Update README.md

README.md

@@ -1,348 +1,306 @@
-# TheoremExplainAgent (TEA) 🍵
-[![arXiv](https://img.shields.io/badge/arXiv-2502.19400-b31b1b.svg)](https://arxiv.org/abs/2502.19400)
-<a href='https://huggingface.co/papers/2502.19400'><img src='https://img.shields.io/static/v1?label=Paper&message=Huggingface&color=orange'></a> 
+---
+title: AI Animation & Voice Studio
+emoji: 🎬
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+suggested_hardware: cpu-upgrade
+suggested_storage: small
+pinned: true
+license: apache-2.0
+short_description: "Create mathematical animations with AI-powered using Manim"
+tags:
+  - text-to-speech
+  - animation
+  - mathematics
+  - manim
+  - ai-voice
+  - educational
+  - visualization
+models:
+  - kokoro-onnx/kokoro-v0_19
+datasets: []
+startup_duration_timeout: 30m
+fullWidth: true
+header: default
+disable_embedding: false
+preload_from_hub: []
+---
+
+# AI Animation & Voice Studio 🎬
+
+A powerful application that combines AI-powered text-to-speech with mathematical animation generation using Manim and Kokoro TTS. Create stunning educational content with synchronized voice narration and mathematical visualizations.
+
+## 🚀 Features
+
+- **Text-to-Speech**: High-quality voice synthesis using Kokoro ONNX models
+- **Mathematical Animations**: Create stunning mathematical visualizations with Manim
+- **LaTeX Support**: Full LaTeX rendering capabilities with TinyTeX
+- **Interactive Interface**: User-friendly Gradio web interface
+- **Audio Processing**: Advanced audio manipulation with FFmpeg and SoX
+
+## 🛠️ Technology Stack
+
+- **Frontend**: Gradio for interactive web interface
+- **Backend**: Python with FastAPI/Flask
+- **Animation**: Manim (Mathematical Animation Engine)
+- **TTS**: Kokoro ONNX for text-to-speech synthesis
+- **LaTeX**: TinyTeX for mathematical typesetting
+- **Audio**: FFmpeg, SoX, PortAudio for audio processing
+- **Deployment**: Docker container optimized for Hugging Face Spaces
+
+## 📦 Models
+
+This application uses the following pre-trained models:
+
+- **Kokoro TTS**: `kokoro-v0_19.onnx` - High-quality neural text-to-speech model
+- **Voice Models**: `voices.bin` - Voice embedding models for different speaker characteristics
+
+Models are automatically downloaded during the Docker build process from the official releases.
+
+## 🏃‍♂️ Quick Start
+
+### Using Hugging Face Spaces
+
+1. Visit the [Space](https://huggingface.co/spaces/your-username/ai-animation-voice-studio)
+2. Wait for the container to load (initial startup may take 3-5 minutes due to model loading)
+3. Upload your script or enter text directly
+4. Choose animation settings and voice parameters
+5. Generate your animated video with AI narration!
+
+### Local Development
+
+```bash
+# Clone the repository
+git clone https://huggingface.co/spaces/your-username/ai-animation-voice-studio
+cd ai-animation-voice-studio
+
+# Build the Docker image
+docker build -t ai-animation-studio .
+
+# Run the container
+docker run -p 7860:7860 ai-animation-studio
+```
 
-[**🌐 Homepage**](https://tiger-ai-lab.github.io/TheoremExplainAgent/)  | [**📖 arXiv**](https://arxiv.org/abs/2502.19400) | [**🤗 HuggingFace Dataset**](https://huggingface.co/datasets/TIGER-Lab/TheoremExplainBench) 
+Access the application at `http://localhost:7860`
 
-[![contributors](https://img.shields.io/github/contributors/TIGER-AI-Lab/TheoremExplainAgent)](https://github.com/TIGER-AI-Lab/TheoremExplainAgent/graphs/contributors)
-[![license](https://img.shields.io/github/license/TIGER-AI-Lab/TheoremExplainAgent.svg)](https://github.com/TIGER-AI-Lab/TheoremExplainAgent/blob/main/LICENSE)
-[![GitHub](https://img.shields.io/github/stars/TIGER-AI-Lab/TheoremExplainAgent?style=social)](https://github.com/TIGER-AI-Lab/TheoremExplainAgent)
-[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FTIGER-AI-Lab%2FTheoremExplainAgent&count_bg=%23C83DB9&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=visitors&edge_flat=false)](https://hits.seeyoufarm.com)
+### Environment Setup
 
-This repo contains the codebase for our paper [TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding](https://arxiv.org/abs/2502.19400)
+Create a `.env` file with your configuration:
 
-## Introduction
-TheoremExplainAgent is an AI system that generates long-form Manim videos to visually explain theorems, proving its deep understanding while uncovering reasoning flaws that text alone often hides.
+```env
+# Application settings
+DEBUG=false
+MAX_WORKERS=4
 
+# Model settings
+MODEL_PATH=/app/models
+CACHE_DIR=/tmp/cache
 
+# Optional: API keys if needed
+# OPENAI_API_KEY=your_key_here
+```
 
-https://github.com/user-attachments/assets/17f2f4f2-8f2c-4abc-b377-ac92ebda69f3
+## 🎯 Usage Examples
 
+### Basic Text-to-Speech
 
-## 📰 News
-* 2025 Mar 3: Generation code and Evaluation code released. Thanks for the wait!
-<!--* 2025 Mar 3: Reach 404 stars without code.-->
-* 2025 Feb 27: Paper available on [Arxiv](https://arxiv.org/abs/2502.19400). Thanks AK for putting our paper on [HF Daily](https://huggingface.co/papers/2502.19400).
+```python
+# Example usage in your code
+from src.tts import generate_speech
+
+audio = generate_speech(
+    text="Hello, this is a test of the text-to-speech system",
+    voice="default",
+    speed=1.0
+)
+```
 
-## Installation
+### Mathematical Animation
 
-> **Look at the [FAQ section in this README doc](https://github.com/TIGER-AI-Lab/TheoremExplainAgent?tab=readme-ov-file#-faq) if you encountered any errors. If that didnt help, create a issue**<br>
+```python
+# Example Manim scene
+from manim import *
 
-1. Setting up conda environment
-```shell
-conda create --name tea python=3.12.8
-conda activate tea
-pip install -r requirements.txt
+class Example(Scene):
+    def construct(self):
+        # Your animation code here
+        pass
 ```
 
-2. You may also need to install latex and other dependencies for Manim Community. Look at [Manim Installation Docs](https://docs.manim.community/en/stable/installation.html) for more details.
-```shell
-# You might need these dependencies if you are using Linux Ubuntu:
-sudo apt-get install portaudio19-dev
-sudo apt-get install libsdl-pango-dev
+## 📁 Project Structure
+
+```
+├── src/                    # Source code
+│   ├── tts/               # Text-to-speech modules
+│   ├── manim_scenes/      # Manim animation scenes
+│   └── utils/             # Utility functions
+├── models/                # Pre-trained models (auto-downloaded)
+├── output/                # Generated content output
+├── requirements.txt       # Python dependencies
+├── Dockerfile            # Container configuration
+├── gradio_app.py         # Main application entry point
+└── README.md             # This file
 ```
 
-3. Then Download the Kokoro model and voices using the commands to enable TTS service.
+## ⚙️ Configuration
 
-```shell
-mkdir -p models && wget -P models https://github.com/thewh1teagle/kokoro-onnx/releases/download/model-files/kokoro-v0_19.onnx && wget -P models https://github.com/thewh1teagle/kokoro-onnx/releases/download/model-files/voices.bin
-```
+### Docker Environment Variables
 
-4. Create `.env` based on `.env.template`, filling in the environmental variables according to the models you choose to use.
-See [LiteLLM](https://docs.litellm.ai/docs/providers) for reference.
+- `GRADIO_SERVER_NAME`: Server host (default: 0.0.0.0)
+- `GRADIO_SERVER_PORT`: Server port (default: 7860)
+- `PYTHONPATH`: Python path configuration
+- `HF_HOME`: Hugging Face cache directory
 
-```shell
-touch .env
-```
-Then open the `.env` file and edit it with whatever text editor you like.
-
-Your `.env` file should look like the following:
-```shell
-# OpenAI
-OPENAI_API_KEY=""
-
-# Azure OpenAI
-AZURE_API_KEY=""
-AZURE_API_BASE=""
-AZURE_API_VERSION=""
-
-# Google Vertex AI
-VERTEXAI_PROJECT=""
-VERTEXAI_LOCATION=""
-GOOGLE_APPLICATION_CREDENTIALS=""
-
-# Google Gemini
-GEMINI_API_KEY=""
-
-...
-
-# Kokoro TTS Settings
-KOKORO_MODEL_PATH="models/kokoro-v0_19.onnx"
-KOKORO_VOICES_PATH="models/voices.bin"
-KOKORO_DEFAULT_VOICE="af"
-KOKORO_DEFAULT_SPEED="1.0"
-KOKORO_DEFAULT_LANG="en-us"
-```
-Fill in the API keys according to the model you wanted to use.
+### Application Settings
 
-5. Configure Python path. Note that you need to configure the python path to make it work. Otherwise you may encounter import issues (like not being able to import src etc.)
-```shell
-export PYTHONPATH=$(pwd):$PYTHONPATH
-```
+Modify settings in your `.env` file or through environment variables:
 
-6. (Optional) To setup RAG, See [https://github.com/TIGER-AI-Lab/TheoremExplainAgent?tab=readme-ov-file#generation-with-rag](https://github.com/TIGER-AI-Lab/TheoremExplainAgent?tab=readme-ov-file#generation-with-rag).
+- Model parameters
+- Audio quality settings
+- Animation render settings
+- Cache configurations
 
-> **Look at the [FAQ section in this README doc](https://github.com/TIGER-AI-Lab/TheoremExplainAgent?tab=readme-ov-file#-faq) if you encountered any errors. If that didnt help, create a issue**<br>
+## 🔧 Development
 
-## Generation
+### Prerequisites
 
-### Supported Models
-<!--You can customize the allowed models by editing the `src/utils/allowed_models.json` file. This file specifies which `model` and `helper_model` the system is permitted to use.--> 
-The model naming follows the LiteLLM convention. For details on how models should be named, please refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/providers).
+- Docker and Docker Compose
+- Python 3.12+
+- Git
 
-### Generation (Single topic)
-```shell
-python generate_video.py \
-      --model "openai/o3-mini" \
-      --helper_model "openai/o3-mini" \
-      --output_dir "output/your_exp_name" \
-      --topic "your_topic" \
-      --context "description of your topic, e.g. 'This is a topic about the properties of a triangle'" \
-```
+### Setting Up Development Environment
 
-Example:
-```shell
-python generate_video.py \
-      --model "openai/o3-mini" \
-      --helper_model "openai/o3-mini" \
-      --output_dir "output/my_exp_name" \
-      --topic "Big O notation" \
-      --context "most common type of asymptotic notation in computer science used to measure worst case complexity" \
-```
+```bash
+# Install dependencies locally for development
+pip install -r requirements.txt
 
-### Generation (in batch)
-```shell
-python generate_video.py \
-      --model "openai/o3-mini" \
-      --helper_model "openai/o3-mini" \
-      --output_dir "output/my_exp_name" \
-      --theorems_path data/thb_easy/math.json \
-      --max_scene_concurrency 7 \
-      --max_topic_concurrency 20 \
-```
+# Run tests (if available)
+python -m pytest tests/
 
-### Generation with RAG
-Before using RAG, download the RAG documentation from this [Google Drive link](https://drive.google.com/file/d/1Tn6J_JKVefFZRgZbjns93KLBtI9ullRv/view?usp=sharing). After downloading, unzip the file. For example, if you unzip it to `data/rag/manim_docs`, then you should set `--manim_docs_path` to `data/rag/manim_docs`. The vector database will be created the first time you run with RAG.
-
-```shell
-python generate_video.py \
-            --model "openai/o3-mini" \
-            --helper_model "openai/o3-mini" \
-            --output_dir "output/with_rag/o3-mini/vtutorbench_easy/math" \
-            --topic "Big O notation" \
-            --context "most common type of asymptotic notation in computer science used to measure worst case complexity" \
-            --use_rag \
-            --chroma_db_path "data/rag/chroma_db" \
-            --manim_docs_path "data/rag/manim_docs" \
-            --embedding_model "vertex_ai/text-embedding-005"
-```
+# Format code
+black .
+isort .
 
-We support more options for generation, see below for more details:
-```shell
-usage: generate_video.py [-h]
-                         [--model]
-                         [--topic TOPIC] [--context CONTEXT]
-                         [--helper_model]
-                         [--only_gen_vid] [--only_combine] [--peek_existing_videos] [--output_dir OUTPUT_DIR] [--theorems_path THEOREMS_PATH]
-                         [--sample_size SAMPLE_SIZE] [--verbose] [--max_retries MAX_RETRIES] [--use_rag] [--use_visual_fix_code]
-                         [--chroma_db_path CHROMA_DB_PATH] [--manim_docs_path MANIM_DOCS_PATH]
-                         [--embedding_model {azure/text-embedding-3-large,vertex_ai/text-embedding-005}] [--use_context_learning]
-                         [--context_learning_path CONTEXT_LEARNING_PATH] [--use_langfuse] [--max_scene_concurrency MAX_SCENE_CONCURRENCY]
-                         [--max_topic_concurrency MAX_TOPIC_CONCURRENCY] [--debug_combine_topic DEBUG_COMBINE_TOPIC] [--only_plan] [--check_status]
-                         [--only_render] [--scenes SCENES [SCENES ...]]
-
-Generate Manim videos using AI
-
-options:
-  -h, --help            show this help message and exit
-  --model               Select the AI model to use
-  --topic TOPIC         Topic to generate videos for
-  --context CONTEXT     Context of the topic
-  --helper_model        Select the helper model to use
-  --only_gen_vid        Only generate videos to existing plans
-  --only_combine        Only combine videos
-  --peek_existing_videos, --peek
-                        Peek at existing videos
-  --output_dir OUTPUT_DIR
-                        Output directory
-  --theorems_path THEOREMS_PATH
-                        Path to theorems json file
-  --sample_size SAMPLE_SIZE, --sample SAMPLE_SIZE
-                        Number of theorems to sample
-  --verbose             Print verbose output
-  --max_retries MAX_RETRIES
-                        Maximum number of retries for code generation
-  --use_rag, --rag      Use Retrieval Augmented Generation
-  --use_visual_fix_code, --visual_fix_code
-                        Use VLM to fix code with rendered visuals
-  --chroma_db_path CHROMA_DB_PATH
-                        Path to Chroma DB
-  --manim_docs_path MANIM_DOCS_PATH
-                        Path to manim docs
-  --embedding_model {azure/text-embedding-3-large,vertex_ai/text-embedding-005}
-                        Select the embedding model to use
-  --use_context_learning
-                        Use context learning with example Manim code
-  --context_learning_path CONTEXT_LEARNING_PATH
-                        Path to context learning examples
-  --use_langfuse        Enable Langfuse logging
-  --max_scene_concurrency MAX_SCENE_CONCURRENCY
-                        Maximum number of scenes to process concurrently
-  --max_topic_concurrency MAX_TOPIC_CONCURRENCY
-                        Maximum number of topics to process concurrently
-  --debug_combine_topic DEBUG_COMBINE_TOPIC
-                        Debug combine videos
-  --only_plan           Only generate scene outline and implementation plans
-  --check_status        Check planning and code status for all theorems
-  --only_render         Only render scenes without combining videos
-  --scenes SCENES [SCENES ...]
-                        Specific scenes to process (if theorems_path is provided)
+# Lint code
+flake8 .
 ```
 
-## Evaluation
-Note that Gemini and GPT4o is required for evaluation.
-
-Currently, evaluation requires a video file and a subtitle file (SRT format).
-
-Video evaluation:
-```shell
-usage: evaluate.py [-h]
-                   [--model_text {gemini/gemini-1.5-pro-002,gemini/gemini-1.5-flash-002,gemini/gemini-2.0-flash-001,vertex_ai/gemini-1.5-flash-002,vertex_ai/gemini-1.5-pro-002,vertex_ai/gemini-2.0-flash-001,openai/o3-mini,gpt-4o,azure/gpt-4o,azure/gpt-4o-mini,bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0,bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0,bedrock/anthropic.claude-3-5-haiku-20241022-v1:0,bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0}]
-                   [--model_video {gemini/gemini-1.5-pro-002,gemini/gemini-2.0-flash-exp,gemini/gemini-2.0-pro-exp-02-05}]
-                   [--model_image {gemini/gemini-1.5-pro-002,gemini/gemini-1.5-flash-002,gemini/gemini-2.0-flash-001,vertex_ai/gemini-1.5-flash-002,vertex_ai/gemini-1.5-pro-002,vertex_ai/gemini-2.0-flash-001,openai/o3-mini,gpt-4o,azure/gpt-4o,azure/gpt-4o-mini,bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0,bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0,bedrock/anthropic.claude-3-5-haiku-20241022-v1:0,bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0}]
-                   [--eval_type {text,video,image,all}] --file_path FILE_PATH --output_folder OUTPUT_FOLDER [--retry_limit RETRY_LIMIT] [--combine] [--bulk_evaluate] [--target_fps TARGET_FPS]
-                   [--use_parent_folder_as_topic] [--max_workers MAX_WORKERS]
-
-Automatic evaluation of theorem explanation videos with LLMs
-
-options:
-  -h, --help            show this help message and exit
-  --model_text {gemini/gemini-1.5-pro-002,gemini/gemini-1.5-flash-002,gemini/gemini-2.0-flash-001,vertex_ai/gemini-1.5-flash-002,vertex_ai/gemini-1.5-pro-002,vertex_ai/gemini-2.0-flash-001,openai/o3-mini,gpt-4o,azure/gpt-4o,azure/gpt-4o-mini,bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0,bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0,bedrock/anthropic.claude-3-5-haiku-20241022-v1:0,bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0}
-                        Select the AI model to use for text evaluation
-  --model_video {gemini/gemini-1.5-pro-002,gemini/gemini-2.0-flash-exp,gemini/gemini-2.0-pro-exp-02-05}
-                        Select the AI model to use for video evaluation
-  --model_image {gemini/gemini-1.5-pro-002,gemini/gemini-1.5-flash-002,gemini/gemini-2.0-flash-001,vertex_ai/gemini-1.5-flash-002,vertex_ai/gemini-1.5-pro-002,vertex_ai/gemini-2.0-flash-001,openai/o3-mini,gpt-4o,azure/gpt-4o,azure/gpt-4o-mini,bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0,bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0,bedrock/anthropic.claude-3-5-haiku-20241022-v1:0,bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0}
-                        Select the AI model to use for image evaluation
-  --eval_type {text,video,image,all}
-                        Type of evaluation to perform
-  --file_path FILE_PATH
-                        Path to a file or a theorem folder
-  --output_folder OUTPUT_FOLDER
-                        Directory to store the evaluation files
-  --retry_limit RETRY_LIMIT
-                        Number of retry attempts for each inference
-  --combine             Combine all results into a single JSON file
-  --bulk_evaluate       Evaluate a folder of theorems together
-  --target_fps TARGET_FPS
-                        Target FPS for video processing. If not set, original video FPS will be used
-  --use_parent_folder_as_topic
-                        Use parent folder name as topic name for single file evaluation
-  --max_workers MAX_WORKERS
-                        Maximum number of concurrent workers for parallel processing
+### Building and Testing
+
+```bash
+# Build the Docker image
+docker build -t your-app-name:dev .
+
+# Test the container locally
+docker run --rm -p 7860:7860 your-app-name:dev
+
+# Check container health
+docker run --rm your-app-name:dev python -c "import src; print('Import successful')"
 ```
-* For `file_path`, it is recommended to pass a folder containing both an MP4 file and an SRT file.
 
-## Misc: Modify the system prompt in TheoremExplainAgent
+## 📊 Performance & Hardware
 
-If you want to modify the system prompt, you need to:
+### Recommended Specs for Hugging Face Spaces
 
-1. Modify files in `task_generator/prompts_raw` folder.
-2. Run `task_generator/parse_prompt.py` to rebuild the `__init__.py` file.
+- **Hardware**: `cpu-upgrade` (recommended for faster rendering)
+- **Storage**: `small` (sufficient for models and temporary files)
+- **Startup Time**: ~3-5 minutes (due to model loading and TinyTeX setup)
+- **Memory Usage**: ~2-3GB during operation
 
-```python
-cd task_generator
-python parse_prompt.py
-cd ..
-```
+### System Requirements
 
-## TheoremExplainBench (TEB)
+- **Memory**: Minimum 2GB RAM, Recommended 4GB+
+- **CPU**: Multi-core processor recommended for faster animation rendering
+- **Storage**: ~1.5GB for models and dependencies
+- **Network**: Stable connection for initial model downloads
 
-TheoremExplainBench can be found on https://huggingface.co/datasets/TIGER-Lab/TheoremExplainBench.
+### Optimization Tips
 
-How to use:
-```python
-import datasets
-dataset = datasets.load_dataset("TIGER-Lab/TheoremExplainBench")
-```
+- Models are cached after first download
+- Gradio interface uses efficient streaming for large outputs
+- Docker multi-stage builds minimize final image size
+- TinyTeX installation is optimized for essential packages only
+
+## 🐛 Troubleshooting
 
-Dataset info:
-```shell
-DatasetDict({
-    train: Dataset({
-        features: ['uid', 'subject', 'difficulty', 'theorem', 'description', 'subfield'],
-        num_rows: 240
-    })
-})
+### Common Issues
+
+**Build Failures**:
+```bash
+# Clear Docker cache if build fails
+docker system prune -a
+docker build --no-cache -t your-app-name .
 ```
 
-## ❓ FAQ
+**Model Download Issues**:
+- Check internet connection
+- Verify model URLs are accessible
+- Models will be re-downloaded if corrupted
 
-The FAQ should cover the most common errors you could encounter. If you see something new, report it on issues.
+**Memory Issues**:
+- Reduce batch sizes in configuration
+- Monitor memory usage with `docker stats`
 
-Q: Error `src.utils.kokoro_voiceover import KokoroService  # You MUST import like this as this is our custom voiceover service. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'src'`. <br>
-A: Please run `export PYTHONPATH=$(pwd):$PYTHONPATH` when you start a new terminal. <br>
+**Audio Issues**:
+- Ensure audio drivers are properly installed
+- Check PortAudio configuration
 
-Q: Error `Files not found` <br>
-A: Check your Manim installation. <br>
+### Getting Help
 
-Q: Error `latex ...` <br>
-A: Check your latex installation. <br>
+1. Check the [Discussions](https://huggingface.co/spaces/your-username/ai-animation-voice-studio/discussions) tab
+2. Review container logs in the Space settings
+3. Enable debug mode in configuration
+4. Report issues in the Community tab
 
-Q: The output log is not showing response? <br>
-A: It could be API-related issues. Make sure your `.env` file is properly configured (fill in your API keys), or you can enable litellm debug mode to figure out the issues. <be>
+### Common Configuration Issues
 
-Q: Plans / Scenes are missing? <br>
-A: It could be API-related issues. Make sure your `.env` file is properly configured (fill in your API keys), or you can enable litellm debug mode to figure out the issues. <br>
+**Space Configuration**:
+- Ensure `app_port: 7860` is set in README.md front matter
+- Check that `sdk: docker` is properly configured
+- Verify hardware suggestions match your needs
 
+**Model Loading**:
+- Models download automatically on first run
+- Check Space logs for download progress
+- Restart Space if models fail to load
 
-## 🖊️ Citation
+## 🤝 Contributing
 
-Please kindly cite our paper if you use our code, data, models or results:
-```bibtex
-@misc{ku2025theoremexplainagentmultimodalexplanationsllm,
-      title={TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding}, 
-      author={Max Ku and Thomas Chong and Jonathan Leung and Krish Shah and Alvin Yu and Wenhu Chen},
-      year={2025},
-      eprint={2502.19400},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2502.19400}, 
-}
-```
+We welcome contributions! Please see our contributing guidelines:
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Add tests if applicable
+5. Submit a pull request
 
-## 🎫 License
+### Code Style
 
-This project is released under the [the MIT License](LICENSE).
+- Follow PEP 8 for Python code
+- Use Black for code formatting
+- Add docstrings for functions and classes
+- Include type hints where appropriate
 
-## ⭐ Star History
+## 📄 License
 
-[![Star History Chart](https://api.star-history.com/svg?repos=TIGER-AI-Lab/TheoremExplainAgent&type=Date)](https://star-history.com/#TIGER-AI-Lab/TheoremExplainAgent&Date)
+This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
 
-## 💞 Acknowledgements
+## 🙏 Acknowledgments
 
-We want to thank [Votee AI](https://votee.ai/) for sponsoring API keys to access the close-sourced models.
+- [Manim Community](https://www.manim.community/) for the animation engine
+- [Kokoro TTS](https://github.com/thewh1teagle/kokoro-onnx) for text-to-speech models
+- [Gradio](https://gradio.app/) for the web interface framework
+- [Hugging Face](https://huggingface.co/) for hosting and infrastructure
 
-The code is built upon the below repositories, we thank all the contributors for open-sourcing.
-* [Manim Community](https://www.manim.community/)
-* [kokoro-manim-voiceover](https://github.com/xposed73/kokoro-manim-voiceover)
-* [manim-physics](https://github.com/Matheart/manim-physics)
-* [manim-Chemistry](https://github.com/UnMolDeQuimica/manim-Chemistry)
-* [ManimML](https://github.com/helblazer811/ManimML)
-* [manim-dsa](https://github.com/F4bbi/manim-dsa)
-* [manim-circuit](https://github.com/Mr-FuzzyPenguin/manim-circuit)
+## 📞 Contact
 
-## 🚨 Disclaimer
+- **Author**: Your Name
+- **Email**: [email protected]
+- **GitHub**: [@your-username](https://github.com/your-username)
+- **Hugging Face**: [@your-username](https://huggingface.co/your-username)
 
-**This work is intended for research purposes only. The authors do not encourage or endorse the use of this codebase for commercial applications. The code is provided "as is" without any warranties, and users assume all responsibility for its use.**
+---
 
-Tested Environment: MacOS, Linux
+*Built with ❤️ for the open-source community*