VisionED : Automated Video Generation for Educators

This project enables educators to upload PowerPoint presentations (PPTs) along with optional images and automatically generates videos from the uploaded content. The project consists of a Django application for managing educator uploads and a FastAPI-based microservice for GPU-accelerated video generation.

Features

• Upload Management: Educators can upload PPTs and optional images through a web interface.
• Background Video Generation: Videos are generated asynchronously from the uploaded content, with the option to leverage GPU acceleration.
• Celery Task Queue: Asynchronous task handling using Celery with RabbitMQ as the message broker.
• Django Integration: The generated videos are linked to the educator's upload and stored in Django's media directory.
• Temporary Directory Management: Temporary directories are used for processing and cleaned up after each request.
• Node Media Server: Serves the generated videos for streaming to students.
• Tailwind CSS: Used for styling the web interface.
• Student Access: Students can consume content uploaded by educators.

---

Project Structure

├── GenerationServer               # FastAPI service for video generation
│   ├── gpu_env.yaml         # Conda environment for GPU
│   ├── app.py                    # FastAPI app for handling video generation requests
│   └── ...
├── edu_project/                   # Django project directory
├── home/                       # Django app for educator uploads and student content consumption
├── media/                         # Directory where generated videos are stored
├── static/                        # Static files for the web UI
├── templates/                     # HTML templates for the web UI
├── requirements.txt               # Python dependencies for Django app
├── manage.py                      # Django management script
├── media_server.js                # Node.js media server for streaming videos
└── README.md                      # Project readme file

---

Requirements

General Dependencies

• Python (>=3.10) for Django
• Python (>=3.7) for GPU services
• Django (>=4.0)
• Celery (>=5.2)
• RabbitMQ (>=3.9)
• FastAPI (>=0.85)
• CUDA (for GPU acceleration)

GPU-Specific Dependencies

To leverage GPU acceleration, you'll need CUDA drivers and the dependencies listed in the GenerationServer/conda-gpu_env.yaml file.

---

Setup Instructions

Django Setup

1. Clone the Repository:

   git clone <repository_url>
   cd <project_directory>

2. Set up the Virtual Environment and Install Dependencies:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Apply Database Migrations:

   python manage.py migrate

4. Create a Superuser for Django Admin:

   python manage.py createsuperuser

5. Run the Django Development Server:

   python manage.py runserver

Access the Django app at http://localhost:8000.

---

FastAPI and GPU Server Setup

1. Navigate to the Generation Server:

   cd GenerationServer

2. Set Up the Conda Environment:

   Install the required GPU dependencies via conda:

   conda env create -f conda-gpuenv.yaml
   conda activate gpuenv

3. Run the FastAPI Video Generation Service:

   uvicorn app:app --host 127.0.0.1 --port 8080

The FastAPI server will now be ready to process video generation requests.

---

Node Media Server Setup

1. Install Node.js:

   If you haven't already, install Node.js from the official website.

2. Run the Media Server:

   In the project root, run the media server using:

   node media_server.js

The media server will serve the generated videos for streaming.

---

Celery & RabbitMQ Setup

1. Install RabbitMQ:

   Install RabbitMQ according to your OS. For Ubuntu, use:

   sudo apt-get install rabbitmq-server
   sudo systemctl enable rabbitmq-server
   sudo systemctl start rabbitmq-server

2. Run Celery Worker:

   In the project root, start a Celery worker to process background tasks:

   celery -A edu_project worker --loglevel=info

3. Run Celery Beat (Optional):

   If you're using scheduled tasks, start the Celery beat scheduler:

   celery -A edu_project beat --loglevel=info

---

Tailwind CSS Setup

1. Install Tailwind CSS:

   Navigate to the project directory where your package.json is located and run:

   npm install

2. Build Tailwind CSS:

   To build the CSS file, use the command:

   npx tailwindcss build -i  static/css/input.css -o static/css/output.css --watch 

This will compile your Tailwind CSS styles and watch for changes.

---

Usage

1. Educator Upload: Educators upload their PowerPoint presentations and optional images using the web UI.

2. Background Video Generation: Uploaded files are sent to the FastAPI service for video generation, which runs asynchronously.

3. Viewing Generated Videos: Once the video is generated, students can stream the videos via the Node media server.

---

Contributing

We welcome contributions! Please follow these steps to contribute:

1. Fork the repository.
2. Create a feature branch.
3. Commit your changes.
4. Push to your branch.
5. Open a pull request.

Please ensure that your code adheres to the project's coding standards and passes all tests.

---

License

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