LLM Code Automation Hub 🤖✨

🌟 Project Overview

This project leverages the power of Large Language Models (LLMs) to automate common software development tasks, reduce repetitive work, and increase developer productivity. Using a combination of GitHub Actions, Flask, and OpenAI's API, we've built a comprehensive system that can analyze code changes, suggest improvements, generate tests, and create meaningful reports.

🧩 Key Components

1. Automated Code Analysis Pipeline 🔍

Our system automatically analyzes code changes in the repository:

• Code Review - LLM reviews code and provides constructive feedback
• Bug Detection - Identifies common programming issues and suggests fixes
• Linting Automation - Finds and resolves style and best practice violations
• Test Generation - Automatically creates test cases for code functions

2. Report Generation System 📊

Every analysis creates markdown reports that are published to GitHub Pages:

• Individual file reports with detailed findings
• Change history tracking
• Beautiful web interface to browse all code changes

3. Pull Request Automation 🔄

Streamlines the PR process:

• Automatically identifies changed files
• Generates meaningful PR descriptions based on code changes
• Creates pull requests with proper metadata

4. Content Upload System 📤

A Flask-based server that allows:

• Drag-and-drop file uploads
• Markdown file processing
• Integration with the reporting system

🚀 Getting Started

Prerequisites

• Python 3.10+
• OpenAI API key
• GitHub repository with Actions enabled

Installation

1. Clone the repository:

   git clone https://github.com/JAdamhub/Tech-Case-Team2.git
   cd Tech-Case-Team2

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   # Create a .env file with your OpenAI API key
   echo "OPENAI_API_KEY=your-api-key-here" > .env

4. Run the server:

   python src/upload_server.py

🔄 Workflow

The system follows this process:

1. 📝 When code is pushed to the repository, GitHub Actions workflow is triggered
2. 🔍 The workflow identifies changed files in the codebase
3. 🧠 LLM analyzes the code changes for issues and improvement opportunities
4. ✅ Tests are generated for new or modified functions
5. 📊 Report files are created in the _llm_changes directory
6. 🌐 GitHub Pages is updated with the latest analysis
7. 🔄 A pull request may be automatically created with LLM-generated description

💻 Usage Examples

Viewing Code Change Reports

Visit the GitHub Pages site at: https://JAdamhub.github.io/Tech-Case-Team2/

Uploading Your Own Files

Access the upload page at: https://JAdamhub.github.io/Tech-Case-Team2/drag-drop

🔧 Customization

You can customize the analysis pipeline by modifying:

• The LLM prompt templates in combined_flow.py
• The GitHub Actions workflow in .github/workflows/main.yml
• The web interface templates in _layouts/

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Contributors

• Team 2 - AAU Case Competition