MomCare Backend

This project serves as the backend for the MomCare application, providing machine learning-powered predictions and recommendations related to maternal health. It is built with FastAPI.

Project Structure

ML/
|-- app/
|   `-- main.py             # FastAPI application logic and API endpoints
|-- random_forest_model.pkl # Trained model for risk prediction
|-- xgb_model.pkl           # Trained model for anxiety prediction
|-- unique_indian_foods_dataset.csv # Dataset for dietary recommendations
|-- requirements.txt        # Python dependencies
`-- README.md               # This file

Setup and Installation

1. Clone the repository:

   git clone https://github.com/jayan110105/MomCareBackend
   cd ML

2. Create a virtual environment: It is highly recommended to use a virtual environment to manage project dependencies.

   # For Windows
   python -m venv venv
   venv\Scripts\activate
   
   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies: Install all the required Python packages using pip.

   pip install -r requirements.txt

Running the Application

To run the FastAPI application locally, use uvicorn. It will start a local server, and the API will be accessible.

uvicorn app.main:app --reload

The application will be available at http://127.0.0.1:8000.

API Endpoints

The following endpoints are available:

1. Risk Prediction

• Endpoint: /predict_risk
• Method: POST
• Description: Predicts a general risk level based on input features.
• Request Body:

  {
    "feature1": 0.0,
    "feature2": 0.0,
    "feature3": 0.0,
    "feature4": 0.0,
    "feature5": 0.0
  }

• Response:

  {
    "prediction": "some_risk_level"
  }

2. Anxiety Prediction

• Endpoint: /predict_anxiety
• Method: POST
• Description: Predicts the level of anxiety based on a series of questions.
• Request Body:

  {
    "Age": "25-30",
    "FeelingSadOrTearful": "Yes",
    "IrritableTowardsBabyAndPartner": "Yes",
    "TroubleSleepingAtNight": "Yes",
    "ProblemsConcentratingOrMakingDecision": "Yes",
    "OvereatingOrLossOfAppetite": "Yes",
    "FeelingOfGuilt": "Yes",
    "ProblemsOfBondingWithBaby": "Yes",
    "SuicideAttempt": "No"
  }

• Response:

  {
    "prediction": "Feeling anxious"
  }

3. Dietary Recommendations

• Endpoint: /recommendations/
• Method: POST
• Description: Provides food recommendations based on nutritional requirements.
• Request Body:

  {
    "calories": 1800,
    "fats": 80,
    "proteins": 150,
    "iron": 20,
    "carbohydrates": 300,
    "fibre": 30,
    "veg_only": true
  }

• Response: A JSON object containing a list of recommended food items.

  {
    "recommendations": [
      {
        "Food_items": "...",
        "Calories": "...",
        "Fats": "...",
        "Proteins": "...",
        "Iron": "...",
        "Carbohydrates": "...",
        "Fibre": "...",
        "VegNovVeg": "..."
      }
    ]
  }