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:

git clone [repository-url]

2. Install required dependencies:

pip install -r requirements.txt

3. Run the application:

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

video_processing_operations.process_video(video_path)

YouTube Integration

youtube_downloader.download_video(url)

Feature Extraction

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
• Sample 2
• Sample 3

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.