GitHub Analytics Dashboard

🌟 Welcome to the GitHub Analytics Dashboard! This project provides detailed analytics about GitHub user repositories and activities using advanced NLP techniques and predictive analytics with MindsDB.

Table of Contents

• Demo
• Features
• Requirements
• Installation
• Usage
• Contributing
• License

Demo

GitHub.Analytics.Dashboard.-.Brave.2024-07-15.11-33-43.mp4

Features

• User Overview: Displays detailed information about a GitHub user, including their repositories, followers, and following counts.
• Repository Insights: Provides metrics such as stars, forks, and watchers for a specific repository.
• Commit Analysis: Analyzes commit messages for keyword extraction and sentiment analysis.
• Issue Trends: Analyzes issues reported in a repository, including open and closed issues, labels, and comments.
• Tech Stack Identification: Identifies the technologies demanded in issues and comments.
• Issue Recommendations: Provides recommendations for resolving issues using NLP techniques.
• Predictive Analytics: Predicts issue priorities using MindsDB.

Requirements

• Python 3.7 or higher
• Required Python libraries:
  • streamlit
  • requests
  • pandas
  • transformers
  • mindsdb_sdk
  • sqlite3
  • torch

Installation

1. Clone the repository:

   git clone https://github.com/Adity20/github-analytics.git
   cd github-analytics

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required libraries:

   pip install -r requirements.txt

4. Set up MindsDB:

   • Sign up for a MindsDB account and obtain your API key.
   • Replace 'your_mindsdb_api_key' in the script with your actual MindsDB API key.

Usage

1. Run the Streamlit app:

   streamlit run main.py

2. Enter the GitHub username and repository name in the sidebar to view analytics and insights.

Project Structure

github-analytics-dashboard/
├── main.py            # Main script for the Streamlit app
├── requirements.txt   # Required Python libraries
├── readme.md          # Project documentation
└── db.db     # SQLite database for storing issues (generated during runtime)