FastAPI ML Application

This project is a FastAPI application that serves a machine learning model for text classification. The model is trained to classify descriptions based on the responsible entity ID.

Project Structure

fastapi-ml-app
├── app
│   ├── main.py              # Entry point of the FastAPI application
│   ├── model
│   │   ├── model.pkl        # Trained machine learning model
│   │   └── vectorizer.pkl   # Vectorizer for transforming input text
│   └── api
│       └── endpoints.py     # API endpoints for handling requests
├── requirements.txt          # Project dependencies
└── README.md                 # Project documentation

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd fastapi-ml-app
   

2. Create a virtual environment (optional but recommended):

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

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Start the FastAPI application:

   uvicorn app.main:app --reload
   

2. Send a POST request to the /predict endpoint with the following JSON body:

   {
       "state": "some_state",
       "description": "some_description"
   }

3. The response will contain the predicted class based on the input description.

Example Request

curl -X POST "http://127.0.0.1:8000/predict" -H "Content-Type: application/json" -d '{"state": "some_state", "description": "some_description"}'

License

This project is licensed under the MIT License.