795 lines
20 KiB
Plaintext
795 lines
20 KiB
Plaintext
---
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title: 'How to Build a React Chatbot: Three Methods Explained'
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description: 'Learn how to build a React chatbot using three methods: no-code, API integration, and full-stack development.'
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publishedAt: '2024-07-11'
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---
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To enhance user engagement in your React application, adding a chatbot can be a game changer. You don't need to get overwhelmed by complex code. React chatbots offer interactive and responsive interfaces.
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Building a React chatbot isn't as hard as it seems. This article explores three methods to build your own chatbot. We'll cover no-code solutions to full-stack implementations. Whether you're an experienced developer or new to React, you'll find an approach that fits your skill level and project needs. Ready to improve your user experience with a **custom React chatbot**? Let's dive in.
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## What is a React Chatbot?
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### Definition of a React Chatbot
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A **React chatbot** is an interactive component in a React application. It simulates human-like conversations with users, enhancing engagement and delivering information efficiently.
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### How Chatbots Work Within React Applications
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React chatbots use React's component-based architecture to create modular and reusable chat interfaces. They usually include:
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1. A chat window component
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2. Message display components
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3. Input field for user messages
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4. State management for conversation flow
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5. API integration for processing messages and generating responses
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The chatbot's logic involves conditional rendering, hooks for state management, and asynchronous functions for API calls.
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### Benefits of Integrating a Chatbot in Your React App
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- **24/7 availability** for user inquiries
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- **Reduced customer support workload**
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- **Personalized user experiences**
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- **Streamlined information gathering**
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- **Improved user engagement and retention**
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### Overview of Typebot as a No-Code Chatbot Builder
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Typebot is an open-source platform that makes it easy to create chatbots for React applications. Its key features include:
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- Drag-and-drop interface for bot design
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- Visual flow builder for conversation logic
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- Pre-built components like text bubbles, input fields, and buttons
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- Easy integration with React via API or embed code
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- Customizable themes to match your app's design
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### Real-World Use Cases of React Chatbots
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<Table
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headers={['Industry', 'Use Case', 'Example']}
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rows={[
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[
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'E-commerce',
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'Product recommendations',
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'Suggesting items based on browsing history',
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],
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[
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'Healthcare',
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'Symptom checker',
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'Guiding users through initial health assessments',
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],
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[
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'Finance',
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'Account management',
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'Helping users check balances and make transfers',
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],
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[
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'Education',
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'Course enrollment',
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'Assisting students in selecting and registering for classes',
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],
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[
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'Travel',
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'Booking assistance',
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'Helping users find and book flights or accommodations',
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],
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]}
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/>
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**React chatbots** are versatile tools. They can significantly enhance user experience across various domains, providing immediate assistance and personalized interactions.
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## Method 1: Creating a Simple React Chatbot Using Typebot
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### Setting Up Typebot Account
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1. Visit typebot.io and sign up for an account.
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2. Verify your email address.
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3. Log in to the Typebot dashboard.
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### Designing Your Chatbot using Typebot's Drag-and-Drop Interface
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1. Click "Create a new typebot".
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2. Choose a template or start from scratch.
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3. Add blocks to your flow:
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- Text bubbles for bot messages.
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- Input fields for user responses.
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- Buttons for multiple-choice options.
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4. Connect blocks to create conversation logic.
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5. Use conditional statements to branch conversations.
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<Callout status="info" title="Info">
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Looking for inspiration to create your first chatbot? We gathered some
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[chatbot script exampes](https://typebot.io/blog/chatbot-script-examples) to
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help you get started.
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</Callout>
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### Exporting and Integrating Typebot Code in React
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1. In Typebot, go to "Share" > "Web".
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2. Choose the "React" embed option.
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3. Copy the provided code snippet.
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4. In your React project, install **Typebot libraries**:
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```bash
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npm install @typebot.io/js @typebot.io/react
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```
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5. Create a new component in your React app:
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```jsx
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import { Bubble } from '@typebot.io/react'
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const ChatbotComponent = () => {
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return <Bubble typebot="YOUR_TYPEBOT_ID" />
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}
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export default ChatbotComponent
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```
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### Adding Typebot Chatbot to Existing React Applications
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1. Import the **ChatbotComponent** where needed:
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```jsx
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import ChatbotComponent from './ChatbotComponent'
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function App() {
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return (
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<div className="App">
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<h1>My React App</h1>
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<ChatbotComponent />
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</div>
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)
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}
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```
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2. Customize the chatbot's appearance:
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```jsx
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<Bubble
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typebot="your-typebot-id"
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theme={{ button: { backgroundColor: '#0042DA' } }}
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prefilledVariables={{
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'User Name': 'John Doe',
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'Page URL': window.location.href,
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}}
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/>
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```
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Here is what you will get:
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<Image
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src="/images/blog/react-chatbot/demo-chat-method-1.jpg"
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alt="Demo Chat Method 1"
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width={2074}
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height={1776}
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/>
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### Running and Testing Your Simple React Chatbot
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1. Start your React development server:
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```bash
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npm start
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```
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2. Open your app in a browser.
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3. Interact with the chatbot to test conversation flows.
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4. Check for:
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- Correct message display.
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- Proper input handling.
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- Accurate conditional logic execution.
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5. Use Typebot's analytics to monitor performance.
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6. Iterate on your design based on user interactions and feedback.
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These steps show you how to quickly create and integrate a functional chatbot into your React application using **Typebot's no-code solution**.
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<Cta />
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## Method 2: Creating the Frontend and Calling the Typebot API
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We will do the same thing, except that we will build our own chat UI. It will allow us to customize as we want.
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### Creating a React App with Vite
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1. Initialize a new React project:
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```bash
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npm create vite@latest my-chatbot -- --template react-ts
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cd my-chatbot
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npm install
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```
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2. Install additional dependencies:
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```bash
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npm install axios daisyui@latest
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```
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3. Configure Tailwind CSS and DaisyUI in `tailwind.config.js`:
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```javascript
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module.exports = {
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content: ['./src/**/*.{js,jsx,ts,tsx}'],
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plugins: [require('daisyui')],
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}
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```
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### Setting Up Typebot API
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1. Create a Typebot account and design your chatbot flow.
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2. Generate an API token in Typebot settings.
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3. Note your Typebot's public ID.
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### Creating Chatbot Components in React using Daisy UI
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Create a `ChatComponent.tsx` file:
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```tsx
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import React, { useState } from 'react'
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const ChatComponent: React.FC = () => {
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const [messages, setMessages] = useState<
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Array<{ text: string; isUser: boolean }>
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>([])
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const [inputMessage, setInputMessage] = useState('')
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const handleSendMessage = async () => {
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if (inputMessage.trim() === '') return
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setMessages([...messages, { text: inputMessage, isUser: true }])
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setInputMessage('')
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// API call will be implemented here
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}
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return (
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<div className="flex flex-col h-screen">
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<div className="flex-1 overflow-y-auto p-4">
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{messages.map((message, index) => (
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<div
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key={index}
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className={`chat ${message.isUser ? 'chat-end' : 'chat-start'}`}
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>
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<div
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className={`chat-bubble ${
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message.isUser ? 'chat-bubble-primary' : 'chat-bubble-secondary'
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}`}
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>
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{message.text}
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</div>
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</div>
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))}
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</div>
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<div className="p-4 bg-base-200">
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<div className="flex">
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<input
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type="text"
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value={inputMessage}
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onChange={(e) => setInputMessage(e.target.value)}
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className="input input-bordered flex-1 mr-2"
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placeholder="Type your message..."
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/>
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<button onClick={handleSendMessage} className="btn btn-primary">
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Send
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</button>
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</div>
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</div>
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</div>
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)
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}
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export default ChatComponent
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```
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### Fetching Responses from Typebot API
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Implement API calls in `handleSendMessage`:
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```typescript
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import axios from 'axios'
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const TYPEBOT_API_URL = 'https://typebot.io/api/v1/typebots'
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const PUBLIC_ID = 'your_public_id'
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const [sessionId, setSessionId] = useState<string | null>(null)
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const handleSendMessage = async () => {
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if (inputMessage.trim() === '') return
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setMessages([...messages, { text: inputMessage, isUser: true }])
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setInputMessage('')
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try {
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let response
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if (!sessionId) {
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response = await axios.post(`${TYPEBOT_API_URL}/${PUBLIC_ID}/startChat`, {
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message: inputMessage,
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})
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setSessionId(response.data.sessionId)
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} else {
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response = await axios.post(
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`${TYPEBOT_API_URL}/${PUBLIC_ID}/continueChat`,
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{
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sessionId,
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message: inputMessage,
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}
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)
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}
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const botMessages = response.data.messages.map(
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(msg: { content: string }) => ({
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text: msg.content.richText[0].children
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.map((children) => children.text)
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.join(' '),
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isUser: false,
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})
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)
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setMessages((prevMessages) => [...prevMessages, ...botMessages])
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} catch (error) {
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console.error('Error sending message:', error)
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}
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}
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```
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### Handling User Inputs and Responses in UI
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Update the UI to display bot responses:
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```tsx
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<div className="flex-1 overflow-y-auto p-4">
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{messages.map((message, index) => (
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<div
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key={index}
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className={`chat ${message.isUser ? 'chat-end' : 'chat-start'}`}
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>
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<div
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className={`chat-bubble ${
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message.isUser ? 'chat-bubble-primary' : 'chat-bubble-secondary'
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}`}
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>
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{message.text}
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</div>
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</div>
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))}
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</div>
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```
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### Error Handling in API Calls
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Implement error handling:
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```typescript
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try {
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// API calls
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} catch (error) {
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console.error('Error in chat flow:', error)
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setMessages((prevMessages) => [
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...prevMessages,
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{ text: 'An error occurred. Please try again.', isUser: false },
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])
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}
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```
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Finally the final result looks like this:
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<Image
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src="/images/blog/react-chatbot/demo-chat-method-2.gif"
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alt="Demo Chat Method 2"
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width={800}
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height={320}
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/>
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### Deploying and Testing
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1. Build your React app:
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```bash
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npm run build
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```
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2. Deploy to a hosting service (e.g., Vercel, Netlify).
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3. Test thoroughly:
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- Conversation flow.
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- Error scenarios.
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- Performance under load.
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By following these steps, you can create a custom **React chatbot frontend** that interacts with the Typebot API, providing a seamless chat experience for your users.
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<Cta />
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## Method 3: Building the Frontend and Backend Yourself
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Here we will do the same thing, but by building our own backend!
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### Initializing a React Project with Vite and TypeScript
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1. Create a new Vite project:
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```bash
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npm create vite@latest my-fullstack-chatbot -- --template react-ts
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cd my-fullstack-chatbot
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npm install
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```
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2. Install additional dependencies:
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```bash
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npm install axios daisyui@latest
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```
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### Setting Up the Backend with Express.js
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1. Create a `server` directory in the project root.
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2. Initialize a new Node.js project:
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```bash
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cd server
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npm init -y
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npm install express cors dotenv
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npm install --save-dev typescript @types/express @types/node @types/cors
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```
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3. Create a `tsconfig.json` file in the `server` directory:
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```json
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{
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"compilerOptions": {
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"target": "es6",
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"module": "commonjs",
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"outDir": "./dist",
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"strict": true,
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"esModuleInterop": true
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}
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}
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```
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### Creating RESTful API Endpoints for Chatbot Responses
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Create a `server.ts` file in the `server` directory:
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```typescript
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import express from 'express'
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import cors from 'cors'
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import dotenv from 'dotenv'
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dotenv.config()
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const app = express()
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app.use(cors())
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app.use(express.json())
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const PORT = process.env.PORT || 3001
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app.post('/api/chat', (req, res) => {
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const userMessage = req.body.message
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// Chatbot logic will be implemented here
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res.json({
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response: `You said: "${userMessage}". This is a placeholder response.`,
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})
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})
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app.listen(PORT, () => console.log(`Server running on port ${PORT}`))
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```
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### Creating Business Logic
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Implement a simple chatbot logic in `server.ts`:
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```typescript
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const generateResponse = (message: string): string => {
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if (message.toLowerCase().includes('hello')) {
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return 'Hello! How can I assist you today?'
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} else if (message.toLowerCase().includes('help')) {
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return "I'm here to help. What do you need assistance with?"
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} else if (message.toLowerCase().includes('bye')) {
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return 'Goodbye! Have a great day!'
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} else {
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return "I'm not sure how to respond to that. Can you please rephrase your question?"
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}
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}
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app.post('/api/chat', (req, res) => {
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const userMessage = req.body.message
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const botResponse = generateResponse(userMessage)
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res.json({ response: botResponse })
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})
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```
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### Designing the Chatbot Interface in React using Daisy UI
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Create a `ChatComponent.tsx` in your React src directory:
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```tsx
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import React, { useState } from 'react'
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import axios from 'axios'
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const ChatComponent: React.FC = () => {
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const [messages, setMessages] = useState<
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Array<{ text: string; isUser: boolean }>
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>([])
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const [inputMessage, setInputMessage] = useState('')
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const handleSendMessage = async () => {
|
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if (inputMessage.trim() === '') return
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setMessages([...messages, { text: inputMessage, isUser: true }])
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setInputMessage('')
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try {
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const response = await axios.post('/api/chat', { message: inputMessage })
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setMessages([
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...messages,
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{ text: inputMessage, isUser: true },
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{ text: response.data.response, isUser: false },
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])
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} catch (error) {
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console.error('Error sending message:', error)
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}
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}
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return (
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<div className="flex flex-col h-screen">
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<div className="flex-1 overflow-y-auto p-4">
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{messages.map((message, index) => (
|
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<div
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key={index}
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className={`chat ${message.isUser ? 'chat-end' : 'chat-start'}`}
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>
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<div
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className={`chat-bubble ${
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message.isUser ? 'chat-bubble-primary' : 'chat-bubble-secondary'
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}`}
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>
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{message.text}
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</div>
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</div>
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))}
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</div>
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<div className="p-4 bg-base-200">
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<div className="flex">
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<input
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type="text"
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value={inputMessage}
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onChange={(e) => setInputMessage(e.target.value)}
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className="input input-bordered flex-1 mr-2"
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placeholder="Type your message..."
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/>
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<button onClick={handleSendMessage} className="btn btn-primary">
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Send
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</button>
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</div>
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</div>
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</div>
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)
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}
|
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|
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export default ChatComponent
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```
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|
|
### Deploying and Testing
|
|
|
|
1. Build your React app:
|
|
|
|
```bash
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|
npm run build
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|
```
|
|
|
|
2. Deploy to a hosting service (e.g., Vercel, Netlify).
|
|
3. Test thoroughly:
|
|
- Conversation flow.
|
|
- Error scenarios.
|
|
- Performance under load.
|
|
|
|
By following these steps, you can create a full-stack **React chatbot** with a custom backend, providing a seamless chat experience for your users.
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|
|
### Connecting Frontend to Backend
|
|
|
|
Update the axios call in `ChatComponent.tsx` to use an environment variable:
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|
|
```typescript
|
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const API_URL = import.meta.env.VITE_API_URL || 'http://localhost:3001'
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|
|
// In handleSendMessage:
|
|
const response = await axios.post(`${API_URL}/api/chat`, {
|
|
message: inputMessage,
|
|
})
|
|
```
|
|
|
|
### Error Handling and Debugging
|
|
|
|
Implement error handling in both frontend and backend:
|
|
|
|
```typescript
|
|
// Frontend (ChatComponent.tsx)
|
|
try {
|
|
// API call
|
|
} catch (error) {
|
|
console.error('Error sending message:', error)
|
|
setMessages((prevMessages) => [
|
|
...prevMessages,
|
|
{ text: 'An error occurred. Please try again.', isUser: false },
|
|
])
|
|
}
|
|
|
|
// Backend (server.ts)
|
|
app.use(
|
|
(
|
|
err: Error,
|
|
req: express.Request,
|
|
res: express.Response,
|
|
next: express.NextFunction
|
|
) => {
|
|
console.error(err.stack)
|
|
res.status(500).send('Something broke!')
|
|
}
|
|
)
|
|
```
|
|
|
|
### Best Practices for State Management
|
|
|
|
For more complex applications, consider using state management libraries like [Redux](https://redux.js.org/) or Zustand:
|
|
|
|
```bash
|
|
npm install @reduxjs/toolkit react-redux
|
|
```
|
|
|
|
### Deploying Full-Stack Chatbot Application
|
|
|
|
1. Build the React frontend:
|
|
|
|
```bash
|
|
npm run build
|
|
```
|
|
|
|
2. Compile TypeScript for the backend:
|
|
|
|
```bash
|
|
npx tsc
|
|
```
|
|
|
|
3. Deploy the backend to a Node.js hosting service (e.g., Heroku, DigitalOcean).
|
|
4. Deploy the frontend to a static hosting service (e.g., Netlify, Vercel).
|
|
5. Set up environment variables for API URL in frontend deployment.
|
|
6. Ensure CORS is properly configured on the backend for the frontend's domain.
|
|
|
|
This method provides full control over both frontend and backend, allowing for custom logic and integrations beyond what's possible with no-code solutions.
|
|
|
|
## Advanced Features and Customization
|
|
|
|
### Using Conditional Logic in Chatbots
|
|
|
|
Implement branching conversations based on user inputs:
|
|
|
|
```typescript
|
|
const generateResponse = (message: string, context: any): string => {
|
|
if (context.stage === 'greeting') {
|
|
if (message.toLowerCase().includes('help')) {
|
|
context.stage = 'help'
|
|
return 'What kind of help do you need? 1. Technical Support 2. Account Issues 3. Billing'
|
|
} else {
|
|
return 'Hello! How can I assist you today?'
|
|
}
|
|
} else if (context.stage === 'help') {
|
|
if (message.includes('1')) {
|
|
return 'For technical support, please describe your issue.'
|
|
} else if (message.includes('2')) {
|
|
return 'For account issues, please provide your account number.'
|
|
} else if (message.includes('3')) {
|
|
return "For billing inquiries, I'll transfer you to our billing department."
|
|
}
|
|
}
|
|
return "I'm not sure how to respond. Can you please clarify?"
|
|
}
|
|
```
|
|
|
|
### Integrations with Third-Party Services
|
|
|
|
#### OpenAI Integration
|
|
|
|
```typescript
|
|
import { Configuration, OpenAIApi } from 'openai'
|
|
|
|
// Initialize the OpenAI configuration
|
|
const configuration = new Configuration({
|
|
apiKey: process.env.OPENAI_API_KEY,
|
|
})
|
|
const openai = new OpenAIApi(configuration)
|
|
|
|
const generateResponse = async (
|
|
message: string,
|
|
context: any
|
|
): Promise<string> => {
|
|
try {
|
|
const completion = await openai.createChatCompletion({
|
|
model: 'gpt-3.5-turbo', // You can change this to a different model if needed
|
|
messages: [
|
|
{
|
|
role: 'system',
|
|
content: 'You are a helpful customer service assistant.',
|
|
},
|
|
{
|
|
role: 'user',
|
|
content: `Current stage: ${context.stage}. User message: ${message}`,
|
|
},
|
|
],
|
|
max_tokens: 150, // Adjust as needed
|
|
temperature: 0.7, // Adjust for more or less randomness in responses
|
|
})
|
|
|
|
return (
|
|
completion.data.choices[0].message?.content ||
|
|
"I'm sorry, I couldn't generate a response."
|
|
)
|
|
} catch (error) {
|
|
console.error('Error calling OpenAI API:', error)
|
|
return "I'm sorry, there was an error processing your request."
|
|
}
|
|
}
|
|
```
|
|
|
|
#### Google Sheets Integration
|
|
|
|
```typescript
|
|
import { google } from 'googleapis'
|
|
|
|
const sheets = google.sheets({
|
|
version: 'v4',
|
|
auth: process.env.GOOGLE_API_KEY,
|
|
})
|
|
|
|
const logToSheet = async (message: string, response: string) => {
|
|
await sheets.spreadsheets.values.append({
|
|
spreadsheetId: 'YOUR_SHEET_ID',
|
|
range: 'Sheet1!A:C',
|
|
valueInputOption: 'USER_ENTERED',
|
|
requestBody: {
|
|
values: [[new Date().toISOString(), message, response]],
|
|
},
|
|
})
|
|
}
|
|
```
|
|
|
|
### Theming and Custom UI Design
|
|
|
|
Customize DaisyUI theme in `tailwind.config.js`:
|
|
|
|
```javascript
|
|
module.exports = {
|
|
// ...
|
|
daisyui: {
|
|
themes: [
|
|
{
|
|
mytheme: {
|
|
primary: '#4B5563',
|
|
secondary: '#60A5FA',
|
|
accent: '#37CDBE',
|
|
neutral: '#3D4451',
|
|
'base-100': '#FFFFFF',
|
|
},
|
|
},
|
|
],
|
|
},
|
|
}
|
|
```
|
|
|
|
### Performance Optimization
|
|
|
|
1. Implement lazy loading for chat history.
|
|
2. Use React.memo for components that don't need frequent re-renders.
|
|
3. Optimize API calls with debouncing or throttling.
|
|
|
|
```typescript
|
|
import { useDebounce } from 'use-debounce'
|
|
|
|
const [value] = useDebounce(text, 1000)
|
|
```
|
|
|
|
Building a **React chatbot** offers diverse approaches, from no-code solutions to full-stack implementations. This article explored three methods: using Typebot for rapid deployment, integrating Typebot's API for customization, and building a complete solution from scratch.
|
|
|
|
Each approach provides unique benefits, allowing developers to choose based on their project requirements and technical expertise.
|
|
|
|
As chatbots continue to evolve, the potential for creating more intelligent, responsive, and personalized conversational interfaces in React applications is boundless.
|
|
|
|
<Cta />
|