Integration of MetaMask wallet in React application

Paulina Lewandowska

19 Dec 2022
Integration of MetaMask wallet in React application

Introduction

In decentralized applications, users typically need a crypto wallet to interact with the blockchain. To enable this, developers working on the frontend layer must integrate their application with users' wallet applications. This article is intended for developers using the React library who want to create user interfaces with the popular browser plugin, MetaMask. Here is a step-by-step guide on how to integrate a React application with MetaMask.

A basic application in React

The source code of the application written for this tutarial can be found at this link. I used the following libraries to create the application:

To work with this tutorial, you can use the application provided at the link above, or you can configure the application yourself in React with the help of, for example, create-react-app or vite.

Once our application is configured, you need to make sure that we have all the necessary dependencies installed, to do this, run the command

npm install wagmi ethers

To prepare the application, I also used a component library called Material-ui, if you also want to use it, install the following packages with the command:

npm install @mui/material @emotion/react @emotion/styled

Once the configuration is complete and all the necessary dependencies are installed, we can move on to the next point.

Wagmi Library

To integrate with the MetaMask wallet application, we will use a dedicated library for React called wagmi that contains a sizable number of hooks and functions needed for daily blockchain interactions in frontend applications.

The first step will be to configure the library, to do this we need to wrap our application in a WagmiConfig component passing a client variable with our configuration:

import { WagmiConfig, createClient } from "wagmi";
import { getDefaultProvider } from "ethers";
 
import { Home } from "./pages";
import "./styles.css";
 
const client = createClient({
 autoConnect: true,
 provider: getDefaultProvider()
});
 
export default function App() {
 return (
   <WagmiConfig client={client}>
     <Home />
   </WagmiConfig>
 );
}

All available configuration options can be found at this link in the official wagmi documentation

Connecting the MetaMask wallet

Once we have completed the configuration of the wagmi library, we can move on to creating the component responsible for connecting to our wallet. The hooks available in the blibliothek will be helpful in the implementation.

To access the function that will allow us to make a request to connect the wallet, use the useConnect() hooka. To indicate that the wallet we want to connect to is MetaMask, pass the created instance of the InjectedConnector class in the configuration object to the hooka under the connecter key

import { useConnect } from "wagmi";
import { InjectedConnector } from "wagmi/dist/connectors/injected";
 
 ...
 
 const { connect } = useConnect({
   connector: new InjectedConnector()
 });
 
 ...

Hook returns us a connect function that we can call, for example, when the button is clicked.

 ...
 
<Button onClick={() => connect()}>Connect</Button>
 
 ...

To get information about the connected wallet or its connection status, you can use the useAccount() hookup, which returns us such information as:

  • the address of the connected wallet
  • whether the wallet connection action is in progress
  • whether the user's wallet is currently connected in the application
...
 
 const { address, isConnected, isConnecting } = useAccount();
 
 ...

If the user of our application managed to connect the wallet we should also allow him to disconnect it, for this we need to use the disconnect function, which we can access with the help of the hookup useDisconnect()

 ...
 
 const { disconnect } = useDisconnect();
 
 ...

With the help of these three simple hooks, we are able to handle wallet connection. The full source code of the component that handles the connection from the sample application:

import { useConnect, useDisconnect, useAccount } from "wagmi";
import { InjectedConnector } from "wagmi/dist/connectors/injected";
import { Card, Button, Heading } from "../../components";
import Typography from "@mui/material/Typography";
import { WalletInfo } from "./WalletInfo";
 
export const WalletConnect = () => {
 const { isConnected, isConnecting } = useAccount();
 
 const { connect } = useConnect({
   connector: new InjectedConnector()
 });
 
 const { disconnect } = useDisconnect();
 
 return (
   <Card>
     <Heading sx={{ mb: 2 }}>
       {isConnected ? "Your connected wallet:" : "Connect your MetaMask"}
     </Heading>
 
     {isConnecting && <Typography>Connecting...</Typography>}
 
     {isConnected ? (
       <>
         <WalletInfo />
         <Button sx={{ mt: 2 }} onClick={() => disconnect()}>
           Disconnect
         </Button>
       </>
     ) : (
       <Button
         disabled={isConnecting}
         sx={{ mt: 2 }}
         onClick={() => connect()}
       >
         Connect
       </Button>
     )}
   </Card>
 );
};

In the above example there is a <WalletInfo /> component, which we will use to display information about the connected wallet, we will deal with its creation in the next step.

Displaying information about the connected wallet

The next step will display to the user information about the connected wallet such as:

  • Wallet address
  • The current ETH balance of the wallet

To do this, we will prepare two simple components <WalletAddress/> and <WalletBalance/> and , which we will then put into the <WalletInfo/> component:

The address of the currently connected wallet

import { WalletAddress } from "./WalletAddress";
import { WalletBalance } from "./WalletBalance";
 
export const WalletInfo = () => {
 return (
   <div>
     <WalletAddress />
     <WalletBalance />
   </div>
 );
};
import { useAccount } from "wagmi";
import Typography from "@mui/material/Typography";
 
export const WalletAddress = () => {
 const { address } = useAccount();
 return (
   <Typography>
     <strong>Address: </strong>
     {address}
   </Typography>
 );
};

In order to display the connected wallet, we will use the previously mentioned useAccount() hookup, which returns us the address variable. The implementation of a simple component to display the address looks like this:

import { useAccount } from "wagmi";
import Typography from "@mui/material/Typography";
 
export const WalletAddress = () => {
 const { address } = useAccount();
 return (
   <Typography>
     <strong>Address: </strong>
     {address}
   </Typography>
 );
};

Balance of the currently connected wallet

The wagmi library also has a useBalance() hook, which will make the process of retrieving the current wallet balance much easier for us. The process of fetching this value from the blockchain is asynchronous, so this hook returns on, among other things, such information in variables as:

  • isLoading - whether the balance download is in progress
  • isFetched - whether the wallet's balance has been downloaded
  • isError - whether an error occurred while downloading the data
  • data - object containing such fields as:
    • value - user balance in WEI units
    • formatted - user's balance formatted to ETH units
    • symbol - Symbol of the asset for which the balance was downloaded
    • decimals - the number of decimal places that the number describing the quantity of the asset can have

For a better understanding of what a WEI, ETH unit or decimals parameter is, I encourage you to read these articles:

To indicate for which wallet we want to retrieve the balance of funds, we need to pass the address of this wallet as a parameter when calling the hookah as follows, to do this we can use the useAccount() hookah again from the previous step:

 ...
 
 const { address } = useAccount();
 const { isLoading, isFetched, isError, data } = useBalance({ address });
 
 ...

With this information, we are able to implement an entire component with support for the data loading process:

import { useAccount, useBalance } from "wagmi";
import Typography from "@mui/material/Typography";
import Skeleton from "@mui/material/Skeleton";
 
export const WalletBalance = () => {
 const { address } = useAccount();
 const { isLoading, isFetched, isError, data } = useBalance({ address });
 
 return (
   <Typography>
     {isLoading && <Skeleton width={200} />}
     {isFetched && (
       <>
         <strong>Balance: </strong>
         {data?.formatted} {data?.symbol}
       </>
     )}
     {isError && "Fetching balance failed!"}
   </Typography>
 );
};

Summary

The application presented here is just an example, in production applications developers often have to deal with integrating multiple wallet applications, supporting connectivity on multiple blockchain networks, and interactions of connected wallets with smart contracts. All of these functionalities and much more are supported by the wagmi library presented in this turorial. Therefore, I encourage you to study the documentation of this library to see what capabilities it offers.

Tagi

Most viewed


Never miss a story

Stay updated about Nextrope news as it happens.

You are subscribed

AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
Nextrope Logo

Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!