Empowering consumers to decode what they eat.
A Next-Gen Food Transparency Backend powered by IBM Watson AI & FastAPI.

Label Padhega India is a mission-driven technical solution designed to bring transparency to the packaged food industry. In a market flooded with misleading "Healthy" labels and hidden harmful ingredients, this application acts as a vigilant personal nutritionist.

By leveraging Generative AI (IBM Watsonx.ai) and Optical Character Recognition (OCR), the backend analyzes complex ingredient lists to flag health risks like hidden sugars, harmful additives (E-numbers), and the infamous "Maida Trap" (refined flour disguised as wheat).

• 🧠 AI-Powered Analysis: Utilizes IBM Watson to semantically understand ingredient quality and health impact, not just keyword matching.
• 📸 Instant Label OCR: Users can upload a photo of a food package, and our system extracts the text using Watson Discovery to perform an instant health audit.
• 🔍 Open Food Facts Integration: Seamlessly connects with the world's largest open database of food products for barcode scanning and search.
• 🛡️ Consumer Protection Guardrails: Automatically detects and flags:
  • Maida Traps (Refined Wheat Flour disguised in "Atta" biscuits).
  • Hidden Sugars (Maltodextrin, High Fructose Corn Syrup).
  • Fake Marketing Claims ("No Sugar Added" validity checks).

Built with a focus on Performance, Scalability, and Developer Experience.

• AI Engine: IBM Watsonx.ai - For LLM-based ingredient analysis.
• Data Processing: IBM Watson Discovery - For robust OCR and document understanding.
• External API: Open Food Facts - Real-time product metadata.
• Architecture: Modular Service-Oriented Architecture (Services, Routes, Models separation).

Follow these steps to set up the project locally.

• Python 3.9+
• Pip
• IBM Cloud Account (for Watson credentials)

1. Clone the Repository

   git clone https://github.com/Aniket-16-S/orbital-aldrin.git
   cd orbital-aldrin

2. Create Virtual Environment (Optional but recommended)

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

3. Install Dependencies

   pip install -r requirements.txt

4. Configure Environment Variables Create a .env file in the root directory and add your IBM credentials:

   IBM_WATSON_API_KEY=your_api_key
   IBM_WATSON_URL=your_service_url
   IBM_PROJECT_ID=your_project_id

5. Run the Server

   uvicorn app.main:app --reload

   or Just run python -m app.main.py

   The server will start at http://127.0.0.1:8000