Overview

AI Nutrition Tracker is a modern web application that helps users understand the nutritional content of their food through two powerful interfaces: image recognition and natural language AI chat. Simply upload a photo of your meal or ask nutrition-related questions to get instant insights.

Key Features

🖼️ Food Detection & Analysis

• Upload food images via drag-and-drop or file selection
• Automatic food identification using Facebook's DETR-ResNet50 object detection model
• Real-time nutritional information retrieval from USDA FoodData Central API

💬 AI Nutrition Assistant

• Chat interface with an AI nutrition expert powered by Qwen3-0.6B language model
• Get answers about calories, nutrients, dietary recommendations, and more
• Intelligent response formatting with highlighted information

📱 User Experience

• Seamless interface switching between chat and image upload modes
• Responsive design that works across devices
• Clean, intuitive UI with smooth animations and visual feedback

Tech Stack

• Backend: Django (Python web framework)
• Machine Learning:
  • Object Detection: Facebook's DETR (DEtection TRansformer) with ResNet-50 backbone
  • NLP: Qwen3-0.6B language model for nutrition-related chat responses
• APIs: USDA FoodData Central API for comprehensive nutritional data
• Frontend: Vanilla JavaScript, HTML5, CSS3 with custom animations
• Deployment: Compatible with standard Python web hosting environments

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ai-nutrition-tracker.git
   cd ai-nutrition-tracker

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # For Unix/MacOS
   # OR
   venv\Scripts\activate  # For Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file with your USDA API key:

   USDA_API_KEY=your_api_key_here
   

   Get your free API key from: USDA FoodData Central

5. Run the development server:

   python3 manage.py runserver

6. Visit http://127.0.0.1:8000/ in your browser to use the application.

Usage

Food Image Analysis

1. Click the "Image Upload" tab
2. Drag and drop a food image or click to select a file
3. Click "Analyze Food"
4. View the detected food items and their detailed nutritional information

Nutrition Chat Assistant

1. Click the "Chat Mode" tab (active by default)
2. Type your nutrition-related question in the input field
3. Get instant answers with key information highlighted
4. Continue the conversation with follow-up questions

Project Structure

ai-nutrition-tracker/
├── food_detector/        # Main Django app
│   ├── templates/        # HTML templates
│   ├── views.py          # View functions handling requests
│   └── urls.py           # URL routing
├── static/               # Static assets
│   ├── css/              # Stylesheets
│   ├── js/               # JavaScript files
│   └── images/           # Images
├── media/                # Uploaded images
├── main.py               # Food detection logic
├── manage.py             # Django management script
└── README.md             # Project documentation

Future Enhancements

• User accounts to save food history and track nutritional intake over time
• Meal planning recommendations based on nutritional goals
• Mobile app version with camera integration
• Expanded food database with regional and cultural specialties

License

MIT License

Credits

• Machine learning models from Hugging Face
• Nutritional data from USDA FoodData Central
• Icons by Font Awesome

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Made with ❤️ for better nutrition