Breast Cancer Classification API

This project demonstrates how to deploy a pre-trained machine learning model as a web service using FastAPI. The machine learning model used in this project is a RandomForestClassifier, trained on a Breast Cancer classification dataset. The trained model is served via an API endpoint created with FastAPI, allowing users to make predictions by sending HTTP requests.

Table of Contents

• Installation
• Usage
• Endpoints
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/emiliodallatorre/unive-breastcancer-classification.git
   cd your-repo-name

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows use `env\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Running the FastAPI Application

1. Start the FastAPI server:

   uvicorn server:app --host 0.0.0.0 # Or use ./start.sh

2. Access the API: Open your browser and navigate to http://127.0.0.1:8000/docs to view the automatically generated API documentation and interact with the API.

Endpoints

/predict [POST]

• Description: This endpoint accepts a JSON payload containing the features required by the model and returns the prediction.

• Request Body:

  {
    "radius": 0,
    "perimeter": 0,
    "area": 0
  }

• Response:

  {
    "diagnosis": "B",
    "probabilities": {
      "B": 1,
      "M": 0
    }
  }

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any bug fixes, improvements, or new features.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.