https://github.com/ezhilezhil/toxic-command-detection
Toxic command detection refers to the process of identifying harmful or inappropriate language in text, particularly in the context of interactions with generative AI and large language models.
https://github.com/ezhilezhil/toxic-command-detection
artificial-intelligence jupyter-notebook toxic-comment-classification
Last synced: about 2 months ago
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Toxic command detection refers to the process of identifying harmful or inappropriate language in text, particularly in the context of interactions with generative AI and large language models.
- Host: GitHub
- URL: https://github.com/ezhilezhil/toxic-command-detection
- Owner: ezhilezhil
- License: mit
- Created: 2024-11-27T05:18:27.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-27T05:47:05.000Z (6 months ago)
- Last Synced: 2025-02-08T15:26:34.314Z (3 months ago)
- Topics: artificial-intelligence, jupyter-notebook, toxic-comment-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 20.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🌟 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
```plaintext
📦 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**:
```bash
git clone
cd Toxic-Command-Detection
```
2. **Set up a Virtual Environment**:
```bash
python -m venv env
source env/bin/activate # On Windows, use `env\Scripts\activate`
```
3. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run Migrations**:
```bash
python manage.py migrate
```
5. **Start the Server**:
```bash
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
---
## 🤝 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](LICENSE).
---
## 💡 Acknowledgements
Special thanks to all contributors and the open-source community for their support.