GPTCheck: AI-Generated Text Detection Website

This project is a Flask-based Website that uses a fine-tuned DeBERTa model to detect whether a given text is AI-generated or human-written. Our model achieve Bronze Medal, Top 8% on Kaggle - LLM Detect AI Generated Text competition[https://www.kaggle.com/competitions/llm-detect-ai-generated-text].

demo

Features

• Accepts a text input and returns a prediction on whether it was generated by an AI or written by a human.
• Returns the probability of the prediction, giving insight into the model's confidence.
• Easy to integrate with web applications or use as a standalone API.

Requirements

• Python 3.8+
• Pip (Python package installer)
• CUDA (optional, for GPU support)

Installation

1. Clone the Repository:

   git clone https://github.com/YuekaiXuEric/GPTCheck.git
   cd GPTCheck

2. Create a Virtual Environment (Optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

   Make sure requirements.txt includes necessary packages like Flask, torch, transformers, etc.

4. Download the Pretrained Model:

   We have provide an DeBERTa v3 small fine tuning model. You can choose other model as you like, make sure you have a fine-tuned DeBERTa model available. Place it in the appropriate directory or adjust the model loading path in app.py.

Usage

1. Run the Flask Server:

   python app.py

   The server will start and listen on http://127.0.0.1:3000.

2. Run the Client:

   npm run start

   The client will start and listen on http://localhost:8080/.

3. Test the API:

   You can test the API using tools like curl, Postman, or Thunder Client. Example using curl:

   curl -X POST -H "Content-Type: application/json" -d '{"text": "Your input text here"}' http://127.0.0.1:3000/predict

   Example response:

   {
       "probability": 0.95
   }

Using Thunder Client

1. Install Thunder Client:
   Thunder Client is a REST API client for Visual Studio Code. Install it from the Extensions view in VS Code.

2. Create a New Request:

   • Method: POST
   • URL: http://127.0.0.1:3000/predict
   • Body: JSON
   • Example:

     {
         "text": "Your input text here"
     }

3. Send the Request:
   View the response directly in Thunder Client.

Contributing

If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are welcome.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Hugging Face Transformers
• Flask
• The AI and Machine Learning community