GitHub Repository Code Analysis System

This system provides end-to-end analysis of GitHub repositories using the complete Lynx parser pipeline.

Features

🔗 GitHub Integration

• Clone any public GitHub repository
• Automatic repository validation
• Clean workspace management

🔍 Code Analysis

• Multilspy-based Python code parsing
• Extract functions, classes, methods, and relationships
• Generate comprehensive dependency graphs

📊 Interactive Visualization

• React-based graph visualization
• Navigate code relationships visually
• Export to multiple formats

🤖 AI-Powered Descriptions

• Gemini AI integration for code explanations
• Context-aware function and class descriptions
• Relationship analysis and architectural insights

Quick Start

Basic Usage (Interactive Mode)

python basic-code-entry.py

Command Line Usage

# Analyze a repository
python basic-code-entry.py https://github.com/user/repository

# With AI features
python basic-code-entry.py https://github.com/user/repo --api-key YOUR_GEMINI_KEY

# Custom workspace
python basic-code-entry.py https://github.com/user/repo --workspace ./my-workspace

Programmatic Usage

from basic_code_entry import GitHubCodeAnalyzer

analyzer = GitHubCodeAnalyzer()
results = analyzer.analyze_repository(
    "https://github.com/user/repository",
    gemini_api_key="your-api-key"  # optional
)

print(f"Analysis completed: {results['stages']}")
print(f"Generated artifacts: {results['artifacts']}")

Pipeline Stages

1. Repository Cloning

• Validates GitHub URL
• Performs shallow clone for efficiency
• Creates isolated workspace

2. Code Parsing

• Uses multilspy language server
• Extracts Python code structure
• Generates graph data (nodes and edges)
• Outputs to react-graph-viewer/public/artifacts/

3. Visualization Building

• Builds React application
• Processes graph data for interactive display
• Generates standalone HTML visualization

4. AI Description Setup

• Initializes code describer
• Prepares for AI-powered analysis
• Enables interactive exploration

Generated Outputs

After successful analysis, you'll have:

workspace/
├── [repository-name]/           # Cloned repository
react-graph-viewer/
├── public/artifacts/
│   ├── combined_graph.json      # Full graph data
│   ├── call_graph.json          # Function call relationships
│   └── declaration_graph.json   # Class/method structure
└── build/
    └── index.html               # Interactive visualization

Next Steps After Analysis

1. Explore with AI Describer

   python basic-describer.py
   
   # Interactive commands:
   > list                        # Show all nodes
   > describe Calculator::main.py # AI description
   > relations add::operations.py # Relationship analysis

2. View Interactive Visualization

   • Open react-graph-viewer/build/index.html in browser
   • Navigate the code graph visually
   • Switch between different graph views

3. Analyze Specific Components

   # Use describer commands
   > context Calculator::main.py  # Show context
   > search "validate"           # Find nodes by name
   > files                       # List all analyzed files

Requirements

• Python 3.8+
• Git (for repository cloning)
• Node.js & npm (for React visualization)
• Dependencies:
  • multilspy
  • google-generativeai (optional, for AI features)

Error Handling

The system gracefully handles:

• Invalid GitHub URLs
• Repositories without Python code
• Network connectivity issues
• Missing dependencies
• Parser failures

Each stage reports success/failure independently, allowing partial analysis even if some stages fail.

Example Analysis Flow

$ python basic-code-entry.py https://github.com/example/python-project

============================================================
GITHUB REPOSITORY ANALYSIS
============================================================
Repository: example/python-project
URL: https://github.com/example/python-project

1. Cloning repository...
Successfully cloned repository to workspace/python-project
✓ Repository cloned successfully

2. Parsing code structure...
Found 15 Python files to analyze
Language server started successfully!
Parser completed successfully
✓ Code parsing completed

3. Building visualization...
Installing npm dependencies...
Building React app...
✓ Visualization built successfully

4. Setting up AI describer...
Code describer ready!
✓ AI describer ready

============================================================
ANALYSIS COMPLETE
============================================================
Completed 4/4 stages successfully

Generated artifacts:
  - combined_graph: 45 nodes, 67 edges
  - call_graph: 45 nodes, 23 edges
  - declaration_graph: 45 nodes, 44 edges

Next steps:
  1. Run: python basic-describer.py
  2. Use 'extract' command to get detailed info
  3. Use 'describe <node_id>' for AI descriptions
  4. Open react-graph-viewer/build/index.html for visualization

Troubleshooting

Repository cloning fails:

• Check internet connection
• Verify GitHub URL is correct and public
• Ensure Git is installed

Parser fails:

• Ensure repository contains Python files
• Check for syntax errors in Python code
• Verify multilspy dependencies are installed

React build fails:

• Ensure Node.js and npm are installed
• Check npm dependencies in react-graph-viewer/
• Try npm install manually in react-graph-viewer/

AI features unavailable:

• Install: pip install google-generativeai
• Obtain Gemini API key from Google AI Studio
• Set as environment variable: export GEMINI_API_KEY=your-key