# nudetech (Adult Content Detector)

A web-based application that detects adult content in videos using AI and machine learning. The system can process both direct video uploads and YouTube URLs.

## Features

- Video content analysis through deep learning
- Support for direct video file uploads
- YouTube URL processing and analysis
- Real-time detection and feedback
- User-friendly dark-themed interface
- Mobile-responsive design

## Tech Stack

- **Frontend:**
  - HTML5
  - CSS3
  - Bootstrap 5
  - JavaScript

- **Backend:**
  - Python
  - TensorFlow/Keras
  - PIL (Python Imaging Library)
  - TensorFlow Hub

## Model Architecture

The system uses a CNN architecture with the following layers:
- Convolutional Layer (32 filters, 3x3 kernel)
- MaxPooling Layer (2x2)
- Flatten Layer
- Dense Layer (128 neurons)
- Output Layer (2 neurons with softmax activation)

## Installation

1. Clone the repository:
```bash
git clone [repository-url]
```

2. Install required dependencies:
```bash
pip install -r requirements.txt
```

3. Run the application:
```bash
python main.py
```

## Usage

1. Access the web interface through your browser
2. Choose one of two options:
   - Upload a video file directly
   - Enter a YouTube URL
3. Click "Start Detection" to begin the analysis
4. View the detection results

## API Reference

### Video Processing Operations
```python
video_processing_operations.process_video(video_path)
```

### YouTube Integration
```python
youtube_downloader.download_video(url)
```

### Feature Extraction
```python
extract_features.extract(video_data)
```

## Project Structure

```
├── main.py
├── page/
│   └── index.html
├── static/
│   ├── css/
│   └── js/
├── models/
└── utils/
```

## Configuration

The application uses the following default configurations:
- Input image size: 224x224 pixels
- Learning rate: 10e-5
- Optimization: Adam
- Loss function: Binary Cross-entropy

## Testing

The application includes sample test videos:
- [Sample 1](https://www.youtube.com/watch?v=eAR2V7PZiIQ)
- [Sample 2](https://www.youtube.com/watch?v=pZs4SYfU6pA)
- [Sample 3](https://www.youtube.com/watch?v=bXlQ3Mw4uGc)

## 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.

## Acknowledgments

- TensorFlow team for the deep learning framework
- Bootstrap team for the UI components
- Contributors and maintainers

## Support

For support, please open an issue in the repository or contact the development team.

## Security

This application processes sensitive content. Please ensure:
- Proper access controls are in place
- Data is handled according to relevant privacy laws
- Regular security updates are maintained

## Performance

The system is optimized for:
- Fast video processing
- Efficient memory usage
- Quick response times
- Scalable architecture

For optimal performance, recommended hardware specifications:
- 8GB RAM minimum
- Modern multi-core processor
- GPU support for faster processing
```

This README provides comprehensive information about the project's features, setup, usage, and technical details while maintaining the specific code patterns and modules used in the original codebase.
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