Detection Model for Textual Data Language

This repository contains a machine learning model designed to detect the language of textual data. It is an efficient and accurate solution for identifying the language of text inputs, making it useful for various applications such as natural language processing, multilingual content management, and more.

Author

Mohammed Shehab

Features

• Language Detection: Accurately identifies the language of given text data.
• High Performance: Optimized for speed and accuracy.
• Scalable: Can handle large volumes of text data efficiently.

Technologies Used

• Docker Containers: The application is containerized using Docker for easy deployment and scalability.
• Powered by Copilot: This project leverages GitHub Copilot for code suggestions and improvements.
• Performance Testing with Locust: This project uses Locust for load testing to ensure the application can handle high traffic and large datasets efficiently.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/language-detection-model.git
   cd language-detection-model

2. Create and activate a virtual environment:

   python -m venv venv
   # On Windows
   .\venv\Scripts\activate
   # On macOS and Linux
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Docker Usage

1. Build the Docker image:

   docker build -t language-detection-model .

2. Run the Docker container:

   docker run -p 8000:8000 language-detection-model

3. Access the application: Open your browser and navigate to http://localhost:8000.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For questions or inquiries, please contact Mohammed Shehab at [shihab@live.cn].