README.md

metadata

title: FoodClassifier-ViT
emoji: 🍕
colorFrom: indigo
colorTo: pink
sdk: gradio
app_file: app.py
pinned: false

FoodViT - Food Classification Application

A production-ready food classification application using Vision Transformer (ViT) that can classify images into three categories: pizza, steak, and sushi.

🍕 Features

• Web Interface: Beautiful Gradio web interface for easy image upload and classification
• Command Line Tool: Batch prediction capabilities for processing multiple images
• High Accuracy: Trained Vision Transformer model with excellent performance
• Production Ready: Modular, well-structured codebase with proper error handling
• Dynamic Example Images: Example images are randomly selected from assets/samples/ at each app launch
• Easy Deployment: Simple setup and configuration
• Model weights hosted on Hugging Face Hub: The model file is not included in this repository; it is automatically downloaded from the Hugging Face Model Hub at runtime.

📁 Project Structure

FoodViT/
├── app.py                 # Main application entry point
├── predict.py            # Command-line prediction script
├── config.py             # Configuration settings
├── requirements.txt      # Python dependencies
├── README.md            # This file
├── INSTALLATION.md      # Installation and troubleshooting guide
├── utils/
│   ├── model_loader.py  # Model loading utilities
│   ├── image_processor.py # Image preprocessing
│   └── predictor.py     # Prediction logic
├── interface/
│   └── gradio_app.py    # Gradio web interface
└── assets/
    └── samples/         # Example images for Gradio interface

🚀 Quick Start

1. Installation

# Clone the repository
git clone <repository-url>
cd FoodViT

# Install dependencies
pip install -r requirements.txt

2. Run the Web Interface

# Start the Gradio web interface
python app.py

The interface will be available at http://localhost:7860

3. Command Line Usage

# Predict a single image
python predict.py path/to/image.jpg

# Predict all images in a directory
python predict.py path/to/image/directory

# Get detailed prediction information
python predict.py path/to/image.jpg --detailed

# Save results to JSON file
python predict.py path/to/image/directory --output results.json

🎯 Usage Examples

Web Interface

1. Open your browser and go to http://localhost:7860
2. Upload an image of pizza, steak, or sushi
3. View the prediction results with confidence scores
4. Try the example images provided (randomly selected from assets/samples/)

Command Line

# Single image prediction
python predict.py pizza.jpg
# Output: ✅ pizza.jpg: Pizza (95.23%)

# Batch prediction with details
python predict.py test_images/ --detailed --output results.json

⚙️ Configuration

Edit config.py to customize:

• Model settings: Model path, device, image size
• Class configuration: Class names and mappings
• Gradio interface: Title, description, theme
• Application settings: Host, port, debug mode

🔧 Advanced Usage

Custom Model Loading

from utils.model_loader import ModelLoader

# Load custom model
loader = ModelLoader()
loader.load_model()
model = loader.get_model()

Image Preprocessing

from utils.image_processor import ImageProcessor

# Preprocess custom image
processor = ImageProcessor()
tensor = processor.preprocess_image("path/to/image.jpg")

Direct Prediction

from utils.predictor import FoodPredictor

# Initialize and predict
predictor = FoodPredictor()
predictor.initialize()
result = predictor.predict("path/to/image.jpg")
print(f"Predicted: {result['class']} ({result['confidence']:.2%})")

📊 Model Information

• Architecture: Vision Transformer (ViT-Base)
• Input Size: 224x224 pixels
• Classes: 3 (pizza, steak, sushi)
• Training Data: Pizza-Steak-Sushi dataset
• Framework: PyTorch with Transformers
• Model weights: Downloaded automatically from the Hugging Face Model Hub (see model repo)

🛠️ Development

Project Structure

• utils/: Core utilities for model loading, image processing, and prediction
• interface/: Web interface components
• assets/samples/: Example images and static assets

Adding New Features

1. New Model: Update config.py and utils/model_loader.py
2. New Classes: Modify config.py CLASS_CONFIG
3. New Interface: Create new files in interface/
4. New Utilities: Add to utils/ directory

🧹 Project Cleanliness & GitHub Readiness

• All unnecessary files and caches have been removed
• Example images are dynamically loaded
• No test or debug files in the repo
• Model weights are not included in the repo (downloaded from the Hub)
• Ready for production and version control

🐛 Troubleshooting

See INSTALLATION.md for detailed troubleshooting, dependency, and environment tips.

📝 License

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

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📞 Support

For questions and support:

• Open an issue on GitHub
• Check the troubleshooting section
• Review the configuration options

---

Enjoy classifying your food images! 🍕🥩🍣