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https://github.com/progremister/codera_ai
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.
https://github.com/progremister/codera_ai
crewai llm nextjs python
Last synced: 9 days ago
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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.
- Host: GitHub
- URL: https://github.com/progremister/codera_ai
- Owner: progremister
- License: apache-2.0
- Created: 2024-04-03T09:31:22.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-04-07T07:28:40.000Z (8 months ago)
- Last Synced: 2024-08-13T07:16:59.997Z (4 months ago)
- Topics: crewai, llm, nextjs, python
- Language: TypeScript
- Homepage: https://codera-ai.vercel.app
- Size: 10.1 MB
- Stars: 9
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - progremister/codera_ai - 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 upgr (TypeScript)
README
# 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/chat**
**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.
```bash
npm install
```
3. Start the development server.
```bash
npm run dev
```
### Building and running the Server Docker image 🐳
1. Navigate to the directory containing the Server `Dockerfile`.
2. Build the Docker image.
```bash
docker build -t server-image .
```
3. Run the Docker container.
```bash
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.
```bash
docker build -t llm-image .
```
3. Run the Docker container.
```bash
docker run -p 11434:11434 llm-image
```
## Contributing 🤝
Guidelines for contributing to the project, including coding standards, pull request process, etc.
## License 📄
Information about the project's license.
## Contact 📬
How to get in touch with the project team.