LLM Translate: Full Stack Application

LLM Translate is an open-source translation tool that uses Large Language Models (LLMs) to deliver high-quality, nuanced translations across numerous languages. This full-stack project features a robust Python-based backend (FastAPI) and an intuitive Node/React-based frontend, working together to provide a seamless translation experience. It aims to offer advanced translation capabilities with a focus on accuracy, contextual understanding, and user accessibility.

Table of Contents

• LLM Translate: Full Stack Application
  • Brief Description
  • Features
  • Project Structure
  • Requirements/Dependencies
  • Installation Instructions
    • Clone the Repository
    • Backend Setup
    • Frontend Setup
    • Docker Compose (Recommended)
  • Usage
  • Configuration
  • API Documentation
  • Related Documentation
  • Contributing Guidelines
  • License
  • Acknowledgments
  • Share

Brief Description

LLM Translate is an open-source tool that leverages Large Language Models (LLMs) to provide high-quality, nuanced translations across a wide range of languages.

Features

Discover what makes LLM Translate a versatile and user-friendly translation solution:

Advanced Translation Capabilities:

• LLM-Powered Translations: Experience intelligent, context-aware translations in real-time.
• Multi-Provider Support: Choose from leading AI providers like OpenAI, Groq, and OpenRouter for translation tasks, ensuring flexibility and access to diverse models.
• Extensive Language Support: Translate between 21 different languages with ease.
• Automatic Source Language Detection: Simply start typing; the tool automatically identifies the input language.
• Text-to-Speech (TTS) Output: Listen to translations with our integrated spoken audio feature. (Please note: This is a beta feature and currently uses an English voice for all languages.)

Enhanced User Experience:

• Sleek Interface: Navigate a clean and modern UI with support for both Light and Dark modes.
• Responsive Design: Enjoy a consistent experience across desktops, tablets, and mobile devices.
• Efficient Workflow:
  • Copy to Clipboard: Easily copy source or translated text.
  • Swap Languages: Instantly switch between source and target languages and their corresponding text with a single click.
  • Keyboard Shortcuts: Speed up common actions using convenient keyboard commands.
• Character Limit Indicators: Keep track of text length to stay within provider limits.
• Accessibility Focused: Designed with ARIA attributes and screen reader compatibility for an inclusive experience.

Translation Management:

• Translation History: Access and manage your recent translations. The last 50 translations are automatically saved for quick reference and reuse.

This combination of powerful translation technology and user-centric design makes LLM Translate an ideal tool for a wide range of translation needs.

Project Structure

A high-level overview of the project:

llm-translate/
├── backend/        # Python FastAPI backend application
│   ├── llm_translate/
│   ├── tests/
│   ├── .env.example
│   ├── Dockerfile
│   ├── main.py
│   └── README.md   # Backend specific documentation
├── frontend/       # React Vite frontend application
│   ├── public/
│   ├── src/
│   ├── .env.example
│   ├── Dockerfile
│   ├── index.html
│   └── README.md   # Frontend specific documentation
├── docker-compose.yml # Docker Compose configuration
└── README.md          # This file (Project Root README)

(Note: The file tree is a representation and might not include all files or exact current structure. Depth is limited for brevity.)

Requirements/Dependencies

• Git
• Python 3.9+ (for backend)
• Node.js (e.g., >=18.x.x) and npm/yarn (for frontend)
• Docker and Docker Compose (recommended for easiest setup)

Installation Instructions

1. Clone the Repository

git clone https://github.com/samestrin/llm-translate.git
cd llm-translate

2. Backend Setup

Navigate to the backend directory and follow the instructions in its README.md file for specific package installations (typically using pip).

cd backend
# Follow instructions in backend/README.md
cd ..

3. Frontend Setup

Navigate to the frontend directory and follow the instructions in its README.md file for specific package installations (typically using npm install or yarn install).

cd frontend
# Follow instructions in frontend/README.md
cd ..

4. Docker Compose (Recommended)

This is the easiest way to get both the backend and frontend services running together. Ensure Docker and Docker Compose are installed. The docker-compose.yml file defines the services.

From the project root directory (llm-translate/):

docker-compose up --build

To run in detached mode:

docker-compose up --build -d

• The backend API will typically be available at http://localhost:8000.
• The frontend application will typically be available at http://localhost:3000.

To stop the services:

docker-compose down

Usage

Once the backend and frontend are running (either individually or via Docker Compose), access the frontend URL (e.g., http://localhost:3000) in your browser. The frontend application provides the user interface to input text, select languages, and receive translations. It interacts with the backend API for translation and text-to-speech functionality.

Configuration

• Backend: Configured using environment variables in a .env file within the backend directory. See backend/.env.example and backend/README.md for details on required variables such as API keys for LLM providers.
• Frontend: Configured using environment variables in a .env file within the frontend directory (e.g., VITE_API_URL to point to the backend). See frontend/.env.example and frontend/README.md for details. User-specific settings like dark/light mode theme preferences are also available in the application UI.

API Documentation

The backend provides a RESTful API for translation and Text-to-Speech (TTS) services. Key endpoints include:

• /translate/: For text translation.
• /speak/: For generating speech from text.

For detailed API documentation, including request/response schemas and parameters, please refer to the API Documentation section in the backend README.md or /docs on the running backend service, e.g., http://localhost:8000/docs). The frontend consumes these APIs.

Related Documentation

• Backend README: Detailed information about the backend service.
• Frontend README: Detailed information about the frontend application.
• Deployment Guide: Deployment guide for various environments.

Contributing Guidelines

Contributions are welcome! Please follow these general guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Write clean, well-documented code.
4. Add or update tests for your changes.
5. Ensure all tests pass.
6. Submit a pull request.

(Detailed contributing guidelines can be found here: CONTRIBUTING)

License

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

Acknowledgments

• Thanks to the creators of FastAPI, OpenAI, Groq, and OpenRouter used by the backend.
• Thanks to the creators of React, Vite, Tailwind CSS, Material UI, and other open-source libraries used in the frontend.

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