This project was developed as a group Industrial Training project. Team members: Sneha Sidhu, Shoaib Ahmad (and others). This repository is my individual submission copy. Original repo: https://github.com/snehasidhu-06/smart-shopper-agent
A GenAI-powered shopping assistant that helps users find products through natural language conversations. Instead of searching with keywords, users can describe what they want, and the assistant recommends suitable products using AI.
- Chat-based shopping assistant
- Understands natural language queries
- Detects shopping requests and casual conversations
- Recommends relevant products using AI
- Displays product images, prices, ratings, and Buy Now links
- Separate frontend and backend architecture
- Frontend: Streamlit
- Backend: FastAPI
- LLM: Groq (Llama 3.1 8B Instant)
- Framework: LangChain
- Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
- Vector Database: FAISS
- Database: SQLite
- Data Processing: Pandas
- The user enters a message in the Streamlit interface.
- The message is sent to the FastAPI backend.
- The AI detects whether the user wants to shop or is simply chatting.
- For shopping requests, similar products are retrieved using FAISS.
- The LLM generates personalized recommendations.
- Product details are displayed to the user.
The project uses a cleaned Amazon product dataset with approximately 700 products, including:
- Product title
- Brand
- Category
- Price
- Rating
- Product image
- Product link
- Clone the repository.
- Create and activate a virtual environment.
- Install dependencies: