GitHub Activity Analyzer

A Streamlit dashboard that analyzes a GitHub user's activity and visualizes:

• Commit patterns over time (heatmap)
• Weekly code additions/deletions (line chart)
• Top active repositories (pie chart)
• Summary metrics (total commits, active repositories, total lines added)

Live Demo

Web URL: https://githu8-activity-analyzer.streamlit.app/

Features

• Clean, responsive Streamlit UI with custom styling
• GitHub API data extraction using PyGithub
• Commit and code frequency processing with pandas
• Visual analytics using matplotlib and seaborn
• Progress logs shown in-app while data is being fetched

Project Structure

github-analyzer/
├── app.py               # Streamlit entrypoint and UI
├── data_extractor.py    # GitHub authentication + data extraction
├── data_processor.py    # Data transformation with pandas
├── visualizer.py        # Chart generation (heatmap/line/pie)
├── test_auth.py         # Quick token authentication check
├── requirements.txt     # Python dependencies
└── .env                 # Environment variables (local only)

How It Works

1. Input Username: User enters a GitHub username in the Streamlit app.
2. Extract Data (data_extractor.py):
   • Loads GITHUB_TOKEN from .env
   • Authenticates with GitHub
   • Iterates through user repositories
   • Collects:
     • Commit metadata (repo, date)
     • Weekly code frequency (week_timestamp, additions, deletions)
3. Process Data (data_processor.py):
   • Converts raw lists to DataFrames
   • Parses timestamps/datetimes
   • Derives analysis columns for visualization
4. Visualize Data (visualizer.py):
   • Commit heatmap (day of week × week of year)
   • Additions vs deletions over time
   • Top 5 active repositories
5. Render Dashboard (app.py):
   • Displays key metrics and charts
   • Shows progress logs during fetch/processing

Prerequisites

• Python 3.10+ (recommended)
• A GitHub Personal Access Token with read access to repositories/activity

Installation

1. Clone the repository:

   git clone https://github.com/Tharindu-Nimsara/github-activity-analyzer.git
   cd github-activity-analyzer

2. Create and activate a virtual environment:

   Windows (PowerShell):

   python -m venv venv
   .\venv\Scripts\Activate.ps1

3. Install dependencies:

   pip install -r requirements.txt

Environment Variables

Create a .env file in the project root:

GITHUB_TOKEN=your_github_personal_access_token

Keep your token private. Do not commit .env to source control.

Run the App

streamlit run app.py

Then open the local URL shown in the terminal (usually http://localhost:8501).

Authentication Test (Optional)

Use the helper script to verify token setup:

python test_auth.py

Expected success output:

Successfully authenticated as: <your_username>

Dashboard Outputs

After generating a report, the app shows:

• Key Metrics
  • Total Commits Found
  • Active Repositories
  • Total Lines Added
• Commit Activity Heatmap
  • Commits by day of week and week of year
• Code Frequency Over Time
  • Weekly additions vs deletions
• Top 5 Active Repositories
  • Distribution by commit count

Notes & Limitations

• GitHub API rate limits can affect data retrieval.
• Some repositories may not return stats (e.g., empty/inactive repos).
• Code frequency data depends on GitHub-generated repository statistics availability.

Tech Stack

• Frontend/App: Streamlit
• Data Access: PyGithub, python-dotenv
• Data Processing: pandas, numpy
• Visualization: matplotlib, seaborn

Troubleshooting

• "No data found or API limit reached"
  • Check GITHUB_TOKEN validity and scopes.
  • Retry after API rate limit reset.
• Authentication failed
  • Recreate token and update .env.
  • Run python test_auth.py to verify credentials.
• Missing dependencies
  • Re-run pip install -r requirements.txt inside your active virtual environment.

License

No license file is currently included in this repository.

Author

Developed by Tharindu Nimsara.

Demo video

Demo.Video.mp4