Labelly

An AI-powered mobile application that instantly analyzes food ingredient labels to provide health insights, safety scores, and healthier product alternatives.

🚀 Features

• Instant Ingredient Analysis: Scan food labels with your camera for immediate health assessments
• AI-Powered Safety Scoring: Get comprehensive safety scores categorized as Safe, Low Risk, Not Great, and Dangerous
• Smart Alternative Recommendations: Discover healthier product alternatives with purchase links
• Allergen Detection: Automatic identification of allergens and harmful additives
• Scan History: Track previously analyzed products with user authentication
• Interactive UI: Clean, intuitive interface with smooth animations and collapsible categories
• Real-time Analysis: Powered by Perplexity Sonar API for up-to-date ingredient research

🛠️ Tech Stack

Frontend (Mobile App)

• React Native with Expo - Cross-platform mobile development
• TypeScript - Type-safe JavaScript
• Expo Router - File-based navigation
• Tamagui - UI components and theming
• React Native Reanimated - Advanced animations
• Expo Camera - Camera functionality for label scanning
• Firebase SDK - Authentication and data management

Backend (API Server)

• Python Flask - REST API framework
• Firebase Admin SDK - Server-side authentication
• Firebase Firestore - NoSQL database
• Perplexity Sonar API - AI ingredient analysis
• Gunicorn - Production WSGI server

External Services

• Firebase Authentication - User management
• Firebase Firestore - Data storage
• Perplexity API - AI-powered analysis engine

📱 Installation

Prerequisites

• Node.js (v16 or higher)
• Python 3.8+
• Firebase project setup
• Perplexity API key
• Expo CLI

Frontend Setup

# Clone the repository
git clone https://github.com/yourusername/labelly-healthanalyzer.git
cd labelly-healthanalyzer

# Install dependencies
npm install

# Start the Expo development server
npx expo start

Backend Setup

# Navigate to backend directory
cd backend

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

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys and Firebase config

# Run the Flask server
python app.py

Environment Variables

Create a .env file in the backend directory:

PERPLEXITY_API_KEY=your_perplexity_api_key
FIREBASE_PROJECT_ID=your_firebase_project_id
FIREBASE_PRIVATE_KEY=your_firebase_private_key
FIREBASE_CLIENT_EMAIL=your_firebase_client_email

🎯 Usage

1. Sign Up/Login: Create an account or sign in using email/password or Google authentication
2. Scan Ingredient Label: Use the camera to capture a food product's ingredient list
3. Review Analysis: Get instant safety scores and detailed ingredient breakdowns
4. Explore Alternatives: Discover healthier product recommendations with purchase links
5. Track History: Access your previous scans in the history section

🔌 API Endpoints

Analyze Ingredients

POST /api/analyze
Content-Type: multipart/form-data
Authorization: Bearer 

Body: image file (JPEG)

Get Alternative Products

POST /api/alternatives
Content-Type: application/json
Authorization: Bearer 

Body: {
  "original_analysis": 
}

📊 Response Format

Analysis Response

{
  "product_name": "Product Name",
  "safety_score": "Safe: 95%",
  "ingredients_summary": "Overall ingredient assessment",
  "ingredient_categories": {
    "safe": {
      "ingredients": ["ingredient1", "ingredient2"],
      "details": [
        {
          "ingredient": "ingredient1",
          "reason": "Natural and safe",
          "amount": "2g"
        }
      ]
    },
    "low_risk": { /* similar structure */ },
    "not_great": { /* similar structure */ },
    "dangerous": { /* similar structure */ }
  },
  "allergen_additive_warnings": ["Contains nuts", "High sodium"],
  "product_summary": "Detailed product health summary"
}

🏗️ Project Structure

labelly-healthanalyzer/
├── frontend/
│   ├── app/                 # Expo Router pages
│   ├── components/          # Reusable components
│   ├── services/           # API services
│   └── types/              # TypeScript definitions
├── backend/
│   ├── app.py              # Flask application
│   ├── requirements.txt    # Python dependencies
│   └── .env.example        # Environment variables template
└── README.md

🚦 Development Status

This project was developed as part of a hackathon submission. Key features are functional, including:

• ✅ Camera-based ingredient scanning
• ✅ AI-powered health analysis
• ✅ User authentication
• ✅ Alternative product recommendations
• ✅ Scan history tracking
• ✅ Cross-platform mobile app

🔗 Demo

Working demo : https://labelly--40iw2plma1.expo.app/onboarding accounts to use : user1@gmail.com(pass: 123456), user2@gmail.com(pass: 123456), deepuser1@gmail.com(pass : 12345678)

Note : Some features may not work properly in web build because the build is meant for Android/IOS

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Made with ❤️ for healthier eating choices