# 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. ```