GitHubGPT

GitHubGPT: GitHub Code Generation AI Model

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NOTE: This repository is not completed. It is still a work in progress. The descriptions below outline the goals of this repository.

GitHubGPT is a powerful GPT plugin allowing you to analyze, explore, and extract insights from code repositories hosted on GitHub.

By leveraging the functionalities of the GitHub API, GitHubGPT facilitates the retrieval of code files from any GitHub repository. It enables you to specify the repository URL and effortlessly fetches the contents, including various code files. This plugin supports both public and private repositories, granting you access to a wide range of codebases.

This empowers developers, researchers, and enthusiasts to gain a deeper understanding of the code, explore potential solutions, and receive assistance in real-time.

GitHubGPT streamlines the process of analyzing code repositories, eliminating the need for manual exploration or switching between different tools and platforms. It enables you to leverage the power of AI-driven natural language processing directly within your GitHub workflow.

This project aims to develop an AI model that can read and generate code specifically for a particular GitHub repository. The model utilizes the power of Hugging Face's models in Python to generate code suggestions and completions based on given prompts or requirements.

Key Features:

• Fetch code files from GitHub repositories effortlessly
• Support for both public and private repositories
• Seamless integration with AutoGPT for enhanced code understanding and analysis
• Quick and efficient retrieval of code files from GitHub repositories
• Ability to handle repositories of any size and complexity
• Access to a wide range of programming languages and file types
• Real-time assistance and insights based on AI-generated responses
• Streamlined workflow within the familiar GitHub environment
• Facilitates code exploration, problem-solving, and idea generation
• Simplifies collaboration and knowledge sharing among developers and researchers
• Helps accelerate development cycles and improve code quality
• Flexible and customizable to suit specific use cases and requirements
• Regular updates and improvements to ensure compatibility and performance

Project Overview

The GitHub Code Generation AI Model follows the following steps:

1. Data Collection: Given a GitHub repository link, the model collects code samples from all files within the repository. It utilizes the GitHub API or web scraping techniques to retrieve the code snippets.
2. Data Preprocessing: The collected code samples undergo preprocessing to ensure they are in a suitable format for training. This involves removing comments, standardizing code formatting, and handling any specific code syntax or language requirements.
3. Model Training: Hugging Face's Transformers library is used to train a language model. Transfer learning techniques are employed to fine-tune a pre-trained model, such as GPT-3 or GPT-2, on the code dataset.
4. Model Evaluation: The trained model's performance is evaluated on a validation set using appropriate metrics for code generation tasks. This evaluation helps assess the model's capabilities and identify areas for improvement.
5. Code Generation: The trained model is then utilized to generate code suggestions and completions. Given prompts or specific requirements, the model generates code snippets that align with the desired functionality, leveraging its understanding of the codebase from the training process.

Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

git clone https://github.com/zeelsheladiya/GitHubGPT.git

2. Install the required dependencies:

pip install -r requirements.txt

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and ensure they are properly tested.
4. Commit and push your changes to your forked repository.
5. Submit a pull request detailing your changes and their benefits.

Please ensure that your contributions align with the project's coding standards and guidelines.

License

This project is licensed under the MIT License.

Acknowledgments

• This project was inspired by the advancements in AI and code generation techniques.
• Special thanks to Hugging Face for their powerful Transformers library.

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