🥗 Calorie Counter App

A smart Flask application that analyzes nutritional content, provides health scores, and suggests recipe videos for food items, with a special focus on Indian cuisine.

✨ Key Features

• 🔍 Instant nutrition analysis for any food item
• 💯 Smart health scoring system (1-10 scale)
• 🎯 Recipe video recommendations
• 🇮🇳 Indian cuisine specialization
• 📱 Mobile-friendly interface
• 🤖 AI-powered analysis using GPT-4o

🛠️ Tech Stack

• Backend: Flask (Python)
• Frontend: HTML, CSS, JavaScript
• AI/ML: OpenAI GPT-4o
• External APIs:
  • OpenAI API
  • YouTube Data API v3

🗂️ Project Structure

app/
├── constants/      # Constants
├── models/         # Data models
├── routes/         # API endpoints
├── services/       # Business logic
├── static/         # Frontend assets
└── templates/      # HTML templates

🚀 Quick Start

1. Clone & Install

git clone https://github.com/yourusername/calorie-counter.git
cd calorie-counter
pip install -r requirements.txt

3. Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key
YOUTUBE_API_KEY=your_youtube_api_key

4. Run the application:

python3 wsgi.py

🔌 API Reference

Food Analysis

POST /calculate_nutrition
Content-Type: application/json

{
  "food_item": "butter chicken",
  "quantity": 100,
  "unit": "g"
}

Auto-Suggestions

GET /get_food_suggestions

🎯 Health Score System

Our health score (1-10) considers:

• Protein content (+)
• Fiber content (+)
• Vitamins & minerals (+)
• Sugar content (-)
• Sodium levels (-)

Score interpretation:

• 8-10: Excellent 🟢
• 6-7.9: Good 🟡
• 4-5.9: Fair 🟠
• 1-3.9: Limited 🔴

🤝 Testing

Running Tests

# Run all tests
pytest

# Run specific test file
pytest pytest tests/test_nutrition_routes.py -v

# Run with verbose output
pytest -v

# Run specific test case
pytest tests/test_nutrition_routes.py::TestNutritionRoutes::test_analyze_real_chicken_breast -v

Test Structure

tests/
├── __init__.py              # Test configurations and shared data
├── conftest.py             # Test fixtures
├── test_nutrition_routes.py # API endpoint tests
└── test_data/              # Test images and mock data
    └── images/
        ├── valid_food.jpg
        ├── chicken_breast.jpg
        └── invalid_format.txt

Key Test Cases

• Food suggestion validation
• Nutrition calculation with various inputs
• Image analysis with real food images
• Error handling and validation
• API response structure verification

🤝 Contributing

1. Fork
2. Create feature branch (git checkout -b feature/NewFeature)
3. Commit (git commit -m 'Add NewFeature')
4. Push (git push origin feature/NewFeature)
5. Open PR

🙏 Acknowledgments

• OpenAI team for GPT-4
• YouTube API team
• Flask community