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---
title: Code Weaver
emoji: 🔥
colorFrom: purple
colorTo: indigo
sdk: docker
pinned: false
short_description: Interact with your github repo
---
# Code Weaver
This application is inspired by the [Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) project by peterw, which demonstrates how to create a chatbot using Streamlit, OpenAI, and Activeloop's Deep Lake. Code Weaver builds upon that foundation, offering a more user-friendly experience and optimized performance.
Code Weaver is designed to let you interact with a GitHub repository through a conversational interface. It indexes the contents of a repository, allowing you to ask questions and receive answers based on the repository's documentation and code.
## Key Improvements Over Original Implementation
Code Weaver offers several key improvements that enhance the user experience and the app's overall performance:
* **Purely Frontend UI:**
* The original application used a command-line interface (CLI) for configuration. Code Weaver provides a completely UI-based experience. All configurations (API keys, repo URLs, dataset names, etc.) are entered directly into the Streamlit app, making it much more accessible to users.
* **Optimized Document Parsing:**
* The document parsing and splitting process have been optimized for speed and handling of large files by using `RecursiveCharacterTextSplitter`.
* **Improved Loading times:**
* The app is now faster because the embeddings are not recalculated each time the app is started, this is achieved by using `InMemoryCache`.
* **Direct OpenAI Embeddings Model:**
* The app now directly specifies the usage of the `"text-embedding-ada-002"` OpenAI embedding model, resulting in faster embedding calculations.
* **In Memory Cache:**
* `InMemoryCache` is used to cache the results from the language model improving loading speeds.
* **Streamlined Setup:**
* No more need for `.env` files, as all necessary variables are inserted directly in the app UI.
* **Easier Deployment**: The app is built to be deployed in Hugging Face Spaces, making the deployment process easier.
## How to Use Code Weaver
1. **Clone the repository:**
```bash
https://github.com/WHITELOTUS0/chat-with-a-repo.git
```
2. **Navigate to the Project Directory:**
```bash
cd your-repo-name
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run the app:**
```bash
streamlit run app.py
```
5. **Enter configurations**: The Streamlit app will open in your web browser. In the sidebar, provide:
* Your OpenAI API Key
* Your Activeloop Token
* Your Activeloop Username
* The GitHub repository URL you want to explore
* The file extensions to include (optional)
* A desired name for the DeepLake dataset
6. **Process the repository**: Click on the "Process Repository" Button, and wait for it to finish.
7. **Start Chatting:** After processing is done, you can start asking questions about the repository in the provided input text area.
## Contributing
Contributions to this project are always welcome. If you find any bugs or would like to suggest new features, feel free to create a pull request or open an issue in this repository.
## Credits
* Inspired by the [Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) project by peterw.
* Built using Python, Streamlit, OpenAI, Activeloop, and Langchain.
## License
[MIT License](LICENSE)
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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