🍎 Food Quality Classifier

An AI-powered web application for classifying food quality using deep learning models. Built with TensorFlow, Flask, and modern web technologies.

✨ Features

• Multi-Food Classification: Support for Tomato, Apple, Mango, and Potato
• AI-Powered Analysis: Deep learning models for quality assessment
• Modern UI: Glassmorphism design with neumorphic elements
• Real-time Results: Instant quality classification with confidence scores
• Responsive Design: Works seamlessly on desktop and mobile devices
• Drag & Drop: Easy image upload with drag and drop support

🚀 Live Demo

Visit the application: Food Quality Classifier

📹 Demo Video

🛠️ Technology Stack

• Backend: Python, Flask, TensorFlow 2.13.0
• Frontend: HTML5, CSS3, JavaScript, Bootstrap 5
• AI Models: EfficientNet-Lite4 architecture
• Deployment: Ready for Heroku, Render, or any cloud platform

📋 Prerequisites

• Python 3.8+
• TensorFlow 2.13.0
• Flask 2.3.3
• Modern web browser

🚀 Installation

1. Clone the repository

   git clone https://github.com/SourabhR23/food-quality-classifier.git
   cd food-quality-classifier

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   python app.py

5. Open in browser Navigate to http://localhost:5000

🎯 Usage

1. Select Food Type: Choose from Tomato, Apple, Mango, or Potato
2. Upload Image: Drag & drop or click to browse for food images
3. Get Results: View quality classification (Poor, Average, Good) with confidence scores
4. Detailed Analysis: See probability breakdown for each quality level

🏗️ Project Structure

food-quality-classifier/
├── app.py                 # Main Flask application
├── food_classifier.py     # AI model loading and classification logic
├── requirements.txt       # Python dependencies
├── templates/
│   └── index.html        # Main web interface
├── static/               # Static assets
├── models/               # Trained AI models
│   ├── tomato/          # Tomato quality model
│   ├── apple/           # Apple quality model
│   ├── mango/           # Mango quality model
│   └── potato/          # Potato quality model
└── README.md            # This file

🔧 Configuration

Environment Variables

• PORT: Server port (default: 5000)
• DEBUG: Debug mode (default: False)

Model Configuration

• Input Size: 300x300 pixels
• Format: RGB images
• Quality Classes: Poor, Average, Good
• Model Architecture: EfficientNet-Lite4

📊 API Endpoints

• GET / - Main web interface
• POST /classify - Image classification endpoint
• GET /models - Model information
• GET /health - Health check
• GET /performance - Performance metrics

🎨 UI Features

• Glassmorphism Cards: Modern transparent glass effects
• Neumorphic Buttons: 3D button designs with shadows
• Floating Action Buttons: Quick access to help and settings
• Skeleton Loading: Animated loading screens
• Food-themed Animations: Custom loading animations
• Responsive Grid Layout: Single-page design without scrolling

🔍 Model Performance

• Accuracy: High accuracy across all food types
• Speed: Fast inference with TensorFlow optimization
• Memory: Efficient memory usage with lazy loading
• Compatibility: Works with TensorFlow 2.13.0+

📝 License

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

🙏 Acknowledgments

• TensorFlow team for the deep learning framework
• EfficientNet-Lite4 model architecture
• Flask community for the web framework
• Bootstrap team for the UI components

📞 Support

If you have any questions or need help:

• Open an issue on GitHub
• Contact: [Your Email]
• Project: GitHub Repository

🔄 Changelog

Version 2.0.0

• ✨ New modern UI with glassmorphism and neumorphic design
• 🎨 Updated color scheme with warm food-themed palette
• 📱 Single-page layout for better user experience
• 🔧 Fixed TensorFlow compatibility issues
• 🚀 Enhanced loading animations and state management

Version 1.0.0

• 🎯 Initial release with basic functionality
• 🤖 AI-powered food quality classification
• 🌐 Web interface for easy interaction

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