Codera AI

This project aims to modernise legacy codebases through an AI-driven analysis and transformation process, legacy systems can be upgraded to meet current standards and technologies.

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Codera AI 🚀

Introduction 📜

This project aims to modernize legacy codebases by transforming them into modern, efficient, and maintainable code. Through an AI-driven analysis and transformation process, legacy systems can be upgraded to meet current standards and technologies.

Product demo: https://codera-ai.vercel.app

Google Colab AI Prototype: https://colab.research.google.com/drive/1XaA3BmUssSqr9G4l3EeKW05fx0eOYf8V?usp=sharing

Implementation Plan 🛠️

1. Parser 📑

  • Creates a single file (e.g., JSON) containing all code from the legacy project.

2. Code Analyser 🔍

  • Reads the code archive.
  • Summarizes the code to create a prompt for further actions.

3. Code Suggestions 💡

  • Generates code suggestions based on the summary, which can either be presented as text or used to directly overwrite the existing files.

4. Personalized Prompts 👤

  • Tailors prompts based on the user's role (Developer, DevOps) and experience.

5. Authentication and WebClient 🔐

  • Manages user sessions and interactions through a web interface.

6. Image Design from Code Summary 🎨

  • Converts code summaries into visual representations.

7. Textual Advice from Code Summary 📝

  • Provides written advice based on the code analysis.

Workflow 🔄

  1. User Registration: Users sign up on the platform and set their role and experience level.
  2. Repository Upload: Users upload their GitHub repository (archive or files) to the platform.
  3. Code Segmentation: The system creates a JSON file containing the entire codebase, which is then used for analysis.
  4. Code Analysis: The code is analyzed by the Code Analyser, interacting with an LLM to produce a logic summary.
  5. Agent Creation: Based on the logic summary, various agents (Developer, UX/UI, etc.) are created to provide specific recommendations and actions.
  6. Personalized Recommendations: Users receive suggestions tailored to their role, which can be used to directly modify and update the code.

Getting Started 🌟

Follow these instructions to set up and run the project on your local machine for development and testing purposes.

Prerequisites 📋

  • Node.js and npm (for the Next.js project)
  • Docker (for running Dockerized services)

Setting up and running the React project 🖥️

  1. Clone the repository to your local machine.
  2. Install the dependencies. npm install
  3. Start the development server. npm run dev

Building and running the Server Docker image 🐳

  1. Navigate to the directory containing the Server Dockerfile.
  2. Build the Docker image. docker build -t server-image .
  3. Run the Docker container. docker run -p 8000:8000 server-image

Building and running the LLM Docker image 🐳

  1. Navigate to the directory containing the LLM Dockerfile.
  2. Build the Docker image. docker build -t llm-image .
  3. Run the Docker container. docker run -p 11434:11434 llm-image

Contributing 🤝

Guidelines for contributing to the project, including coding standards, pull request process, etc.

License 📄

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/

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