HUFS Course Catalog Chatbot

HUFS Course Catalog Chatbot is a conversational assistant designed to provide information about course registration, graduation requirements, major-specific details, and academic policies at Hankuk University of Foreign Studies (HUFS).

Features

• Conversational AI: Powered by OpenAI's GPT model for natural language understanding.
• Hybrid Search System: Combines semantic and keyword-based search for precise information retrieval.
• Contextual Responses: Utilizes relevant data to generate step-by-step answers in Korean.
• User-Friendly Interface: Simple web-based chat interface built with HTML, CSS, and JavaScript.

How It Works

1. Users ask questions about course registration, graduation, or other academic policies.
2. The chatbot processes the query using hybrid search algorithms.
3. The chatbot fetches and formats the answer using relevant course catalog data.
4. Results are displayed in the chat interface.

Technologies Used

• Backend: Python, Flask
• AI Model: OpenAI GPT, jhgan/ko-sbert-nli for semantic embeddings
• Frontend: HTML, CSS, JavaScript
• Data Management: JSON-based course catalog data
• Libraries:
  • flask
  • flask-cors
  • sentence-transformers
  • rank-bm25
  • numpy
  • python-dotenv

Project Structure

E:.
│   .env                # Environment variables
│   .gitignore          # Ignored files
│   LICENSE             # License information
│   README.md           # Project documentation
│
├───Eval                # Evaluation scripts and datasets
│       eval_code.py
│       main.py
│       processed_data.json
│
├───server              # Backend server
│       server.py
│       utils.py
│       search.py
│
├───static              # Frontend resources
│       index.html
│       style.css
│       script.js
│       bot-avatar.png
│       user-avatar.png
│
└───__pycache__         # Python compiled files (ignored in .gitignore)

Installation

1. Clone the repository:

   git clone https://github.com/YourUsername/HufsCourseCatalogChat.git
   cd HufsCourseCatalogChat

2. Create a virtual environment and activate it:

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

3. Install the dependencies:

   pip install -r requirements.txt

4. Set up the environment variables:

   • Create a .env file in the root directory.
   • Add your OpenAI API key:

     OPENAI_API_KEY=your_api_key_here
     

5. Start the Flask server:

   python server/server.py

6. Open the chat interface:

   • Navigate to http://127.0.0.1:1954 in your browser.

Usage

• Ask Questions: Enter your query in the input box, and click "Send" to get a response.
• Supported Queries: Course registration schedules, graduation requirements, and major-related questions.

Contributing

Contributions are welcome! Please follow the guidelines in CONTRIBUTING.md.

License

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