🌸 Iris Predictor

A Django web application that predicts the species of an Iris flower based on its measurements, using a trained Random Forest model.

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📋 Description

This project exposes a web form and a REST API endpoint to classify Iris flowers into one of three species:

• Iris setosa
• Iris versicolor
• Iris virginica

It uses a RandomForestClassifier trained on the classic Iris dataset from scikit-learn.

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💻 Local Installation

1. Clone the repository

git clone https://github.com/Kenkyoo/django-iris-predictor.git
cd mlapp

2. Create and activate virtual environment

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Train and save the model

python train.py

5. Run the development server

python manage.py runserver

Visit http://localhost:8000 in your browser.

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🐳 Docker

Build the image

docker build -t mlapp .

Run the container

docker run -p 8000:8000 mlapp

Visit http://localhost:8000 in your browser.

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🚀 Deploy on Render

1. Push your code to GitHub (make sure predictor/model/iris_rf.joblib is included).
2. Go to render.com and create a new Web Service.
3. Connect your GitHub repository.
4. Render will detect the Dockerfile automatically.
5. Click Deploy.

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🔌 API Usage

Endpoint: POST /api/predict/

Content-Type: application/json

Request

{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2
}

Response

{
  "class_index": 0,
  "class_name": "setosa",
  "probabilities": {
    "setosa": 0.980,
    "versicolor": 0.015,
    "virginica": 0.005
  }
}

Example with curl

curl -X POST http://localhost:8000/api/predict/ \
  -H "Content-Type: application/json" \
  -d '{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2}'

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🛠 Tech Stack

• Python 3.12
• Django
• scikit-learn
• Gunicorn
• Docker
• Bulma CSS