-117            modules=modules,
-118            package=package,
-119            path=path,
-120            pipe=pipe,
-121            size=size,
-122            tags=tags,
-123            tasks=tasks,
-124            timestamp=timestamp or int(datetime.now().timestamp()),  # Default to current time if not provided
-125            tokenizer=tokenizer,
-126        )
-127        dbuq(entry)
-128        return entry
-
- - -
-
- -
- - class - RegistryEntry(pydantic.main.BaseModel): - - - -
- - 
 18class RegistryEntry(BaseModel):
- 19    """Validate Hub / Ollama / LMStudio model input"""
- 20
- 21    cuetype: CueType
- 22
- 23    model: str
- 24    size: int
- 25    tags: List[str]
- 26    timestamp: int
- 27    mode: str | None = None
- 28    api_kwargs: Optional[dict] = None
- 29    mir: Optional[List[str]] = None
- 30    bundle: Optional[List[List[str]]] = None
- 31    model_family: Optional[List[str]] = None
- 32    modules: Optional[dict[str, dict]] = None
- 33    package: Optional[Union[PkgType, CueType]] = None
- 34    path: Optional[Union[str, Path, List[Union[str, Path]]]] = None
- 35    pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = (None,)
- 36    tasks: Optional[List[Union[str, List[str]]]] = (None,)
- 37    tokenizer: Optional[Path] = None
- 38
- 39    @computed_field
- 40    @property
- 41    def available_tasks(self) -> List[Tuple]:
- 42        """Filter tag tasks into edge coordinates for graphing"""
- 43        # This is a best effort at parsing tags; it is not perfect, and there is room for improvement
- 44        # particularly: Tokenizers, being locatable here, should be assigned to their model entry
- 45        # the logistics of how this occurs have been difficult to implement
- 46        # additionally, tag recognition of tasks needs cleaner, which requires practical testing to solve
- 47        import re
- 48
- 49        default_task = None
- 50        processed_tasks = []
- 51        dbuq(self.model)
- 52        if self.cuetype in [x for x in list(CueType) if x != CueType.HUB]:  # Literal list of CueType, must use list()
- 53            default_task = ("text", "text")  # usually these are txt gen libraries
- 54        elif self.cuetype == CueType.HUB:
- 55            dbuq(self.cuetype)  # pair tags from the hub such 'x-to-y' such as 'text-to-text' etc
- 56            pattern = re.compile(r"(\w+)-to-(\w+)")
- 57            for tag in [*self.tags, self.mode]:
- 58                if tag:
- 59                    match = pattern.search(tag.lower())
- 60                    if match and all(group in VALID_CONVERSIONS for group in match.groups()) and (match.group(1), match.group(2)) not in processed_tasks:
- 61                        processed_tasks.append((match.group(1), match.group(2)))
- 62        for tag in self.tags:  # when pair-tagged elements are not available, potential to duplicate HUB tags here
- 63            for (graph_src, graph_dest), tags in VALID_TASKS[self.cuetype].items():
- 64                if tag.lower() in tags and (graph_src, graph_dest) not in processed_tasks:
- 65                    processed_tasks.append((graph_src, graph_dest))
- 66        if default_task and default_task not in processed_tasks:
- 67            processed_tasks.append(default_task)
- 68        return processed_tasks
- 69
- 70    @classmethod
- 71    def create_entry(
- 72        cls,
- 73        cuetype: CueType,
- 74        model: str,
- 75        size: int,
- 76        tags: List[str],
- 77        api_kwargs: dict = None,
- 78        mir: Optional[List[str]] = None,
- 79        bundle: Optional[List[List[str]]] = None,
- 80        mode: str | None = None,
- 81        model_family: Optional[List[str]] = None,
- 82        modules: Optional[dict[str, dict]] = None,
- 83        package: Optional[Union[PkgType, CueType]] = None,
- 84        path: Optional[Union[str, Path, List[Union[str, Path]]]] = None,
- 85        pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = None,
- 86        tasks: Optional[List[Union[str, List[str]]]] = None,
- 87        timestamp: Optional[int] = None,
- 88        tokenizer=Optional[str],
- 89    ):
- 90        """API specific data to call models\n
- 91        :param cuetype: Provider to trigger loading
- 92        :param model:Cache location for model
- 93        :param size: File size (usually in bytes)
- 94        :param tags: List of available machine tasks for model
- 95        :param api_kwargs: Localhost server defaults, defaults to None
- 96        :param keys: List of available data buckets inside the MIR tree, defaults to None
- 97        :param mir: MIR information, defaults to None
- 98        :param model_family: Compatibility information for the model, defaults to None
- 99        :param modules: List of packages that can support the model, defaults to None
-100        :param path: Location of the model on disk, defaults to None
-101        :param pipe: List of components to build the execution for the model, defaults to None
-102        :param package: Package name and availability, defaults to None
-103        :param tasks: Available methods to run the model
-104        :param timestamp: Download time of model, defaults to None
-105        :param tokenizer: Tokenizer configuration location, defaults to None
-106        :return: An instance of RegistryEntry with the provided values
-107        """
-108        from datetime import datetime
-109
-110        entry = cls(
-111            api_kwargs=api_kwargs,
-112            bundle=bundle,
-113            cuetype=cuetype,
-114            mir=mir,
-115            mode=mode,
-116            model_family=model_family,
-117            model=model,
-118            modules=modules,
-119            package=package,
-120            path=path,
-121            pipe=pipe,
-122            size=size,
-123            tags=tags,
-124            tasks=tasks,
-125            timestamp=timestamp or int(datetime.now().timestamp()),  # Default to current time if not provided
-126            tokenizer=tokenizer,
-127        )
-128        dbuq(entry)
-129        return entry
-
- - -

Validate Hub / Ollama / LMStudio model input

-
- - -
-
- cuetype: zodiac.providers.constants.CueType = -PydanticUndefined - - -
- - - - -
-
-
- model: str = -PydanticUndefined - - -
- - - - -
-
-
- size: int = -PydanticUndefined - - -
- - - - -
-
-
- tags: List[str] = -PydanticUndefined - - -
- - - - -
-
-
- timestamp: int = -PydanticUndefined - - -
- - - - -
-
-
- mode: str | None = -None - - -
- - - - -
-
-
- api_kwargs: Optional[dict] = -None - - -
- - - - -
-
-
- mir: Optional[List[str]] = -None - - -
- - - - -
-
-
- bundle: Optional[List[List[str]]] = -None - - -
- - - - -
-
-
- model_family: Optional[List[str]] = -None - - -
- - - - -
-
-
- modules: Optional[dict[str, dict]] = -None - - -
- - - - -
-
-
- package: Union[zodiac.providers.constants.PkgType, zodiac.providers.constants.CueType, NoneType] = -None - - -
- - - - -
-
-
- path: Union[str, pathlib._local.Path, List[Union[str, pathlib._local.Path]], NoneType] = -None - - -
- - - - -
-
-
- pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = -(None,) - - -
- - - - -
-
-
- tasks: Optional[List[Union[str, List[str]]]] = -(None,) - - -
- - - - -
-
-
- tokenizer: Optional[pathlib._local.Path] = -None - - -
- - - - -
-
- -
- available_tasks: List[Tuple] - - - -
- - 
39    @computed_field
-40    @property
-41    def available_tasks(self) -> List[Tuple]:
-42        """Filter tag tasks into edge coordinates for graphing"""
-43        # This is a best effort at parsing tags; it is not perfect, and there is room for improvement
-44        # particularly: Tokenizers, being locatable here, should be assigned to their model entry
-45        # the logistics of how this occurs have been difficult to implement
-46        # additionally, tag recognition of tasks needs cleaner, which requires practical testing to solve
-47        import re
-48
-49        default_task = None
-50        processed_tasks = []
-51        dbuq(self.model)
-52        if self.cuetype in [x for x in list(CueType) if x != CueType.HUB]:  # Literal list of CueType, must use list()
-53            default_task = ("text", "text")  # usually these are txt gen libraries
-54        elif self.cuetype == CueType.HUB:
-55            dbuq(self.cuetype)  # pair tags from the hub such 'x-to-y' such as 'text-to-text' etc
-56            pattern = re.compile(r"(\w+)-to-(\w+)")
-57            for tag in [*self.tags, self.mode]:
-58                if tag:
-59                    match = pattern.search(tag.lower())
-60                    if match and all(group in VALID_CONVERSIONS for group in match.groups()) and (match.group(1), match.group(2)) not in processed_tasks:
-61                        processed_tasks.append((match.group(1), match.group(2)))
-62        for tag in self.tags:  # when pair-tagged elements are not available, potential to duplicate HUB tags here
-63            for (graph_src, graph_dest), tags in VALID_TASKS[self.cuetype].items():
-64                if tag.lower() in tags and (graph_src, graph_dest) not in processed_tasks:
-65                    processed_tasks.append((graph_src, graph_dest))
-66        if default_task and default_task not in processed_tasks:
-67            processed_tasks.append(default_task)
-68        return processed_tasks
-
- - -

Filter tag tasks into edge coordinates for graphing

-
- - -
-
- -
-
@classmethod
- - def - create_entry( cls, cuetype: zodiac.providers.constants.CueType, model: str, size: int, tags: List[str], api_kwargs: dict = None, mir: Optional[List[str]] = None, bundle: Optional[List[List[str]]] = None, mode: str | None = None, model_family: Optional[List[str]] = None, modules: Optional[dict[str, dict]] = None, package: Union[zodiac.providers.constants.PkgType, zodiac.providers.constants.CueType, NoneType] = None, path: Union[str, pathlib._local.Path, List[Union[str, pathlib._local.Path]], NoneType] = None, pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = None, tasks: Optional[List[Union[str, List[str]]]] = None, timestamp: Optional[int] = None, tokenizer=typing.Optional[str]): - - - -
- - 
 70    @classmethod
- 71    def create_entry(
- 72        cls,
- 73        cuetype: CueType,
- 74        model: str,
- 75        size: int,
- 76        tags: List[str],
- 77        api_kwargs: dict = None,
- 78        mir: Optional[List[str]] = None,
- 79        bundle: Optional[List[List[str]]] = None,
- 80        mode: str | None = None,
- 81        model_family: Optional[List[str]] = None,
- 82        modules: Optional[dict[str, dict]] = None,
- 83        package: Optional[Union[PkgType, CueType]] = None,
- 84        path: Optional[Union[str, Path, List[Union[str, Path]]]] = None,
- 85        pipe: Optional[dict[str, Union[List[List[str]], List[str], str]]] = None,
- 86        tasks: Optional[List[Union[str, List[str]]]] = None,
- 87        timestamp: Optional[int] = None,
- 88        tokenizer=Optional[str],
- 89    ):
- 90        """API specific data to call models\n
- 91        :param cuetype: Provider to trigger loading
- 92        :param model:Cache location for model
- 93        :param size: File size (usually in bytes)
- 94        :param tags: List of available machine tasks for model
- 95        :param api_kwargs: Localhost server defaults, defaults to None
- 96        :param keys: List of available data buckets inside the MIR tree, defaults to None
- 97        :param mir: MIR information, defaults to None
- 98        :param model_family: Compatibility information for the model, defaults to None
- 99        :param modules: List of packages that can support the model, defaults to None
-100        :param path: Location of the model on disk, defaults to None
-101        :param pipe: List of components to build the execution for the model, defaults to None
-102        :param package: Package name and availability, defaults to None
-103        :param tasks: Available methods to run the model
-104        :param timestamp: Download time of model, defaults to None
-105        :param tokenizer: Tokenizer configuration location, defaults to None
-106        :return: An instance of RegistryEntry with the provided values
-107        """
-108        from datetime import datetime
-109
-110        entry = cls(
-111            api_kwargs=api_kwargs,
-112            bundle=bundle,
-113            cuetype=cuetype,
-114            mir=mir,
-115            mode=mode,
-116            model_family=model_family,
-117            model=model,
-118            modules=modules,
-119            package=package,
-120            path=path,
-121            pipe=pipe,
-122            size=size,
-123            tags=tags,
-124            tasks=tasks,
-125            timestamp=timestamp or int(datetime.now().timestamp()),  # Default to current time if not provided
-126            tokenizer=tokenizer,
-127        )
-128        dbuq(entry)
-129        return entry
-
- - -

API specific data to call models

- -
Parameters
- -
    -
  • cuetype: Provider to trigger loading
    • -
  • model: Cache location for model
    • -
  • size: File size (usually in bytes)
    • -
  • tags: List of available machine tasks for model
    • -
  • api_kwargs: Localhost server defaults, defaults to None
    • -
  • keys: List of available data buckets inside the MIR tree, defaults to None
    • -
  • mir: MIR information, defaults to None
    • -
  • model_family: Compatibility information for the model, defaults to None
    • -
  • modules: List of packages that can support the model, defaults to None
    • -
  • path: Location of the model on disk, defaults to None
    • -
  • pipe: List of components to build the execution for the model, defaults to None
    • -
  • package: Package name and availability, defaults to None
    • -
  • tasks: Available methods to run the model
    • -
  • timestamp: Download time of model, defaults to None
    • -
  • tokenizer: Tokenizer configuration location, defaults to None
    • -
- -
Returns
- -
-

An instance of RegistryEntry with the provided values

-
-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams.html b/docs/zodiac/streams.html deleted file mode 100644 index 6278acb..0000000 --- a/docs/zodiac/streams.html +++ /dev/null @@ -1,251 +0,0 @@ - - - - - - - zodiac.streams API documentation - - - - - - - - - -
-
-

-zodiac.streams

- - - - - - - 
1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
-2# <!-- // /*  d a r k s h a p e s */ -->
-3
-4from zodiac.streams.model_stream import ModelStream
-5from zodiac.streams.task_stream import TaskStream
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/class_stream.html b/docs/zodiac/streams/class_stream.html deleted file mode 100644 index 370fbfd..0000000 --- a/docs/zodiac/streams/class_stream.html +++ /dev/null @@ -1,549 +0,0 @@ - - - - - - - zodiac.streams.class_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.class_stream

- - - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4from typing import List, Tuple, Callable, Union, Any, Generator
-  5from zodiac.providers.registry_entry import RegistryEntry
-  6from zodiac.providers.constants import MIR_DB, VERSIONS_CONFIG, ChipType
-  7
-  8
-  9async def ancestor_data(mir_tag_or_registry_entry: RegistryEntry | list, field_name: str = "pkg") -> Generator:
- 10    """Trace lineage of a model for the specified field \n
- 11    :param registry_entry: RegistryEntry for the model that needs to be traced
- 12    :param field_name: The name of the database field containing the data sought
- 13    :return: A generator populated with matching data fields"""
- 14    mir_db = MIR_DB.database
- 15    if isinstance(mir_tag_or_registry_entry, RegistryEntry):
- 16        mir_prefix = mir_tag_or_registry_entry.mir[0]
- 17    else:
- 18        mir_prefix = mir_tag_or_registry_entry[0]
- 19    base_fields = [
- 20        "diffusers",
- 21        "*",
- 22    ]  # "prior"
- 23    return [mir_db[mir_prefix][x].get(field_name) for x in base_fields if mir_db[mir_prefix].get(x, {}).get(field_name, {})]
- 24
- 25
- 26async def best_package(pkg_data: RegistryEntry | dict[int | str, Any], ready_list: list[tuple[ChipType]] = ChipType._show_ready()) -> tuple[str]:
- 27    """Identify the best package based on model data and package sets.\n
- 28    :param mir_db_pkg: Dictionary containing package data to match
- 29    :param ready_pkg_types: List of priority package processors to evaluate
- 30    :return: Tuple containing (class name, package type) if match found, otherwise None"""
- 31
- 32    if isinstance(pkg_data, RegistryEntry):
- 33        pkg_loop: list = await ancestor_data(pkg_data)
- 34        # print(pkg_loop)
- 35        pkg_loop.insert(0, pkg_data.modules | pkg_loop[0])
- 36    else:
- 37        pkg_loop = [pkg_data]  # await ancestor_data(pkg_data)  # normalize to list
- 38    for processor in ready_list:
- 39        for pkg_type in processor[2]:
- 40            if pkg_type.value[0]:  # Determine if the package is available
- 41                package_name = pkg_type.value[1].lower()
- 42                for index, data in next(iter(pkg_loop)).items():
- 43                    if package_name in data:
- 44                        return (index, data, pkg_type)
- 45
- 46
- 47async def find_package(entry: RegistryEntry = None, mir_entry: list[str] | None = None) -> Tuple[str]:
- 48    """Look up class and package in MIR from RegistryEntry.\n
- 49    :param entry: A RegistryEntry object containing MIR (Model Identifier Resource) details.
- 50    :return: A tuple containing the class name of the package and its type if found; otherwise, None.
- 51    :raises: AttributeError: If PkgType or ChipType classes are not properly defined."""
- 52    import re
- 53
- 54    mir_base = entry.mir[0] if not mir_entry else mir_entry[0]
- 55    mir_comp = entry.mir[1] if not mir_entry else mir_entry[1]
- 56    mir_ids = [mir_comp, "diffusers", "*"]
- 57    suffixes = VERSIONS_CONFIG.get("suffixes")
- 58    if suffixes:
- 59        for compatibility, model_data in MIR_DB.database[mir_base].items():
- 60            package_key = model_data.get("pkg")
- 61            if package_key and (any(re.match(pattern, compatibility) for pattern in suffixes) or compatibility in mir_ids):  # Fallback to CPU packages if no match found in GPU packages
- 62                package_data = await best_package(package_key)
- 63                if package_data:
- 64                    return package_data
- 65    return
- 66
- 67
- 68async def stage_class(class_object: Callable) -> List[Tuple[Union[str, Callable]]]:
- 69    """Returns a tuple of data for each sub-class of a module
- 70    :param class_obj: The class item to inspect.
- 71    ex:('diffusers', 'models.autoencoders.autoencoder_kl', 'AutoencoderKL', <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'>),"""
- 72    import typing
- 73
- 74    pipe_args = typing.get_type_hints(class_object.__init__).values()
- 75    sub_classes = [
- 76        (*pipe.__module__.split(".", 1), pipe.__name__, pipe)  #
- 77        for pipe in pipe_args  #
- 78        if "builtins" not in pipe.__module__ and "typing" not in pipe.__module__
- 79    ]
- 80    return sub_classes
- 81
- 82
- 83# async def show_transformers_tasks(class_name: str) -> List[str]:
- 84#     """Retrieves a list of task classes associated with a specified transformer class.\n
- 85#     :param class_name: The name of the transformer class to inspect.
- 86#     :param pkg_type: The dependency for the module
- 87#     :return: A list of task classes associated with the specified transformer.
- 88#     """
- 89#     class_obj: Callable = make_callable(class_name, PkgType.TRANSFORMERS.value[1].lower())
- 90#     class_module: Callable = make_callable(*class_obj.__module__.split(".", 1)[-1:], class_obj.__module__.split(".", 1)[0])
- 91#     task_classes = getattr(class_module, "__all__")
- 92#     return task_classes
- 93
- 94
- 95# from mir.mappers import make_callable
- 96# from zodiac.providers.constants import MIR_DB
- 97# from zodiac.class_stream import lookup_package
- 98# from zodiac.class_stream import trace_class
- 99
-100# model_data = lookup_package(entry)
-101# package_data = [content for content in model_data.get("pkg").values() if next(iter(content)) in ["diffusers", "transformers"]]
-102# class_data = trace_class(make_callable(package_data[0], "diffusers"))
-103# # [terminal_gen(data[3],getattr(PkgType,data[0].upper())) for data in class_data]
-
- - -
-
- -
- - async def - ancestor_data( mir_tag_or_registry_entry: zodiac.providers.registry_entry.RegistryEntry | list, field_name: str = 'pkg') -> Generator: - - - -
- - 
10async def ancestor_data(mir_tag_or_registry_entry: RegistryEntry | list, field_name: str = "pkg") -> Generator:
-11    """Trace lineage of a model for the specified field \n
-12    :param registry_entry: RegistryEntry for the model that needs to be traced
-13    :param field_name: The name of the database field containing the data sought
-14    :return: A generator populated with matching data fields"""
-15    mir_db = MIR_DB.database
-16    if isinstance(mir_tag_or_registry_entry, RegistryEntry):
-17        mir_prefix = mir_tag_or_registry_entry.mir[0]
-18    else:
-19        mir_prefix = mir_tag_or_registry_entry[0]
-20    base_fields = [
-21        "diffusers",
-22        "*",
-23    ]  # "prior"
-24    return [mir_db[mir_prefix][x].get(field_name) for x in base_fields if mir_db[mir_prefix].get(x, {}).get(field_name, {})]
-
- - -

Trace lineage of a model for the specified field

- -
Parameters
- -
    -
  • registry_entry: RegistryEntry for the model that needs to be traced
    • -
  • field_name: The name of the database field containing the data sought
    • -
- -
Returns
- -
-

A generator populated with matching data fields

-
-
- - -
-
- -
- - async def - best_package( pkg_data: zodiac.providers.registry_entry.RegistryEntry | dict[int | str, typing.Any], ready_list: list[tuple[zodiac.providers.constants.ChipType]] = [(True, 'MPS', [<PkgType.MFLUX: (True, 'MFLUX', [])>, <PkgType.MLX_AUDIO: (True, 'MLX_AUDIO', [])>, <PkgType.MLX_LM: (True, 'MLX_LM', [])>, <PkgType.BAGEL: (False, 'BAGEL', ['bytedance-seed/BAGEL'])>]), (True, 'CPU', [<PkgType.AUDIOGEN: (False, 'AUDIOCRAFT', ['exdysa/facebookresearch-audiocraft-revamp'])>, <PkgType.PARLER_TTS: (False, 'PARLER_TTS', ['huggingface/parler-tts'])>, <PkgType.LLAMA: (True, 'LLAMA_CPP', [])>, <PkgType.HIDIFFUSION: (False, 'HIDIFFUSION', ['megvii-research/HiDiffusion'])>, <PkgType.SENTENCE_TRANSFORMERS: (False, 'SENTENCE_TRANSFORMERS', [])>, <PkgType.DIFFUSERS: (True, 'DIFFUSERS', [])>, <PkgType.TRANSFORMERS: (True, 'TRANSFORMERS', [])>, <PkgType.TORCH: (True, 'TORCH', [])>])]) -> tuple[str]: - - - -
- - 
27async def best_package(pkg_data: RegistryEntry | dict[int | str, Any], ready_list: list[tuple[ChipType]] = ChipType._show_ready()) -> tuple[str]:
-28    """Identify the best package based on model data and package sets.\n
-29    :param mir_db_pkg: Dictionary containing package data to match
-30    :param ready_pkg_types: List of priority package processors to evaluate
-31    :return: Tuple containing (class name, package type) if match found, otherwise None"""
-32
-33    if isinstance(pkg_data, RegistryEntry):
-34        pkg_loop: list = await ancestor_data(pkg_data)
-35        # print(pkg_loop)
-36        pkg_loop.insert(0, pkg_data.modules | pkg_loop[0])
-37    else:
-38        pkg_loop = [pkg_data]  # await ancestor_data(pkg_data)  # normalize to list
-39    for processor in ready_list:
-40        for pkg_type in processor[2]:
-41            if pkg_type.value[0]:  # Determine if the package is available
-42                package_name = pkg_type.value[1].lower()
-43                for index, data in next(iter(pkg_loop)).items():
-44                    if package_name in data:
-45                        return (index, data, pkg_type)
-
- - -

Identify the best package based on model data and package sets.

- -
Parameters
- -
    -
  • mir_db_pkg: Dictionary containing package data to match
    • -
  • ready_pkg_types: List of priority package processors to evaluate
    • -
- -
Returns
- -
-

Tuple containing (class name, package type) if match found, otherwise None

-
-
- - -
-
- -
- - async def - find_package( entry: zodiac.providers.registry_entry.RegistryEntry = None, mir_entry: list[str] | None = None) -> Tuple[str]: - - - -
- - 
48async def find_package(entry: RegistryEntry = None, mir_entry: list[str] | None = None) -> Tuple[str]:
-49    """Look up class and package in MIR from RegistryEntry.\n
-50    :param entry: A RegistryEntry object containing MIR (Model Identifier Resource) details.
-51    :return: A tuple containing the class name of the package and its type if found; otherwise, None.
-52    :raises: AttributeError: If PkgType or ChipType classes are not properly defined."""
-53    import re
-54
-55    mir_base = entry.mir[0] if not mir_entry else mir_entry[0]
-56    mir_comp = entry.mir[1] if not mir_entry else mir_entry[1]
-57    mir_ids = [mir_comp, "diffusers", "*"]
-58    suffixes = VERSIONS_CONFIG.get("suffixes")
-59    if suffixes:
-60        for compatibility, model_data in MIR_DB.database[mir_base].items():
-61            package_key = model_data.get("pkg")
-62            if package_key and (any(re.match(pattern, compatibility) for pattern in suffixes) or compatibility in mir_ids):  # Fallback to CPU packages if no match found in GPU packages
-63                package_data = await best_package(package_key)
-64                if package_data:
-65                    return package_data
-66    return
-
- - -

Look up class and package in MIR from RegistryEntry.

- -
Parameters
- -
    -
  • entry: A RegistryEntry object containing MIR (Model Identifier Resource) details.
    • -
- -
Returns
- -
-

A tuple containing the class name of the package and its type if found; otherwise, None.

-
- -
Raises
- -
    -
  • AttributeError: If PkgType or ChipType classes are not properly defined.
    • -
-
- - -
-
- -
- - async def - stage_class(class_object: Callable) -> List[Tuple[Union[str, Callable]]]: - - - -
- - 
69async def stage_class(class_object: Callable) -> List[Tuple[Union[str, Callable]]]:
-70    """Returns a tuple of data for each sub-class of a module
-71    :param class_obj: The class item to inspect.
-72    ex:('diffusers', 'models.autoencoders.autoencoder_kl', 'AutoencoderKL', <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'>),"""
-73    import typing
-74
-75    pipe_args = typing.get_type_hints(class_object.__init__).values()
-76    sub_classes = [
-77        (*pipe.__module__.split(".", 1), pipe.__name__, pipe)  #
-78        for pipe in pipe_args  #
-79        if "builtins" not in pipe.__module__ and "typing" not in pipe.__module__
-80    ]
-81    return sub_classes
-
- - -

Returns a tuple of data for each sub-class of a module

- -
Parameters
- -
    -
  • class_obj: The class item to inspect. -ex:('diffusers', 'models.autoencoders.autoencoder_kl', 'AutoencoderKL', ),
    • -
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/media_stream.html b/docs/zodiac/streams/media_stream.html deleted file mode 100644 index 0ff026a..0000000 --- a/docs/zodiac/streams/media_stream.html +++ /dev/null @@ -1,470 +0,0 @@ - - - - - - - zodiac.streams.media_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.media_stream

- - - - - - - 
 1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
- 2#  # # <!-- // /*  d a r k s h a p e s */ -->
- 3
- 4import sounddevice as sd
- 5from nnll.monitor.file import dbuq
- 6
- 7
- 8class AudioMachine:
- 9    audio_stream = [0]
-10    frequency = 0
-11    duration: float = 3.0
-12    precision = duration * frequency
-13    sample_length = 0.0
-14
-15
-16async def record_audio(self, frequency: int = 16000) -> None:
-17    """Get audio from mic"""
-18    self.frequency = frequency
-19    self.audio_stream = [0]
-20    self.audio_stream = sd.rec(int(self.precision), samplerate=self.frequency, channels=1)
-21    sd.wait()
-22    self.sample_length = str(float(len(self.audio_stream) / frequency))
-23    return self.audio_stream
-24
-25
-26async def play_audio(self) -> None:
-27    """Playback audio recordings"""
-28    try:
-29        sd.play(self.audio_stream, samplerate=self.frequency)
-30        sd.wait()
-31    except TypeError as error_log:
-32        dbuq(error_log)
-33
-34
-35async def erase_audio(self) -> None:
-36    """Clear audio graph and recording"""
-37    self.audio_stream = [0]
-38    self.frequency = 0.0
-39    self.sample_length = 0.0
-
- - -
-
- -
- - class - AudioMachine: - - - -
- - 
 9class AudioMachine:
-10    audio_stream = [0]
-11    frequency = 0
-12    duration: float = 3.0
-13    precision = duration * frequency
-14    sample_length = 0.0
-
- - - - -
-
- audio_stream = -[0] - - -
- - - - -
-
-
- frequency = -0 - - -
- - - - -
-
-
- duration: float = -3.0 - - -
- - - - -
-
-
- precision = -0.0 - - -
- - - - -
-
-
- sample_length = -0.0 - - -
- - - - -
-
-
- -
- - async def - record_audio(self, frequency: int = 16000) -> None: - - - -
- - 
17async def record_audio(self, frequency: int = 16000) -> None:
-18    """Get audio from mic"""
-19    self.frequency = frequency
-20    self.audio_stream = [0]
-21    self.audio_stream = sd.rec(int(self.precision), samplerate=self.frequency, channels=1)
-22    sd.wait()
-23    self.sample_length = str(float(len(self.audio_stream) / frequency))
-24    return self.audio_stream
-
- - -

Get audio from mic

-
- - -
-
- -
- - async def - play_audio(self) -> None: - - - -
- - 
27async def play_audio(self) -> None:
-28    """Playback audio recordings"""
-29    try:
-30        sd.play(self.audio_stream, samplerate=self.frequency)
-31        sd.wait()
-32    except TypeError as error_log:
-33        dbuq(error_log)
-
- - -

Playback audio recordings

-
- - -
-
- -
- - async def - erase_audio(self) -> None: - - - -
- - 
36async def erase_audio(self) -> None:
-37    """Clear audio graph and recording"""
-38    self.audio_stream = [0]
-39    self.frequency = 0.0
-40    self.sample_length = 0.0
-
- - -

Clear audio graph and recording

-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/model_stream.html b/docs/zodiac/streams/model_stream.html deleted file mode 100644 index ceb7001..0000000 --- a/docs/zodiac/streams/model_stream.html +++ /dev/null @@ -1,625 +0,0 @@ - - - - - - - zodiac.streams.model_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.model_stream

- - - - - - - 
 1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
- 2#  # # <!-- // /*  d a r k s h a p e s */ -->
- 3
- 4from typing import List, Tuple
- 5
- 6from toga.sources import Source
- 7
- 8nfo = print
- 9
-10
-11class ModelStream(Source):
-12    async def model_graph(self) -> None:
-13        """Build an intent graph from models using the IntentProcessor class"""
-14        from zodiac.graph import IntentProcessor
-15
-16        self._graph = {}
-17        self._graph = IntentProcessor()
-18        await self._graph.calc_graph()
-19
-20    async def show_edges(self, target: bool = False) -> List[str]:
-21        """Retrieve and sort edges from the intent graph.\n
-22        :param target: If True, sorts based on the second element of each edge pair; defaults to False.
-23        :return: A sorted list of unique elements from the edge pairs."""
-24
-25        if self._graph.intent_graph:
-26            edge_pairs = list(self._graph.intent_graph.edges)
-27            if edge_pairs:
-28                pair = 0 if not target else 1
-29                seen = []
-30                for edge in edge_pairs:
-31                    if edge[pair] not in seen:
-32                        seen.append(edge[pair])
-33                seen.sort(key=len)
-34                return seen
-35
-36    async def trace_models(self, mode_in: str, mode_out: str) -> List[Tuple[str, int]]:
-37        """Trace model path through input to output mode, then updates the internal model list..\n
-38        :param mode_in: The input mode for tracing.
-39        :param mode_out: The output mode for tracing.
-40        :return: A list of traced models."""
-41
-42        from nnll.monitor.file import dbuq
-43
-44        self._graph.set_path(mode_in=mode_in, mode_out=mode_out)
-45        self._graph.set_registry_entries()
-46        nfo(f"calculated : {self._graph.coord_path}")
-47        dbuq(f"calculated : {self._graph.coord_path} {self._graph.registry_entries}")
-48        self._models = self._graph.models
-49        return self._models
-50
-51    async def chart_path(self) -> List[str]:
-52        """Return hop names of current path\n
-53        :return: List of [x,y,z] node names along the chosen path
-54        """
-55        return self._graph.coord_path
-56
-57    def __len__(self):
-58        return len(list(self._models()))
-59
-60    def __getitem__(self, index):
-61        return self._models()[index]
-62
-63    def index(self, entry):
-64        return self._models().index(entry)
-65
-66    async def clear(self):
-67        self._models = []
-68        self.notify("clear")
-
- - -
-
-
- - def - nfo(*args, sep=' ', end='\n', file=None, flush=False): - - -
- - -

Prints the values to a stream, or to sys.stdout by default.

- -

sep - string inserted between values, default a space. -end - string appended after the last value, default a newline. -file - a file-like object (stream); defaults to the current sys.stdout. -flush - whether to forcibly flush the stream.

-
- - -
-
- -
- - class - ModelStream(toga.sources.base.Source): - - - -
- - 
12class ModelStream(Source):
-13    async def model_graph(self) -> None:
-14        """Build an intent graph from models using the IntentProcessor class"""
-15        from zodiac.graph import IntentProcessor
-16
-17        self._graph = {}
-18        self._graph = IntentProcessor()
-19        await self._graph.calc_graph()
-20
-21    async def show_edges(self, target: bool = False) -> List[str]:
-22        """Retrieve and sort edges from the intent graph.\n
-23        :param target: If True, sorts based on the second element of each edge pair; defaults to False.
-24        :return: A sorted list of unique elements from the edge pairs."""
-25
-26        if self._graph.intent_graph:
-27            edge_pairs = list(self._graph.intent_graph.edges)
-28            if edge_pairs:
-29                pair = 0 if not target else 1
-30                seen = []
-31                for edge in edge_pairs:
-32                    if edge[pair] not in seen:
-33                        seen.append(edge[pair])
-34                seen.sort(key=len)
-35                return seen
-36
-37    async def trace_models(self, mode_in: str, mode_out: str) -> List[Tuple[str, int]]:
-38        """Trace model path through input to output mode, then updates the internal model list..\n
-39        :param mode_in: The input mode for tracing.
-40        :param mode_out: The output mode for tracing.
-41        :return: A list of traced models."""
-42
-43        from nnll.monitor.file import dbuq
-44
-45        self._graph.set_path(mode_in=mode_in, mode_out=mode_out)
-46        self._graph.set_registry_entries()
-47        nfo(f"calculated : {self._graph.coord_path}")
-48        dbuq(f"calculated : {self._graph.coord_path} {self._graph.registry_entries}")
-49        self._models = self._graph.models
-50        return self._models
-51
-52    async def chart_path(self) -> List[str]:
-53        """Return hop names of current path\n
-54        :return: List of [x,y,z] node names along the chosen path
-55        """
-56        return self._graph.coord_path
-57
-58    def __len__(self):
-59        return len(list(self._models()))
-60
-61    def __getitem__(self, index):
-62        return self._models()[index]
-63
-64    def index(self, entry):
-65        return self._models().index(entry)
-66
-67    async def clear(self):
-68        self._models = []
-69        self.notify("clear")
-
- - -

A base class for data sources, providing an implementation of data -notifications.

-
- - -
- -
- - async def - model_graph(self) -> None: - - - -
- - 
13    async def model_graph(self) -> None:
-14        """Build an intent graph from models using the IntentProcessor class"""
-15        from zodiac.graph import IntentProcessor
-16
-17        self._graph = {}
-18        self._graph = IntentProcessor()
-19        await self._graph.calc_graph()
-
- - -

Build an intent graph from models using the IntentProcessor class

-
- - -
-
- -
- - async def - show_edges(self, target: bool = False) -> List[str]: - - - -
- - 
21    async def show_edges(self, target: bool = False) -> List[str]:
-22        """Retrieve and sort edges from the intent graph.\n
-23        :param target: If True, sorts based on the second element of each edge pair; defaults to False.
-24        :return: A sorted list of unique elements from the edge pairs."""
-25
-26        if self._graph.intent_graph:
-27            edge_pairs = list(self._graph.intent_graph.edges)
-28            if edge_pairs:
-29                pair = 0 if not target else 1
-30                seen = []
-31                for edge in edge_pairs:
-32                    if edge[pair] not in seen:
-33                        seen.append(edge[pair])
-34                seen.sort(key=len)
-35                return seen
-
- - -

Retrieve and sort edges from the intent graph.

- -
Parameters
- -
    -
  • target: If True, sorts based on the second element of each edge pair; defaults to False.
    • -
- -
Returns
- -
-

A sorted list of unique elements from the edge pairs.

-
-
- - -
-
- -
- - async def - trace_models(self, mode_in: str, mode_out: str) -> List[Tuple[str, int]]: - - - -
- - 
37    async def trace_models(self, mode_in: str, mode_out: str) -> List[Tuple[str, int]]:
-38        """Trace model path through input to output mode, then updates the internal model list..\n
-39        :param mode_in: The input mode for tracing.
-40        :param mode_out: The output mode for tracing.
-41        :return: A list of traced models."""
-42
-43        from nnll.monitor.file import dbuq
-44
-45        self._graph.set_path(mode_in=mode_in, mode_out=mode_out)
-46        self._graph.set_registry_entries()
-47        nfo(f"calculated : {self._graph.coord_path}")
-48        dbuq(f"calculated : {self._graph.coord_path} {self._graph.registry_entries}")
-49        self._models = self._graph.models
-50        return self._models
-
- - -

Trace model path through input to output mode, then updates the internal model list..

- -
Parameters
- -
    -
  • mode_in: The input mode for tracing.
    • -
  • mode_out: The output mode for tracing.
    • -
- -
Returns
- -
-

A list of traced models.

-
-
- - -
-
- -
- - async def - chart_path(self) -> List[str]: - - - -
- - 
52    async def chart_path(self) -> List[str]:
-53        """Return hop names of current path\n
-54        :return: List of [x,y,z] node names along the chosen path
-55        """
-56        return self._graph.coord_path
-
- - -

Return hop names of current path

- -
Returns
- -
-

List of [x,y,z] node names along the chosen path

-
-
- - -
-
- -
- - def - index(self, entry): - - - -
- - 
64    def index(self, entry):
-65        return self._models().index(entry)
-
- - - - -
-
- -
- - async def - clear(self): - - - -
- - 
67    async def clear(self):
-68        self._models = []
-69        self.notify("clear")
-
- - - - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/plot_stream.html b/docs/zodiac/streams/plot_stream.html deleted file mode 100644 index b2b0086..0000000 --- a/docs/zodiac/streams/plot_stream.html +++ /dev/null @@ -1,340 +0,0 @@ - - - - - - - zodiac.streams.plot_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.plot_stream

- - - - - - - 
 1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
- 2# <!-- // /*  d a r k s h a p e s */ -->
- 3
- 4
- 5import asyncio
- 6
- 7import matplotlib as mpl
- 8import matplotlib.pyplot as plt
- 9import networkx as nx
-10
-11from zodiac.streams import ModelStream
-12
-13
-14async def main():
-15    model_source = ModelStream()
-16    await model_source.model_graph()
-17    await model_source.trace_models("image", "speech")
-18    graph_copy = model_source._graph.intent_graph.to_undirected()
-19    nx_graph = nx.Graph(graph_copy)
-20
-21    pos = nx.spring_layout(nx_graph, seed=7)  # positions for all nodes - seed for reproducibility
-22
-23    # nodes
-24    nx.draw_networkx_nodes(nx_graph, pos, node_size=700)
-25
-26    # edges
-27    # print(nx_graph.edges())
-28    # print(nx_graph.adjacency())
-29    path = nx.bidirectional_shortest_path(nx_graph, "image", "speech")
-30    path_list = [nx_graph[path[x]][path[x + 1]] for x in range(len(path) - 1)]
-31    nx.draw_networkx_edges(nx_graph, pos, edgelist=nx_graph.edges(), width=6)
-32    nx.draw_networkx_edges(nx_graph, pos, edgelist=path_list, width=6, alpha=0.5, edge_color="b", style="dashed")
-33
-34    # node labels
-35    nx.draw_networkx_labels(nx_graph, pos, font_size=20, font_family="sans-serif")
-36    # edge weight labels
-37    edge_labels = nx.get_edge_attributes(nx_graph, "weight")
-38    nx.draw_networkx_edge_labels(nx_graph, pos, edge_labels)
-39
-40    ax = plt.gca()
-41    ax.margins(0.08)
-42    plt.axis("off")
-43    plt.tight_layout()
-44    plt.show()
-45
-46
-47if __name__ == "__main__":
-48    asyncio.run(main())
-
- - -
-
- -
- - async def - main(): - - - -
- - 
15async def main():
-16    model_source = ModelStream()
-17    await model_source.model_graph()
-18    await model_source.trace_models("image", "speech")
-19    graph_copy = model_source._graph.intent_graph.to_undirected()
-20    nx_graph = nx.Graph(graph_copy)
-21
-22    pos = nx.spring_layout(nx_graph, seed=7)  # positions for all nodes - seed for reproducibility
-23
-24    # nodes
-25    nx.draw_networkx_nodes(nx_graph, pos, node_size=700)
-26
-27    # edges
-28    # print(nx_graph.edges())
-29    # print(nx_graph.adjacency())
-30    path = nx.bidirectional_shortest_path(nx_graph, "image", "speech")
-31    path_list = [nx_graph[path[x]][path[x + 1]] for x in range(len(path) - 1)]
-32    nx.draw_networkx_edges(nx_graph, pos, edgelist=nx_graph.edges(), width=6)
-33    nx.draw_networkx_edges(nx_graph, pos, edgelist=path_list, width=6, alpha=0.5, edge_color="b", style="dashed")
-34
-35    # node labels
-36    nx.draw_networkx_labels(nx_graph, pos, font_size=20, font_family="sans-serif")
-37    # edge weight labels
-38    edge_labels = nx.get_edge_attributes(nx_graph, "weight")
-39    nx.draw_networkx_edge_labels(nx_graph, pos, edge_labels)
-40
-41    ax = plt.gca()
-42    ax.margins(0.08)
-43    plt.axis("off")
-44    plt.tight_layout()
-45    plt.show()
-
- - - - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/task_stream.html b/docs/zodiac/streams/task_stream.html deleted file mode 100644 index 7ab94d0..0000000 --- a/docs/zodiac/streams/task_stream.html +++ /dev/null @@ -1,712 +0,0 @@ - - - - - - - zodiac.streams.task_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.task_stream

- - - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4from typing import List, Any, Set
-  5from toga.sources import Source
-  6from zodiac.providers.registry_entry import RegistryEntry
-  7
-  8nfo = print
-  9
- 10flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative]
- 11flatten_map.__annotations__ = {"nested": List[str], "unpack": str}
- 12
- 13
- 14class TaskStream(Source):
- 15    def __init__(self) -> None:
- 16        self.basic_tasks = {
- 17            "speech": ["Audio"],
- 18            "image": ["ControlNet", "PAG"],
- 19            "text": [
- 20                "QuestionAnswering",
- 21                "SequenceClassification",
- 22            ],
- 23        }
- 24        self.exclusive_tasks = {
- 25            ("image", "text"): ["Vision"],
- 26            ("image", "image"): ["Img2Img", "Inpaint", "PAG", "Vision"],
- 27            ("text", "text"): ["Text", "CasualLM", "SequenceClassification", "QuestionAnswering"],
- 28        }
- 29        self.all_tasks = set().union(*self.basic_tasks.values()).union(*self.exclusive_tasks.values())
- 30
- 31    async def set_filter_type(self, mode_in: str = "image", mode_out: str = "image") -> None:
- 32        """Filter class items by modality
- 33        :param mode_in: Input modality operation, defaults to "image"
- 34        :param mode_out: Output modality operation, defaults to "image"""
- 35
- 36        self.tasks = None
- 37        self.exclude = None
- 38        self.tasks = set()
- 39        self.exclude = set()
- 40
- 41        if (mode_in, mode_out) in self.exclusive_tasks:
- 42            self.tasks = self.exclusive_tasks[(mode_in, mode_out)]
- 43        else:
- 44            if mode_in in self.basic_tasks:
- 45                self.tasks.update(self.basic_tasks[mode_in])
- 46            if mode_out in self.basic_tasks:
- 47                self.tasks.update(self.basic_tasks[mode_out])
- 48        self.exclude = self.all_tasks.difference(self.tasks)
- 49
- 50    async def filter_tasks(self, registry_entry: RegistryEntry) -> List[str]:
- 51        """Processes preformatted task data by removing specified prefixes and keywords, then adds valid data to task_data.\n
- 52        :param preformatted_task_data: A list of strings to be processed.
- 53        :param snip_words: A list of prefixes or suffixes to be removed from each pipe.
- 54        :return: A sorted list of unique task_data entries after processing."""
- 55        import re
- 56
- 57        if registry_entry.package == "mflux":
- 58            return registry_entry.tasks
- 59        task_names = registry_entry.tasks
- 60        snip_words: Set[str] = {"Model", "PreTrained", "ForConditionalGeneration", "Pipeline", "For"}
- 61        class_snippets = snip_words | self.all_tasks
- 62        if task_names:
- 63            for task_class in task_names:
- 64                for snip in class_snippets:
- 65                    if task_class and isinstance(task_class, str):
- 66                        snip_words.add(task_class.replace(snip, ""))
- 67                    elif task_class[0]:
- 68                        snip_words.add(task_class[0].replace(snip, ""))
- 69                task_data = set()
- 70                for pipe in task_names:
- 71                    if isinstance(pipe, list):
- 72                        for sub_pipe in pipe:
- 73                            for word in snip_words:
- 74                                sub_pipe = sub_pipe.replace(word, "")
- 75                                if sub_pipe:
- 76                                    for task in self.tasks:
- 77                                        if task in sub_pipe:
- 78                                            task_data.add(sub_pipe)
- 79                    else:
- 80                        for word in snip_words:
- 81                            pipe = pipe.replace(word, "")
- 82                            if pipe:
- 83                                for task in self.tasks:
- 84                                    if task in pipe:
- 85                                        task_data.add(pipe)
- 86                task_data = list(task_data)
- 87                task_data.sort()
- 88            return task_data
- 89
- 90    def __len__(self):
- 91        return len(list(self._task_data()))
- 92
- 93    def __getitem__(self, index):
- 94        return self._task_data()[index]
- 95
- 96    def index(self, entry):
- 97        return self._task_data().index(entry)
- 98
- 99    async def clear(self):
-100        self._task_data = []
-101        self.notify("clear")
-
- - -
-
-
- - def - nfo(*args, sep=' ', end='\n', file=None, flush=False): - - -
- - -

Prints the values to a stream, or to sys.stdout by default.

- -

sep - string inserted between values, default a space. -end - string appended after the last value, default a newline. -file - a file-like object (stream); defaults to the current sys.stdout. -flush - whether to forcibly flush the stream.

-
- - -
-
- -
- - def - flatten_map(nested: List[str], unpack: str): - - - -
- - 
11flatten_map: List[Any] = lambda nested, unpack: [element for iterative in getattr(nested, unpack)() for element in iterative]
-
- - - - -
-
- -
- - class - TaskStream(toga.sources.base.Source): - - - -
- - 
 15class TaskStream(Source):
- 16    def __init__(self) -> None:
- 17        self.basic_tasks = {
- 18            "speech": ["Audio"],
- 19            "image": ["ControlNet", "PAG"],
- 20            "text": [
- 21                "QuestionAnswering",
- 22                "SequenceClassification",
- 23            ],
- 24        }
- 25        self.exclusive_tasks = {
- 26            ("image", "text"): ["Vision"],
- 27            ("image", "image"): ["Img2Img", "Inpaint", "PAG", "Vision"],
- 28            ("text", "text"): ["Text", "CasualLM", "SequenceClassification", "QuestionAnswering"],
- 29        }
- 30        self.all_tasks = set().union(*self.basic_tasks.values()).union(*self.exclusive_tasks.values())
- 31
- 32    async def set_filter_type(self, mode_in: str = "image", mode_out: str = "image") -> None:
- 33        """Filter class items by modality
- 34        :param mode_in: Input modality operation, defaults to "image"
- 35        :param mode_out: Output modality operation, defaults to "image"""
- 36
- 37        self.tasks = None
- 38        self.exclude = None
- 39        self.tasks = set()
- 40        self.exclude = set()
- 41
- 42        if (mode_in, mode_out) in self.exclusive_tasks:
- 43            self.tasks = self.exclusive_tasks[(mode_in, mode_out)]
- 44        else:
- 45            if mode_in in self.basic_tasks:
- 46                self.tasks.update(self.basic_tasks[mode_in])
- 47            if mode_out in self.basic_tasks:
- 48                self.tasks.update(self.basic_tasks[mode_out])
- 49        self.exclude = self.all_tasks.difference(self.tasks)
- 50
- 51    async def filter_tasks(self, registry_entry: RegistryEntry) -> List[str]:
- 52        """Processes preformatted task data by removing specified prefixes and keywords, then adds valid data to task_data.\n
- 53        :param preformatted_task_data: A list of strings to be processed.
- 54        :param snip_words: A list of prefixes or suffixes to be removed from each pipe.
- 55        :return: A sorted list of unique task_data entries after processing."""
- 56        import re
- 57
- 58        if registry_entry.package == "mflux":
- 59            return registry_entry.tasks
- 60        task_names = registry_entry.tasks
- 61        snip_words: Set[str] = {"Model", "PreTrained", "ForConditionalGeneration", "Pipeline", "For"}
- 62        class_snippets = snip_words | self.all_tasks
- 63        if task_names:
- 64            for task_class in task_names:
- 65                for snip in class_snippets:
- 66                    if task_class and isinstance(task_class, str):
- 67                        snip_words.add(task_class.replace(snip, ""))
- 68                    elif task_class[0]:
- 69                        snip_words.add(task_class[0].replace(snip, ""))
- 70                task_data = set()
- 71                for pipe in task_names:
- 72                    if isinstance(pipe, list):
- 73                        for sub_pipe in pipe:
- 74                            for word in snip_words:
- 75                                sub_pipe = sub_pipe.replace(word, "")
- 76                                if sub_pipe:
- 77                                    for task in self.tasks:
- 78                                        if task in sub_pipe:
- 79                                            task_data.add(sub_pipe)
- 80                    else:
- 81                        for word in snip_words:
- 82                            pipe = pipe.replace(word, "")
- 83                            if pipe:
- 84                                for task in self.tasks:
- 85                                    if task in pipe:
- 86                                        task_data.add(pipe)
- 87                task_data = list(task_data)
- 88                task_data.sort()
- 89            return task_data
- 90
- 91    def __len__(self):
- 92        return len(list(self._task_data()))
- 93
- 94    def __getitem__(self, index):
- 95        return self._task_data()[index]
- 96
- 97    def index(self, entry):
- 98        return self._task_data().index(entry)
- 99
-100    async def clear(self):
-101        self._task_data = []
-102        self.notify("clear")
-
- - -

A base class for data sources, providing an implementation of data -notifications.

-
- - -
-
- basic_tasks - - -
- - - - -
-
-
- exclusive_tasks - - -
- - - - -
-
-
- all_tasks - - -
- - - - -
-
- -
- - async def - set_filter_type(self, mode_in: str = 'image', mode_out: str = 'image') -> None: - - - -
- - 
32    async def set_filter_type(self, mode_in: str = "image", mode_out: str = "image") -> None:
-33        """Filter class items by modality
-34        :param mode_in: Input modality operation, defaults to "image"
-35        :param mode_out: Output modality operation, defaults to "image"""
-36
-37        self.tasks = None
-38        self.exclude = None
-39        self.tasks = set()
-40        self.exclude = set()
-41
-42        if (mode_in, mode_out) in self.exclusive_tasks:
-43            self.tasks = self.exclusive_tasks[(mode_in, mode_out)]
-44        else:
-45            if mode_in in self.basic_tasks:
-46                self.tasks.update(self.basic_tasks[mode_in])
-47            if mode_out in self.basic_tasks:
-48                self.tasks.update(self.basic_tasks[mode_out])
-49        self.exclude = self.all_tasks.difference(self.tasks)
-
- - -

Filter class items by modality

- -
Parameters
- -
    -
  • mode_in: Input modality operation, defaults to "image"
    • -
  • mode_out: Output modality operation, defaults to "image
    • -
-
- - -
-
- -
- - async def - filter_tasks( self, registry_entry: zodiac.providers.registry_entry.RegistryEntry) -> List[str]: - - - -
- - 
51    async def filter_tasks(self, registry_entry: RegistryEntry) -> List[str]:
-52        """Processes preformatted task data by removing specified prefixes and keywords, then adds valid data to task_data.\n
-53        :param preformatted_task_data: A list of strings to be processed.
-54        :param snip_words: A list of prefixes or suffixes to be removed from each pipe.
-55        :return: A sorted list of unique task_data entries after processing."""
-56        import re
-57
-58        if registry_entry.package == "mflux":
-59            return registry_entry.tasks
-60        task_names = registry_entry.tasks
-61        snip_words: Set[str] = {"Model", "PreTrained", "ForConditionalGeneration", "Pipeline", "For"}
-62        class_snippets = snip_words | self.all_tasks
-63        if task_names:
-64            for task_class in task_names:
-65                for snip in class_snippets:
-66                    if task_class and isinstance(task_class, str):
-67                        snip_words.add(task_class.replace(snip, ""))
-68                    elif task_class[0]:
-69                        snip_words.add(task_class[0].replace(snip, ""))
-70                task_data = set()
-71                for pipe in task_names:
-72                    if isinstance(pipe, list):
-73                        for sub_pipe in pipe:
-74                            for word in snip_words:
-75                                sub_pipe = sub_pipe.replace(word, "")
-76                                if sub_pipe:
-77                                    for task in self.tasks:
-78                                        if task in sub_pipe:
-79                                            task_data.add(sub_pipe)
-80                    else:
-81                        for word in snip_words:
-82                            pipe = pipe.replace(word, "")
-83                            if pipe:
-84                                for task in self.tasks:
-85                                    if task in pipe:
-86                                        task_data.add(pipe)
-87                task_data = list(task_data)
-88                task_data.sort()
-89            return task_data
-
- - -

Processes preformatted task data by removing specified prefixes and keywords, then adds valid data to task_data.

- -
Parameters
- -
    -
  • preformatted_task_data: A list of strings to be processed.
    • -
  • snip_words: A list of prefixes or suffixes to be removed from each pipe.
    • -
- -
Returns
- -
-

A sorted list of unique task_data entries after processing.

-
-
- - -
-
- -
- - def - index(self, entry): - - - -
- - 
97    def index(self, entry):
-98        return self._task_data().index(entry)
-
- - - - -
-
- -
- - async def - clear(self): - - - -
- - 
100    async def clear(self):
-101        self._task_data = []
-102        self.notify("clear")
-
- - - - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/streams/token_stream.html b/docs/zodiac/streams/token_stream.html deleted file mode 100644 index 1899325..0000000 --- a/docs/zodiac/streams/token_stream.html +++ /dev/null @@ -1,503 +0,0 @@ - - - - - - - zodiac.streams.token_stream API documentation - - - - - - - - - -
-
-

-zodiac.streams.token_stream

- - - - - - - 
 1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
- 2#  # # <!-- // /*  d a r k s h a p e s */ -->
- 3
- 4# pylint: disable=import-error
- 5
- 6
- 7import warnings
- 8from pathlib import Path
- 9from typing import Callable, Optional
-10
-11warnings.filterwarnings("ignore", category=DeprecationWarning)
-12
-13from litellm.utils import create_tokenizer, token_counter
-14from toga.sources import Source
-15from zodiac.providers.registry_entry import RegistryEntry
-16
-17
-18class TokenStream(Source):
-19    def __init__(self):
-20        self.tokenizer: Optional[str] = None
-21        self.message: Optional[str] = None
-22        self.tokenizer_args = {}
-23
-24    async def set_tokenizer(self, registry_entry: RegistryEntry) -> Callable:
-25        """Pass message to model routine\n
-26        :param model: Path to model
-27        :param message: Text to encode
-28        :return: Token embeddings for the model"""
-29
-30        import json
-31
-32        if registry_entry.tokenizer:
-33            with open(str(registry_entry.tokenizer), encoding="UTF-8") as file_obj:
-34                tokenizer_json = json.load(file_obj)
-35                tokenizer_data = json.dumps(tokenizer_json)
-36                self.tokenizer_args = {"custom_tokenizer": create_tokenizer(tokenizer_data)}
-37        else:
-38            # model_name = os.path.split(registry_entry.model)
-39            # model_name = os.path.join(os.path.split(model_name[0])[-1], model_name[-1])
-40            # self.status_log.registry_entry.model
-41            self.tokenizer_args = {"model": registry_entry.model}
-42
-43    async def token_count(
-44        self,
-45        message: str,
-46    ) -> Callable:
-47        """Return token count of message based on model\n
-48        :param model: Model path to lookup tokenizer for
-49        :param message: Message to tokenize
-50        :return: `int` Number of tokens needed to represent message"""
-51        import warnings
-52
-53        warnings.filterwarnings("ignore", category=DeprecationWarning)
-54        character_count = len(message)
-55        return token_counter(text=message, **self.tokenizer_args), character_count
-
- - -
-
- -
- - class - TokenStream(toga.sources.base.Source): - - - -
- - 
19class TokenStream(Source):
-20    def __init__(self):
-21        self.tokenizer: Optional[str] = None
-22        self.message: Optional[str] = None
-23        self.tokenizer_args = {}
-24
-25    async def set_tokenizer(self, registry_entry: RegistryEntry) -> Callable:
-26        """Pass message to model routine\n
-27        :param model: Path to model
-28        :param message: Text to encode
-29        :return: Token embeddings for the model"""
-30
-31        import json
-32
-33        if registry_entry.tokenizer:
-34            with open(str(registry_entry.tokenizer), encoding="UTF-8") as file_obj:
-35                tokenizer_json = json.load(file_obj)
-36                tokenizer_data = json.dumps(tokenizer_json)
-37                self.tokenizer_args = {"custom_tokenizer": create_tokenizer(tokenizer_data)}
-38        else:
-39            # model_name = os.path.split(registry_entry.model)
-40            # model_name = os.path.join(os.path.split(model_name[0])[-1], model_name[-1])
-41            # self.status_log.registry_entry.model
-42            self.tokenizer_args = {"model": registry_entry.model}
-43
-44    async def token_count(
-45        self,
-46        message: str,
-47    ) -> Callable:
-48        """Return token count of message based on model\n
-49        :param model: Model path to lookup tokenizer for
-50        :param message: Message to tokenize
-51        :return: `int` Number of tokens needed to represent message"""
-52        import warnings
-53
-54        warnings.filterwarnings("ignore", category=DeprecationWarning)
-55        character_count = len(message)
-56        return token_counter(text=message, **self.tokenizer_args), character_count
-
- - -

A base class for data sources, providing an implementation of data -notifications.

-
- - -
-
- tokenizer: Optional[str] - - -
- - - - -
-
-
- message: Optional[str] - - -
- - - - -
-
-
- tokenizer_args - - -
- - - - -
-
- -
- - async def - set_tokenizer( self, registry_entry: zodiac.providers.registry_entry.RegistryEntry) -> Callable: - - - -
- - 
25    async def set_tokenizer(self, registry_entry: RegistryEntry) -> Callable:
-26        """Pass message to model routine\n
-27        :param model: Path to model
-28        :param message: Text to encode
-29        :return: Token embeddings for the model"""
-30
-31        import json
-32
-33        if registry_entry.tokenizer:
-34            with open(str(registry_entry.tokenizer), encoding="UTF-8") as file_obj:
-35                tokenizer_json = json.load(file_obj)
-36                tokenizer_data = json.dumps(tokenizer_json)
-37                self.tokenizer_args = {"custom_tokenizer": create_tokenizer(tokenizer_data)}
-38        else:
-39            # model_name = os.path.split(registry_entry.model)
-40            # model_name = os.path.join(os.path.split(model_name[0])[-1], model_name[-1])
-41            # self.status_log.registry_entry.model
-42            self.tokenizer_args = {"model": registry_entry.model}
-
- - -

Pass message to model routine

- -
Parameters
- -
    -
  • model: Path to model
    • -
  • message: Text to encode
    • -
- -
Returns
- -
-

Token embeddings for the model

-
-
- - -
-
- -
- - async def - token_count(self, message: str) -> Callable: - - - -
- - 
44    async def token_count(
-45        self,
-46        message: str,
-47    ) -> Callable:
-48        """Return token count of message based on model\n
-49        :param model: Model path to lookup tokenizer for
-50        :param message: Message to tokenize
-51        :return: `int` Number of tokens needed to represent message"""
-52        import warnings
-53
-54        warnings.filterwarnings("ignore", category=DeprecationWarning)
-55        character_count = len(message)
-56        return token_counter(text=message, **self.tokenizer_args), character_count
-
- - -

Return token count of message based on model

- -
Parameters
- -
    -
  • model: Model path to lookup tokenizer for
    • -
  • message: Message to tokenize
    • -
- -
Returns
- -
-

int Number of tokens needed to represent message

-
-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/toga.html b/docs/zodiac/toga.html deleted file mode 100644 index 828cc79..0000000 --- a/docs/zodiac/toga.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - zodiac.toga API documentation - - - - - - - - - -
-
-

-zodiac.toga

- - - - - - - 
1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0  */ -->
-2#  # # <!-- // /*  d a r k s h a p e s */ -->
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/toga/app.html b/docs/zodiac/toga/app.html deleted file mode 100644 index b499ce2..0000000 --- a/docs/zodiac/toga/app.html +++ /dev/null @@ -1,2239 +0,0 @@ - - - - - - - zodiac.toga.app API documentation - - - - - - - - - -
-
-

-zodiac.toga.app

- - - - - - - 
  1#  # # <!-- // /*  SPDX-License-Identifier: MPL-2.0*/ -->
-  2#  # # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4import os
-  5import asyncio
-  6import requests
-  7from requests.exceptions import ConnectionError, ConnectTimeout
-  8from urllib3.exceptions import MaxRetryError, NewConnectionError
-  9from typing import Callable
- 10
- 11import toga
- 12import toga.app
- 13from toga import Key
- 14from toga.constants import Direction
- 15from toga.style import Pack
- 16
- 17from zodiac.streams.model_stream import ModelStream
- 18from zodiac.streams.task_stream import TaskStream
- 19from zodiac.streams.token_stream import TokenStream
- 20import platform
- 21from dspy import Prediction, streamify, context as dspy_context, inspect_history
- 22
- 23OS_NAME = platform.system  # replace with config from sdbx later
- 24
- 25
- 26class Interface(toga.App):
- 27    formatted_units = [" ❖ chr", " ⟐ tok", ' " sec ']
- 28    bg_graph = "#070708"
- 29    bg_text = "#1B1B1B"  # "#1B1B1B"  # "#09090B"
- 30    bg = bg_text
- 31    bg_static = "#5D5E62"
- 32    activity = "#8122C4"
- 33
- 34    static = Pack(color="#727378")
- 35    fg_static = Pack(color="#8D8E94")
- 36    scroll_buffer = 5000  # chunks required to scroll down
- 37    graph_disabled = "http://localhost"
- 38    graph_server = "http://127.0.0.1:8188"
- 39    status_info = ("Connecting...", "Server?", "Ready.", "Done.", "No File.", "Read Failed.", "Attached.", "Copied.")
- 40    _is_cancelled = False
- 41
- 42    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
- 43        """Process and synthesize input data based on selected model.\n
- 44        :param widget: The UI widget that triggered this action, typically used for state management.\n
- 45        :type widget: toga.widgets
- 46        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
- 47        :type external: bool"""
- 48        from zodiac.toga.signatures import ready_predictor
- 49
- 50        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
- 51        await self.token_stream.set_tokenizer(self.registry_entry)
- 52        prompts = {}
- 53        if self.message_panel.value:
- 54            cache = False
- 55            prompts.setdefault("text", self.message_panel.value)
- 56        # prompts.setdefault("audio",[0]) if else []
- 57        # prompts.setdefault("image",[]) if image": []
- 58        if stream := self.output_types.value == "text":
- 59            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
- 60            await self.stream_text(prompts, context_data, predictor_data)
- 61        else:
- 62            content = await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
- 63            return widget
- 64        return widget
- 65
- 66    async def stream_text(self, prompts, context_data, predictor_data):
- 67        from zodiac.toga.signatures import Predictor
- 68        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
- 69        from dspy.streaming import StatusMessage, StreamResponse
- 70
- 71        self.response_panel.scroll_to_bottom()
- 72        with dspy_context(**context_data):
- 73            self.program = streamify(Predictor(), **predictor_data)
- 74            async for prediction in self.program(question=prompts["text"]):
- 75                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
- 76                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
- 77                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
- 78                    self.response_panel.value += str(prediction.chunk)
- 79                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
- 80                    self.response_panel.value += str(prediction.answer)
- 81                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
- 82                    self.status_display.text = self.status_text_prefix + str(prediction.message)
- 83        self.response_panel.value += "\n--\n\n"
- 84        return prediction
- 85
- 86    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
- 87        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
- 88        from nnll.tensor_pipe.inference import run_inference
- 89        from zodiac.streams.class_stream import best_package
- 90        from zodiac.providers.constants import MIR_DB
- 91
- 92        pkg_data = await best_package(pkg_data=registry_entry)
- 93        print(pkg_data)
- 94        constructor = ConstructPipeline()
- 95        pipe_data = await constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
- 96        content = await run_inference(pipe_data, prompts, out_type=self.output_types.value)
- 97        return content
- 98        # from zodiac.toga.signatures import Predictor
- 99
-100        # return prediction
-101
-102    async def halt(self, widget, **kwargs) -> None:
-103        """Stop processing prompt\n
-104        :param widget: The calling widget object"""
-105        if not self.program.done():
-106            import gc
-107
-108            del self.program
-109            gc.collect()
-110            self.status_display.text = self.status_text_prefix + "Cancelled."
-111
-112    async def empty_prompt(self, widget, **kwargs) -> None:
-113        """Clears the prompt input area.
-114        :param widget: Triggering widget"""
-115        self.message_panel.value = ""
-116
-117    async def copy_reply(self, widget, **kwargs) -> None:
-118        """Push the reply into the clipboard
-119        :param widget: Triggering widget"""
-120        import pyperclip
-121
-122        pyperclip.copy(self.response_panel.value)
-123        self.status_display.text = self.status_text_prefix + self.status_info[7]
-124
-125    async def attach_file(self, widget, **kwargs) -> None:
-126        """Attaches a file's contents to the prompt area.
-127        :param widget: Triggering widget"""
-128        import json
-129
-130        try:
-131            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-132            self.status_display.text = f"Read. {file_path_named}"
-133            if file_path_named is not None:
-134                from nnll.metadata.json_io import read_json_file
-135
-136                file_contents = read_json_file(file_path_named)
-137                self.message_panel.scroll_to_bottom()
-138                self.message_panel.value = json.dumps(file_contents)
-139                self.status_display.text = self.status_text_prefix + self.status_info[6]
-140            else:
-141                self.status_display.text = self.status_text_prefix + self.status_info[4]
-142        except (ValueError, json.JSONDecodeError):
-143            self.status_display.text = self.status_text_prefix + self.status_info[5]
-144
-145    async def reset_position(self, widget, **kwargs) -> None:
-146        """Scrolls text panel to bottom after content update.
-147        :param widget: text panel widget
-148        """
-149        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-150        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-151            self.position_counter = 0
-152            widget.scroll_to_bottom()
-153
-154    async def on_select_handler(self, widget, **kwargs) -> None:
-155        """React to input/output choice\n
-156        :param widget: The widget that triggered the event."""
-157        selection = widget.value
-158        if self.model_stream._graph.registry_entries is not None:
-159            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-160            await self.populate_task_stack()
-161            await self.token_stream.set_tokenizer(self.registry_entry)
-162        else:
-163            self.registry_entry = "No model..."
-164
-165    async def model_graph(self):
-166        """Builds the model graph."""
-167        await self.model_stream.model_graph()
-168
-169    async def token_estimate(self, widget, **kwargs) -> None:
-170        """Updates character and token count based on user input.
-171        :param widget: Input widget providing text"""
-172        token_count, character_count = await self.token_stream.token_count(message=self.message_panel.value)
-173        self.character_stats.text = "{:02}".format(character_count) + "".join(self.formatted_units[0])
-174        self.token_stats.text = "{:02}".format(token_count) + "".join(self.formatted_units[1])
-175        self.time_stats.text = "{:02}".format(0.0) + "".join(self.formatted_units[2])
-176
-177    async def populate_in_types(self) -> None:
-178        """Builds the input types selection."""
-179        in_edge_names = await self.model_stream.show_edges()
-180        self.input_types.items = in_edge_names
-181
-182    async def populate_out_types(self) -> None:
-183        """Builds the output types selection."""
-184        out_edges = await self.model_stream.show_edges(target=True)
-185        self.output_types.items = out_edges
-186
-187    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-188        """Builds the model stack selection dropdown."""
-189
-190        await self.model_stream.clear()
-191        if self.input_types.value and self.output_types.value:
-192            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-193            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-194            await self.token_estimate(widget=self.message_panel)
-195
-196    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-197        """Builds the task stack selection dropdown."""
-198        selection = self.model_stack.value
-199        if self.model_stream._graph.registry_entries:
-200            registry_entry = next(
-201                iter(
-202                    registry["entry"]  # formatting
-203                    for registry in self.model_stream._graph.registry_entries  # formatting
-204                    if selection in registry["entry"].model
-205                )
-206            )
-207        else:
-208            registry_entry = "No models..."
-209        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-210        if registry_entry and not isinstance(registry_entry, str):
-211            tasks = await self.task_stream.filter_tasks(registry_entry)
-212        else:
-213            tasks = ""
-214
-215        self.task_stack.items = tasks
-216
-217    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-218        """Switches between text and graph tabs.
-219        :param widget: The triggering widget (optional), defaults to None"""
-220        self.browser_panel.evaluate_javascript("location.reload();")
-221        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-222        self.final_layout.style.background_color = self.bg
-223        self.final_layout.refresh()
-224        self.status_display.text += self.status_info[0]
-225        await self.ping_server(widget=self.status_display)
-226
-227    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-228        self.browser_panel.url = self.graph_server
-229        try:
-230            request = requests.get(self.graph_server, timeout=(3, 3))
-231            if request is not None:
-232                if hasattr(request, "status_code"):
-233                    status = request.status_code
-234                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-235                    await self.active_server()
-236                elif hasattr(request, "json"):
-237                    status = request.json()
-238                    if status.get("result") == "OK":
-239                        await self.active_server()
-240                else:
-241                    self.browser_panel.url = self.graph_disabled
-242                    await self.active_server(False)
-243            else:
-244                self.browser_panel.url = self.graph_disabled
-245                await self.active_server(False)
-246        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-247            await self.active_server(False)
-248            pass
-249        return widget
-250
-251    async def active_server(self, enabled: bool = True):
-252        if not enabled:
-253            status_info = self.status_info[1]
-254            self.browser_panel.url = self.graph_disabled
-255        else:
-256            status_info = self.status_info[2]
-257            self.browser_panel.url = self.graph_server
-258        for info in self.status_info:
-259            self.status_display.text = self.status_display.text.replace(info, "")
-260        self.status_display.text += status_info
-261
-262    def initialize_inputs(self):
-263        """Initializes UI elements for input handling."""
-264        self.character_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[0]), **self.fg_static)
-265        self.token_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[1]), **self.fg_static)
-266        self.time_stats = toga.Label("{:02}".format(0.0) + "".join(self.formatted_units[2]), **self.fg_static)
-267        self.input_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-268        self.output_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-269        self.model_stack = toga.Selection(items=[], on_change=self.on_select_handler)
-270        self.task_stack = toga.Selection(items=[], style=Pack(align_items="end"))
-271        self.message_panel = toga.MultilineTextInput(placeholder="Prompt", on_change=self.token_estimate, style=Pack(flex=0.66, margin=10))
-272        self.browser_panel = toga.WebView(url=self.graph_server, id="Graph ")
-273        self.audio_panel = toga.Canvas()
-274        self.response_panel = toga.MultilineTextInput(readonly=True, placeholder="Response", style=Pack(flex=5), on_change=self.reset_position)
-275
-276    def initialize_static(self) -> None:
-277        """Create the main input fields"""
-278
-279        status_bar = toga.Row(
-280            children=[
-281                toga.Column(
-282                    children=[
-283                        toga.Row(
-284                            children=[self.input_types, toga.Label("➾"), self.output_types, self.task_stack],
-285                            style=Pack(align_items="end", gap=5),
-286                        ),
-287                        toga.Row(
-288                            children=[self.model_stack, toga.Label("↪︎")],  # , live_stats
-289                            style=Pack(align_items="end", text_direction="rtl", gap=5),
-290                        ),
-291                    ],
-292                    style=Pack(vertical_align_items="center", gap=5, justify_content="end", align_items="end"),
-293                ),
-294                toga.Row(
-295                    children=[
-296                        toga.Column(children=[self.character_stats, self.token_stats, self.time_stats]),
-297                        toga.Column(
-298                            children=[
-299                                toga.Button("▶︎", on_press=self.ticker, style=Pack(width=30, height=20, font_size="12")),
-300                                toga.Button("⧉", on_press=self.copy_reply, style=Pack(width=30, height=20, font_size=15, vertical_align_items="start")),
-301                            ],
-302                            style=Pack(gap=5),
-303                        ),
-304                        toga.Column(
-305                            children=[
-306                                toga.Button(
-307                                    """📎
-308                                _""",
-309                                    on_press=self.attach_file,
-310                                    style=Pack(width=30, height=20, font_size="10", align_items="start", justify_content="start"),
-311                                ),
-312                                toga.Button("⌫", on_press=self.empty_prompt, style=Pack(width=30, height=20, font_size="14")),
-313                            ],
-314                            style=Pack(font_size="15", gap=5),
-315                        ),
-316                    ],
-317                    style=Pack(vertical_align_items="center", gap=5, justify_content="start", align_items="start"),
-318                ),
-319            ],
-320            style=Pack(margin=10, gap=5, vertical_align_items="center", justify_content="start", align_items="start"),
-321        )
-322        self.status_log = toga.Label(f"{inspect_history()}")  # show llm history
-323        self.status_tab = toga.OptionItem(text="|  Connecting...", content=self.status_log, enabled=False)
-324        # self.response_array = toga.Box(children=[self.response_panel], style=Pack(flex=1))
-325        # self.endless_response = toga.ScrollContainer(content=self.response_array)
-326        resize_area = toga.SplitContainer(
-327            content=[
-328                toga.OptionContainer(
-329                    content=[
-330                        ("Output", self.response_panel),
-331                        ("Graph", self.browser_panel),
-332                        self.status_tab,
-333                    ],
-334                    on_select=self.switch_tabs,
-335                    style=Pack(background_color="#000000", flex=2),
-336                    id="tab_panel",
-337                ),
-338                toga.Row(
-339                    children=[
-340                        toga.Column(justify_content="start", style=Pack(flex=0.33)),
-341                        toga.Box(children=[self.message_panel], style=Pack(flex=1)),
-342                        toga.Column(style=Pack(flex=0.33, justify_content="start")),
-343                    ]
-344                ),
-345            ],
-346            direction=Direction.HORIZONTAL,
-347            style=Pack(flex=3),
-348        )
-349
-350        self.final_layout = toga.Column(children=[status_bar, resize_area], style=Pack(background_color=self.bg_text, flex=1))
-351
-352    def initialize_layout(self) -> None:
-353        """Create the layout of the application."""
-354        self.main_window.content = self.final_layout
-355
-356    def startup(self) -> None:
-357        """Startup Logic. Initialize widgets and layout, then asynchronous tasks for populating datagets"""
-358        self.main_window = toga.MainWindow()
-359        self.model_stream = ModelStream()
-360        self.task_stream = TaskStream()
-361        self.token_stream = TokenStream()
-362        self.token_stream = TokenStream()
-363
-364        start = toga.Command(
-365            self.ticker,
-366            text="Start",
-367            tooltip="Run the current available prompts.",
-368            shortcut=Key.MOD_1 + Key.ENTER,
-369            group=toga.Group.APP,
-370            section=-1,
-371        )
-372        attach = toga.Command.standard(
-373            self,
-374            toga.Command.OPEN,
-375            text="Attach File...",
-376            tooltip="Attach a file to the prompt.",
-377            shortcut=Key.MOD_1 + Key.O,
-378            action=self.attach_file,
-379            group=toga.Group.APP,
-380            section=0,
-381        )
-382        copy_reply = toga.Command(
-383            self.copy_reply,
-384            text="Copy Response",
-385            tooltip="Copy the response provided by the system",
-386            group=toga.Group.APP,
-387            section=0,
-388        )
-389        clear = toga.Command(
-390            self.empty_prompt,
-391            text="Clear Prompt",
-392            tooltip="Empty the user prompt field.",
-393            shortcut=Key.MOD_3 + Key.BACKSPACE,
-394            group=toga.Group.APP,
-395            section=1,
-396        )
-397        stop = toga.Command(
-398            self.halt,
-399            text="Stop",
-400            tooltip="Cancel the current sequence generation.",
-401            shortcut=Key.MOD_1 + Key.ESCAPE,  #
-402            group=toga.Group.APP,
-403            section=1,
-404        )
-405        self.commands.add(start, attach, copy_reply, clear, stop)
-406
-407        self.initialize_inputs()
-408        self.initialize_static()
-409        self.initialize_layout()
-410        asyncio.create_task(self.model_graph())
-411        asyncio.create_task(self.token_estimate(self))
-412        asyncio.create_task(self.populate_in_types())
-413        asyncio.create_task(self.populate_out_types())
-414        asyncio.create_task(self.populate_model_stack())
-415        asyncio.create_task(self.populate_task_stack())
-416        self.main_window.show()
-417        self.status_display = self.status_tab
-418        self.bg = self.bg_graph
-419        self.status_text_prefix = "|  "
-420
-421        asyncio.create_task(self.switch_tabs())
-422
-423
-424def main(url: str = "http://127.0.0.1:8188"):
-425    """The entry point for the application."""
-426    app = Interface(
-427        formal_name="Shadowbox",
-428        app_id="org.darkshapes.shadowbox",
-429        app_name="sdbx",
-430        author="Darkshapes",
-431        home_page="https://darkshapes.org",
-432        description=" A generative AI instrument. ",
-433    )
-434
-435    app.icon = toga.Icon(path="resources/anomaly_128x")
-436    try:
-437        app.main_loop()
-438    except Exception as error_log:
-439        print(error_log)
-
- - -
-
- -
- - def - OS_NAME(): - - - -
- - 
1067def system():
-1068
-1069    """ Returns the system/OS name, e.g. 'Linux', 'Windows' or 'Java'.
-1070
-1071        An empty string is returned if the value cannot be determined.
-1072
-1073    """
-1074    return uname().system
-
- - -

Returns the system/OS name, e.g. 'Linux', 'Windows' or 'Java'.

- -

An empty string is returned if the value cannot be determined.

-
- - -
-
- -
- - class - Interface(toga.app.App): - - - -
- - 
 27class Interface(toga.App):
- 28    formatted_units = [" ❖ chr", " ⟐ tok", ' " sec ']
- 29    bg_graph = "#070708"
- 30    bg_text = "#1B1B1B"  # "#1B1B1B"  # "#09090B"
- 31    bg = bg_text
- 32    bg_static = "#5D5E62"
- 33    activity = "#8122C4"
- 34
- 35    static = Pack(color="#727378")
- 36    fg_static = Pack(color="#8D8E94")
- 37    scroll_buffer = 5000  # chunks required to scroll down
- 38    graph_disabled = "http://localhost"
- 39    graph_server = "http://127.0.0.1:8188"
- 40    status_info = ("Connecting...", "Server?", "Ready.", "Done.", "No File.", "Read Failed.", "Attached.", "Copied.")
- 41    _is_cancelled = False
- 42
- 43    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
- 44        """Process and synthesize input data based on selected model.\n
- 45        :param widget: The UI widget that triggered this action, typically used for state management.\n
- 46        :type widget: toga.widgets
- 47        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
- 48        :type external: bool"""
- 49        from zodiac.toga.signatures import ready_predictor
- 50
- 51        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
- 52        await self.token_stream.set_tokenizer(self.registry_entry)
- 53        prompts = {}
- 54        if self.message_panel.value:
- 55            cache = False
- 56            prompts.setdefault("text", self.message_panel.value)
- 57        # prompts.setdefault("audio",[0]) if else []
- 58        # prompts.setdefault("image",[]) if image": []
- 59        if stream := self.output_types.value == "text":
- 60            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
- 61            await self.stream_text(prompts, context_data, predictor_data)
- 62        else:
- 63            content = await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
- 64            return widget
- 65        return widget
- 66
- 67    async def stream_text(self, prompts, context_data, predictor_data):
- 68        from zodiac.toga.signatures import Predictor
- 69        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
- 70        from dspy.streaming import StatusMessage, StreamResponse
- 71
- 72        self.response_panel.scroll_to_bottom()
- 73        with dspy_context(**context_data):
- 74            self.program = streamify(Predictor(), **predictor_data)
- 75            async for prediction in self.program(question=prompts["text"]):
- 76                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
- 77                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
- 78                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
- 79                    self.response_panel.value += str(prediction.chunk)
- 80                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
- 81                    self.response_panel.value += str(prediction.answer)
- 82                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
- 83                    self.status_display.text = self.status_text_prefix + str(prediction.message)
- 84        self.response_panel.value += "\n--\n\n"
- 85        return prediction
- 86
- 87    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
- 88        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
- 89        from nnll.tensor_pipe.inference import run_inference
- 90        from zodiac.streams.class_stream import best_package
- 91        from zodiac.providers.constants import MIR_DB
- 92
- 93        pkg_data = await best_package(pkg_data=registry_entry)
- 94        print(pkg_data)
- 95        constructor = ConstructPipeline()
- 96        pipe_data = await constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
- 97        content = await run_inference(pipe_data, prompts, out_type=self.output_types.value)
- 98        return content
- 99        # from zodiac.toga.signatures import Predictor
-100
-101        # return prediction
-102
-103    async def halt(self, widget, **kwargs) -> None:
-104        """Stop processing prompt\n
-105        :param widget: The calling widget object"""
-106        if not self.program.done():
-107            import gc
-108
-109            del self.program
-110            gc.collect()
-111            self.status_display.text = self.status_text_prefix + "Cancelled."
-112
-113    async def empty_prompt(self, widget, **kwargs) -> None:
-114        """Clears the prompt input area.
-115        :param widget: Triggering widget"""
-116        self.message_panel.value = ""
-117
-118    async def copy_reply(self, widget, **kwargs) -> None:
-119        """Push the reply into the clipboard
-120        :param widget: Triggering widget"""
-121        import pyperclip
-122
-123        pyperclip.copy(self.response_panel.value)
-124        self.status_display.text = self.status_text_prefix + self.status_info[7]
-125
-126    async def attach_file(self, widget, **kwargs) -> None:
-127        """Attaches a file's contents to the prompt area.
-128        :param widget: Triggering widget"""
-129        import json
-130
-131        try:
-132            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-133            self.status_display.text = f"Read. {file_path_named}"
-134            if file_path_named is not None:
-135                from nnll.metadata.json_io import read_json_file
-136
-137                file_contents = read_json_file(file_path_named)
-138                self.message_panel.scroll_to_bottom()
-139                self.message_panel.value = json.dumps(file_contents)
-140                self.status_display.text = self.status_text_prefix + self.status_info[6]
-141            else:
-142                self.status_display.text = self.status_text_prefix + self.status_info[4]
-143        except (ValueError, json.JSONDecodeError):
-144            self.status_display.text = self.status_text_prefix + self.status_info[5]
-145
-146    async def reset_position(self, widget, **kwargs) -> None:
-147        """Scrolls text panel to bottom after content update.
-148        :param widget: text panel widget
-149        """
-150        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-151        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-152            self.position_counter = 0
-153            widget.scroll_to_bottom()
-154
-155    async def on_select_handler(self, widget, **kwargs) -> None:
-156        """React to input/output choice\n
-157        :param widget: The widget that triggered the event."""
-158        selection = widget.value
-159        if self.model_stream._graph.registry_entries is not None:
-160            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-161            await self.populate_task_stack()
-162            await self.token_stream.set_tokenizer(self.registry_entry)
-163        else:
-164            self.registry_entry = "No model..."
-165
-166    async def model_graph(self):
-167        """Builds the model graph."""
-168        await self.model_stream.model_graph()
-169
-170    async def token_estimate(self, widget, **kwargs) -> None:
-171        """Updates character and token count based on user input.
-172        :param widget: Input widget providing text"""
-173        token_count, character_count = await self.token_stream.token_count(message=self.message_panel.value)
-174        self.character_stats.text = "{:02}".format(character_count) + "".join(self.formatted_units[0])
-175        self.token_stats.text = "{:02}".format(token_count) + "".join(self.formatted_units[1])
-176        self.time_stats.text = "{:02}".format(0.0) + "".join(self.formatted_units[2])
-177
-178    async def populate_in_types(self) -> None:
-179        """Builds the input types selection."""
-180        in_edge_names = await self.model_stream.show_edges()
-181        self.input_types.items = in_edge_names
-182
-183    async def populate_out_types(self) -> None:
-184        """Builds the output types selection."""
-185        out_edges = await self.model_stream.show_edges(target=True)
-186        self.output_types.items = out_edges
-187
-188    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-189        """Builds the model stack selection dropdown."""
-190
-191        await self.model_stream.clear()
-192        if self.input_types.value and self.output_types.value:
-193            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-194            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-195            await self.token_estimate(widget=self.message_panel)
-196
-197    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-198        """Builds the task stack selection dropdown."""
-199        selection = self.model_stack.value
-200        if self.model_stream._graph.registry_entries:
-201            registry_entry = next(
-202                iter(
-203                    registry["entry"]  # formatting
-204                    for registry in self.model_stream._graph.registry_entries  # formatting
-205                    if selection in registry["entry"].model
-206                )
-207            )
-208        else:
-209            registry_entry = "No models..."
-210        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-211        if registry_entry and not isinstance(registry_entry, str):
-212            tasks = await self.task_stream.filter_tasks(registry_entry)
-213        else:
-214            tasks = ""
-215
-216        self.task_stack.items = tasks
-217
-218    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-219        """Switches between text and graph tabs.
-220        :param widget: The triggering widget (optional), defaults to None"""
-221        self.browser_panel.evaluate_javascript("location.reload();")
-222        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-223        self.final_layout.style.background_color = self.bg
-224        self.final_layout.refresh()
-225        self.status_display.text += self.status_info[0]
-226        await self.ping_server(widget=self.status_display)
-227
-228    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-229        self.browser_panel.url = self.graph_server
-230        try:
-231            request = requests.get(self.graph_server, timeout=(3, 3))
-232            if request is not None:
-233                if hasattr(request, "status_code"):
-234                    status = request.status_code
-235                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-236                    await self.active_server()
-237                elif hasattr(request, "json"):
-238                    status = request.json()
-239                    if status.get("result") == "OK":
-240                        await self.active_server()
-241                else:
-242                    self.browser_panel.url = self.graph_disabled
-243                    await self.active_server(False)
-244            else:
-245                self.browser_panel.url = self.graph_disabled
-246                await self.active_server(False)
-247        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-248            await self.active_server(False)
-249            pass
-250        return widget
-251
-252    async def active_server(self, enabled: bool = True):
-253        if not enabled:
-254            status_info = self.status_info[1]
-255            self.browser_panel.url = self.graph_disabled
-256        else:
-257            status_info = self.status_info[2]
-258            self.browser_panel.url = self.graph_server
-259        for info in self.status_info:
-260            self.status_display.text = self.status_display.text.replace(info, "")
-261        self.status_display.text += status_info
-262
-263    def initialize_inputs(self):
-264        """Initializes UI elements for input handling."""
-265        self.character_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[0]), **self.fg_static)
-266        self.token_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[1]), **self.fg_static)
-267        self.time_stats = toga.Label("{:02}".format(0.0) + "".join(self.formatted_units[2]), **self.fg_static)
-268        self.input_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-269        self.output_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-270        self.model_stack = toga.Selection(items=[], on_change=self.on_select_handler)
-271        self.task_stack = toga.Selection(items=[], style=Pack(align_items="end"))
-272        self.message_panel = toga.MultilineTextInput(placeholder="Prompt", on_change=self.token_estimate, style=Pack(flex=0.66, margin=10))
-273        self.browser_panel = toga.WebView(url=self.graph_server, id="Graph ")
-274        self.audio_panel = toga.Canvas()
-275        self.response_panel = toga.MultilineTextInput(readonly=True, placeholder="Response", style=Pack(flex=5), on_change=self.reset_position)
-276
-277    def initialize_static(self) -> None:
-278        """Create the main input fields"""
-279
-280        status_bar = toga.Row(
-281            children=[
-282                toga.Column(
-283                    children=[
-284                        toga.Row(
-285                            children=[self.input_types, toga.Label("➾"), self.output_types, self.task_stack],
-286                            style=Pack(align_items="end", gap=5),
-287                        ),
-288                        toga.Row(
-289                            children=[self.model_stack, toga.Label("↪︎")],  # , live_stats
-290                            style=Pack(align_items="end", text_direction="rtl", gap=5),
-291                        ),
-292                    ],
-293                    style=Pack(vertical_align_items="center", gap=5, justify_content="end", align_items="end"),
-294                ),
-295                toga.Row(
-296                    children=[
-297                        toga.Column(children=[self.character_stats, self.token_stats, self.time_stats]),
-298                        toga.Column(
-299                            children=[
-300                                toga.Button("▶︎", on_press=self.ticker, style=Pack(width=30, height=20, font_size="12")),
-301                                toga.Button("⧉", on_press=self.copy_reply, style=Pack(width=30, height=20, font_size=15, vertical_align_items="start")),
-302                            ],
-303                            style=Pack(gap=5),
-304                        ),
-305                        toga.Column(
-306                            children=[
-307                                toga.Button(
-308                                    """📎
-309                                _""",
-310                                    on_press=self.attach_file,
-311                                    style=Pack(width=30, height=20, font_size="10", align_items="start", justify_content="start"),
-312                                ),
-313                                toga.Button("⌫", on_press=self.empty_prompt, style=Pack(width=30, height=20, font_size="14")),
-314                            ],
-315                            style=Pack(font_size="15", gap=5),
-316                        ),
-317                    ],
-318                    style=Pack(vertical_align_items="center", gap=5, justify_content="start", align_items="start"),
-319                ),
-320            ],
-321            style=Pack(margin=10, gap=5, vertical_align_items="center", justify_content="start", align_items="start"),
-322        )
-323        self.status_log = toga.Label(f"{inspect_history()}")  # show llm history
-324        self.status_tab = toga.OptionItem(text="|  Connecting...", content=self.status_log, enabled=False)
-325        # self.response_array = toga.Box(children=[self.response_panel], style=Pack(flex=1))
-326        # self.endless_response = toga.ScrollContainer(content=self.response_array)
-327        resize_area = toga.SplitContainer(
-328            content=[
-329                toga.OptionContainer(
-330                    content=[
-331                        ("Output", self.response_panel),
-332                        ("Graph", self.browser_panel),
-333                        self.status_tab,
-334                    ],
-335                    on_select=self.switch_tabs,
-336                    style=Pack(background_color="#000000", flex=2),
-337                    id="tab_panel",
-338                ),
-339                toga.Row(
-340                    children=[
-341                        toga.Column(justify_content="start", style=Pack(flex=0.33)),
-342                        toga.Box(children=[self.message_panel], style=Pack(flex=1)),
-343                        toga.Column(style=Pack(flex=0.33, justify_content="start")),
-344                    ]
-345                ),
-346            ],
-347            direction=Direction.HORIZONTAL,
-348            style=Pack(flex=3),
-349        )
-350
-351        self.final_layout = toga.Column(children=[status_bar, resize_area], style=Pack(background_color=self.bg_text, flex=1))
-352
-353    def initialize_layout(self) -> None:
-354        """Create the layout of the application."""
-355        self.main_window.content = self.final_layout
-356
-357    def startup(self) -> None:
-358        """Startup Logic. Initialize widgets and layout, then asynchronous tasks for populating datagets"""
-359        self.main_window = toga.MainWindow()
-360        self.model_stream = ModelStream()
-361        self.task_stream = TaskStream()
-362        self.token_stream = TokenStream()
-363        self.token_stream = TokenStream()
-364
-365        start = toga.Command(
-366            self.ticker,
-367            text="Start",
-368            tooltip="Run the current available prompts.",
-369            shortcut=Key.MOD_1 + Key.ENTER,
-370            group=toga.Group.APP,
-371            section=-1,
-372        )
-373        attach = toga.Command.standard(
-374            self,
-375            toga.Command.OPEN,
-376            text="Attach File...",
-377            tooltip="Attach a file to the prompt.",
-378            shortcut=Key.MOD_1 + Key.O,
-379            action=self.attach_file,
-380            group=toga.Group.APP,
-381            section=0,
-382        )
-383        copy_reply = toga.Command(
-384            self.copy_reply,
-385            text="Copy Response",
-386            tooltip="Copy the response provided by the system",
-387            group=toga.Group.APP,
-388            section=0,
-389        )
-390        clear = toga.Command(
-391            self.empty_prompt,
-392            text="Clear Prompt",
-393            tooltip="Empty the user prompt field.",
-394            shortcut=Key.MOD_3 + Key.BACKSPACE,
-395            group=toga.Group.APP,
-396            section=1,
-397        )
-398        stop = toga.Command(
-399            self.halt,
-400            text="Stop",
-401            tooltip="Cancel the current sequence generation.",
-402            shortcut=Key.MOD_1 + Key.ESCAPE,  #
-403            group=toga.Group.APP,
-404            section=1,
-405        )
-406        self.commands.add(start, attach, copy_reply, clear, stop)
-407
-408        self.initialize_inputs()
-409        self.initialize_static()
-410        self.initialize_layout()
-411        asyncio.create_task(self.model_graph())
-412        asyncio.create_task(self.token_estimate(self))
-413        asyncio.create_task(self.populate_in_types())
-414        asyncio.create_task(self.populate_out_types())
-415        asyncio.create_task(self.populate_model_stack())
-416        asyncio.create_task(self.populate_task_stack())
-417        self.main_window.show()
-418        self.status_display = self.status_tab
-419        self.bg = self.bg_graph
-420        self.status_text_prefix = "|  "
-421
-422        asyncio.create_task(self.switch_tabs())
-
- - - - -
-
- formatted_units = -[' ❖ chr', ' ⟐ tok', ' " sec '] - - -
- - - - -
-
-
- bg_graph = -'#070708' - - -
- - - - -
-
-
- bg_text = -'#1B1B1B' - - -
- - - - -
-
-
- bg = -'#1B1B1B' - - -
- - - - -
-
-
- bg_static = -'#5D5E62' - - -
- - - - -
-
-
- activity = -'#8122C4' - - -
- - - - -
-
-
- static = -Pack(color=rgb(114, 115, 120)) - - -
- - - - -
-
-
- fg_static = -Pack(color=rgb(141, 142, 148)) - - -
- - - - -
-
-
- scroll_buffer = -5000 - - -
- - - - -
-
-
- graph_disabled = -'http://localhost' - - -
- - - - -
-
-
- graph_server = -'http://127.0.0.1:8188' - - -
- - - - -
-
-
- status_info = -('Connecting...', 'Server?', 'Ready.', 'Done.', 'No File.', 'Read Failed.', 'Attached.', 'Copied.') - - -
- - - - -
-
- -
- - async def - ticker( self, widget: Callable, external: bool = False, **kwargs) -> toga.widgets.base.Widget: - - - -
- - 
43    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
-44        """Process and synthesize input data based on selected model.\n
-45        :param widget: The UI widget that triggered this action, typically used for state management.\n
-46        :type widget: toga.widgets
-47        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
-48        :type external: bool"""
-49        from zodiac.toga.signatures import ready_predictor
-50
-51        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
-52        await self.token_stream.set_tokenizer(self.registry_entry)
-53        prompts = {}
-54        if self.message_panel.value:
-55            cache = False
-56            prompts.setdefault("text", self.message_panel.value)
-57        # prompts.setdefault("audio",[0]) if else []
-58        # prompts.setdefault("image",[]) if image": []
-59        if stream := self.output_types.value == "text":
-60            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
-61            await self.stream_text(prompts, context_data, predictor_data)
-62        else:
-63            content = await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
-64            return widget
-65        return widget
-
- - -

Process and synthesize input data based on selected model.

- -
Parameters
- -
    -

  • widget: The UI widget that triggered this action, typically used for state management.

    • -

  • external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False

    • -
-
- - -
-
- -
- - async def - stream_text(self, prompts, context_data, predictor_data): - - - -
- - 
67    async def stream_text(self, prompts, context_data, predictor_data):
-68        from zodiac.toga.signatures import Predictor
-69        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
-70        from dspy.streaming import StatusMessage, StreamResponse
-71
-72        self.response_panel.scroll_to_bottom()
-73        with dspy_context(**context_data):
-74            self.program = streamify(Predictor(), **predictor_data)
-75            async for prediction in self.program(question=prompts["text"]):
-76                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
-77                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
-78                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
-79                    self.response_panel.value += str(prediction.chunk)
-80                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
-81                    self.response_panel.value += str(prediction.answer)
-82                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
-83                    self.status_display.text = self.status_text_prefix + str(prediction.message)
-84        self.response_panel.value += "\n--\n\n"
-85        return prediction
-
- - - - -
-
- -
- - async def - generate_media(self, prompts, registry_entry) -> None: - - - -
- - 
 87    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
- 88        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
- 89        from nnll.tensor_pipe.inference import run_inference
- 90        from zodiac.streams.class_stream import best_package
- 91        from zodiac.providers.constants import MIR_DB
- 92
- 93        pkg_data = await best_package(pkg_data=registry_entry)
- 94        print(pkg_data)
- 95        constructor = ConstructPipeline()
- 96        pipe_data = await constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
- 97        content = await run_inference(pipe_data, prompts, out_type=self.output_types.value)
- 98        return content
- 99        # from zodiac.toga.signatures import Predictor
-100
-101        # return prediction
-
- - - - -
-
- -
- - async def - halt(self, widget, **kwargs) -> None: - - - -
- - 
103    async def halt(self, widget, **kwargs) -> None:
-104        """Stop processing prompt\n
-105        :param widget: The calling widget object"""
-106        if not self.program.done():
-107            import gc
-108
-109            del self.program
-110            gc.collect()
-111            self.status_display.text = self.status_text_prefix + "Cancelled."
-
- - -

Stop processing prompt

- -
Parameters
- -
    -
  • widget: The calling widget object
    • -
-
- - -
-
- -
- - async def - empty_prompt(self, widget, **kwargs) -> None: - - - -
- - 
113    async def empty_prompt(self, widget, **kwargs) -> None:
-114        """Clears the prompt input area.
-115        :param widget: Triggering widget"""
-116        self.message_panel.value = ""
-
- - -

Clears the prompt input area.

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - copy_reply(self, widget, **kwargs) -> None: - - - -
- - 
118    async def copy_reply(self, widget, **kwargs) -> None:
-119        """Push the reply into the clipboard
-120        :param widget: Triggering widget"""
-121        import pyperclip
-122
-123        pyperclip.copy(self.response_panel.value)
-124        self.status_display.text = self.status_text_prefix + self.status_info[7]
-
- - -

Push the reply into the clipboard

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - attach_file(self, widget, **kwargs) -> None: - - - -
- - 
126    async def attach_file(self, widget, **kwargs) -> None:
-127        """Attaches a file's contents to the prompt area.
-128        :param widget: Triggering widget"""
-129        import json
-130
-131        try:
-132            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-133            self.status_display.text = f"Read. {file_path_named}"
-134            if file_path_named is not None:
-135                from nnll.metadata.json_io import read_json_file
-136
-137                file_contents = read_json_file(file_path_named)
-138                self.message_panel.scroll_to_bottom()
-139                self.message_panel.value = json.dumps(file_contents)
-140                self.status_display.text = self.status_text_prefix + self.status_info[6]
-141            else:
-142                self.status_display.text = self.status_text_prefix + self.status_info[4]
-143        except (ValueError, json.JSONDecodeError):
-144            self.status_display.text = self.status_text_prefix + self.status_info[5]
-
- - -

Attaches a file's contents to the prompt area.

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - reset_position(self, widget, **kwargs) -> None: - - - -
- - 
146    async def reset_position(self, widget, **kwargs) -> None:
-147        """Scrolls text panel to bottom after content update.
-148        :param widget: text panel widget
-149        """
-150        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-151        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-152            self.position_counter = 0
-153            widget.scroll_to_bottom()
-
- - -

Scrolls text panel to bottom after content update.

- -
Parameters
- -
    -
  • widget: text panel widget
    • -
-
- - -
-
- -
- - async def - on_select_handler(self, widget, **kwargs) -> None: - - - -
- - 
155    async def on_select_handler(self, widget, **kwargs) -> None:
-156        """React to input/output choice\n
-157        :param widget: The widget that triggered the event."""
-158        selection = widget.value
-159        if self.model_stream._graph.registry_entries is not None:
-160            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-161            await self.populate_task_stack()
-162            await self.token_stream.set_tokenizer(self.registry_entry)
-163        else:
-164            self.registry_entry = "No model..."
-
- - -

React to input/output choice

- -
Parameters
- -
    -
  • widget: The widget that triggered the event.
    • -
-
- - -
-
- -
- - async def - model_graph(self): - - - -
- - 
166    async def model_graph(self):
-167        """Builds the model graph."""
-168        await self.model_stream.model_graph()
-
- - -

Builds the model graph.

-
- - -
-
- -
- - async def - token_estimate(self, widget, **kwargs) -> None: - - - -
- - 
170    async def token_estimate(self, widget, **kwargs) -> None:
-171        """Updates character and token count based on user input.
-172        :param widget: Input widget providing text"""
-173        token_count, character_count = await self.token_stream.token_count(message=self.message_panel.value)
-174        self.character_stats.text = "{:02}".format(character_count) + "".join(self.formatted_units[0])
-175        self.token_stats.text = "{:02}".format(token_count) + "".join(self.formatted_units[1])
-176        self.time_stats.text = "{:02}".format(0.0) + "".join(self.formatted_units[2])
-
- - -

Updates character and token count based on user input.

- -
Parameters
- -
    -
  • widget: Input widget providing text
    • -
-
- - -
-
- -
- - async def - populate_in_types(self) -> None: - - - -
- - 
178    async def populate_in_types(self) -> None:
-179        """Builds the input types selection."""
-180        in_edge_names = await self.model_stream.show_edges()
-181        self.input_types.items = in_edge_names
-
- - -

Builds the input types selection.

-
- - -
-
- -
- - async def - populate_out_types(self) -> None: - - - -
- - 
183    async def populate_out_types(self) -> None:
-184        """Builds the output types selection."""
-185        out_edges = await self.model_stream.show_edges(target=True)
-186        self.output_types.items = out_edges
-
- - -

Builds the output types selection.

-
- - -
-
- -
- - async def - populate_model_stack(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
188    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-189        """Builds the model stack selection dropdown."""
-190
-191        await self.model_stream.clear()
-192        if self.input_types.value and self.output_types.value:
-193            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-194            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-195            await self.token_estimate(widget=self.message_panel)
-
- - -

Builds the model stack selection dropdown.

-
- - -
-
- -
- - async def - populate_task_stack(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
197    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-198        """Builds the task stack selection dropdown."""
-199        selection = self.model_stack.value
-200        if self.model_stream._graph.registry_entries:
-201            registry_entry = next(
-202                iter(
-203                    registry["entry"]  # formatting
-204                    for registry in self.model_stream._graph.registry_entries  # formatting
-205                    if selection in registry["entry"].model
-206                )
-207            )
-208        else:
-209            registry_entry = "No models..."
-210        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-211        if registry_entry and not isinstance(registry_entry, str):
-212            tasks = await self.task_stream.filter_tasks(registry_entry)
-213        else:
-214            tasks = ""
-215
-216        self.task_stack.items = tasks
-
- - -

Builds the task stack selection dropdown.

-
- - -
-
- -
- - async def - switch_tabs(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
218    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-219        """Switches between text and graph tabs.
-220        :param widget: The triggering widget (optional), defaults to None"""
-221        self.browser_panel.evaluate_javascript("location.reload();")
-222        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-223        self.final_layout.style.background_color = self.bg
-224        self.final_layout.refresh()
-225        self.status_display.text += self.status_info[0]
-226        await self.ping_server(widget=self.status_display)
-
- - -

Switches between text and graph tabs.

- -
Parameters
- -
    -
  • widget: The triggering widget (optional), defaults to None
    • -
-
- - -
-
- -
- - async def - ping_server( self, widget: toga.widgets.base.Widget, **kwargs) -> toga.widgets.base.Widget: - - - -
- - 
228    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-229        self.browser_panel.url = self.graph_server
-230        try:
-231            request = requests.get(self.graph_server, timeout=(3, 3))
-232            if request is not None:
-233                if hasattr(request, "status_code"):
-234                    status = request.status_code
-235                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-236                    await self.active_server()
-237                elif hasattr(request, "json"):
-238                    status = request.json()
-239                    if status.get("result") == "OK":
-240                        await self.active_server()
-241                else:
-242                    self.browser_panel.url = self.graph_disabled
-243                    await self.active_server(False)
-244            else:
-245                self.browser_panel.url = self.graph_disabled
-246                await self.active_server(False)
-247        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-248            await self.active_server(False)
-249            pass
-250        return widget
-
- - - - -
-
- -
- - async def - active_server(self, enabled: bool = True): - - - -
- - 
252    async def active_server(self, enabled: bool = True):
-253        if not enabled:
-254            status_info = self.status_info[1]
-255            self.browser_panel.url = self.graph_disabled
-256        else:
-257            status_info = self.status_info[2]
-258            self.browser_panel.url = self.graph_server
-259        for info in self.status_info:
-260            self.status_display.text = self.status_display.text.replace(info, "")
-261        self.status_display.text += status_info
-
- - - - -
-
- -
- - def - initialize_inputs(self): - - - -
- - 
263    def initialize_inputs(self):
-264        """Initializes UI elements for input handling."""
-265        self.character_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[0]), **self.fg_static)
-266        self.token_stats = toga.Label("{:02}".format(0) + "".join(self.formatted_units[1]), **self.fg_static)
-267        self.time_stats = toga.Label("{:02}".format(0.0) + "".join(self.formatted_units[2]), **self.fg_static)
-268        self.input_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-269        self.output_types = toga.Selection(items=[], on_change=self.populate_model_stack)
-270        self.model_stack = toga.Selection(items=[], on_change=self.on_select_handler)
-271        self.task_stack = toga.Selection(items=[], style=Pack(align_items="end"))
-272        self.message_panel = toga.MultilineTextInput(placeholder="Prompt", on_change=self.token_estimate, style=Pack(flex=0.66, margin=10))
-273        self.browser_panel = toga.WebView(url=self.graph_server, id="Graph ")
-274        self.audio_panel = toga.Canvas()
-275        self.response_panel = toga.MultilineTextInput(readonly=True, placeholder="Response", style=Pack(flex=5), on_change=self.reset_position)
-
- - -

Initializes UI elements for input handling.

-
- - -
-
- -
- - def - initialize_static(self) -> None: - - - -
- - 
277    def initialize_static(self) -> None:
-278        """Create the main input fields"""
-279
-280        status_bar = toga.Row(
-281            children=[
-282                toga.Column(
-283                    children=[
-284                        toga.Row(
-285                            children=[self.input_types, toga.Label("➾"), self.output_types, self.task_stack],
-286                            style=Pack(align_items="end", gap=5),
-287                        ),
-288                        toga.Row(
-289                            children=[self.model_stack, toga.Label("↪︎")],  # , live_stats
-290                            style=Pack(align_items="end", text_direction="rtl", gap=5),
-291                        ),
-292                    ],
-293                    style=Pack(vertical_align_items="center", gap=5, justify_content="end", align_items="end"),
-294                ),
-295                toga.Row(
-296                    children=[
-297                        toga.Column(children=[self.character_stats, self.token_stats, self.time_stats]),
-298                        toga.Column(
-299                            children=[
-300                                toga.Button("▶︎", on_press=self.ticker, style=Pack(width=30, height=20, font_size="12")),
-301                                toga.Button("⧉", on_press=self.copy_reply, style=Pack(width=30, height=20, font_size=15, vertical_align_items="start")),
-302                            ],
-303                            style=Pack(gap=5),
-304                        ),
-305                        toga.Column(
-306                            children=[
-307                                toga.Button(
-308                                    """📎
-309                                _""",
-310                                    on_press=self.attach_file,
-311                                    style=Pack(width=30, height=20, font_size="10", align_items="start", justify_content="start"),
-312                                ),
-313                                toga.Button("⌫", on_press=self.empty_prompt, style=Pack(width=30, height=20, font_size="14")),
-314                            ],
-315                            style=Pack(font_size="15", gap=5),
-316                        ),
-317                    ],
-318                    style=Pack(vertical_align_items="center", gap=5, justify_content="start", align_items="start"),
-319                ),
-320            ],
-321            style=Pack(margin=10, gap=5, vertical_align_items="center", justify_content="start", align_items="start"),
-322        )
-323        self.status_log = toga.Label(f"{inspect_history()}")  # show llm history
-324        self.status_tab = toga.OptionItem(text="|  Connecting...", content=self.status_log, enabled=False)
-325        # self.response_array = toga.Box(children=[self.response_panel], style=Pack(flex=1))
-326        # self.endless_response = toga.ScrollContainer(content=self.response_array)
-327        resize_area = toga.SplitContainer(
-328            content=[
-329                toga.OptionContainer(
-330                    content=[
-331                        ("Output", self.response_panel),
-332                        ("Graph", self.browser_panel),
-333                        self.status_tab,
-334                    ],
-335                    on_select=self.switch_tabs,
-336                    style=Pack(background_color="#000000", flex=2),
-337                    id="tab_panel",
-338                ),
-339                toga.Row(
-340                    children=[
-341                        toga.Column(justify_content="start", style=Pack(flex=0.33)),
-342                        toga.Box(children=[self.message_panel], style=Pack(flex=1)),
-343                        toga.Column(style=Pack(flex=0.33, justify_content="start")),
-344                    ]
-345                ),
-346            ],
-347            direction=Direction.HORIZONTAL,
-348            style=Pack(flex=3),
-349        )
-350
-351        self.final_layout = toga.Column(children=[status_bar, resize_area], style=Pack(background_color=self.bg_text, flex=1))
-
- - -

Create the main input fields

-
- - -
-
- -
- - def - initialize_layout(self) -> None: - - - -
- - 
353    def initialize_layout(self) -> None:
-354        """Create the layout of the application."""
-355        self.main_window.content = self.final_layout
-
- - -

Create the layout of the application.

-
- - -
-
- -
- - def - startup(self) -> None: - - - -
- - 
357    def startup(self) -> None:
-358        """Startup Logic. Initialize widgets and layout, then asynchronous tasks for populating datagets"""
-359        self.main_window = toga.MainWindow()
-360        self.model_stream = ModelStream()
-361        self.task_stream = TaskStream()
-362        self.token_stream = TokenStream()
-363        self.token_stream = TokenStream()
-364
-365        start = toga.Command(
-366            self.ticker,
-367            text="Start",
-368            tooltip="Run the current available prompts.",
-369            shortcut=Key.MOD_1 + Key.ENTER,
-370            group=toga.Group.APP,
-371            section=-1,
-372        )
-373        attach = toga.Command.standard(
-374            self,
-375            toga.Command.OPEN,
-376            text="Attach File...",
-377            tooltip="Attach a file to the prompt.",
-378            shortcut=Key.MOD_1 + Key.O,
-379            action=self.attach_file,
-380            group=toga.Group.APP,
-381            section=0,
-382        )
-383        copy_reply = toga.Command(
-384            self.copy_reply,
-385            text="Copy Response",
-386            tooltip="Copy the response provided by the system",
-387            group=toga.Group.APP,
-388            section=0,
-389        )
-390        clear = toga.Command(
-391            self.empty_prompt,
-392            text="Clear Prompt",
-393            tooltip="Empty the user prompt field.",
-394            shortcut=Key.MOD_3 + Key.BACKSPACE,
-395            group=toga.Group.APP,
-396            section=1,
-397        )
-398        stop = toga.Command(
-399            self.halt,
-400            text="Stop",
-401            tooltip="Cancel the current sequence generation.",
-402            shortcut=Key.MOD_1 + Key.ESCAPE,  #
-403            group=toga.Group.APP,
-404            section=1,
-405        )
-406        self.commands.add(start, attach, copy_reply, clear, stop)
-407
-408        self.initialize_inputs()
-409        self.initialize_static()
-410        self.initialize_layout()
-411        asyncio.create_task(self.model_graph())
-412        asyncio.create_task(self.token_estimate(self))
-413        asyncio.create_task(self.populate_in_types())
-414        asyncio.create_task(self.populate_out_types())
-415        asyncio.create_task(self.populate_model_stack())
-416        asyncio.create_task(self.populate_task_stack())
-417        self.main_window.show()
-418        self.status_display = self.status_tab
-419        self.bg = self.bg_graph
-420        self.status_text_prefix = "|  "
-421
-422        asyncio.create_task(self.switch_tabs())
-
- - -

Startup Logic. Initialize widgets and layout, then asynchronous tasks for populating datagets

-
- - -
-
-
- -
- - def - main(url: str = 'http://127.0.0.1:8188'): - - - -
- - 
425def main(url: str = "http://127.0.0.1:8188"):
-426    """The entry point for the application."""
-427    app = Interface(
-428        formal_name="Shadowbox",
-429        app_id="org.darkshapes.shadowbox",
-430        app_name="sdbx",
-431        author="Darkshapes",
-432        home_page="https://darkshapes.org",
-433        description=" A generative AI instrument. ",
-434    )
-435
-436    app.icon = toga.Icon(path="resources/anomaly_128x")
-437    try:
-438        app.main_loop()
-439    except Exception as error_log:
-440        print(error_log)
-
- - -

The entry point for the application.

-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/toga/interface.html b/docs/zodiac/toga/interface.html deleted file mode 100644 index e299705..0000000 --- a/docs/zodiac/toga/interface.html +++ /dev/null @@ -1,513 +0,0 @@ - - - - - - - zodiac.toga.interface API documentation - - - - - - - - - -
-
-

-zodiac.toga.interface

- - - - - - - 
  1# # SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
-  2# # <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4# import os
-  5# import asyncio
-  6# from typing import Callable, Optional
-  7
-  8# import toga
-  9# from toga import Key
- 10# from zodiac.streams.model_stream import ModelStream
- 11# from zodiac.streams.task_stream import TaskStream
- 12# from zodiac.streams.token_stream import TokenStream
- 13
- 14
- 15# class DynamicApp:
- 16#     character_stats = None
- 17
- 18#     def __init__(self):
- 19#         self.registry_entry = None
- 20#         self.model_source = ModelStream()
- 21#         self.task_source = TaskStream()
- 22#         self.token_source = TokenStream()
- 23
- 24#     async def initialize(self):
- 25#         asyncio.create_task(self.token_estimate(self))
- 26#         asyncio.create_task(self.model_graph())
- 27#         asyncio.create_task(self.populate_in_types())
- 28#         asyncio.create_task(self.populate_out_types())
- 29#         asyncio.create_task(self.populate_model_stack())
- 30#         asyncio.create_task(self.populate_task_stack())
- 31
- 32#     async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> None:
- 33#         """Process and synthesize input data based on selected model.\n
- 34#         :param widget: The UI widget that triggered this action, typically used for state management.\n
- 35#         :type widget: toga.widgets
- 36#         :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
- 37#         :type external: bool"""
- 38#         from zodiac.toga.signatures import text_qa_stream
- 39#         from dspy.streaming import StatusMessage
- 40
- 41#         prompts = {"text": self.message_panel.value, "audio": [0], "image": []}
- 42#         # self.status.text = "Processing..."
- 43#         async for chunk in text_qa_stream(registry_entry=self.registry_entry, prompt=prompts["text"]):
- 44#             if chunk and isinstance(chunk, StatusMessage):
- 45#                 self.status.text = chunk.message
- 46#             elif chunk:
- 47#                 self.response_panel.value += chunk
- 48
- 49#     async def empty_prompt(self, widget, **kwargs) -> None:
- 50#         self.message_panel.value = ""
- 51
- 52#     async def halt(self, widget, **kwargs) -> None:
- 53#         """Stop processing prompt\n
- 54#         :param widget: The calling widget object"""
- 55#         self.status.text = "Cancelled."
- 56
- 57#     async def include_file(self, widget, **kwargs) -> None:
- 58#         import json
- 59
- 60#         try:
- 61#             file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
- 62#             self.status.text = f"Read. {file_path_named}"
- 63#             if file_path_named is not None:
- 64#                 from nnll.metadata.json_io import read_json_file
- 65
- 66#                 file_contents = read_json_file(file_path_named)
- 67#                 self.message_panel.scroll_to_bottom()
- 68#                 self.message_panel.value = json.dumps(file_contents)
- 69#                 self.status.text = f"Attached {os.path.basename(file_path_named)}."
- 70#             else:
- 71#                 self.status.text = "No file. "
- 72#         except (ValueError, json.JSONDecodeError):
- 73#             self.status.text = "Read failed... "
- 74
- 75#     async def reset_position(self, widget, **kwargs) -> None:
- 76#         widget.scroll_to_bottom()
- 77
- 78#     async def on_select_handler(self, widget, **kwargs):
- 79#         """React to input/output choice\n
- 80#         :param widget: The widget that triggered the event."""
- 81#         selection = widget.value
- 82#         registry_entry = next(iter(registry["entry"] for registry in self.model_source._graph.registry_entries if selection in registry["entry"].model))
- 83#         self.registry_entry = registry_entry
- 84#         await self.populate_task_stack()
- 85#         await self.token_source.set_tokenizer(registry_entry)
- 86
- 87#     async def model_graph(self):
- 88#         """Builds the model graph."""
- 89#         await self.model_source.model_graph()
- 90
- 91#     async def token_estimate(self, widget, **kwargs):
- 92#         token_count, character_count = await self.token_source.token_count(widget.value)
- 93#         self.character_stats.text = "{:02}".format(character_count) + "".join(self.formatted_units[0])
- 94#         self.token_stats.text = "{:02}".format(token_count) + "".join(self.formatted_units[1])
- 95#         self.time_stats.text = "{:02}".format(0.0) + "".join(self.formatted_units[2])
- 96
- 97#     async def populate_in_types(self):
- 98#         """Builds the input types selection."""
- 99
-100#         in_edge_names = await self.model_source.show_edges()
-101#         self.input_types.items = in_edge_names
-102
-103#     async def populate_out_types(self):
-104#         """Builds the output types selection."""
-105
-106#         out_edges = await self.model_source.show_edges(target=True)
-107#         self.output_types.items = out_edges
-108
-109#     async def populate_model_stack(self, widget: Optional[Callable] = None):
-110#         """Builds the model stack selection dropdown."""
-111
-112#         await self.model_source.clear()
-113#         if self.input_types.value and self.output_types.value:
-114#             models = await self.model_source.trace_models(self.input_types.value, self.output_types.value)
-115#             self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-116
-117#     async def populate_task_stack(self, widget: Optional[Callable] = None):
-118#         """Builds the task stack selection dropdown."""
-119#         selection = self.model_stack.value
-120#         registry_entry = next(
-121#             iter(
-122#                 registry["entry"]  # formatting
-123#                 for registry in self.model_source._graph.registry_entries  # formatting
-124#                 if selection in registry["entry"].model
-125#             )
-126#         )
-127#         await self.task_source.set_filter_type(self.input_types.value, self.output_types.value)
-128#         tasks = await self.task_source.trace_tasks(registry_entry)
-129
-130#         self.task_stack.items = tasks
-131
-132#     async def switch_tabs(self, widget: Optional[Callable] = None):
-133#         self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-134#         self.final_layout.style.background_color = self.bg
-135#         self.final_layout.refresh()
-136#         self.browser_panel.evaluate_javascript("location.reload();")
-137
-138#     async def add_commands(self, parent):
-139#         # control_group = toga.Group("Controls", order=40)
-140#         start = toga.Command(
-141#             self.ticker,
-142#             text="Start",
-143#             tooltip="Run the current available prompts.",
-144#             shortcut=Key.MOD_1 + Key.ENTER,
-145#             group=toga.Group.APP,
-146#             section=-1,
-147#         )
-148#         stop = toga.Command(
-149#             self.halt,
-150#             text="Stop",
-151#             tooltip="Cancel the current sequence generation.",
-152#             shortcut=Key.ESCAPE,
-153#             group=toga.Group.APP,
-154#             section=0,
-155#         )
-156#         attach = toga.Command.standard(
-157#             self,
-158#             toga.Command.OPEN,
-159#             text="Attach File...",
-160#             tooltip="Attach a file to the prompt.",
-161#             shortcut=Key.MOD_1 + Key.O,
-162#             action=self.include_file,
-163#             group=toga.Group.APP,
-164#             section=1,
-165#         )
-166#         clear = toga.Command(
-167#             self.empty_prompt,
-168#             text="Clear Prompt",
-169#             tooltip="Empty the prompt field.",
-170#             shortcut=Key.MOD_3 + Key.BACKSPACE,
-171#             group=toga.Group.APP,
-172#             section=2,
-173#         )
-174#         parent.commands.add(start, stop, attach, clear)
-175
-176#     # async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> None:
-177#     #     """Process and synthesize input data based on selected model.\n
-178#     #     :param widget: The UI widget that triggered this action, typically used for state management.\n
-179#     #     :type widget: toga.widgets
-180#     #     :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
-181#     #     :type external: bool"""
-182#     #     from zodiac.toga.signatures import text_qa_stream
-183#     #     from dspy.streaming import StatusMessage
-184
-185#     #     prompts = {"text": self.message_panel.value, "audio": [0], "image": []}
-186#     #     # self.status.text = "Processing..."
-187#     #     async for chunk in text_qa_stream(registry_entry=self.registry_entry, prompt=prompts["text"]):
-188#     #         if chunk and isinstance(chunk, StatusMessage):
-189#     #             self.status.text = chunk.message
-190#     #         elif chunk:
-191#     #             self.response_panel.value += chunk
-192
-193#     # async def empty_prompt(self, widget, **kwargs) -> None:
-194#     #     self.message_panel.value = ""
-195
-196#     # async def halt(self, widget, **kwargs) -> None:
-197#     #     """Stop processing prompt\n
-198#     #     :param widget: The calling widget object"""
-199#     #     self.status.text = "Cancelled."
-200
-201#     # async def include_file(self, widget, **kwargs) -> None:
-202#     #     import json
-203
-204#     #     try:
-205#     #         file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-206#     #         self.status.text = f"Read. {file_path_named}"
-207#     #         if file_path_named is not None:
-208#     #             from nnll.metadata.json_io import read_json_file
-209
-210#     #             file_contents = read_json_file(file_path_named)
-211#     #             self.message_panel.scroll_to_bottom()
-212#     #             self.message_panel.value = json.dumps(file_contents)
-213#     #             self.status.text = f"Attached {os.path.basename(file_path_named)}."
-214#     #         else:
-215#     #             self.status.text = "No file. "
-216#     #     except (ValueError, json.JSONDecodeError):
-217#     #         self.status.text = "Read failed... "
-218
-219#     # async def reset_position(self, widget, **kwargs) -> None:
-220#     #     self.widget.scroll_to_bottom()
-221
-222#     # async def on_select_handler(self, widget, **kwargs):
-223#     #     """React to input/output choice\n
-224#     #     :param widget: The widget that triggered the event."""
-225#     #     selection = widget.value
-226#     #     registry_entry = next(iter(registry["entry"] for registry in self.model_source._graph.registry_entries if selection in registry["entry"].model))
-227#     #     self.registry_entry = registry_entry
-228#     #     await self.populate_task_stack()
-229#     #     await self.token_source.set_tokenizer(registry_entry)
-230
-231#     # async def token_estimate(self, widget, **kwargs):
-232#     #     token_count, character_count = await self.token_source.token_count(widget.value)
-233#     #     self.static_interface.character_stats.text = "{:02}".format(character_count) + "".join(self.static_interface.formatted_units[0])
-234#     #     self.static_interface.token_stats.text = "{:02}".format(token_count) + "".join(self.static_interface.formatted_units[1])
-235#     #     self.static_interface.time_stats.text = "{:02}".format(0.0) + "".join(self.static_interface.formatted_units[2])
-236
-237#     # async def populate_in_types(self):
-238#     #     """Builds the input types selection."""
-239
-240#     #     in_edge_names = await self.model_source.show_edges()
-241#     #     self.input_types.items = in_edge_names
-242
-243#     # async def populate_out_types(self):
-244#     #     """Builds the output types selection."""
-245
-246#     #     out_edges = await self.model_source.show_edges(target=True)
-247#     #     self.output_types.items = out_edges
-248
-249#     # async def populate_model_stack(self, widget: Optional[Callable] = None):
-250#     #     """Builds the model stack selection dropdown."""
-251
-252#     #     await self.model_source.clear()
-253#     #     if self.input_types.value and self.output_types.value:
-254#     #         models = await self.model_source.trace_models(self.input_types.value, self.output_types.value)
-255#     #         self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-256
-257#     # async def populate_task_stack(self, widget: Optional[Callable] = None):
-258#     #     """Builds the task stack selection dropdown."""
-259#     #     selection = self.model_stack.value
-260#     #     registry_entry = next(
-261#     #         iter(
-262#     #             registry["entry"]  # formatting
-263#     #             for registry in self.model_source._graph.registry_entries  # formatting
-264#     #             if selection in registry["entry"].model
-265#     #         )
-266#     #     )
-267#     #     await self.task_source.set_filter_type(self.input_types.value, self.output_types.value)
-268#     #     tasks = await self.task_source.trace_tasks(registry_entry)
-269
-270#     #     self.task_stack.items = tasks
-271
-272#     # async def switch_tabs(self, widget: Optional[Callable] = None):
-273#     #     self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-274#     #     self.final_layout.style.background_color = self.bg
-275#     #     self.final_layout.refresh()
-276#     #     self.browser_panel.evaluate_javascript("location.reload();")
-
- - -
-
- - \ No newline at end of file diff --git a/docs/zodiac/toga/palette.html b/docs/zodiac/toga/palette.html deleted file mode 100644 index 23ca51d..0000000 --- a/docs/zodiac/toga/palette.html +++ /dev/null @@ -1,1308 +0,0 @@ - - - - - - - zodiac.toga.palette API documentation - - - - - - - - - -
-
-

-zodiac.toga.palette

- - - - - - - 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
-  2# <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4from typing import Callable
-  5import toga
-  6
-  7
-  8class CommandPalette:
-  9    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
- 10        """Process and synthesize input data based on selected model.\n
- 11        :param widget: The UI widget that triggered this action, typically used for state management.\n
- 12        :type widget: toga.widgets
- 13        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
- 14        :type external: bool"""
- 15        from zodiac.toga.signatures import ready_predictor
- 16
- 17        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
- 18
- 19        await self.token_stream.set_tokenizer(self.registry_entry)
- 20        prompts = {}
- 21        if self.message_panel.value:
- 22            cache = False
- 23            prompts.setdefault("text", self.message_panel.value)
- 24        stream = True if self.output_types.value == "text" else False
- 25        # prompts.setdefault("audio",[0]) if else []
- 26        # prompts.setdefault("image",[]) if image": []
- 27
- 28        if stream:
- 29            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
- 30            await self.stream_text(prompts, context_data, predictor_data)
- 31        else:
- 32            await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
- 33        return widget
- 34
- 35    async def stream_text(self, prompts, context_data, predictor_data):
- 36        from zodiac.toga.signatures import Predictor
- 37        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
- 38        from dspy.streaming import StatusMessage, StreamResponse
- 39
- 40        self.response_panel.scroll_to_bottom()
- 41        with dspy_context(**context_data):
- 42            self.program = streamify(Predictor(), **predictor_data)
- 43            async for prediction in self.program(question=prompts["text"]):
- 44                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
- 45                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
- 46                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
- 47                    self.response_panel.value += str(prediction.chunk)
- 48                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
- 49                    self.response_panel.value += str(prediction.answer)
- 50                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
- 51                    self.status_display.text = self.status_text_prefix + str(prediction.message)
- 52        self.response_panel.value += "\n--\n\n"
- 53        return prediction
- 54
- 55    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
- 56        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
- 57        from nnll.tensor_pipe.inference import run_inference
- 58        from zodiac.streams.class_stream import best_package
- 59        from zodiac.providers.constants import MIR_DB
- 60
- 61        pkg_data = await best_package(pkg_data=registry_entry)
- 62        constructor = ConstructPipeline()
- 63        pipe_data = constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
- 64        return run_inference(pipe_data, prompts)
- 65
- 66        # from zodiac.toga.signatures import Predictor
- 67
- 68        # return prediction
- 69
- 70    async def halt(self, widget, **kwargs) -> None:
- 71        """Stop processing prompt\n
- 72        :param widget: The calling widget object"""
- 73        if not self.program.done():
- 74            import gc
- 75
- 76            del self.program
- 77            gc.collect()
- 78            self.status_display.text = self.status_text_prefix + "Cancelled."
- 79
- 80    async def empty_prompt(self, widget, **kwargs) -> None:
- 81        """Clears the prompt input area.
- 82        :param widget: Triggering widget"""
- 83        self.message_panel.value = ""
- 84
- 85    async def copy_reply(self, widget, **kwargs) -> None:
- 86        """Push the reply into the clipboard
- 87        :param widget: Triggering widget"""
- 88        import pyperclip
- 89
- 90        pyperclip.copy(self.response_panel.value)
- 91        self.status_display.text = self.status_text_prefix + self.status_info[7]
- 92
- 93    async def attach_file(self, widget, **kwargs) -> None:
- 94        """Attaches a file's contents to the prompt area.
- 95        :param widget: Triggering widget"""
- 96        import json
- 97
- 98        try:
- 99            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-100            self.status_display.text = f"Read. {file_path_named}"
-101            if file_path_named is not None:
-102                from nnll.metadata.json_io import read_json_file
-103
-104                file_contents = read_json_file(file_path_named)
-105                self.message_panel.scroll_to_bottom()
-106                self.message_panel.value = json.dumps(file_contents)
-107                self.status_display.text = self.status_text_prefix + self.status_info[6]
-108            else:
-109                self.status_display.text = self.status_text_prefix + self.status_info[4]
-110        except (ValueError, json.JSONDecodeError):
-111            self.status_display.text = self.status_text_prefix + self.status_info[5]
-112
-113    async def reset_position(self, widget, **kwargs) -> None:
-114        """Scrolls text panel to bottom after content update.
-115        :param widget: text panel widget
-116        """
-117        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-118        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-119            self.position_counter = 0
-120            widget.scroll_to_bottom()
-121
-122    async def on_select_handler(self, widget, **kwargs) -> None:
-123        """React to input/output choice\n
-124        :param widget: The widget that triggered the event."""
-125        selection = widget.value
-126        if self.model_stream._graph.registry_entries is not None:
-127            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-128            await self.populate_task_stack()
-129            await self.token_stream.set_tokenizer(self.registry_entry)
-130        else:
-131            self.registry_entry = "No model..."
-132
-133    async def model_graph(self):
-134        """Builds the model graph."""
-135        await self.model_stream.model_graph()
-136
-137    async def populate_in_types(self) -> None:
-138        """Builds the input types selection."""
-139        in_edge_names = await self.model_stream.show_edges()
-140        self.input_types.items = in_edge_names
-141
-142    async def populate_out_types(self) -> None:
-143        """Builds the output types selection."""
-144        out_edges = await self.model_stream.show_edges(target=True)
-145        self.output_types.items = out_edges
-146
-147    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-148        """Builds the model stack selection dropdown."""
-149
-150        await self.model_stream.clear()
-151        if self.input_types.value and self.output_types.value:
-152            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-153            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-154            await self.token_estimate(widget=self.message_panel)
-155
-156    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-157        """Builds the task stack selection dropdown."""
-158        selection = self.model_stack.value
-159        if self.model_stream._graph.registry_entries:
-160            registry_entry = next(
-161                iter(
-162                    registry["entry"]  # formatting
-163                    for registry in self.model_stream._graph.registry_entries  # formatting
-164                    if selection in registry["entry"].model
-165                )
-166            )
-167        else:
-168            registry_entry = "No models..."
-169        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-170        if registry_entry and not isinstance(registry_entry, str):
-171            tasks = await self.task_stream.filter_tasks(registry_entry)
-172        else:
-173            tasks = ""
-174
-175        self.task_stack.items = tasks
-176
-177    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-178        """Switches between text and graph tabs.
-179        :param widget: The triggering widget (optional), defaults to None"""
-180        self.browser_panel.evaluate_javascript("location.reload();")
-181        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-182        self.final_layout.style.background_color = self.bg
-183        self.final_layout.refresh()
-184        self.status_display.text += self.status_info[0]
-185        await self.ping_server(widget=self.status_display)
-186
-187    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-188        self.browser_panel.url = self.graph_server
-189        try:
-190            request = requests.get(self.graph_server, timeout=(3, 3))
-191            if request is not None:
-192                if hasattr(request, "status_code"):
-193                    status = request.status_code
-194                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-195                    await self.active_server()
-196                elif hasattr(request, "json"):
-197                    status = request.json()
-198                    if status.get("result") == "OK":
-199                        await self.active_server()
-200                else:
-201                    self.browser_panel.url = self.graph_disabled
-202                    await self.active_server(False)
-203            else:
-204                self.browser_panel.url = self.graph_disabled
-205                await self.active_server(False)
-206        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-207            await self.active_server(False)
-208            pass
-209        return widget
-210
-211    async def active_server(self, enabled: bool = True):
-212        if not enabled:
-213            status_info = self.status_info[1]
-214            self.browser_panel.url = self.graph_disabled
-215        else:
-216            status_info = self.status_info[2]
-217            self.browser_panel.url = self.graph_server
-218        for info in self.status_info:
-219            self.status_display.text = self.status_display.text.replace(info, "")
-220        self.status_display.text += status_info
-
- - -
-
- -
- - class - CommandPalette: - - - -
- - 
  9class CommandPalette:
- 10    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
- 11        """Process and synthesize input data based on selected model.\n
- 12        :param widget: The UI widget that triggered this action, typically used for state management.\n
- 13        :type widget: toga.widgets
- 14        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
- 15        :type external: bool"""
- 16        from zodiac.toga.signatures import ready_predictor
- 17
- 18        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
- 19
- 20        await self.token_stream.set_tokenizer(self.registry_entry)
- 21        prompts = {}
- 22        if self.message_panel.value:
- 23            cache = False
- 24            prompts.setdefault("text", self.message_panel.value)
- 25        stream = True if self.output_types.value == "text" else False
- 26        # prompts.setdefault("audio",[0]) if else []
- 27        # prompts.setdefault("image",[]) if image": []
- 28
- 29        if stream:
- 30            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
- 31            await self.stream_text(prompts, context_data, predictor_data)
- 32        else:
- 33            await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
- 34        return widget
- 35
- 36    async def stream_text(self, prompts, context_data, predictor_data):
- 37        from zodiac.toga.signatures import Predictor
- 38        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
- 39        from dspy.streaming import StatusMessage, StreamResponse
- 40
- 41        self.response_panel.scroll_to_bottom()
- 42        with dspy_context(**context_data):
- 43            self.program = streamify(Predictor(), **predictor_data)
- 44            async for prediction in self.program(question=prompts["text"]):
- 45                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
- 46                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
- 47                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
- 48                    self.response_panel.value += str(prediction.chunk)
- 49                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
- 50                    self.response_panel.value += str(prediction.answer)
- 51                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
- 52                    self.status_display.text = self.status_text_prefix + str(prediction.message)
- 53        self.response_panel.value += "\n--\n\n"
- 54        return prediction
- 55
- 56    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
- 57        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
- 58        from nnll.tensor_pipe.inference import run_inference
- 59        from zodiac.streams.class_stream import best_package
- 60        from zodiac.providers.constants import MIR_DB
- 61
- 62        pkg_data = await best_package(pkg_data=registry_entry)
- 63        constructor = ConstructPipeline()
- 64        pipe_data = constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
- 65        return run_inference(pipe_data, prompts)
- 66
- 67        # from zodiac.toga.signatures import Predictor
- 68
- 69        # return prediction
- 70
- 71    async def halt(self, widget, **kwargs) -> None:
- 72        """Stop processing prompt\n
- 73        :param widget: The calling widget object"""
- 74        if not self.program.done():
- 75            import gc
- 76
- 77            del self.program
- 78            gc.collect()
- 79            self.status_display.text = self.status_text_prefix + "Cancelled."
- 80
- 81    async def empty_prompt(self, widget, **kwargs) -> None:
- 82        """Clears the prompt input area.
- 83        :param widget: Triggering widget"""
- 84        self.message_panel.value = ""
- 85
- 86    async def copy_reply(self, widget, **kwargs) -> None:
- 87        """Push the reply into the clipboard
- 88        :param widget: Triggering widget"""
- 89        import pyperclip
- 90
- 91        pyperclip.copy(self.response_panel.value)
- 92        self.status_display.text = self.status_text_prefix + self.status_info[7]
- 93
- 94    async def attach_file(self, widget, **kwargs) -> None:
- 95        """Attaches a file's contents to the prompt area.
- 96        :param widget: Triggering widget"""
- 97        import json
- 98
- 99        try:
-100            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-101            self.status_display.text = f"Read. {file_path_named}"
-102            if file_path_named is not None:
-103                from nnll.metadata.json_io import read_json_file
-104
-105                file_contents = read_json_file(file_path_named)
-106                self.message_panel.scroll_to_bottom()
-107                self.message_panel.value = json.dumps(file_contents)
-108                self.status_display.text = self.status_text_prefix + self.status_info[6]
-109            else:
-110                self.status_display.text = self.status_text_prefix + self.status_info[4]
-111        except (ValueError, json.JSONDecodeError):
-112            self.status_display.text = self.status_text_prefix + self.status_info[5]
-113
-114    async def reset_position(self, widget, **kwargs) -> None:
-115        """Scrolls text panel to bottom after content update.
-116        :param widget: text panel widget
-117        """
-118        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-119        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-120            self.position_counter = 0
-121            widget.scroll_to_bottom()
-122
-123    async def on_select_handler(self, widget, **kwargs) -> None:
-124        """React to input/output choice\n
-125        :param widget: The widget that triggered the event."""
-126        selection = widget.value
-127        if self.model_stream._graph.registry_entries is not None:
-128            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-129            await self.populate_task_stack()
-130            await self.token_stream.set_tokenizer(self.registry_entry)
-131        else:
-132            self.registry_entry = "No model..."
-133
-134    async def model_graph(self):
-135        """Builds the model graph."""
-136        await self.model_stream.model_graph()
-137
-138    async def populate_in_types(self) -> None:
-139        """Builds the input types selection."""
-140        in_edge_names = await self.model_stream.show_edges()
-141        self.input_types.items = in_edge_names
-142
-143    async def populate_out_types(self) -> None:
-144        """Builds the output types selection."""
-145        out_edges = await self.model_stream.show_edges(target=True)
-146        self.output_types.items = out_edges
-147
-148    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-149        """Builds the model stack selection dropdown."""
-150
-151        await self.model_stream.clear()
-152        if self.input_types.value and self.output_types.value:
-153            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-154            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-155            await self.token_estimate(widget=self.message_panel)
-156
-157    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-158        """Builds the task stack selection dropdown."""
-159        selection = self.model_stack.value
-160        if self.model_stream._graph.registry_entries:
-161            registry_entry = next(
-162                iter(
-163                    registry["entry"]  # formatting
-164                    for registry in self.model_stream._graph.registry_entries  # formatting
-165                    if selection in registry["entry"].model
-166                )
-167            )
-168        else:
-169            registry_entry = "No models..."
-170        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-171        if registry_entry and not isinstance(registry_entry, str):
-172            tasks = await self.task_stream.filter_tasks(registry_entry)
-173        else:
-174            tasks = ""
-175
-176        self.task_stack.items = tasks
-177
-178    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-179        """Switches between text and graph tabs.
-180        :param widget: The triggering widget (optional), defaults to None"""
-181        self.browser_panel.evaluate_javascript("location.reload();")
-182        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-183        self.final_layout.style.background_color = self.bg
-184        self.final_layout.refresh()
-185        self.status_display.text += self.status_info[0]
-186        await self.ping_server(widget=self.status_display)
-187
-188    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-189        self.browser_panel.url = self.graph_server
-190        try:
-191            request = requests.get(self.graph_server, timeout=(3, 3))
-192            if request is not None:
-193                if hasattr(request, "status_code"):
-194                    status = request.status_code
-195                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-196                    await self.active_server()
-197                elif hasattr(request, "json"):
-198                    status = request.json()
-199                    if status.get("result") == "OK":
-200                        await self.active_server()
-201                else:
-202                    self.browser_panel.url = self.graph_disabled
-203                    await self.active_server(False)
-204            else:
-205                self.browser_panel.url = self.graph_disabled
-206                await self.active_server(False)
-207        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-208            await self.active_server(False)
-209            pass
-210        return widget
-211
-212    async def active_server(self, enabled: bool = True):
-213        if not enabled:
-214            status_info = self.status_info[1]
-215            self.browser_panel.url = self.graph_disabled
-216        else:
-217            status_info = self.status_info[2]
-218            self.browser_panel.url = self.graph_server
-219        for info in self.status_info:
-220            self.status_display.text = self.status_display.text.replace(info, "")
-221        self.status_display.text += status_info
-
- - - - -
- -
- - async def - ticker( self, widget: Callable, external: bool = False, **kwargs) -> toga.widgets.base.Widget: - - - -
- - 
10    async def ticker(self, widget: Callable, external: bool = False, **kwargs) -> toga.Widget:
-11        """Process and synthesize input data based on selected model.\n
-12        :param widget: The UI widget that triggered this action, typically used for state management.\n
-13        :type widget: toga.widgets
-14        :param external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False
-15        :type external: bool"""
-16        from zodiac.toga.signatures import ready_predictor
-17
-18        self.response_panel.value += f"{os.path.basename(self.registry_entry.model)} :\n"
-19
-20        await self.token_stream.set_tokenizer(self.registry_entry)
-21        prompts = {}
-22        if self.message_panel.value:
-23            cache = False
-24            prompts.setdefault("text", self.message_panel.value)
-25        stream = True if self.output_types.value == "text" else False
-26        # prompts.setdefault("audio",[0]) if else []
-27        # prompts.setdefault("image",[]) if image": []
-28
-29        if stream:
-30            context_data, predictor_data = await ready_predictor(self.registry_entry, dspy_stream=stream, async_stream=stream, cache=cache)
-31            await self.stream_text(prompts, context_data, predictor_data)
-32        else:
-33            await self.generate_media(prompts, self.registry_entry)  # context_data, predictor_data)
-34        return widget
-
- - -

Process and synthesize input data based on selected model.

- -
Parameters
- -
    -

  • widget: The UI widget that triggered this action, typically used for state management.

    • -

  • external: Indicates whether the processing should be handled externally (e.g., via clipboard), defaults to False

    • -
-
- - -
-
- -
- - async def - stream_text(self, prompts, context_data, predictor_data): - - - -
- - 
36    async def stream_text(self, prompts, context_data, predictor_data):
-37        from zodiac.toga.signatures import Predictor
-38        from litellm.types.utils import ModelResponseStream  # StatusStreamingCallback
-39        from dspy.streaming import StatusMessage, StreamResponse
-40
-41        self.response_panel.scroll_to_bottom()
-42        with dspy_context(**context_data):
-43            self.program = streamify(Predictor(), **predictor_data)
-44            async for prediction in self.program(question=prompts["text"]):
-45                if isinstance(prediction, ModelResponseStream) and prediction["choices"][0]["delta"]["content"]:
-46                    self.response_panel.value += prediction["choices"][0]["delta"]["content"]
-47                elif isinstance(prediction, StreamResponse) or hasattr(prediction, "chunk"):
-48                    self.response_panel.value += str(prediction.chunk)
-49                elif isinstance(prediction, Prediction) or hasattr(prediction, "answer"):
-50                    self.response_panel.value += str(prediction.answer)
-51                elif isinstance(prediction, StatusMessage) or hasattr(prediction, "message"):
-52                    self.status_display.text = self.status_text_prefix + str(prediction.message)
-53        self.response_panel.value += "\n--\n\n"
-54        return prediction
-
- - - - -
-
- -
- - async def - generate_media(self, prompts, registry_entry) -> None: - - - -
- - 
56    async def generate_media(self, prompts, registry_entry) -> None:  # , predictor_data
-57        from nnll.tensor_pipe.construct_pipe import ConstructPipeline
-58        from nnll.tensor_pipe.inference import run_inference
-59        from zodiac.streams.class_stream import best_package
-60        from zodiac.providers.constants import MIR_DB
-61
-62        pkg_data = await best_package(pkg_data=registry_entry)
-63        constructor = ConstructPipeline()
-64        pipe_data = constructor.create_pipeline(registry_entry, pkg_data, MIR_DB)
-65        return run_inference(pipe_data, prompts)
-66
-67        # from zodiac.toga.signatures import Predictor
-68
-69        # return prediction
-
- - - - -
-
- -
- - async def - halt(self, widget, **kwargs) -> None: - - - -
- - 
71    async def halt(self, widget, **kwargs) -> None:
-72        """Stop processing prompt\n
-73        :param widget: The calling widget object"""
-74        if not self.program.done():
-75            import gc
-76
-77            del self.program
-78            gc.collect()
-79            self.status_display.text = self.status_text_prefix + "Cancelled."
-
- - -

Stop processing prompt

- -
Parameters
- -
    -
  • widget: The calling widget object
    • -
-
- - -
-
- -
- - async def - empty_prompt(self, widget, **kwargs) -> None: - - - -
- - 
81    async def empty_prompt(self, widget, **kwargs) -> None:
-82        """Clears the prompt input area.
-83        :param widget: Triggering widget"""
-84        self.message_panel.value = ""
-
- - -

Clears the prompt input area.

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - copy_reply(self, widget, **kwargs) -> None: - - - -
- - 
86    async def copy_reply(self, widget, **kwargs) -> None:
-87        """Push the reply into the clipboard
-88        :param widget: Triggering widget"""
-89        import pyperclip
-90
-91        pyperclip.copy(self.response_panel.value)
-92        self.status_display.text = self.status_text_prefix + self.status_info[7]
-
- - -

Push the reply into the clipboard

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - attach_file(self, widget, **kwargs) -> None: - - - -
- - 
 94    async def attach_file(self, widget, **kwargs) -> None:
- 95        """Attaches a file's contents to the prompt area.
- 96        :param widget: Triggering widget"""
- 97        import json
- 98
- 99        try:
-100            file_path_named = await self.main_window.dialog(toga.OpenFileDialog(title="Attach a file to the prompt"))
-101            self.status_display.text = f"Read. {file_path_named}"
-102            if file_path_named is not None:
-103                from nnll.metadata.json_io import read_json_file
-104
-105                file_contents = read_json_file(file_path_named)
-106                self.message_panel.scroll_to_bottom()
-107                self.message_panel.value = json.dumps(file_contents)
-108                self.status_display.text = self.status_text_prefix + self.status_info[6]
-109            else:
-110                self.status_display.text = self.status_text_prefix + self.status_info[4]
-111        except (ValueError, json.JSONDecodeError):
-112            self.status_display.text = self.status_text_prefix + self.status_info[5]
-
- - -

Attaches a file's contents to the prompt area.

- -
Parameters
- -
    -
  • widget: Triggering widget
    • -
-
- - -
-
- -
- - async def - reset_position(self, widget, **kwargs) -> None: - - - -
- - 
114    async def reset_position(self, widget, **kwargs) -> None:
-115        """Scrolls text panel to bottom after content update.
-116        :param widget: text panel widget
-117        """
-118        setattr(self, "position_counter", getattr(self, "position_counter", 0) + 1)
-119        if max(self.scroll_buffer, self.position_counter) >= self.scroll_buffer:
-120            self.position_counter = 0
-121            widget.scroll_to_bottom()
-
- - -

Scrolls text panel to bottom after content update.

- -
Parameters
- -
    -
  • widget: text panel widget
    • -
-
- - -
-
- -
- - async def - on_select_handler(self, widget, **kwargs) -> None: - - - -
- - 
123    async def on_select_handler(self, widget, **kwargs) -> None:
-124        """React to input/output choice\n
-125        :param widget: The widget that triggered the event."""
-126        selection = widget.value
-127        if self.model_stream._graph.registry_entries is not None:
-128            self.registry_entry = next(iter(registry["entry"] for registry in self.model_stream._graph.registry_entries if selection in registry["entry"].model))
-129            await self.populate_task_stack()
-130            await self.token_stream.set_tokenizer(self.registry_entry)
-131        else:
-132            self.registry_entry = "No model..."
-
- - -

React to input/output choice

- -
Parameters
- -
    -
  • widget: The widget that triggered the event.
    • -
-
- - -
-
- -
- - async def - model_graph(self): - - - -
- - 
134    async def model_graph(self):
-135        """Builds the model graph."""
-136        await self.model_stream.model_graph()
-
- - -

Builds the model graph.

-
- - -
-
- -
- - async def - populate_in_types(self) -> None: - - - -
- - 
138    async def populate_in_types(self) -> None:
-139        """Builds the input types selection."""
-140        in_edge_names = await self.model_stream.show_edges()
-141        self.input_types.items = in_edge_names
-
- - -

Builds the input types selection.

-
- - -
-
- -
- - async def - populate_out_types(self) -> None: - - - -
- - 
143    async def populate_out_types(self) -> None:
-144        """Builds the output types selection."""
-145        out_edges = await self.model_stream.show_edges(target=True)
-146        self.output_types.items = out_edges
-
- - -

Builds the output types selection.

-
- - -
-
- -
- - async def - populate_model_stack(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
148    async def populate_model_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-149        """Builds the model stack selection dropdown."""
-150
-151        await self.model_stream.clear()
-152        if self.input_types.value and self.output_types.value:
-153            models = await self.model_stream.trace_models(self.input_types.value, self.output_types.value)
-154            self.model_stack.items = models  # [model[0][:20] for model in models if len(model[0]) > 20]
-155            await self.token_estimate(widget=self.message_panel)
-
- - -

Builds the model stack selection dropdown.

-
- - -
-
- -
- - async def - populate_task_stack(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
157    async def populate_task_stack(self, widget: toga.Widget = None, **kwargs) -> None:
-158        """Builds the task stack selection dropdown."""
-159        selection = self.model_stack.value
-160        if self.model_stream._graph.registry_entries:
-161            registry_entry = next(
-162                iter(
-163                    registry["entry"]  # formatting
-164                    for registry in self.model_stream._graph.registry_entries  # formatting
-165                    if selection in registry["entry"].model
-166                )
-167            )
-168        else:
-169            registry_entry = "No models..."
-170        await self.task_stream.set_filter_type(self.input_types.value, self.output_types.value)
-171        if registry_entry and not isinstance(registry_entry, str):
-172            tasks = await self.task_stream.filter_tasks(registry_entry)
-173        else:
-174            tasks = ""
-175
-176        self.task_stack.items = tasks
-
- - -

Builds the task stack selection dropdown.

-
- - -
-
- -
- - async def - switch_tabs(self, widget: toga.widgets.base.Widget = None, **kwargs) -> None: - - - -
- - 
178    async def switch_tabs(self, widget: toga.Widget = None, **kwargs) -> None:
-179        """Switches between text and graph tabs.
-180        :param widget: The triggering widget (optional), defaults to None"""
-181        self.browser_panel.evaluate_javascript("location.reload();")
-182        self.bg = self.bg_graph if self.bg == self.bg_text else self.bg_text
-183        self.final_layout.style.background_color = self.bg
-184        self.final_layout.refresh()
-185        self.status_display.text += self.status_info[0]
-186        await self.ping_server(widget=self.status_display)
-
- - -

Switches between text and graph tabs.

- -
Parameters
- -
    -
  • widget: The triggering widget (optional), defaults to None
    • -
-
- - -
-
- -
- - async def - ping_server( self, widget: toga.widgets.base.Widget, **kwargs) -> toga.widgets.base.Widget: - - - -
- - 
188    async def ping_server(self, widget: toga.Widget, **kwargs) -> toga.Widget:
-189        self.browser_panel.url = self.graph_server
-190        try:
-191            request = requests.get(self.graph_server, timeout=(3, 3))
-192            if request is not None:
-193                if hasattr(request, "status_code"):
-194                    status = request.status_code
-195                if (hasattr(request, "ok") and request.ok) or (hasattr(request, "reason") and request.reason == "OK"):
-196                    await self.active_server()
-197                elif hasattr(request, "json"):
-198                    status = request.json()
-199                    if status.get("result") == "OK":
-200                        await self.active_server()
-201                else:
-202                    self.browser_panel.url = self.graph_disabled
-203                    await self.active_server(False)
-204            else:
-205                self.browser_panel.url = self.graph_disabled
-206                await self.active_server(False)
-207        except (ConnectTimeout, ConnectionError, ConnectionRefusedError, MaxRetryError, NewConnectionError, OSError):
-208            await self.active_server(False)
-209            pass
-210        return widget
-
- - - - -
-
- -
- - async def - active_server(self, enabled: bool = True): - - - -
- - 
212    async def active_server(self, enabled: bool = True):
-213        if not enabled:
-214            status_info = self.status_info[1]
-215            self.browser_panel.url = self.graph_disabled
-216        else:
-217            status_info = self.status_info[2]
-218            self.browser_panel.url = self.graph_server
-219        for info in self.status_info:
-220            self.status_display.text = self.status_display.text.replace(info, "")
-221        self.status_display.text += status_info
-
- - - - -
-
-
- - \ No newline at end of file diff --git a/docs/zodiac/toga/signatures.html b/docs/zodiac/toga/signatures.html deleted file mode 100644 index e9e10ef..0000000 --- a/docs/zodiac/toga/signatures.html +++ /dev/null @@ -1,1179 +0,0 @@ - - - - - - - zodiac.toga.signatures API documentation - - - - - - - - - -
-
-

-zodiac.toga.signatures

- - - - - - - 
  1# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
-  2# <!-- // /*  d a r k s h a p e s */ -->
-  3
-  4import dspy
-  5from zodiac.providers.registry_entry import RegistryEntry
-  6
-  7from zodiac.providers.constants import CueType, PkgType, MIR_DB
-  8
-  9dspy.configure_cache(enable_disk_cache=False)
- 10
- 11
- 12class StreamActivity(dspy.streaming.StatusMessageProvider):
- 13    def lm_start_status_message(self, instance, inputs):
- 14        return "Processing..."
- 15
- 16    def module_start_status_message(self, instance, inputs):
- 17        return "Module started."
- 18
- 19    def lm_end_status_message(self, outputs):
- 20        return "Done."
- 21
- 22    def tool_start_status_message(self, instance, inputs):
- 23        return "Tool started..."
- 24
- 25    def tool_end_status_message(self, outputs):
- 26        return "Tool finished."
- 27
- 28
- 29class QATask(dspy.Signature):
- 30    """Reply with short responses within 60-90 word/10k character code limits"""
- 31
- 32    question: str = dspy.InputField(desc="The question to respond to")
- 33    answer = dspy.OutputField(desc="Often between 60 and 90 words and limited to 10000 character code blocks")
- 34
- 35
- 36TARGET_LANGUAGE = "English"
- 37
- 38
- 39class TranslateTask(dspy.Signature):
- 40    f"""Translate from a language to {TARGET_LANGUAGE}"""
- 41
- 42    message: dspy.Image | dspy.Audio | str = dspy.InputField(desc="The input to translate.")
- 43    translation: dspy.Image | dspy.Audio | str = dspy.OutputField(desc="A translation of the input")
- 44
- 45
- 46class VisionTask(dspy.Signature):
- 47    """Describe the image in detail."""
- 48
- 49    image: dspy.Image = dspy.InputField(desc="An image")
- 50    description: str = dspy.OutputField(desc="A detailed description of the image.")
- 51
- 52
- 53class TranscribeTask(dspy.Signature):
- 54    """Transcribe spoken words into text"""
- 55
- 56    message: dspy.Audio = dspy.InputField(desc="The speech to transcribe.")
- 57    answer: str = dspy.OutputField(desc="A transcript of the recorded speech")
- 58
- 59
- 60class GenerativeImageTask(dspy.Signature):
- 61    message: str = dspy.InputField(desc="Description of the image to generate")
- 62    image: dspy.Image = dspy.OutputField(desc="An image matching the description")
- 63
- 64
- 65class GenerativeAudioTask(dspy.Signature):
- 66    message: str = dspy.InputField(desc="Description of the audio to generate")
- 67    audio: dspy.Audio = dspy.OutputField(desc="An audio file matching the description")
- 68
- 69
- 70# cherry pick examples
- 71# dspy.Adapter.format(demos=[{:,:}],signatures:,inputs:)
- 72
- 73
- 74class QuestionAnswer(dspy.Module):
- 75    def __init__(self):
- 76        super().__init__()
- 77        self.predict = dspy.Predict(QATask)
- 78
- 79    def forward(self, question, **kwargs):
- 80        self.predict(question=question, **kwargs)
- 81        return self.predict(question=question, **kwargs)
- 82
- 83
- 84class Predictor(dspy.Module):
- 85    def __init__(self):
- 86        super().__init__
- 87        self.program = dspy.Predict(signature=QATask)
- 88
- 89    def __call__(self, question: str):
- 90        from litellm.exceptions import APIConnectionError
- 91        from litellm.llms.ollama.common_utils import OllamaError
- 92        from httpx import ConnectError
- 93        from dspy.utils.exceptions import AdapterParseError
- 94        from aiohttp.client_exceptions import ClientConnectorError
- 95
- 96        try:
- 97            return self.program(question=question)
- 98        except (ClientConnectorError, ConnectError, AdapterParseError, APIConnectionError, OllamaError, OSError):
- 99            pass
-100
-101    # from nnll.tensor_pipe import segments
-102    # from nnll.configure.init_gpu import seed_planter
-103    # mir_db = MIR_DB.database
-104    # mir_arch = registry_entries.mir
-105    # arch_data = mir_db[mir_arch[0][mir_arch[1]]]
-106    # pkg_data = mir_db[mir_arch[0]]["pkg"][0]
-107    # init_modules = find_package
-108    # self.pipe, model, self.import_pkg, self.pipe_kwargs = self.factory.create_pipeline(arch_data=registry_entry.mir, init_modules=init_modules)
-109
-110    #     if lora is not None:
-111    #         lora_arch = self.mir_db.database[series].get(lora[1])
-112    #         lora_repo = next(iter(lora_arch["repo"]))  # <- user location here OR this
-113    #         scheduler = self.mir_db.database[series]["[init]"].get("scheduler")
-114    #         kwargs = {}
-115    #         if scheduler:
-116    #             sched = self.mir_db.database[scheduler]["[init]"]
-117    #             scheduler_kwargs = self.mir_db.database[series]["[init]"].get("scheduler_kwargs")
-118    #             kwargs = {sched: sched, scheduler_kwargs: scheduler_kwargs}
-119    #         init_kwargs = lora_arch.get("init_kwargs")
-120    #         if lora:
-121    #             self.pipe = self.factory.add_lora(self.pipe, lora_repo=lora_repo, init_kwargs=init_kwargs, **kwargs)
-122
-123    #     noise_seed = seed_planter(device=self.device)
-124    #     user_set = {
-125    #         "output_type": "pil",
-126    #     }
-127    #     self.pipe_kwargs.update(user_set)
-128
-129    #     if ChipType.MPS[0]:
-130    #         self.pipe.enable_attention_slicing()
-131    #     nfo(f"Pre-generator Model {model}  Pipe {self.pipe} Arguments {self.pipe_kwargs}")  # Lora {lora_opt}
-132    #     if "diffusers" in self.import_pkg:
-133    #         self.pipe.to(self.device)
-134    #         self.pipe = segments.add_generator(pipe=self.pipe, noise_seed=noise_seed)
-135    #     else:
-136    #         self.pipe = self.pipe[0]
-137    #         self.pipe.to(self.device)
-138    #     if "audiogen" in self.import_pkg:
-139    #         self.pipe_kwargs.update({"sample_rate": self.pipe.config.sampling_rate})
-140    #     elif "parler_tts" in self.import_pkg:
-141    #         self.pipe_kwargs.update({"sampling_rate": self.pipe.config.sampling_rate})
-142
-143
-144async def ready_predictor(
-145    registry_entry: RegistryEntry,
-146    async_stream: bool = True,
-147    dspy_stream: bool = True,
-148    max_workers: int = 8,
-149    cache: bool = True,
-150):
-151    from zodiac.providers.constants import ChipType
-152
-153    if registry_entry.cuetype == CueType.HUB:
-154        device = getattr(ChipType, next(iter(ChipType._show_ready())), "CPU")
-155
-156    else:
-157        lm_kwargs = {"async_max_workers": max_workers, "cache": cache}
-158        lm_model = dspy.LM(
-159            registry_entry.model,
-160            **registry_entry.api_kwargs,
-161            **lm_kwargs,
-162        )
-163    stream_listeners = [dspy.streaming.StreamListener(signature_field_name="answer")]
-164    context_kwargs = {"lm": lm_model}  # , "adapter": dspy.ChatAdapter()}
-165    predictor_kwargs = {
-166        "status_message_provider": StreamActivity(),
-167        "async_streaming": async_stream,
-168        "include_final_prediction_in_output_stream": False,
-169    }
-170    if dspy_stream:
-171        predictor_kwargs["stream_listeners"] = stream_listeners
-172
-173    return context_kwargs, predictor_kwargs
-174
-175
-176# import sounddevice as sd
-
- - -
-
- -
- - class - StreamActivity(dspy.streaming.messages.StatusMessageProvider): - - - -
- - 
13class StreamActivity(dspy.streaming.StatusMessageProvider):
-14    def lm_start_status_message(self, instance, inputs):
-15        return "Processing..."
-16
-17    def module_start_status_message(self, instance, inputs):
-18        return "Module started."
-19
-20    def lm_end_status_message(self, outputs):
-21        return "Done."
-22
-23    def tool_start_status_message(self, instance, inputs):
-24        return "Tool started..."
-25
-26    def tool_end_status_message(self, outputs):
-27        return "Tool finished."
-
- - -

Provides customizable status message streaming for DSPy programs.

- -

This class serves as a base for creating custom status message providers. Users can subclass -and override its methods to define specific status messages for different stages of program execution, -each method must return a string.

- -

Example:

- -
-
class MyStatusMessageProvider(StatusMessageProvider):
-    def lm_start_status_message(self, instance, inputs):
-        return f"Calling LM with inputs {inputs}..."
-
-    def module_end_status_message(self, outputs):
-        return f"Module finished with output: {outputs}!"
-
-program = dspy.streamify(dspy.Predict("q->a"), status_message_provider=MyStatusMessageProvider())
-
-
-
- - -
- -
- - def - lm_start_status_message(self, instance, inputs): - - - -
- - 
14    def lm_start_status_message(self, instance, inputs):
-15        return "Processing..."
-
- - -

Status message before a dspy.LM is called.

-
- - -
-
- -
- - def - module_start_status_message(self, instance, inputs): - - - -
- - 
17    def module_start_status_message(self, instance, inputs):
-18        return "Module started."
-
- - -

Status message before a dspy.Module or dspy.Predict is called.

-
- - -
-
- -
- - def - lm_end_status_message(self, outputs): - - - -
- - 
20    def lm_end_status_message(self, outputs):
-21        return "Done."
-
- - -

Status message after a dspy.LM is called.

-
- - -
-
- -
- - def - tool_start_status_message(self, instance, inputs): - - - -
- - 
23    def tool_start_status_message(self, instance, inputs):
-24        return "Tool started..."
-
- - -

Status message before a dspy.Tool is called.

-
- - -
-
- -
- - def - tool_end_status_message(self, outputs): - - - -
- - 
26    def tool_end_status_message(self, outputs):
-27        return "Tool finished."
-
- - -

Status message after a dspy.Tool is called.

-
- - -
-
-
- -
- - class - QATask(dspy.signatures.signature.Signature): - - - -
- - 
30class QATask(dspy.Signature):
-31    """Reply with short responses within 60-90 word/10k character code limits"""
-32
-33    question: str = dspy.InputField(desc="The question to respond to")
-34    answer = dspy.OutputField(desc="Often between 60 and 90 words and limited to 10000 character code blocks")
-
- - -

Reply with short responses within 60-90 word/10k character code limits

-
- - -
-
- question: str = -PydanticUndefined - - -
- - - - -
-
-
- answer: str = -PydanticUndefined - - -
- - - - -
-
-
-
- TARGET_LANGUAGE = -'English' - - -
- - - - -
-
- -
- - class - TranslateTask(dspy.signatures.signature.Signature): - - - -
- - 
40class TranslateTask(dspy.Signature):
-41    f"""Translate from a language to {TARGET_LANGUAGE}"""
-42
-43    message: dspy.Image | dspy.Audio | str = dspy.InputField(desc="The input to translate.")
-44    translation: dspy.Image | dspy.Audio | str = dspy.OutputField(desc="A translation of the input")
-
- - -

Given the fields message, produce the fields translation.

-
- - -
-
- message: dspy.adapters.types.image.Image | dspy.adapters.types.audio.Audio | str = -PydanticUndefined - - -
- - - - -
-
-
- translation: dspy.adapters.types.image.Image | dspy.adapters.types.audio.Audio | str = -PydanticUndefined - - -
- - - - -
-
-
- -
- - class - VisionTask(dspy.signatures.signature.Signature): - - - -
- - 
47class VisionTask(dspy.Signature):
-48    """Describe the image in detail."""
-49
-50    image: dspy.Image = dspy.InputField(desc="An image")
-51    description: str = dspy.OutputField(desc="A detailed description of the image.")
-
- - -

Describe the image in detail.

-
- - -
-
- image: dspy.adapters.types.image.Image = -PydanticUndefined - - -
- - - - -
-
-
- description: str = -PydanticUndefined - - -
- - - - -
-
-
- -
- - class - TranscribeTask(dspy.signatures.signature.Signature): - - - -
- - 
54class TranscribeTask(dspy.Signature):
-55    """Transcribe spoken words into text"""
-56
-57    message: dspy.Audio = dspy.InputField(desc="The speech to transcribe.")
-58    answer: str = dspy.OutputField(desc="A transcript of the recorded speech")
-
- - -

Transcribe spoken words into text

-
- - -
-
- message: dspy.adapters.types.audio.Audio = -PydanticUndefined - - -
- - - - -
-
-
- answer: str = -PydanticUndefined - - -
- - - - -
-
-
- -
- - class - GenerativeImageTask(dspy.signatures.signature.Signature): - - - -
- - 
61class GenerativeImageTask(dspy.Signature):
-62    message: str = dspy.InputField(desc="Description of the image to generate")
-63    image: dspy.Image = dspy.OutputField(desc="An image matching the description")
-
- - -

Given the fields message, produce the fields image.

-
- - -
-
- message: str = -PydanticUndefined - - -
- - - - -
-
-
- image: dspy.adapters.types.image.Image = -PydanticUndefined - - -
- - - - -
-
-
- -
- - class - GenerativeAudioTask(dspy.signatures.signature.Signature): - - - -
- - 
66class GenerativeAudioTask(dspy.Signature):
-67    message: str = dspy.InputField(desc="Description of the audio to generate")
-68    audio: dspy.Audio = dspy.OutputField(desc="An audio file matching the description")
-
- - -

Given the fields message, produce the fields audio.

-
- - -
-
- message: str = -PydanticUndefined - - -
- - - - -
-
-
- audio: dspy.adapters.types.audio.Audio = -PydanticUndefined - - -
- - - - -
-
-
- -
- - class - QuestionAnswer(dspy.primitives.module.Module): - - - -
- - 
75class QuestionAnswer(dspy.Module):
-76    def __init__(self):
-77        super().__init__()
-78        self.predict = dspy.Predict(QATask)
-79
-80    def forward(self, question, **kwargs):
-81        self.predict(question=question, **kwargs)
-82        return self.predict(question=question, **kwargs)
-
- - - - -
-
- predict - - -
- - - - -
-
- -
- - def - forward(self, question, **kwargs): - - - -
- - 
80    def forward(self, question, **kwargs):
-81        self.predict(question=question, **kwargs)
-82        return self.predict(question=question, **kwargs)
-
- - - - -
-
-
- -
- - class - Predictor(dspy.primitives.module.Module): - - - -
- - 
 85class Predictor(dspy.Module):
- 86    def __init__(self):
- 87        super().__init__
- 88        self.program = dspy.Predict(signature=QATask)
- 89
- 90    def __call__(self, question: str):
- 91        from litellm.exceptions import APIConnectionError
- 92        from litellm.llms.ollama.common_utils import OllamaError
- 93        from httpx import ConnectError
- 94        from dspy.utils.exceptions import AdapterParseError
- 95        from aiohttp.client_exceptions import ClientConnectorError
- 96
- 97        try:
- 98            return self.program(question=question)
- 99        except (ClientConnectorError, ConnectError, AdapterParseError, APIConnectionError, OllamaError, OSError):
-100            pass
-101
-102    # from nnll.tensor_pipe import segments
-103    # from nnll.configure.init_gpu import seed_planter
-104    # mir_db = MIR_DB.database
-105    # mir_arch = registry_entries.mir
-106    # arch_data = mir_db[mir_arch[0][mir_arch[1]]]
-107    # pkg_data = mir_db[mir_arch[0]]["pkg"][0]
-108    # init_modules = find_package
-109    # self.pipe, model, self.import_pkg, self.pipe_kwargs = self.factory.create_pipeline(arch_data=registry_entry.mir, init_modules=init_modules)
-110
-111    #     if lora is not None:
-112    #         lora_arch = self.mir_db.database[series].get(lora[1])
-113    #         lora_repo = next(iter(lora_arch["repo"]))  # <- user location here OR this
-114    #         scheduler = self.mir_db.database[series]["[init]"].get("scheduler")
-115    #         kwargs = {}
-116    #         if scheduler:
-117    #             sched = self.mir_db.database[scheduler]["[init]"]
-118    #             scheduler_kwargs = self.mir_db.database[series]["[init]"].get("scheduler_kwargs")
-119    #             kwargs = {sched: sched, scheduler_kwargs: scheduler_kwargs}
-120    #         init_kwargs = lora_arch.get("init_kwargs")
-121    #         if lora:
-122    #             self.pipe = self.factory.add_lora(self.pipe, lora_repo=lora_repo, init_kwargs=init_kwargs, **kwargs)
-123
-124    #     noise_seed = seed_planter(device=self.device)
-125    #     user_set = {
-126    #         "output_type": "pil",
-127    #     }
-128    #     self.pipe_kwargs.update(user_set)
-129
-130    #     if ChipType.MPS[0]:
-131    #         self.pipe.enable_attention_slicing()
-132    #     nfo(f"Pre-generator Model {model}  Pipe {self.pipe} Arguments {self.pipe_kwargs}")  # Lora {lora_opt}
-133    #     if "diffusers" in self.import_pkg:
-134    #         self.pipe.to(self.device)
-135    #         self.pipe = segments.add_generator(pipe=self.pipe, noise_seed=noise_seed)
-136    #     else:
-137    #         self.pipe = self.pipe[0]
-138    #         self.pipe.to(self.device)
-139    #     if "audiogen" in self.import_pkg:
-140    #         self.pipe_kwargs.update({"sample_rate": self.pipe.config.sampling_rate})
-141    #     elif "parler_tts" in self.import_pkg:
-142    #         self.pipe_kwargs.update({"sampling_rate": self.pipe.config.sampling_rate})
-
- - - - -
-
- program - - -
- - - - -
-
-
- -
- - async def - ready_predictor( registry_entry: zodiac.providers.registry_entry.RegistryEntry, async_stream: bool = True, dspy_stream: bool = True, max_workers: int = 8, cache: bool = True): - - - -
- - 
145async def ready_predictor(
-146    registry_entry: RegistryEntry,
-147    async_stream: bool = True,
-148    dspy_stream: bool = True,
-149    max_workers: int = 8,
-150    cache: bool = True,
-151):
-152    from zodiac.providers.constants import ChipType
-153
-154    if registry_entry.cuetype == CueType.HUB:
-155        device = getattr(ChipType, next(iter(ChipType._show_ready())), "CPU")
-156
-157    else:
-158        lm_kwargs = {"async_max_workers": max_workers, "cache": cache}
-159        lm_model = dspy.LM(
-160            registry_entry.model,
-161            **registry_entry.api_kwargs,
-162            **lm_kwargs,
-163        )
-164    stream_listeners = [dspy.streaming.StreamListener(signature_field_name="answer")]
-165    context_kwargs = {"lm": lm_model}  # , "adapter": dspy.ChatAdapter()}
-166    predictor_kwargs = {
-167        "status_message_provider": StreamActivity(),
-168        "async_streaming": async_stream,
-169        "include_final_prediction_in_output_stream": False,
-170    }
-171    if dspy_stream:
-172        predictor_kwargs["stream_listeners"] = stream_listeners
-173
-174    return context_kwargs, predictor_kwargs
-
- - - - -
-
- - \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 8812ceb..877edce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,7 +44,6 @@ classifiers = [ ] dependencies = [ "dspy>=2.6.27", - "litellm>=1.72.0", "tokenizers==0.21.2", #force for non linux "networkx>=3.5",