🌟 Toxic Command Detection

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

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🚀 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.

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✨ 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.

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🛠️ Technologies

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

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📂 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  

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📖 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  

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🎯 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.

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📊 Screenshots

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🤝 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

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📜 License

This project is licensed under the MIT License.

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💡 Acknowledgements

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