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Build Your Own Browser-Based AI Coding Assistant with Gradio Lite and Transformers.js

Community Article Published November 24, 2024
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Unlock the power of AI directly in your browser with a serverless, interactive coding environment.

Introduction

Artificial Intelligence (AI) has revolutionized the way developers code, debug, and learn new programming concepts. Imagine having an AI coding assistant that runs entirely in your browser, providing real-time code execution, natural language conversations, and support for multiple file types—all without any server-side processing.

In this comprehensive guide, we’ll walk you through building a browser-based AI tool using Gradio Lite, Transformers.js, and Pyodide. This assistant will enable you to:

  • Chat with an AI model using natural language.
  • Execute Python code directly in the browser.
  • Upload and process files seamlessly.
  • Experience a serverless, scalable, and secure application.

Whether you’re a developer exploring AI chatbot development, an educator seeking interactive tools for teaching, or someone interested in machine learning in the browser, this tutorial is for you.

AI Coding Assistant Banner

Table of Contents

  1. Prerequisites
  2. Project Overview
  3. Setting Up the Development Environment
  4. Understanding the Tools and Libraries
  5. Creating the User Interface with Gradio Lite
  6. Implementing AI Chat Functionality with Transformers.js
  7. Enabling Python Code Execution with Pyodide
  8. Handling File Uploads and Real-Time Logs
  9. Optimizing Performance
  10. Customization Options
  11. Deploying Your AI Application Online
  12. Conclusion
  13. Additional Resources

Prerequisites

Before diving in, ensure you have:

  • A basic understanding of HTML, CSS, and JavaScript.
  • Familiarity with Python programming.
  • A modern web browser that supports WebGPU (e.g., the latest versions of Chrome or Firefox).

Project Overview

What You’ll Build

A browser-based AI coding assistant capable of:

  • AI-Powered Conversations: Chat with an AI model using Transformers.js.
  • Python Code Execution: Run Python scripts directly in the browser with real-time feedback using Pyodide Integration.
  • File Upload Support: Handle image and text uploads for interactive coding environments.
  • Serverless Design: Everything runs on the client-side, ensuring privacy, scalability, and enhanced security.

Who Is This For?

This tutorial is perfect for:

  • Developers exploring AI chatbot development.
  • Educators building interactive coding tools.
  • Anyone interested in serverless AI applications.

Gradio Lite

Setting Up the Development Environment

Step 1: Create a Project Directory

Create a new directory for your project:

mkdir ai-coding-assistant
cd ai-coding-assistant

Step 2: Initialize Project Files

Create the following files:

  • index.html: The main HTML file.
  • style.css: CSS for styling.
  • app.js: JavaScript file for logic.

Understanding the Tools and Libraries

Gradio Lite

A lightweight framework for building interactive web interfaces directly in the browser.

Transformers.js

A library to run Hugging Face models in JavaScript, enabling machine learning in the browser.

Pyodide

A Python runtime compiled to WebAssembly, allowing for client-side Python execution.

Creating the User Interface with Gradio Lite

We'll start by setting up the basic HTML structure and styling.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>AI Coding Assistant</title>
    <script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css">
    <link rel="stylesheet" href="style.css">
</head>
<body>
    <div class="container">
        <h1>AI Coding Assistant</h1>
        <div id="chat-interface">
            <div id="chat-container" class="chat-container"></div>
            <input type="text" id="chat-input" placeholder="Ask your coding question here..." aria-label="Chat Input">
            <button id="chat-submit" aria-label="Send Message">Send</button>
        </div>
    </div>
</body>
</html>

Code Execution

Implementing AI Chat Functionality with Transformers.js

Use Transformers.js to enable AI-powered conversation capabilities:

import { pipeline } from '@xenova/transformers';

// Initialize the model pipeline
let generator;

async function initAIModel() {
    generator = await pipeline('text-generation', 'Xenova/llama-2-7b-chat');
}

// Generate responses
async function generateResponse(userInput) {
    if (!generator) await initAIModel();
    const output = await generator(userInput, { max_new_tokens: 50 });
    return output[0].generated_text;
}

Enabling Python Code Execution with Pyodide

We’ll allow users to execute Python code directly in the browser:

// Initialize Pyodide
let pyodideReadyPromise = loadPyodide();

async function executePythonCode() {
    let pyodide = await pyodideReadyPromise;
    const code = document.getElementById('python-code').value;
    try {
        let output = await pyodide.runPythonAsync(code);
        console.log(`Output:\n${output}`);
    } catch (err) {
        console.log(`Error:\n${err.message}`);
    }
}

Deploying Your AI Application Online

Deployment Platforms

  • GitHub Pages: Ideal for hosting static websites.
  • Vercel: Easy deployment with automatic HTTPS.
  • Netlify: Offers continuous deployment from Git repositories.

Steps to Deploy

  1. Push your project to a GitHub repository.
  2. Configure your hosting platform for deployment.
  3. Deploy and test your application live.

Conclusion

You’ve built a powerful, serverless AI application combining AI-driven conversations and real-time code execution in the browser. Expand on this foundation to include new features, and share it with the community!

Interactive Coding Environment

Additional Resources

Happy coding!

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