Water Quality Monitoring API

This project is a FastAPI application for monitoring water quality using a machine learning model. The application provides an API that allows users to submit water quality data and receive predictions regarding the quality of the water.

Project Structure

water-quality-api
├── app
│   ├── __init__.py          # Initializes the app package
│   ├── main.py              # Entry point of the API
│   ├── schemas.py           # Data models for request and response validation
│   └── predictor.py         # Logic for loading the model and making predictions
├── models
│   └── water_quality_model.pkl # Saved machine learning model
├── .gitignore                # Files and directories to ignore by Git
├── Dockerfile                # Instructions for building the Docker image
├── Procfile                  # Command to run the application on Render
├── README.md                 # Documentation for the project
└── requirements.txt          # Python dependencies for the project

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd water-quality-api

2. Create a virtual environment:

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

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   uvicorn app.main:app --reload

Usage

Once the application is running, you can access the API at http://localhost:8000. The API provides endpoints for submitting water quality data and receiving predictions.

Example Request

To get a prediction, send a POST request to the /predict endpoint with the required data in JSON format.

{
  "parameter1": value1,
  "parameter2": value2,
  ...
}

Example Response

The API will return a JSON response with the prediction result.

{
  "prediction": "safe",
  "confidence": 0.95
}

License

This project is licensed under the MIT License. See the LICENSE file for more details.