CourseTA - AI Teaching Assistant

CourseTA is an AI-powered teaching assistant that helps educators process educational content, generate questions, create summaries, and build Q&A systems.

Features

• File Upload: Upload PDF documents or audio/video files for automatic text extraction
• Question Generation: Create True/False or Multiple Choice questions from your content
• Content Summarization: Extract main points and generate comprehensive summaries
• Question Answering: Ask questions and get answers specific to your uploaded content

Requirements

• Python 3.9+
• Dependencies listed in requirements.txt
• FFmpeg (for audio/video processing)
• Ollama (optional, for local LLM support)

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/CourseTA.git
   cd CourseTA

2. Install FFmpeg:

   Linux (Ubuntu/Debian):

   sudo apt update
   sudo apt install ffmpeg

3. Install the required Python packages:

   pip install -r requirements.txt

4. (Optional) Install Ollama for local LLM support:

   Windows/macOS/Linux:

   • Download and install from https://ollama.ai/
   • Or use the installation script:

   curl -fsSL https://ollama.ai/install.sh | sh

   Pull the recommended model:

   ollama pull qwen2.5:1.5b

5. Set up your environment variables (API keys, etc.) in a .env file.

Usage

Running the Application

1. Start the FastAPI backend:

   python main.py

2. In a separate terminal, start the Gradio UI:

   python gradio_ui.py

How to Use

1. Upload Content:

   • Go to the "Upload Content" tab
   • Upload a PDF document or audio/video file
   • Copy the generated Asset ID

2. Generate Questions:

   • Go to the "Question Generation" tab
   • Paste your Asset ID
   • Choose a question type (T/F or MCQ)
   • Click "Generate Question"
   • Provide feedback to refine the questions

3. Summarize Content:

   • Go to the "Content Summarization" tab
   • Paste your Asset ID
   • Click "Generate Summary"
   • View the main points and full summary

4. Ask Questions:

   • Go to the "Question Answering" tab
   • Paste your Asset ID
   • Ask questions about your content

Architecture

CourseTA uses a microservice architecture:

• FastAPI backend for API endpoints
• LangGraph-based processing pipelines
• Gradio frontend for user interface
• LangChain for LLM orchestration