Upload README.md

README.md

@@ -0,0 +1,151 @@
+---
+title: PyTorch Neural Network Classifier
+emoji: 🧠
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: "4.0.0"
+app_file: app.py
+pinned: false
+---
+
+# PyTorch Neural Network Classifier
+
+This is a Gradio interface for a convolutional neural network based on the [PyTorch Neural Networks Tutorial](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html). The model is a simplified version of LeNet-5, designed for image classification tasks.
+
+## Model Architecture
+
+The neural network has the following architecture (exactly as shown in the PyTorch tutorial):
+- Two convolutional layers with ReLU activation and max pooling
+- Three fully connected layers
+- Designed for 32x32 grayscale input images
+
+```
+Input → Conv2d(1, 6, 5) → ReLU → MaxPool2d(2, 2) → 
+Conv2d(6, 16, 5) → ReLU → MaxPool2d(2, 2) → 
+Flatten → Linear(16*5*5, 120) → ReLU → 
+Linear(120, 84) → ReLU → Linear(84, 10) → Output
+```
+
+## Features
+
+- Interactive image classification interface with modern UI
+- Example images for quick testing
+- Real-time predictions with probability scores
+- Support for custom image uploads
+- Built-in drawing tool for creating test images
+- Responsive design with gradient backgrounds and animations
+- Automatic image preprocessing (resize, grayscale conversion)
+
+## How to Use with Your Existing Model
+
+1. Place your trained PyTorch model file in the app directory and name it `model.pth`
+2. Ensure your model uses the same architecture as defined in the Net class
+3. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Run the application:
+   ```bash
+   python app.py
+   ```
+
+5. Access the interface at `http://localhost:7860` (or the URL provided in the terminal)
+
+## Image Input Capabilities
+
+The model can handle various types of image inputs:
+
+### Supported Image Formats
+- JPG, PNG, BMP, TIFF, and other common image formats
+- Color images (automatically converted to grayscale)
+- Any resolution (automatically resized to 32×32 pixels)
+
+### Robustness Features
+- **Resolution Independence**: Works with images of any size (resized to 32×32)
+- **Color Conversion**: Automatically converts color images to grayscale
+- **Contrast Handling**: Works with both high and low contrast images
+- **Noise Tolerance**: Can handle some image noise
+- **Rotation Tolerance**: Some tolerance to slight rotations
+
+### Best Practices for Good Results
+1. **Center the digit** in the image area
+2. **Use clear contrast** between the digit and background
+3. **Fill most of the image** area with the digit
+4. **Avoid excessive noise** or artifacts
+5. **Use dark digits on light background** or vice versa
+
+## Deployment to Hugging Face Spaces
+
+This application can be deployed to Hugging Face Spaces by:
+
+1. Creating a new Space on Hugging Face
+2. Uploading these files to the repository
+3. Setting the SDK to "Gradio"
+4. Adding the requirements in the requirements.txt file
+5. Uploading your `model.pth` file
+
+The Space will automatically run the `app.py` file as the entry point.
+
+## Example Usage
+
+The interface comes with hand-drawn example images that demonstrate how the classifier works. You can:
+1. Click on any example image to load it
+2. Upload your own image using the file browser
+3. Draw an image using the built-in sketch tool
+4. View the classification probabilities for each class
+
+Try these examples:
+- Handwritten digits of different styles
+- Printed digits
+- Digits with varying thickness
+- Digits with different backgrounds
+
+## Technical Details
+
+This implementation follows the PyTorch tutorial exactly and includes:
+- Gradio interface with custom CSS styling
+- Image preprocessing pipeline (resize to 32x32, grayscale conversion)
+- Softmax probability output (as shown in the tutorial)
+- Example generation for demonstration
+- Model loading functionality for your trained weights
+
+The prediction function exactly matches the tutorial:
+```python
+model.eval()
+with torch.no_grad():
+    output = model(input_tensor)
+    probabilities = F.softmax(output, dim=1)
+```
+
+The UI features:
+- Animated gradient background
+- Glass-morphism design elements
+- Responsive layout for all screen sizes
+- Interactive buttons with hover effects
+- Clean, modern typography
+
+## Requirements
+
+- Python 3.6+
+- PyTorch >= 1.7.0
+- TorchVision >= 0.8.0
+- Gradio >= 4.0.0
+- Pillow >= 8.0.0
+- NumPy >= 1.19.0
+
+Install with:
+```bash
+pip install -r requirements.txt
+```
+
+## Troubleshooting
+
+If you encounter issues:
+1. Ensure your `model.pth` file is in the same directory as `app.py`
+2. Verify that your model uses the same architecture as defined in the Net class
+3. Check that all required dependencies are installed
+4. Make sure you're using a compatible version of Python (3.6+)
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference