README.md

Cloudburst Prediction Model

Overview

This project is a cloudburst prediction model. The model predicts the likelihood of a cloudburst based on various meteorological parameters.

Features

• Support Vector Classifier: Model trained for predictions.
• FastAPI: Used for building the API.
• Pydantic: Used for data validation.
• CORS Middleware: Configured to allow cross-origin requests.
• Model Prediction Endpoint: Exposes an endpoint to make predictions using the trained model.

Requirements

• Python 3.8+
• requirements.txt lists the necessary packages:

  fastapi
  uvicorn
  pydantic
  scikit-learn==1.2.2
  numpy==1.23.5
  

Installation

1. Clone the repository:

   git clone https://github.com/manthankhawse/Cloudburst-prediction-model-trained.git
   cd cloudburst-prediction

2. Create a virtual environment:

   python -m venv .venv

3. Activate the virtual environment:

   • On macOS/Linux:

     source .venv/bin/activate

   • On Windows:

     .venv\Scripts\activate

4. Install the dependencies:

   pip install -r requirements.txt

Usage

1. Start the FastAPI server:

   uvicorn main:app --reload

2. Access the API documentation: Open your browser and navigate to http://127.0.0.1:8000/docs to see the interactive API documentation.

3. Make a prediction: Send a POST request to the /predict endpoint with the following JSON payload:

   {
     "temperature": 25.0,
     "humidity": 80,
     "dew_point": 22.0,
     "sea_level_pressure": 1012.0,
     "cloud": 75,
     "wind_speed": 5.5
   }

   The response will be a JSON object with the prediction result.

File Structure

.
├── main.py
├── requirements.txt
├── cloudburst_prediction_trained.sav
└── README.md

• main.py: The main application file containing the FastAPI app.
• requirements.txt: Lists the dependencies required for the project.
• cloudburst_prediction_trained.sav: The serialized trained model file.
• README.md: This README file.

Troubleshooting

Binary Compatibility Issue

If you encounter a binary compatibility issue similar to ValueError: numpy.dtype size changed, ensure you have the correct versions of numpy and scikit-learn installed as specified in the requirements.txt. Recreate the virtual environment if necessary.

Steps to Recreate the Virtual Environment

1. Remove the existing virtual environment:

   rm -rf .venv

2. Create a new virtual environment:

   python -m venv .venv

3. Activate the virtual environment:

   • On macOS/Linux:

     source .venv/bin/activate

   • On Windows:

     .venv\Scripts\activate

4. Install the dependencies:

   pip install -r requirements.txt

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an issue to discuss what you would like to change.