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

Clone the repository:

git clone [repository-url]

Install required dependencies:

pip install -r requirements.txt

Run the application:

python main.py

Usage

Access the web interface through your browser
Choose one of two options:
- Upload a video file directly
- Enter a YouTube URL
Click "Start Detection" to begin the analysis
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:

Contributing

Fork the repository
Create your feature branch
Commit your changes
Push to the branch
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.