Building a Language Translator with CopilotKit for AI-Powered Translation
Language is an essential tool that connects us, but language barriers can often stand in the way of effective communication. In this project, I’ll walk you through building a web-based language translator that translates text between over 20 languages using CopilotKit, an AI library that enables easy interaction with copilot actions and makes setting up actions and responses a breeze.
Live Link -> https://language-copilot.vercel.app/
Why CopilotKit?
CopilotKit offers a flexible interface for defining copilot-readable variables and copilot actions—key elements for processing and interacting with language data in real-time. This streamlined workflow allowed us to focus on building a responsive UI and implementing the translation functionality without getting bogged down by complex backend integrations.
Features of Our Translator App
Our translator supports features like:
Language selection: Choose an input and output language from a diverse set of 20+ languages.
Real-time translation: Users get their translated text with a single click.
Interactive UI: Built with responsive elements using Tailwind CSS to enhance the user experience.
Building the Translator: Code Walkthrough
Step 1: Define the Language List and State Variables
We start by defining the list of languages and managing them through useState to track the selected input and output languages, the input text, and the translated output.
Step 2: Setting Up CopilotKit for AI-Powered Translation
With CopilotKit, we define copilot-readable values to give our translation model context on available languages, selected languages, and input text. This ensures that CopilotKit understands the application state and can provide an appropriate response.
Step 3: Define Copilot Actions for Translation
We then define a Translate action that takes inputLang, outputLang, and inputText as parameters and fetches the translated text from our /api/translate endpoint. By using CopilotKit’s useCopilotAction hook, this action becomes accessible and enables the app to handle translations seamlessly.
Step 4: Designing the UI
Using Tailwind CSS and components, we crafted a user-friendly UI. Here, we used Select for language options, Textarea for user input, and a button that triggers the translation action.
Step 5: Implementing the Translation API
Our app connects to a translation API that processes the request and returns the translated text. This part of the code is written as a server-side function to handle the POST request with the selected languages and text as parameters.
Conclusion
By integrating CopilotKit into our translator, we transformed a simple translation app into a flexible, scalable solution. CopilotKit’s capabilities for managing actions and readable states were essential in keeping our code clean and easy to manage.
You can try it out by deploying it on a Next.js server with Tailwind CSS and a translation API of your choice, and start breaking down language barriers with AI-powered CopilotKit at the heart of your solution!
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