Building AI App with React NextJs TypeScript Google & Stripe
Building AI App with React NextJs TypeScript Google & Stripe
The rapidly evolving field of artificial intelligence (AI) has opened up countless opportunities for developers to build innovative applications.
Enroll Now
Whether you're creating a virtual assistant, building predictive models, or designing an intelligent customer support bot, AI technology can significantly improve user experience.
In this guide, we will walk through building an AI-powered web application using React, Next.js, TypeScript, Google APIs, and Stripe. The app will leverage Google’s machine learning capabilities and Stripe for handling payments. We’ll also emphasize modern development practices using TypeScript for type safety and maintainability.
Tech Stack Overview
- React: A popular JavaScript library for building user interfaces, especially single-page applications.
- Next.js: A React framework that provides server-side rendering, static site generation, and API routes. Perfect for building performant web apps.
- TypeScript: A strongly typed superset of JavaScript that improves code reliability and readability.
- Google APIs: In this case, we’ll leverage Google's AI and machine learning tools like Google Cloud's Natural Language API or Vision API.
- Stripe: A robust platform for handling online payments, ideal for integrating monetization into your AI app.
1. Setting up the Development Environment
Start by creating a Next.js application with TypeScript support. Next.js has built-in support for TypeScript, so it's easy to get started.
bashnpx create-next-app@latest --typescript
This command will scaffold a Next.js app with TypeScript configured. Once the setup is done, run:
bashnpm run dev
The development server will start, and your basic app is now ready. Visit http://localhost:3000 to see it in action.
2. Creating the AI Functionality with Google APIs
To integrate AI into our app, we'll use Google Cloud’s AI APIs. The Google Cloud Natural Language API, Vision API, and Machine Learning Engine are examples of tools we can use depending on the AI functionality we need. For this guide, we'll focus on using the Natural Language API for sentiment analysis.
Setting Up Google Cloud API
- Create a Google Cloud Account: If you don't have a Google Cloud account, create one at Google Cloud Console.
- Enable APIs: Once you're in the console, enable the Google Natural Language API from the API Library.
- Create Credentials: Generate a new API key from the "Credentials" section to authenticate requests from your app.
Installing Google Cloud SDK in your App
In your Next.js project, install the Google Cloud client library:
bashnpm install @google-cloud/language
Now, create a utility file (utils/googleAI.ts) to handle interactions with the Google Natural Language API.
typescriptimport language from '@google-cloud/language';
const client = new language.LanguageServiceClient();
export const analyzeSentiment = async (text: string) => {
const document = {
content: text,
type: 'PLAIN_TEXT',
};
const [result] = await client.analyzeSentiment({ document });
const sentiment = result.documentSentiment;
return sentiment;
};
This function takes a string as input and returns the sentiment score by communicating with Google’s API.
3. Building the Frontend with React and TypeScript
In your Next.js app, you'll want to create a page that allows users to input text and receive sentiment analysis results. We can create a simple React form that submits text to our API and displays the results.
Create a new component (components/SentimentForm.tsx):
tsximport { useState } from 'react';
type SentimentResponse = {
score: number;
magnitude: number;
};
const SentimentForm = () => {
const [inputText, setInputText] = useState('');
const [sentiment, setSentiment] = useState<SentimentResponse | null>(null);
const [loading, setLoading] = useState(false);
const handleSubmit = async (e: React.FormEvent) => {
e.preventDefault();
setLoading(true);
const response = await fetch('/api/sentiment', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text: inputText }),
});
const data = await response.json();
setSentiment(data);
setLoading(false);
};
return (
<form onSubmit={handleSubmit}>
<textarea
value={inputText}
onChange={(e) => setInputText(e.target.value)}
placeholder="Enter text for sentiment analysis"
/>
<button type="submit" disabled={loading}>
{loading ? 'Analyzing...' : 'Analyze Sentiment'}
</button>
{sentiment && (
<div>
<p>Sentiment Score: {sentiment.score}</p>
<p>Magnitude: {sentiment.magnitude}</p>
</div>
)}
</form>
);
};
export default SentimentForm;
4. Setting up the API Route in Next.js
In Next.js, API routes allow you to create server-side API endpoints easily. Create an API route (pages/api/sentiment.ts) that handles sentiment analysis requests.
typescriptimport { NextApiRequest, NextApiResponse } from 'next';
import { analyzeSentiment } from '../../utils/googleAI';
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
if (req.method === 'POST') {
const { text } = req.body;
const sentiment = await analyzeSentiment(text);
res.status(200).json(sentiment);
} else {
res.status(405).json({ message: 'Method not allowed' });
}
}
This route receives the text input from the frontend, calls the Google API function we defined earlier, and returns the sentiment analysis.
5. Adding Payments with Stripe
To monetize the AI app, we can integrate Stripe for handling payments. We'll allow users to pay for detailed sentiment analysis reports.
Setting Up Stripe
- Create a Stripe Account: If you don’t have one, sign up at Stripe.
- Install Stripe SDK: Add the Stripe SDK to your project:
bashnpm install @stripe/stripe-js npm install @stripe/react-stripe-js
- Add Payment Form: Create a payment form using Stripe’s React components.
In components/CheckoutForm.tsx, we’ll create a simple payment form:
tsximport { useStripe, useElements, CardElement } from '@stripe/react-stripe-js';
const CheckoutForm = () => {
const stripe = useStripe();
const elements = useElements();
const handleSubmit = async (event: React.FormEvent) => {
event.preventDefault();
if (!stripe || !elements) {
return;
}
const cardElement = elements.getElement(CardElement);
const result = await stripe.createPaymentMethod({
type: 'card',
card: cardElement!,
});
if (result.error) {
console.error(result.error.message);
} else {
console.log('Payment successful!');
}
};
return (
<form onSubmit={handleSubmit}>
<CardElement />
<button type="submit" disabled={!stripe}>
Pay
</button>
</form>
);
};
export default CheckoutForm;
Now, users can enter their card information and make payments using Stripe.
6. Securing and Deploying the App
Once you’ve finished building the core features, consider the following steps before deploying the app:
Environment Variables: Use environment variables to store sensitive information like your Google Cloud API key and Stripe secret key. Next.js supports
.env.localfiles for this purpose.Authentication: Consider adding user authentication, especially if you want to provide a personalized experience or manage user subscriptions.
Deployment: For deploying the app, you can use Vercel, which provides seamless integration with Next.js. Run the following command to deploy:
bashvercel
Follow the prompts, and your app will be live!
Conclusion
By combining React, Next.js, TypeScript, Google APIs, and Stripe, you’ve built a powerful AI-driven web application that allows users to analyze text sentiment and make payments. This setup can be extended with more complex machine learning models and additional monetization options, creating endless possibilities for your AI app.
The architecture of using Next.js for server-side logic, React for building a responsive interface, TypeScript for type safety, Google APIs for AI, and Stripe for payment processing is both flexible and scalable for future development.