Skip to main content

Deployment Manual

Class Explorer is a three-part application designed to enhance interactive learning and classroom engagement. It is composed of the following components:

  1. Unity Game
  2. React Teacher Dashboard
  3. FastAPI Model Backend

1. Unity Game​

The Unity game component of Class Explorer provides an immersive, interactive learning environment. Students can engage with educational content through gamified elements, making it fun and motivating to explore various topics.

  • Deployment: The game can be downloaded directly over the network and installed on student devices.
  • Getting Started:
    1. Download the Unity game client.
    2. Install and run the client on a compatible device (PC, Mac, or mobile).

2. React Teacher Dashboard​

The React-based teacher dashboard serves as the control center for the game. It provides tools for managing game content and game settings.

  • Repository: teacher-ui on GitHub
  • Features:
    • Configure or customize game scenarios
    • Manage Game settings
    • Generate and upload quiz questions
    • Object detection for quiz generation
  • Getting Started:
    1. Clone the teacher-ui repository.
    2. Install dependencies by running npm install.
    3. Start the development server with npm run dev to launch the dashboard locally.
    4. Access the dashboard through your browser to log in and manage classrooms.

3. FastAPI Model Backend​

The FastAPI Model Backend will handle data processing, storage, and AI model integrations for the Class Explorer system.

  • Repository:
  • Features:
    • AI model integration for question generation
    • Data processing and storage
    • API endpoints for game interactions
  • Getting Started:
    1. Option 1 (Local Deployment):
      1. Make sure you have a Nvidia GPU with CUDA support for AI model acceleration and python 3.8 or later.
      2. Clone the Educational Question Generator
      3. Install torch, torchvision for your CUDA version Click here for more information to install right torch.
      4. Install llama-cpp-python Click here for more information to install llama-cpp-python
      5. Install dependencies by running pip install -r requirements.txt.
      6. Run the desktop application for generating questions by running python ./main.py.
      7. Start the FastAPI server by running uvicorn app.main:app --reload to launch the backend server.
    2. Option 2 (Docker Deployment):
      1. Make sure you have a Nvidia GPU with CUDA support for AI model acceleration.
      2. Build the Docker image by running docker build -t educational-quiz-generator ..
      3. Run the Docker container by running docker run --gpus all -p 8000:8000 educational-quiz-generator.
      4. This will start the FastAPI server inside a Docker container, and you can access the API at http://127.0.0.1:8000.
  • Troubleshooting:
    • If your server is not using GPU, make sure you have installed the right version of CUDA and torch. And also make sure you install with CUDA support (set the GGML_CUDA=on environment variable before installing).
    • Check NVIDIA drivers are up to date
    • For MacOS users, verify Metal support requirements