The Anti-India Detection System is a project aimed at identifying and flagging content that contains anti-India sentiment across digital platforms. It utilizes Natural Language Processing (NLP) and machine learning techniques to analyze and detect potentially harmful or sensitive content efficiently.

• Automated Content Scanning: Analyzes text data for anti-India sentiment using advanced algorithms.
• Customizable Detection: Fine-tune detection parameters for different platforms and sources.
• Reporting & Dashboard: Provides actionable reports and an interactive dashboard for monitoring flagged content.
• Extensible Design: Easily integrates additional data sources and detection logic.
• Machine Learning Models: Uses both supervised and unsupervised techniques for high accuracy.

A live prototype of the Anti-India Detection System is available for testing and demonstration.
Prototype Link: Streamlit App

The prototype allows users to input textual data and visualize detection outputs. It demonstrates the core detection capabilities and reporting features in a user-friendly interface.

1. Clone the repository:

   git clone https://github.com/Om-ingle/anti-india-detection-system.git
   cd anti-india-detection-system

2. Install dependencies:

   pip install -r requirements.txt

3. Configure settings:

   • Update configuration files as needed for your sources and detection preferences.

1. Run the detection system:

   streamlit run app.py

2. Interact with the prototype:

   • Visit the Streamlit App to test detection interactively.

For more detailed information about the system's design, features, and usage, please refer to the official documentation:
Google Doc

Contributions are welcome! Please open issues or submit pull requests to propose improvements or new features.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-name).
5. Open a Pull Request.

This project is licensed under the MIT License.

Questions or suggestions? Reach out to Om-ingle.