Architechure

Installation

1. Clone the Repository:

2. Create a Virtual Environment and Activate It:

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

3. Install Dependencies:

   pip install -r requirements.txt

   Required Dependencies:

   • fastapi
   • uvicorn
   • pymongo
   • pandas
   • numpy
   • fuzzywuzzy
   • jellyfish
   • scipy

4. Set Up MongoDB:

   Ensure you have a running MongoDB instance. The default connection URI is mongodb://localhost:27017/. Adjust the URI in the code if needed.

Data Ingestion

This script uses the FoodDataProcessor class to load data from the CSV files and store them into respective collections (nutrition, recipes, carbon_footprint, and interactions).

API Endpoints

The API is built using FastAPI. Key endpoints include:

• Health Check:

  GET /health
  

  Returns a simple status check.

• Score Calculation:

  POST /score
  

  Request Body:

  {
      "dish_name": "Paneer Butter Masala",
      "user_context": {
          "medications": ["Warfarin"],
          "conditions": ["Hypertension"]
      }
  }

  Response:

  {
      "dish_name": "Paneer Butter Masala",
      "unified_score": 85.0,
      "score_components": {
          "health": 90.0,
          "carbon": 65.0,
          "interactions": 70.0
      },
      "confidence_interval": 87.5,
      "explanation": {
          "health_breakdown": "ICMR-aligned nutrient balance",
          "carbon_factors": "MP-adjusted CO2e values",
          "interaction_risks": "Drug-food interaction hierarchy"
      },
      "alternatives": [{"name": "Suggested Alternative", "score": 85}]
  }

Usage

1. Start the API Server:

   uvicorn api:app --host 0.0.0.0 --port 8000

2. Access the Documentation:

   Open your browser and navigate to http://localhost:8000/docs to view the interactive API documentation provided by Swagger UI.

3. Testing:

   Use any API client (like Postman) or the built-in Swagger UI to test the endpoints.

Future Enhancements

• ML Ensemble Integration: Enhance scoring by training ensemble models (Random Forest and XGBoost) on aggregated ingredient features.
• Improved Confidence Metrics: Develop more robust measures for data completeness and reliability.
• Interactive Dashboard: Build a frontend UI for visualizing scores and recommendations.
• Expanded Dataset Support: Integrate additional regional datasets for broader coverage.

License

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