In recent years, building side projects has become increasingly popular among developers, as it allows them to explore new technologies and experiment with ideas that might not fit within the scope of their day job. One technology stack that has gained popularity for side project development is React, Node, and the Chat GPT4 API. In this article, we will explore how to build a side project using these technologies.

Getting Started

Before we dive into the specifics of building a side project with React, Node, and the Chat GPT4 API, let's take a moment to briefly discuss each of these technologies.

React is a popular front-end framework developed by Facebook that allows developers to build user interfaces using a component-based approach. It has gained popularity due to its ease of use and its ability to render components efficiently.

Node is a server-side JavaScript runtime built on Chrome's V8 JavaScript engine. It allows developers to write server-side applications using JavaScript, making it a popular choice for building back-end applications.

The Chat GPT4 API is a natural language processing API developed by OpenAI that allows developers to create conversational interfaces for their applications. It uses advanced machine learning algorithms to understand and respond to user input in a natural way.

With that out of the way, let's dive into building our side project.

Step 1: Setting Up the Back-End with Node

The first step in building our side project is to set up the back-end with Node. We will be using Express, a popular Node framework, to create a simple API that we can use to communicate with the Chat GPT4 API.

To get started, we will need to create a new Node project. We can do this by running the following commands in our terminal:

perlCopy codemkdir my-project
cd my-project
npm init -y

This will create a new Node project with a default package.json file.

Next, we need to install the express package. We can do this by running the following command:

Copy codenpm install express

Once the express package is installed, we can create a new file called index.js in the root of our project with the following code:

javascriptCopy codeconst express = require("express");
const app = express();
const port = 3000;

app.get("/", (req, res) => {
  res.send("Hello World!");
});

app.listen(port, () => {
  console.log(`Server running at http://localhost:${port}`);
});

This code sets up a basic Express server that listens on port 3000 and responds to requests to the root URL with a "Hello World!" message.

Step 2: Integrating the Chat GPT4 API

Now that we have our back-end set up, we can integrate the Chat GPT4 API into our project. To do this, we will need to create an account on the OpenAI website and obtain an API key.

Once we have our API key, we can install the openai package by running the following command:

Copy codenpm install openai

Next, we can update our index.js file to include a new route that will allow us to send a message to the Chat GPT4 API and receive a response:

javascriptCopy codeconst express = require("express");
const openai = require("openai");
const app = express();
const port = 3000;

const openaiApiKey = process.env.OPENAI_API_KEY;

if (!openaiApiKey) {
  console.error("OPENAI_API_KEY environment variable is not set");
  process.exit(1);
}

const openaiApi = new openai.OpenAI(openaiApiKey);

app.get("/", (req, res) => {
  res.send("Hello World!");
});

app.get("/// Endpoint for sending a message to the Chat GPT4 API
app.get("/message", async (req, res) => {
const { message } = req.query;

try {
const response = await openaiApi.complete({
engine: "text-davinci-002",
prompt: Conversation:\nUser: ${message}\nAI:,
maxTokens: 50,
n: 1,
stop: "\n",
});

scssCopy codeconst aiMessage = response.choices[0].text.trim();

res.json({ message: aiMessage });

} catch (error) { console.error(error); res.status(500).json({ error: "An error occurred" }); } });

app.listen(port, () => { console.log(Server running at http://localhost:${port}); });

vbnetCopy code
This code sets up a new route at `/message` that expects a `message` query parameter. It then sends this message to the Chat GPT4 API using the `openai` package and returns the AI's response as JSON.

Step 3: Building the Front-End with React

Now that we have our back-end set up and integrated with the Chat GPT4 API, we can move on to building the front-end with React.

To get started, we will need to create a new React project. We can do this by running the following command in our terminal:

npx create-react-app my-app cd my-app

vbnetCopy code
This will create a new React project in a directory called `my-app`.

Next, we need to install the `axios` package, which we will use to make requests to our back-end API:

npm install axios

vbnetCopy code
Once `axios` is installed, we can create a new file called `Chat.js` in the `src` directory with the following code:

```javascript
import { useState } from "react";
import axios from "axios";

function Chat() {
  const [inputValue, setInputValue] = useState("");
  const [messages, setMessages] = useState([]);

  const sendMessage = async () => {
    if (inputValue) {
      const response = await axios.get("/message", {
        params: {
          message: inputValue,
        },
      });

      setMessages((prevMessages) => [
        ...prevMessages,
        { text: inputValue, user: true },
        { text: response.data.message, user: false },
      ]);
      setInputValue("");
    }
  };

  return (
    <div>
      <div>
        {messages.map((message, index) => (
          <div key={index} style={{ textAlign: message.user ? "right" : "left" }}>
            {message.text}
          </div>
        ))}
      </div>
      <div>
        <input value={inputValue} onChange={(event) => setInputValue(event.target.value)} />
        <button onClick={sendMessage}>Send</button>
      </div>
    </div>
  );
}

export default Chat;

This code sets up a simple chat interface that allows the user to send messages to the Chat GPT4 API and receive responses. It uses the useState hook to manage the state of the input value and the list of messages.

When the user clicks the "Send" button, the sendMessage function is called. This function sends a request to our back-end API with the current input value, receives a response from the Chat GPT4 API, and updates the list of messages with the user's message and the AI's response.

Step 4: Bringing It All Together

Finally, we can bring our back-end and front-end together by updating the App.js file in our React project with the following code:

javascriptCopy codeimport Chat from "./Chat";

function App() {
  return (
    <div>
      <h1>Chat with Chat GPT4</h1>
      <Chat />
    </div>
  );
}

export default App;

This code simply renders a header and our Chat component.

And that's it! We've successfully built a simple chat application that uses the Chat GPT4 API to generate responses.

Conclusion

In this article, we've explored how to build a side project with React, Node, and the Chat GPT4 API. We've walked through the steps of setting up a back-end API with Node and the openai package, and building a front-end with React that sends messages to the API and displays the AI's responses.

While this project is relatively simple, it demonstrates the power of machine learning and natural language processing in building conversational applications. With the Chat GPT4 API, we can create chatbots and virtual assistants that can understand and respond to natural language, making them more intuitive and user-friendly.

If you're interested in building more advanced applications with the Chat GPT4 API, be sure to check out the OpenAI documentation for more information and examples. And if you're looking for more resources on building side projects with React and Node, there are many great tutorials and courses available online to help you get started.