Regex Pattern Matching and Replacement Web Application

This project is a web application built using Django and React for regex pattern matching and replacement in CSV files. It integrates a Large Language Model (LLM) from Meta-Llama-3-8B-Instruct to convert natural language descriptions into regex patterns. A brief demonstration can be found here.

Installation

1. Clone the repository:

   git clone https://github.com/DoDucNhan/regex_pattern_web.git
   cd regex_pattern_web

2. Backend Setup (Django):

   • Ensure Python 3.x and pip are installed.
   • Install required Python packages:

   pip install -r requirements.txt

   • Set up Django environment variables and configuration.

3. Frontend Setup (React):

   • Navigate to the frontend directory:

   cd regex_frontend

   • Install dependencies using npm:

   npm install axios react-bootstrap bootstrap

4. Hugging Face API Token Setup:

   • Obtain an API token from Hugging Face following their documentation. (Remember to accept meta terms to be granted access to their models)
   • Replace the token in settings.py:

   # regex_app/settings.py
   HUGGINGFACE_API_KEY = os.getenv('HUGGINGFACE_API_KEY', 'YOUR_KEY')

Usage

NOTE: On your first run, it will take time to download the checkpoint for the LLAMA 3 model.

1. Running the Development Servers:

   • Navigate to the regex_app directory.

   cd regex_app

   • Start the Django backend server:

   python manage.py runserver

   • Start the React frontend server (in another terminal window):

   cd regex_frontend
   npm start

2. Using the Application:

   • Access the application in your web browser at http://localhost:3000.
   • Upload a CSV or Excel file and describe the pattern you want to match in natural language.
   • The application will convert your description into a regex pattern using Meta-Llama-3-8B-Instruct.
   • Download the processed CSV file.

Acknowledgments

• Meta-Llama-3-8B-Instruct for providing the LLM capabilities.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.