Project

Co Op Translator

Easily automate multilingual translations for your projects powered by advanced LLM technology.

Overview of Co Op Translator

Co Op Translator is a tool designed to automate multilingual translations for various projects using advanced Large Language Model (LLM) technology. This project aims to simplify the process of translating content into multiple languages, making it accessible and efficient for developers.

Python Package

Co Op Translator Python package designed to automate multilingual translations across all files in your project. Powered by advanced Language Learning Models (LLMs), it simplifies the localization process by providing accurate and efficient translations.

Co Op Translator scans all your project files and translates all Markdown documents and even text within images found in your folders. This comprehensive approach ensures that every part of your project is accessible to a global audience.

By integrating Co Op Translator into your workflow, you can:

• Automate Translations: Easily translate text in your project files into multiple languages.
• Comprehensive Coverage: Translate all Markdown files and text within images in your project directories.
• Advanced LLM Technology: Utilize cutting-edge language models for high-quality translations.
• Simplify Localization: Streamline the process of localizing your project for international markets.
• Easy Integration: Seamlessly integrate with your existing project setup.

Co Op Translator empowers developers to focus on core functionalities while effortlessly managing multilingual support.

Here is an example of how Co Op Translator can be used to translate text in images and Markdown files in your project:

Key Features:

• Multilingual Translation: Supports translation into multiple languages, leveraging the power of LLMs.
• Automation: Automates the translation process, reducing manual effort and increasing consistency.
• Integration: Easily integrates with existing projects, providing a seamless translation experience.

Getting Started:

Environment setup

GitHub Codespaces

You can run this samples virtually by using GitHub Codespaces and no additional settings or setup are required.

The button will open a web-based VS Code instance in your browser:

1. Open the template (this may take several minutes):

   

Running Locally using VS Code Dev Containers

⚠️ This option will only work if your Docker Desktop is allocated at least 16 GB of RAM. If you have less than 16 GB of RAM, you can try the GitHub Codespaces option or set it up locally.

A related option is VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:

1. Start Docker Desktop (install it if not already installed)

2. Open the project:

   

Setting Up a Local Development Environment:

• Create a virtual environment:

  python -m venv venv

• Activate the virtual environment:
  • For Windows:

    venv\Scripts\activate.bat

  • For Mac/Linux:

    source venv/bin/activate

2. Installing Required Packages:

   • Install the necessary packages:

     pip install -r requirements.txt

3. Environment Variables:

   • Create an .env file in the root directory by copying the provided .env.template file.
   • Fill in the environment variables as guided.

4. Installing the Package:

   • Install the package in editable mode:

     pip install -e .

5. Running Tests:

   • Ensure the virtual environment is activated and run tests:

     pytest tests/

Sample Notebooks:

• Getting Started with notebook: basic version
• Getting Started with notebook: module version

Repository Structure:

• data/: Contains data files.
• notebooks/: Jupyter notebooks for examples and tutorials.
• src/co_op_translator/: Source code for the translator.
• tests/: Test cases for the project.

Getting started with Co Op Translator

How to Use Co Op Translator

1. Set up Azure resources before starting
2. Create an '.env' file in the root directory
3. Install the Co Op translator package
4. Use Co Op translator in your project

Prerequisites

• Azure AI Services resource
• Azure OpenAI resource
• Python 3.10 or higher

Responsible AI

Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.

The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using generation quality and risk and safety metrics.

You can evaluate your AI application in your development environment using the prompt flow SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.