Portfolio

GitHub Repo Analyzer

GitHub Repo Analyzer is a web application that analyzes a user’s starred GitHub repositories, providing insights and summaries.

Live Demo

You can try out the live version of this application here: GitHub Repo Analyzer on Replit

Features

• Fetch and analyze starred repositories for a given GitHub username
• Generate summaries of repository README files
• Visualize language distribution of starred repos
• Show cumulative stars over time
• Analyze and display top topics
• Suggest interesting repositories based on a scoring system
• Generate a downloadable markdown summary of all repos

Technologies Used

• Python 3.10+
• Flask web framework
• PyGithub for GitHub API interactions
• Pandas for data manipulation
• Matplotlib for data visualization
• NLTK and sumy for text summarization

Setup

1. Clone the repository:

   git clone https://github.com/stochastic-sisyphus/github-repo-analyzer.git
   cd github-repo-analyzer
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your GitHub token:
   • Create a personal access token on GitHub
   • Set it as an environment variable:

     export GITHUB_TOKEN=your_token_here
     

4. Run the application:

   python main.py
   

5. Open a web browser and navigate to http://localhost:8080

Usage

1. Enter a GitHub username in the provided form
2. Click “Analyze” to process the user’s starred repositories
3. View the generated insights, including:
   • Top 5 starred repositories
   • Language distribution
   • Cumulative stars over time
   • Top 10 topics
   • Suggested interesting repositories
4. Download the markdown summary if desired

Deployment

This application is deployed on Replit. To deploy your own instance:

1. Fork this repository to your Replit account
2. Set up the GITHUB_TOKEN secret in your Replit project
3. Click the “Run” button in Replit

Recent Updates

• Updated the caching mechanism to improve performance
• Enhanced the text summarization algorithm for better accuracy
• Improved the visualization of cumulative stars over time

Additional Information

• The application now includes a caching mechanism to reduce the number of API calls and improve performance.
• The text summarization algorithm has been enhanced to provide more accurate summaries of repository README files.
• The visualization of cumulative stars over time has been improved for better clarity and understanding.