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🌟 Toxic Command Detection

A Python-powered project utilizing Machine Learning and Django to detect and mitigate harmful commands.

🚀 Overview

Toxic Command Detection is a cutting-edge solution designed to identify toxic or abusive commands using trained machine learning models. With a user-friendly web interface powered by Django, this project bridges machine learning and web technologies for real-time detection.

✨ Features

✔️ Toxicity Detection: Identify and flag harmful commands instantly.
✔️ Interactive Web Interface: Simple and intuitive design for easy use.
✔️ Customizable Model: Trained using Jupyter Notebook for flexibility.
✔️ Scalable: Django-powered backend supports extensibility.

🛠️ Technologies

  • Python 🐍: Core language for implementation.
  • Jupyter Notebook 📓: Dataset preprocessing and ML training.
  • Django 🌐: Framework for building the web interface.

📂 Project Structure

📦 Toxic Command Detection  
├── 📁 dataset/             # Dataset used for training  
├── 📁 notebooks/           # Jupyter notebooks for ML workflows  
├── 📁 django_project/      # Django application files  
├── 📄 requirements.txt     # Python dependencies  
└── README.md               # Project documentation

📖 Getting Started

🔧 Prerequisites

Ensure you have the following installed:

  • Python 3.8+
  • pip package manager
  • Virtual Environment (recommended)

⚙️ Installation

  1. Clone the Repository: 
    git clone <repository-url>  
cd Toxic-Command-Detection
  2. Set up a Virtual Environment: 
    python -m venv env  
source env/bin/activate  # On Windows, use `env\Scripts\activate`
  3. Install Dependencies: 
    pip install -r requirements.txt
  4. Run Migrations: 
    python manage.py migrate
  5. Start the Server: 
    python manage.py runserver

🎯 Usage

  1. Open your browser and navigate to: http://127.0.0.1:8000.
  2. Use the web interface to upload commands or text for toxicity analysis.
  3. View real-time results on the dashboard.

📊 Screenshots

screenshot

🤝 Contributions

We welcome contributions from the community!

  • Fork the repository
  • Create a new branch (git checkout -b feature-branch)
  • Commit changes (git commit -m 'Add new feature')
  • Push to the branch (git push origin feature-branch)
  • Submit a pull request

📜 License

This project is licensed under the MIT License.

💡 Acknowledgements

Special thanks to all contributors and the open-source community for their support.