Multi-language code converter contribution
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Blaise Alako
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week4/community-contributions/ai_code_converter/README.md
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week4/community-contributions/ai_code_converter/README.md
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# CodeXchange AI
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[](https://python.org)
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[](LICENSE)
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[](https://github.com/alakob/ai_code_converter/actions/workflows/python-test.yml)
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A powerful tool for converting and executing code between different programming languages using AI models.
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## Overview
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CodeXchangeAI is a Python application that leverages various AI models to translate code between programming languages while maintaining functionality and idiomatic patterns.
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### Key Features
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- Multi-language code conversion
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- Real-time code execution
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- Multiple AI model support (GPT, Claude, DeepSeek, GROQ, Gemini)
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- File upload/download functionality
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- Syntax highlighting
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- Detailed logging system
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- Docker support
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## Quick Start
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```bash
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# Clone the repository
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git clone git@github.com:alakob/ai_code_converter.git
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cd ai_code_converter
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# Configure environment
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cp .env.example .env
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# Edit .env with your API keys
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```
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### Using the Docker Wrapper Script (Recommended)
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For a more convenient way to run the application with Docker, you can use the provided wrapper script:
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```bash
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# Make the script executable
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chmod +x run-docker.sh
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# Run the application
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./run-docker.sh # Build and run normally
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./run-docker.sh -d # Run in detached mode
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./run-docker.sh -p 8080 # Run on port 8080
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./run-docker.sh -s # Stop the container
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./run-docker.sh -h # Show help message
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```
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## CI/CD Pipeline
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CodeXchange AI uses GitHub Actions for continuous integration and deployment. The pipeline includes:
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### Automated Testing
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- Runs Python tests on multiple Python versions (3.9, 3.10, 3.11)
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- Performs code linting and style checks
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- Generates test coverage reports
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### Docker Image Validation
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- Builds the Docker image to verify Dockerfile integrity
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- Performs vulnerability scanning with Trivy
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- Validates container startup and dependencies
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### Deployment Automation
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- Automatically deploys to staging environment when changes are pushed to develop branch
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- Creates production releases with semantic versioning
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- Publishes Docker images to Docker Hub
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### Setting Up for Development
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To use the CI/CD pipeline in your fork, you'll need to add these secrets to your GitHub repository:
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1. `DOCKERHUB_USERNAME`: Your Docker Hub username
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2. `DOCKERHUB_TOKEN`: A Docker Hub access token (not your password)
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See the [CI/CD documentation](docs/ci_cd_pipeline.md) for detailed setup instructions.
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The wrapper script provides several options for customizing the Docker deployment:
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```bash
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Usage: ./run-docker.sh [OPTIONS]
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Options:
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-b, --build Build the Docker image without running the container
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-d, --detach Run container in detached mode (background)
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-e, --env FILE Specify an environment file (default: .env)
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-p, --port PORT Specify the port to expose (default: 7860)
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-l, --logs Follow the container logs after starting
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-s, --stop Stop the running container
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-r, --restart Restart the container
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-D, --down Stop and remove the container
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-k, --keys Check for API keys and show setup instructions if missing
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-h, --help Display this help message
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-v, --version Display script version
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```
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Examples:
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- Run on a different port: `./run-docker.sh -p 8080`
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- Run in background: `./run-docker.sh -d`
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- Stop the application: `./run-docker.sh -s`
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- View logs: `./run-docker.sh -l`
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The application will be available at `http://localhost:7860`
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### Manual Installation
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```bash
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# Create and activate virtual environment
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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# Install dependencies
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pip install -r requirements.txt
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# Configure environment
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cp .env.example .env
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# Edit .env with your API keys
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# Run the application
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python run.py
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```
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### Using Make Commands
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The project includes a Makefile with useful commands to streamline development:
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```bash
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# Install dependencies
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make install
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# Run the application
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make run
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# Run with development settings (hot-reloading)
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make dev
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# Build Docker container
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make docker-build
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# Run Docker container
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make docker-run
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# Or use the Docker wrapper script for more options
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./run-docker.sh
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# Run tests
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make test
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# Clean Python cache and build artifacts
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make clean
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```
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## Basic Usage
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1. Select source and target programming languages
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2. Enter or upload source code
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3. Choose AI model and temperature
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4. Click "Convert" to translate the code
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5. Use "Run" buttons to execute original or converted code
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6. Download the results including compiled binaries (for compiled languages)
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## Supported Languages
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Currently supports 17 programming languages including Python, JavaScript, Java, C++, Julia, Go, Ruby, Swift, Rust, C#, TypeScript, R, Perl, Lua, PHP, Kotlin, and SQL.
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See [Supported Languages](./docs/languages.md) for detailed information on each language.
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## Documentation
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For detailed documentation, please refer to the [docs](./docs) directory:
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- [Supported Languages](./docs/languages.md) - Details on all supported programming languages
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- [Configuration Guide](./docs/configuration.md) - How to configure the application
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- [Development Guide](./docs/development.md) - Guide for developers extending the application
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- [Contributing Guidelines](./docs/contributing.md) - How to contribute to the project
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- [Project Structure](./docs/project_structure.md) - Overview of the codebase architecture
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- [Architecture Diagram](./docs/architecture_diagram.md) - Visual representation of the application architecture
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## License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## Acknowledgments
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- OpenAI for GPT models
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- Anthropic for Claude
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- Google for Gemini
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- DeepSeek and GROQ for their AI models
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- The Gradio team for the web interface framework
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