🎬 Manimate Backend

Turn your text prompts into beautiful math & educational animations!

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📝 Project Description

Manimate Backend is a FastAPI-powered service that transforms natural language prompts into stunning mathematical and educational animations using OpenAI's LLM and the Manim engine. Simply send a prompt, and receive a rendered video—perfect for educators, content creators, and anyone who wants to visualize concepts programmatically.

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✨ Features

• 🚀 /generate API: Send a prompt, get a video URL back!
• 🤖 LLM Integration: Uses OpenAI to generate Manim code from your ideas.
• 🎥 Manim Rendering: Renders animations and manages output files.
• 📂 Static Video Hosting: Serves generated videos via /videos.
• 🐳 Docker Support: Easy deployment anywhere.

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🛠️ Requirements

• Python 3.10+
• Manim & system dependencies (see Dockerfile)
• OpenAI API key

All Python dependencies are in requirements.txt.

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🔑 Environment Variables

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key
OPENAI_BASE_URL=your_openai_base_url  # Optional

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⚡ Quickstart

🖥️ Local Development

1. Clone the repo:

   git clone https://github.com/nitindavegit/Manimate-backend.git
   cd Manimate-backend

2. Install system dependencies:

   sudo apt-get update && sudo apt-get install -y ffmpeg libcairo2-dev libpango1.0-dev texlive-full build-essential pkg-config

3. Install Python dependencies:

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

4. Set up your .env file (see above).
5. Run the server:

   uvicorn app.main:app --reload

🐳 Docker

1. Build the image:

   docker build -t manimate-backend .

2. Run the container:

   docker run -p 8000:8000 --env-file .env manimate-backend

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📡 API Usage

🎬 Generate Animation

• POST /generate/
• Request Body:

  {
    "prompt": "Show a diagram of a client-server API interaction"
  }

• Response:

  {
    "video_url": "/videos/output.mp4"
  }

📺 Access Generated Videos

• GET /videos/{filename}

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🗂️ Project Structure

app/
  config.py         # Loads environment variables
  llm_handler.py    # Handles LLM prompt and Manim code generation
  main.py           # FastAPI app entry point
  manim_runner.py   # Runs Manim and manages output files
  routes.py         # API endpoints
generate/           # Stores generated videos and code
requirements.txt    # Python dependencies
Dockerfile          # Docker build instructions

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🧪 Testing

Test Manim rendering without the API:

python app/test_debug.py

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📝 Notes

• 💡 Prompts should describe the animation you want. The LLM generates Manim code, which is rendered and returned as a video.
• 🎬 All generated videos are saved in the generate/ directory and served via /videos.
• 🔒 For production, restrict CORS and secure your API keys.

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📄 License

MIT License