Building a chat app with GPT-3, ReactJS, and NextJS: A step-by-step guide

 GPT-3, or Generative Pretrained Transformer 3, is the latest version of OpenAI’s powerful language model. It can generate natural-sounding text in a variety of languages and styles, making it a useful tool for building chat apps and other applications that involve natural language processing.

In this article, we will show you how to use the GPT-3 API to build a simple web chat app using ReactJS, JavaScript, and NextJS. We will start by setting up our development environment and creating a NextJS app, then we will add the GPT-3 API and build the chat interface. Finally, we will discuss some ways to enhance the chat experience and make it more engaging for users.

Whether you are a seasoned developer or a beginner looking to learn more about GPT-3 and natural language processing, this article will provide you with the necessary tools and knowledge to get started. Let’s dive in!

Table of Contents

• Setting up your development environment:
• Adding the GPT-3 API:
• Building the chat interface:
• Sending and receiving messages:
• Ways to enhance the chat experience and make it more engaging for users
• Conclusion:

Setting up your development environment:

Before we can start using the GPT-3 API, we need to set up our development environment and install the necessary tools and libraries.

1. The first thing you will need is Node.js, a JavaScript runtime that allows you to run JavaScript on the server-side. You can download and install Node.js from the official website (https://nodejs.org/en/).
2. Next, you will need to install ReactJS, a popular JavaScript library for building user interfaces. You can do this using the npm (Node Package Manager) that comes with Node.js:

npm install -g create-react-app


3. Finally, you will need to install NextJS, a framework for building server-rendered React applications. You can do this using the following command:

npm install -g next


Once you have installed these tools and libraries, you can verify that everything is working correctly by creating a new NextJS app using the create-next-app command:

create-next-app my-chat-app


This will create a new directory called my-chat-app with the necessary files and dependencies for a NextJS app. You can navigate to this directory and run the app using the npm run dev command, which will start a local development server at http://localhost:3000. You should see a welcome message in your browser, indicating that your app is running correctly.

In the next section, we will show you how to add the GPT-3 API to your app and test it using a sample query.

Adding the GPT-3 API:

Now that we have a working NextJS app, we are ready to add the GPT-3 API and start using it in our app.

To use the GPT-3 API, you will first need to sign up for an API key. You can do this by visiting the OpenAI website (https://beta.openai.com/) and following the instructions to create an account and request an API key.

Once you have an API key, you can install the GPT-3 client library for Node.js using the following command:

npm install openai


This will add the openai package to your project’s dependencies, allowing you to use the GPT-3 API in your app.

To test the API, you can use the following code to send a sample query and print the response in the console:

const openai = require('openai');


openai.apiKey = "<your API key here>";


openai.completions({


         engine: "text-davinci-003",


                   prompt: "Hello, how are you?",


                   max_tokens: 32,


                   n: 1,
stop: ".",


                   temperature: 0.5,


              }).then((response) => {
console.log(response.data.choices[0].text);
});

Replace <your API key here> with your actual API key, and run this code using the node command. You should see the GPT-3 model’s response to your query printed in the console, indicating that the API is working correctly.

In the next section, we will show you how to build the chat interface and start sending and receiving messages using the GPT-3 API.

Building the chat interface:

Now that we have added the GPT-3 API to our app, we are ready to build the chat interface and start sending and receiving messages.

To build the chat interface, we will use ReactJS to create a simple form with an input field for the user’s message and a display area for the chat messages. We will also add some basic CSS styles to make the interface look more appealing.

First, create a new file called components/Chat.js and add the following code to it:

import React from ‘react’;
import ‘./Chat.css’;

const Chat = () => {
return (
<div className=”chat-container”>
<div className=”chat-messages”>
{/* Add messages here */}
</div>
<form className=”chat-form”>
<input type=”text” placeholder=”Enter your message” />
<button type=”submit”>Send</button>
</form>
</div>
);
};

export default Chat;

This code defines a Chat component that renders a div element with the chat-container class, which will contain the chat messages and the input form. The chat-messages class is used for the display area, and the chat-form class is used for the input form.

Next, create a new file called components/Chat.css and add the following styles to it:

.chat-container {
width: 500px;
margin: 0 auto;
border: 1px solid #ccc;
padding: 10px;
}

.chat-messages {
height: 300px;
overflow-y: scroll;
padding: 10px;
border: 1px solid #ccc;
margin-bottom: 10px;
}

.chat-form {
display: flex;
}

.chat-form input {
flex-grow: 1;
padding: 5px;
border: 1px solid #ccc;
margin-right: 5px;
}

.chat-form button {
padding: 5px 10px;
border: none;
background-color: #0099ff;
color: #fff;
}

These styles define the layout and appearance of the chat interface, including the size, margins, padding, and colors of the various elements.

Finally, we need to update the pages/index.js file to use the Chat component we just created. Replace the existing code with the following:

import Chat from ‘../components/Chat’;

const Home = () => {
return (
<>
<Chat />
</>
);
};

export default Home;

This code imports the Chat component and uses it as the default route for the app. When you run the app using the npm run dev command and open it in your web browser, you should see the chat interface with an input field and a display area.

In the next section, we will show you how to send and receive messages using the GPT-3 API.

Sending and receiving messages:

Now that we have built the chat interface, we are ready to send and receive messages using the GPT-3 API.

To send a message, we need to add a form submission event handler to the Chat component. This handler will be called when the user submits the form, and it will use the GPT-3 API to send the user’s message and display the model’s response in the chat interface.

First, update the Chat component to include a state variable for the chat messages and a method for adding a new message:

import React, { useState } from ‘react’;
import ‘./Chat.css’;

const Chat = () => {
const [messages, setMessages] = useState([]);

const addMessage = (message) => {
setMessages([…messages, message]);
};

return (
<div className=”chat-container”>
<div className=”chat-messages”>
{messages.map((message, index) => (
<div key={index}>{message}</div>
))}
</div>
<form
className=”chat-form”
onSubmit={(event) => {
event.preventDefault();
const input = event.target.elements.message;
const text = input.value;
input.value = ”;
// Send the message to the GPT-3 model and add the response to the chat
// Add your code here…
}}
>
<input type=”text” placeholder=”Enter your message” name=”message” />
<button type=”submit”>Send</button>
</form>
</div>
);
};

export default Chat;

This code adds a messages state variable to the Chat component, which is an array of chat messages. It also adds a addMessage method, which is used to add a new message to the messages array.

The Chat the component also includes an onSubmit event handler for the form, which is called when the user submits the form. This handler prevents the default form submission behavior, extracts the user’s message from the input field, and clears the input field.

Next, we need to add the code that sends the user’s message to the GPT-3 model and adds the model’s response to the chat. To do this, we will use the openai.completions method we used earlier to test the API:

import openai from ‘openai’;

// Add this code inside the form’s onSubmit event handler…
openai.apiKey = “<your API key here>”;

openai.completions({
engine: “text-davinci-002”,
prompt: text,
max_tokens: 32,
n: 1,
stop: “.”,
temperature: 0.5,
}).then((response) => {
const message = response.data.choices[0].text;
addMessage(message);
});

This code sends the user’s message to the GPT-3 model using the openai.completions method and then adds the model’s response to the chat using the addMessage method.

When you run the app and try sending a message, you should see the model’s response appear in the chat interface. You can continue sending messages and the model will respond in a way that is appropriate and natural-sounding.

In the next section, we will discuss some ways to enhance the chat experience and make it more engaging for users.

Ways to enhance the chat experience and make it more engaging for users

here are a few possible ways to enhance the chat experience and make it more engaging for users:

• Custom responses: You can use the GPT-3 API to train the model on specific topics or scenarios, and then use the trained model to generate more personalized and relevant responses. For example, if you are building a chat app for a travel website, you can train the model on common travel-related questions and then use it to answer user queries about destinations, flights, and hotels.
• Multiple users: You can extend the chat app to support multiple users, allowing them to chat with each other and with the GPT-3 model. You can use a real-time database, such as Firebase, to synchronize the messages and users across different devices and sessions.

Conclusion:

In this article, we showed you how to use the GPT-3 API to build a simple web chat app using ReactJS, JavaScript, and NextJS. We started by setting up our development environment and creating a NextJS app, then we added the GPT-3 API and built the chat interface. Finally, we discussed some ways to enhance the chat experience and make it more engaging for users.

By following the steps in this article, you can create your own chat app and experiment with the powerful capabilities of GPT-3. Whether you are a developer looking to build natural language processing applications or a beginner looking to learn more about GPT-3, this article provides a useful starting point for your journey. Happy coding!