GPTeacher

GPTeacher is a Streamlit-based web application designed to help users solve programming tasks through interactive guidance and step-by-step learning using OpenAI's GPT-4o model. The chatbot utilizes an instructional scaffolding approach, encouraging active engagement rather than passive learning.

Features

• Test Selection: Choose from multiple programming challenges loaded from a JSON file.
• Stepwise Tutoring: Instead of providing direct answers, the chatbot guides users step by step.
• Interactive Chat: Engage in a conversation with an AI assistant that provides guidance and answers to your coding questions.
• Conversation History: Keeps track of user queries to maintain context-aware assistance.
• Korean Support: Responds in Korean to cater to native Korean speakers.

Installation

Prerequisites

• Python 3.7+ installed on your system.
• OpenAI API Key (needed for GPT-4o usage).
• Streamlit (for running the web application).

Setup Instructions

1. Clone the Repository

   git clone https://github.com/username/gpteacher.git
   cd gpteacher

2. Create a Virtual Environment

   It's recommended to use a virtual environment to manage dependencies.

   python3 -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

4. Set Up API Key

   Create a .env file in the root directory and add:

   OPENAI_API_KEY=your_openai_api_key_here

   Replace your_openai_api_key_here with your actual OpenAI API key.

5. Prepare Data

   Ensure that data.json exists in the root directory. The structure should be:

   [
       {
           "title": "Sample Test 1",
           "text": "Description of the first programming task."
       },
       {
           "title": "Sample Test 2",
           "text": "Description of another coding task."
       }
       // Add more tests as needed
   ]

Usage

Run the Streamlit web app using:

streamlit run streamlit_app.py

This will start the application and open it in your default web browser. If it doesn't open automatically, navigate to http://localhost:8501 in your browser.

Project Structure

GPTeacher/
│
├── .devcontainer/             # VSCode devcontainer setup  
│   ├── devcontainer.json  
│
├── app.py                     # Main chatbot application
├── data.json                  # JSON file containing programming tasks
├── README.md                  # Project documentation
├── requirements.txt           # Python dependencies
|
├── .env                       # Environment variables (not committed)

How GPTeacher Works

1. Retriever – Generates a long-term dialogue plan based on the user's input.
2. Generator – Produces structured responses based on the learning steps.
3. Checker – Evaluates user responses to determine whether to move forward, provide hints, or reset the plan.

Unlike traditional chatbots, GPTeacher engages users in an active learning process, ensuring they fully understand coding concepts rather than just copying solutions.