Building an AI-Powered News Search Dashboard with CopilotKit

PriyanshuDas01 — Thu, 31 Oct 2024 14:14:02 +0000

Here's a draft for the Dev.to post without code blocks.

Building an AI-Powered News Search Dashboard with CopilotKit
Hey, developers! 🌟 Today, I’m excited to share my latest project: an AI-powered news search dashboard built using CopilotKit, a tool that simplifies integrating AI and other assistive features into your apps. In this post, I’ll walk through how I used CopilotKit to create a seamless news search experience with real-time AI responses.

Live Link: https://copilot-news.vercel.app/
Why CopilotKit?
CopilotKit offers powerful features for building accessible and intelligent UIs. With built-in state management, UI components, and easy-to-use APIs, it’s perfect for anyone looking to add a layer of AI-powered assistance to their applications.

Project Overview: AI News Dashboard
The goal was simple: build a dashboard where users can search for news topics and see AI-generated summaries or assistance based on their search. Here's the final structure of the project:

Frontend: Created with React, featuring a search bar, AI-driven news summaries, and a Copilot popup assistant.
Backend: A Node.js server handling requests to an external news API.
Step-by-Step Implementation
Here's how I implemented the AI-powered news search dashboard:

Step 1: Install Dependencies
First, I installed the necessary packages, including @copilotkit/react-ui for CopilotKit components and React Toastify for notifications.

Step 2: Set Up the Dashboard Component
I created a Dashboard component that manages the state and UI. This component includes a search bar, a list of news items, and a Copilot popup that guides users through the news summaries.

The imports included useState, CopilotPopup, Input, Button, Card components, Loader, X, and Search icons, plus toast notifications and the useCopilotReadable hook. This setup formed the foundation for the user experience and interactions.

Step 3: Fetch News Data from the API
I set up a helper function, fetchNews, to handle API requests to a news aggregator service. This function handles errors, displays notifications, and returns an array of news items. The function uses a POST request and manages response errors to keep users informed when no data is available.

Step 4: Integrate CopilotKit for AI Functionality
With CopilotKit’s CopilotPopup and useCopilotReadable hooks, I enabled real-time AI guidance in the component. The useCopilotReadable hook allowed me to make the state of the searched news topics accessible to the AI assistant, enabling context-aware suggestions.

Using CopilotPopup, I added a popup assistant that appears at the bottom of the screen to help guide users as they search for news. The popup assistant comes with customizable titles and prompts to create a friendly and intuitive user experience.

Step 5: Create the Search Functionality
I then added a search input and a button for user interaction. When users enter a topic and click the search button, handleSearch is triggered to fetch news articles and update the history.

The search functionality checks if a topic is entered, then retrieves results or displays messages if nothing relevant is found. It’s styled with Tailwind CSS for an elegant look and easy adaptability.

Final UI
With everything in place, the final UI consists of a header, search bar, and a responsive news grid. CopilotKit’s integration added a layer of smart AI guidance, turning a standard search tool into an interactive assistant for news discovery.

Conclusion
Integrating CopilotKit into this project made it easy to add AI assistance without heavy customization, making it a great tool for quickly adding intelligence to UI components. If you're looking to integrate similar features into your own projects, I highly recommend trying out CopilotKit!

Building a Language Translator with CopilotKit for AI-Powered Translation

PriyanshuDas01 — Thu, 31 Oct 2024 12:56:58 +0000

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!

DEV Community: PriyanshuDas01

Building an AI-Powered News Search Dashboard with CopilotKit

Building a Language Translator with CopilotKit for AI-Powered Translation