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@@ -6,7 +6,6 @@ colorTo: pink
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  sdk: gradio
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  app_file: app.py
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  pinned: false
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- license: mit
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  ---
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  # FoodViT - Food Classification Application
@@ -21,6 +20,7 @@ A production-ready food classification application using Vision Transformer (ViT
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  - **Production Ready**: Modular, well-structured codebase with proper error handling
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  - **Dynamic Example Images**: Example images are randomly selected from `assets/samples/` at each app launch
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  - **Easy Deployment**: Simple setup and configuration
 
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  ## πŸ“ Project Structure
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@@ -32,8 +32,6 @@ FoodViT/
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  β”œβ”€β”€ requirements.txt # Python dependencies
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  β”œβ”€β”€ README.md # This file
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  β”œβ”€β”€ INSTALLATION.md # Installation and troubleshooting guide
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- β”œβ”€β”€ model/
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- β”‚ └── bestViT_PT.pth # Trained PyTorch model
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  β”œβ”€β”€ utils/
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  β”‚ β”œβ”€β”€ model_loader.py # Model loading utilities
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  β”‚ β”œβ”€β”€ image_processor.py # Image preprocessing
@@ -153,6 +151,7 @@ print(f"Predicted: {result['class']} ({result['confidence']:.2%})")
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  - **Classes**: 3 (pizza, steak, sushi)
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  - **Training Data**: Pizza-Steak-Sushi dataset
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  - **Framework**: PyTorch with Transformers
 
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  ## πŸ› οΈ Development
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@@ -160,7 +159,6 @@ print(f"Predicted: {result['class']} ({result['confidence']:.2%})")
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  - **`utils/`**: Core utilities for model loading, image processing, and prediction
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  - **`interface/`**: Web interface components
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- - **`model/`**: Trained model files
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  - **`assets/samples/`**: Example images and static assets
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  ### Adding New Features
@@ -175,6 +173,7 @@ print(f"Predicted: {result['class']} ({result['confidence']:.2%})")
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  - All unnecessary files and caches have been removed
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  - Example images are dynamically loaded
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  - No test or debug files in the repo
 
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  - Ready for production and version control
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  ## πŸ› Troubleshooting
@@ -202,4 +201,4 @@ For questions and support:
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  ---
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- **Enjoy classifying your food images! πŸ•πŸ₯©πŸ£**
 
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  sdk: gradio
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  app_file: app.py
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  pinned: false
 
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  ---
10
 
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  # FoodViT - Food Classification Application
 
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  - **Production Ready**: Modular, well-structured codebase with proper error handling
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  - **Dynamic Example Images**: Example images are randomly selected from `assets/samples/` at each app launch
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  - **Easy Deployment**: Simple setup and configuration
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+ - **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.
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  ## πŸ“ Project Structure
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  β”œβ”€β”€ requirements.txt # Python dependencies
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  β”œβ”€β”€ README.md # This file
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  β”œβ”€β”€ INSTALLATION.md # Installation and troubleshooting guide
 
 
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  β”œβ”€β”€ utils/
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  β”‚ β”œβ”€β”€ model_loader.py # Model loading utilities
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  β”‚ β”œβ”€β”€ image_processor.py # Image preprocessing
 
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  - **Classes**: 3 (pizza, steak, sushi)
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  - **Training Data**: Pizza-Steak-Sushi dataset
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  - **Framework**: PyTorch with Transformers
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+ - **Model weights**: Downloaded automatically from the Hugging Face Model Hub ([see model repo](https://huggingface.co/mahmoudalrefaey/FoodViT-weights))
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  ## πŸ› οΈ Development
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  - **`utils/`**: Core utilities for model loading, image processing, and prediction
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  - **`interface/`**: Web interface components
 
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  - **`assets/samples/`**: Example images and static assets
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  ### Adding New Features
 
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  - All unnecessary files and caches have been removed
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  - Example images are dynamically loaded
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  - No test or debug files in the repo
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+ - Model weights are not included in the repo (downloaded from the Hub)
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  - Ready for production and version control
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  ## πŸ› Troubleshooting
 
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  ---
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+ **Enjoy classifying your food images! πŸ•πŸ₯©πŸ£**