Update README.md

README.md

@@ -1,90 +1,99 @@
-# Combined ANPR and Helmet Detection System
-
-A comprehensive traffic violation detection system that combines Automatic Number Plate Recognition (ANPR) and Helmet Detection using YOLOv8.
-
-## Features
-
-- Real-time license plate detection and recognition
-- Helmet detection for two-wheeler riders
-- Modern Gradio interface with real-time processing
-- Adjustable confidence threshold for detection
-- Combined visual annotations from both models
-- Queue support for multiple users
-- Comprehensive error handling
-
-## Prerequisites
-
-- Python 3.8 or higher
-- CUDA-capable GPU (recommended for better performance)
-- 8GB RAM minimum
-
-## Installation
-
-1. Clone the repository:
-```bash
-git clone <repository-url>
-cd <repository-name>
-```
-
-2. Create and activate a virtual environment:
-```bash
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-```
-
-3. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-
-1. Start the application:
-```bash
-python app.py
-```
-
-2. Open your web browser and navigate to:
-```
-http://localhost:7860
-```
-
-3. Upload an image or use the example images to test the system.
-
-## Model Files
-
-The following model files are required:
-- `ANPR_IND/licence_plat.pt`: License plate detection model
-- `ANPR_IND/licence_character.pt`: Character recognition model
-- `Helmet-Detect-model/best.pt`: Helmet detection model
-
-## API Endpoints
-
-The application exposes the following endpoints:
-- `/api/predict`: POST endpoint for image processing
-- `/api/health`: GET endpoint for health check
-
-## Deployment
-
-### Local Deployment
-```bash
-python app.py
-```
-
-### Docker Deployment
-```bash
-docker build -t traffic-detection .
-docker run -p 7860:7860 traffic-detection
-```
-
-## Contributing
-
-1. Fork the repository
-2. Create your feature branch
-3. Commit your changes
-4. Push to the branch
-5. Create a new Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details. 
+---
+license: mit
+title: Helmet_Detection_OCR_ANPR
+sdk: gradio
+emoji: 📚
+colorFrom: red
+colorTo: yellow
+short_description: Helmet_Detection_OCR_ANPR
+---
+# Combined ANPR and Helmet Detection System
+
+A comprehensive traffic violation detection system that combines Automatic Number Plate Recognition (ANPR) and Helmet Detection using YOLOv8.
+
+## Features
+
+- Real-time license plate detection and recognition
+- Helmet detection for two-wheeler riders
+- Modern Gradio interface with real-time processing
+- Adjustable confidence threshold for detection
+- Combined visual annotations from both models
+- Queue support for multiple users
+- Comprehensive error handling
+
+## Prerequisites
+
+- Python 3.8 or higher
+- CUDA-capable GPU (recommended for better performance)
+- 8GB RAM minimum
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone <repository-url>
+cd <repository-name>
+```
+
+2. Create and activate a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+1. Start the application:
+```bash
+python app.py
+```
+
+2. Open your web browser and navigate to:
+```
+http://localhost:7860
+```
+
+3. Upload an image or use the example images to test the system.
+
+## Model Files
+
+The following model files are required:
+- `ANPR_IND/licence_plat.pt`: License plate detection model
+- `ANPR_IND/licence_character.pt`: Character recognition model
+- `Helmet-Detect-model/best.pt`: Helmet detection model
+
+## API Endpoints
+
+The application exposes the following endpoints:
+- `/api/predict`: POST endpoint for image processing
+- `/api/health`: GET endpoint for health check
+
+## Deployment
+
+### Local Deployment
+```bash
+python app.py
+```
+
+### Docker Deployment
+```bash
+docker build -t traffic-detection .
+docker run -p 7860:7860 traffic-detection
+```
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch
+3. Commit your changes
+4. Push to the branch
+5. Create a new Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.