User-Controlled Censorship (UCC)

A Machine Learning-powered text filtering system that provides user-controlled profanity censorship. Instead of rigid blacklists, this model scores words based on severity, allowing dynamic filtering based on user-defined thresholds.

🚀 Features

• ML-based Text Classification using Naïve Bayes + TF-IDF
• Weighted Censorship (Profanity is filtered based on severity scores)
• User-defined Censorship Thresholds (0.1 - 0.9)
• Dynamic Profanity Detection with an adjustable filtering model

⚙️ How It Works

1. Text is analyzed using a trained Naïve Bayes model to detect vulgarity.
2. If vulgar, words are filtered based on their severity weight (from Version_2.py).
3. Users can set the censorship level (e.g., mild filtering at 0.9 vs. strict filtering at 0.1).
4. Final output replaces words above the threshold with [CENSORED].

📦 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/UCC-User-Controlled-Censorship.git
   cd UCC-User-Controlled-Censorship

2. Install dependencies:

   pip install -r requirements.txt

🚀 Usage

Run the censorship script interactively:

python Version_2.py

Then enter the text and censorship threshold when prompted.

🔬 Future Enhancements

• FastAPI-based API for real-time censorship.
• Integration with chat applications to test live moderation.
• Fine-tuning with deep learning models like BERT for improved accuracy.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Final Thoughts

I am taking a break from this project for now EXAMS!

💡 Contributors

Developed by Kinjal Choudhary 🎯