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-# manimator
-
-![manimator](https://github.com/HyperCluster-Tech/manimator/blob/main/assets/manimator.png)
-[![GitHub Stars](https://img.shields.io/github/stars/HyperCluster-Tech/manimator?style=social)](https://github.com/HyperCluster-Tech/manimator/stargazers)
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-[![Website](https://img.shields.io/badge/Website-manimator.hypercluster.tech-blue)](https://manimator.hypercluster.tech/)
-
-### What is _manimator_?
-
-manimator is a tool to transform research papers and mathematical concepts into stunning visual explanations, powered by AI and the [manim](https://github.com/ManimCommunity/manim) engine
-
-Building on the incredible work by 3Blue1Brown and the manim community, _manimator_ turns complex research papers and user prompts into clear, animated explainer videos.
-
-### 🔗 Try it out:
-
-- Gradio Demo: [![On Gradio (Hugging Face)](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-md-dark.svg)](https://huggingface.co/spaces/HyperCluster/manimator)
-- Or replace `arxiv.org` with `manimator.hypercluster.tech` in any arXiv PDF URL for instant visualizations!
-
-### 🌟 Highlights so far:
-
-- Over **1000+ uses** within 24 hours of launch and over **5000 uses** within a week
-- Featured as Hugging Face's **Space of the Week**!
-- 16th in Hugging Face's Top Trending Spaces
-- Take a look at the paper on arXiv here: https://www.arxiv.org/abs/2507.14306
-
-## 🎥 Demo Videos:
-
-<table border="0" style="width: 100%; text-align: center; margin: 20px 0;">
-    <tr>
-        <td width="50%">
-            <video src="https://github.com/user-attachments/assets/5aa9ebea-06f1-489d-9a68-c8c62cdc6915" width="100%" controls autoplay loop></video>
-            <p align="center">ArXiv usage Walkthrough</p>
-        </td>
-        <td width="50%">
-            <video src="https://github.com/user-attachments/assets/820142c4-7931-4aa8-b9b7-c1d5d46b23b5" width="100%" controls autoplay loop></video>
-            <p align="center">Gradio Walkthrough</p>
-        </td>
-    </tr>
-</table>
-
-## Installation
-
-> [!IMPORTANT]
-> This project is built using the [poetry](https://python-poetry.org/) tool to manage Python packages and dependencies. Download it from [here](https://python-poetry.org/docs/#installing-with-the-official-installer) to run this project or use the Docker image.
-> This project is dependent on the [manim](https://github.com/ManimCommunity/manim) engine and hence has certain dependencies for running the engine properly which can be found [here](https://docs.manim.community/en/stable/installation.html).
-
-```
-bash
-git clone https://github.com/HyperCluster-Tech/manimator
-cd manimator
-```
-
-Install Dependencies:
-`poetry install`
-
-Activate the environment:
-`poetry env activate`
-
-(If you're using a version before Poetry 2.0, you should use `poetry shell`)
-
-## Usage
-
-After successfully installing all the project dependencies and manim dependencies, set the environment variables in a .env file according to the .env.example:
-
-Run the FastAPI server:
-
-```
-poetry run app
-```
-
-and visit `localhost:8000/docs` to open SwaggerUI
-
-Run the Gradio interface:
-
-```
-poetry run gradio-app
-```
-
-and open `localhost:7860`
-
-### Notes
-
-To change the models being used, you can set the environment variables for the models according to [LiteLLM syntax](https://docs.litellm.ai/docs/providers) and set the corresponding API keys accordingly.
-
-To prompt engineer to better suit your use case, you can modify the system prompts in `utils/system_prompts.py` and change the few shot examples in `few_shot/few_shot_prompts.py`.
-
-## 🛳️ Docker
-
-To use manimator with Docker, execute the following commands:
-
-1. Clone the manimator repo to get the Docker image (we will be publishing the image in DockerHub soon)
-2. Run the Docker container, exposing port 8000 for the FastAPI server or 7860 for the Gradio interface
-
-Build the Docker image locally. Then, run the Docker container as follows:
-
-`docker build -t manimator .`
-
-If you are running the FastAPI server
-
-`docker run -p 8000:8000 manimator`
-
-Else for the Gradio interface
-
-`docker run -p 7860:7860 manimator`
-
-<details>
-<summary><h2>API Endpoints</h2></summary>
-
-- [API Endpoints](#api-endpoints)
-  - [Health Check](#health-check)
-  - [PDF Processing](#pdf-processing)
-    - [Generate PDF Scene](#generate-pdf-scene)
-    - [Process ArXiv PDF](#process-arxiv-pdf)
-  - [Scene Generation](#scene-generation)
-    - [Generate Prompt Scene](#generate-prompt-scene)
-  - [Animation Generation](#animation-generation)
-    - [Generate Animation](#generate-animation)
-
-### Health Check
-
-#### Check API Health Status
-
-Endpoint: `/health-check`  
-Method: GET
-
-Returns the health status of the API.
-
-Response:
-
-```json
-{
-  "status": "ok"
-}
-```
-
-Curl command:
-
-```bash
-curl http://localhost:8000/health-check
-```
-
-### PDF Processing
-
-#### Generate PDF Scene
-
-Endpoint: `/generate-pdf-scene`  
-Method: POST
-
-Processes a PDF file and generates a scene description for animation.
-
-Request:
-
-- Content-Type: `multipart/form-data`
-- Body: PDF file
-
-Response:
-
-```json
-{
-  "scene_description": "Generated scene description based on PDF content"
-}
-```
-
-Curl command:
-
-```bash
-curl -X POST -F "file=@/path/to/file.pdf" http://localhost:8000/generate-pdf-scene
-```
-
-#### Process ArXiv PDF
-
-Endpoint: `/pdf/{arxiv_id}`  
-Method: GET
-
-Downloads and processes an arXiv paper by ID to generate a scene description.
-
-Parameters:
-
-- `arxiv_id`: The arXiv paper identifier
-
-Response:
-
-```json
-{
-  "scene_description": "Generated scene description based on arXiv paper"
-}
-```
-
-Curl command:
-
-```bash
-curl http://localhost:8000/pdf/2312.12345
-```
-
-### Scene Generation
-
-#### Generate Prompt Scene
-
-Endpoint: `/generate-prompt-scene`  
-Method: POST
-
-Generates a scene description from a text prompt.
-
-Request:
-
-- Content-Type: `application/json`
-- Body:
-
-```json
-{
-  "prompt": "Your scene description prompt"
-}
-```
-
-Response:
-
-```json
-{
-  "scene_description": "Generated scene description based on prompt"
-}
-```
-
-Curl command:
-
-```bash
-curl -X POST \
-     -H "Content-Type: application/json" \
-     -d '{"prompt": "Explain how neural networks work"}' \
-     http://localhost:8000/generate-prompt-scene
-```
-
-### Animation Generation
-
-#### Generate Animation
-
-Endpoint: `/generate-animation`  
-Method: POST
-
-Generates a Manim animation based on a text prompt.
-
-Request:
-
-- Content-Type: `application/json`
-- Body:
-
-```json
-{
-  "prompt": "Your animation prompt"
-}
-```
-
-Response:
-
-- Content-Type: `video/mp4`
-- Body: Generated MP4 animation file
-
-Curl command:
-
-```bash
-curl -X POST \
-     -H "Content-Type: application/json" \
-     -d '{"prompt": "Create an animation explaining quantum computing"}' \
-     --output animation.mp4 \
-     http://localhost:8000/generate-animation
-```
-
-### Error Handling
-
-All endpoints follow consistent error handling:
-
-- 400: Bad Request - Invalid input or missing required fields
-- 500: Internal Server Error - Processing or generation failure
-
-Error responses include a detail message:
-
-```json
-{
-  "detail": "Error description"
-}
-```
-
-### Notes
-
-1. The API processes PDFs and generates animations using the Manim library
-2. Scene descriptions are generated using Language Models (LLMs)
-3. Animations are rendered using Manim with specific quality settings (-pql flag)
-4. All generated files are handled in temporary directories and cleaned up automatically
-5. PDF processing includes automatic compression for optimal performance
-
-</details>
-
-## Coming Soon
-
-- **Improved Generation Quality**  
-  Enhance the clarity and precision of generated animations and videos.
-
-- **Video Transcription**  
-  Automatically generate scripts explaining how concepts in the video relate to the research paper.
-
-- **Adding Audio**  
-  Support for adding voiceovers and background music to create more engaging visualizations.
-
-- **Chrome Extension**
-  Based on the code graciously contributed by [Dr. Seth Dobrin](https://drsethdobrin.com/) under the [Creative Commons License](https://github.com/HyperCluster-Tech/manimator-chrome-extension/blob/main/LICENSE), we will be releasing a Chrome Extension on the Chrome Web Store soon!
-
-## Limitations
-
-- **LLM Limitations**  
-  For accurate document parsing and code generation, we require large models like Gemini, DeepSeek V3 and Qwen 2.5 Coder 32B, which cannot be run locally.
-
-- **Video Generation Limitations**  
-  The generated video may sometimes exhibit overlap between scenes and rendered elements, leading to visual inconsistencies. Additionally, it sometimes fails to effectively visualize complex papers in a relevant and meaningful manner.
-
-## License
-
-manimator is licensed under the MIT License. See `LICENSE` for more information.
-The project uses the [Manim engine](https://github.com/ManimCommunity/manim) under the hood, which is double-licensed under the MIT license, with copyright by 3blue1brown LLC and copyright by Manim Community Developers.
-
-## Acknowledgements
-
-We acknowledge the [Manim Community](https://www.manim.community/) and [3Blue1Brown](https://github.com/3b1b/manim) for developing and maintaining the Manim library, which serves as the foundation for this project. Project developers include: [Samarth P](https://github.com/samarth777), [Vyoman Jain](https://github.com/VyoJ), [Shiva Golugula](https://github.com/Shiva4113), and [M Sai Sathvik](https://github.com/User-LazySloth) for their efforts in developing **manimator**.
-
-Models and Providers being used:
-
-- DeepSeek-V3
-- Llama 3.3 70B via Groq
-- Gemini 1.5 Flash / 2.0 Flash-experimental
-
----
-
-## Contact
-
-For any inquiries, please contact us at [email protected] or refer to our website [hypercluster.tech](https://www.hypercluster.tech/)
-
-<img src="https://api.star-history.com/svg?repos=HyperCluster-Tech/manimator&type=Date" alt="Star History Chart">