Welcome to the third and final installment in this series. In Part 2, we created and connected the backend with Strapi to help save our meetings and transcriptions. In this part of the series, we will use ChatGPT with Strapi to gain insights about the transcribed text at the click of a button. We will also look at some testing and how to deploy the application to Strapi cloud.
Outline
You can find the outline for this series below:
- Part 1: Implement Audio Recording and User Interface
- Part 2: Incorporate Strapi CMS and Save Transcriptions
- Part 3: Implement connection to chatGPT and deploy to Strapi cloud
Create Custom API Endpoints in Strapi
We will need our custom endpoints in Strapi CMS to connect with ChatGPT, so navigate to the terminal, change the directory to strapi-transcribe-api, and run the below command:
yarn strapi generate
Doing this will begin the process of generating our custom API. Choose the API option, give it the name transcribe-insight-gpt, and select "no" when it asks us if this is for a plugin.
Make Strapi API Public
Inside the src directory, If we check the api directory in our code editor, we should see the newly created API for transcribe-insight-gpt with it's routes, controllers, and services directories.
Let's check if it works by uncommenting the code in each file, restarting the server, and navigating to the admin dashboard. We will want to make access to this route public, so click Settings > Users & permissions plugin > Roles > Public, then scroll down to Select all on the transcribe-insight-gpt API to make the permissions public, and click save in the top right.
If we enter the following into our browser and click enter, we should get an "ok" message.
http://localhost:1337/api/transcribe-insight-gpt
Using ChatGPT with Strapi
We have confirmed the API endpoint is working, let's connect it to OpenAI first, install the OpenAI package, navigate to the route directory, and run the below command in the terminal
yarn add openai
Then, in the .env file, add the API key to the OPENAI environment variable:
OPENAI=<OpenAI api key here>
Now, under the transcribe-insight-gpt directory, change the code in the routes directory to the following:
module.exports = {
routes: [
{
method: "POST",
path: "/transcribe-insight-gpt/exampleAction",
handler: "transcribe-insight-gpt.exampleAction",
config: {
policies: [],
middlewares: [],
},
},
],
};
Change the code in the controller directory to the following:
"use strict";
module.exports = {
exampleAction: async (ctx) => {
try {
const response = await strapi
.service("api::transcribe-insight-gpt.transcribe-insight-gpt")
.insightService(ctx);
ctx.body = { data: response };
} catch (err) {
console.log(err.message);
throw new Error(err.message);
}
},
};
And the code in the services directory to the following:
"use strict";
const { OpenAI } = require("openai");
const openai = new OpenAI({
apiKey: process.env.OPENAI,
});
/**
* transcribe-insight-gpt service
*/
module.exports = ({ strapi }) => ({
insightService: async (ctx) => {
try {
const input = ctx.request.body.data?.input;
const operation = ctx.request.body.data?.operation;
if (operation === "analysis") {
const analysisResult = await gptAnalysis(input);
return {
message: analysisResult,
};
} else if (operation === "answer") {
const answerResult = await gptAnswer(input);
return {
message: answerResult,
};
} else {
return { error: "Invalid operation specified" };
}
} catch (err) {
ctx.body = err;
}
},
});
async function gptAnalysis(input) {
const analysisPrompt =
"Analyse the following text and give me a brief overview of what it means:";
const completion = await openai.chat.completions.create({
messages: [{ role: "user", content: `${analysisPrompt} ${input}` }],
model: "gpt-3.5-turbo",
});
const analysis = completion.choices[0].message.content;
return analysis;
}
async function gptAnswer(input) {
const answerPrompt =
"Analyse the following text and give me an answer to the question posed: ";
const completion = await openai.chat.completions.create({
messages: [{ role: "user", content: `${answerPrompt} ${input}` }],
model: "gpt-3.5-turbo",
});
const answer = completion.choices[0].message.content;
return answer;
}
Here, we pass two parameters to our API: the input text, which will be our transcriptions, and the operation, which will be either analysis or answer depending on what operation we want it to perform. Each operation will have a different prompt for ChatGPT.
Confirm That ChatGPT Works
We can check the connection to our POST route by pasting the below code in our terminal:
curl -X POST \
http://localhost:1337/api/transcribe-insight-gpt/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "Comparatively, four-dimensional space has an extra coordinate axis, orthogonal to the other three, which is usually labeled w. To describe the two additional cardinal directions",
"operation": "analysis"
}
}'
And to check the answer operation, you can use the below command:
curl -X POST \
http://localhost:1337/api/transcribe-insight-gpt/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?",
"operation": "answer"
}
}'
That's great. Now that we have our analysis and answer capabilities within a Strapi API route, we need to connect this to our front-end code and ensure we can save this information for our meetings and transcriptions.
Connect Custom API for Anaylysis in Next.js
To maintain a clear separation of concerns, let's create a separate API file for our app's analysis functionality.
Get Analysis from ChatGPT
In transcribe-frontend under the api directory, create a new file called analysis.js and paste in the following code:
const baseUrl = 'http://localhost:1337';
const url = `${baseUrl}/api/transcribe-insight-gpt/exampleAction`;
export async function callInsightGpt(operation, input) {
console.log('operation - ', operation);
const payload = {
data: {
input: input,
operation: operation,
},
};
try {
const response = await fetch(url, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(payload),
});
const data = await response.json();
return data;
} catch (error) {
console.error('Error:', error);
}
}
The code above is a POST request to call the insight API and get the analysis back from ChatGPT.
Update Transcriptions with Analysis and Answers
Let's add a way to update our transcriptions with analysis and answers. Paste the following code into the transcriptions.js file.
export async function updateTranscription(
updatedTranscription,
transcriptionId
) {
const updateURL = `${url}/${transcriptionId}`;
const payload = {
data: updatedTranscription,
};
try {
const res = await fetch(updateURL, {
method: 'PUT',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(payload),
});
return await res.json();
} catch (error) {
console.error('Error updating meeting:', error);
throw error;
}
}
The code above is a PUT request to handle an update of the analysis or answer field on each transcription.
Create a Custom Hook to Handle the Overview and Analysis of Transcriptions
Now, let's create a hook where we can use this method. Create a file named useInsightGpt under the hooks directory and paste in the following code:
import { useState } from 'react';
import { callInsightGpt } from '../api/analysis';
import { updateMeeting } from '../api/meetings';
import { updateTranscription } from '../api/transcriptions';
export const useInsightGpt = () => {
const [loadingAnalysis, setLoading] = useState(false);
const [transcriptionIdLoading, setTranscriptionIdLoading] = useState('');
const [analysisError, setError] = useState(null);
const getAndSaveTranscriptionAnalysis = async (
operation,
input,
transcriptionId
) => {
try {
setTranscriptionIdLoading(transcriptionId);
// Get insight analysis / answer
const { data } = await callInsightGpt(operation, input);
// Use transcriptionId to save it to the transcription
const updateTranscriptionDetails =
operation === 'analysis'
? { analysis: data.message }
: { answer: data.message };
await updateTranscription(updateTranscriptionDetails, transcriptionId);
setTranscriptionIdLoading('');
} catch (e) {
setTranscriptionIdLoading('');
setError('Error getting analysis', e);
}
};
const getAndSaveOverviewAnalysis = async (operation, input, meetingId) => {
try {
setLoading(true);
// Get overview insight
const {
data: { message },
} = await callInsightGpt(operation, input);
// Use meetingId to save it to the meeting
const updateMeetingDetails = { overview: message };
await updateMeeting(updateMeetingDetails, meetingId);
setLoading(false);
} catch (e) {
setLoading(false);
setError('Error getting overview', e);
}
};
return {
loadingAnalysis,
transcriptionIdLoading,
analysisError,
getAndSaveTranscriptionAnalysis,
getAndSaveOverviewAnalysis,
};
};
This hook handles the logic to get and save the overview for our meeting when it has ended. It also handles getting the analysis or answers to our transcriptions and saving them, too. It keeps track of which transcription we are requesting analysis for so we can show specific loading states.
Display Analysis of a Transcription
Import the functionality above into the TranscribeContainer and use it. Paste the following updated code into TranscribeContainer.jsx
import React, { useState, useEffect } from "react";
import styles from "../styles/Transcribe.module.css";
import { useAudioRecorder } from "../hooks/useAudioRecorder";
import RecordingControls from "../components/transcription/RecordingControls";
import TranscribedText from "../components/transcription/TranscribedText";
import { useRouter } from "next/router";
import { useMeetings } from "../hooks/useMeetings";
import { useInsightGpt } from "../hooks/useInsightGpt";
import { createNewTranscription } from "../api/transcriptions";
const TranscribeContainer = ({ streaming = true, timeSlice = 1000 }) => {
const router = useRouter();
const [meetingId, setMeetingId] = useState(null);
const [meetingTitle, setMeetingTitle] = useState("");
const {
getMeetingDetails,
saveTranscriptionToMeeting,
updateMeetingDetails,
loading,
error,
meetingDetails,
} = useMeetings();
const {
loadingAnalysis,
transcriptionIdLoading,
analysisError,
getAndSaveTranscriptionAnalysis,
getAndSaveOverviewAnalysis,
} = useInsightGpt();
const apiKey = process.env.NEXT_PUBLIC_OPENAI_API_KEY;
const whisperApiEndpoint = "https://api.openai.com/v1/audio/";
const {
recording,
transcribed,
handleStartRecording,
handleStopRecording,
setTranscribed,
} = useAudioRecorder(streaming, timeSlice, apiKey, whisperApiEndpoint);
const { ended } = meetingDetails;
const transcribedHistory = meetingDetails?.transcribed_chunks?.data;
useEffect(() => {
const fetchDetails = async () => {
if (router.isReady) {
const { meetingId } = router.query;
if (meetingId) {
try {
await getMeetingDetails(meetingId);
setMeetingId(meetingId);
} catch (err) {
console.log("Error getting meeting details - ", err);
}
}
}
};
fetchDetails();
}, [router.isReady, router.query]);
useEffect(() => {
setMeetingTitle(meetingDetails.title);
}, [meetingDetails]);
const handleGetAnalysis = async (input, transcriptionId) => {
await getAndSaveTranscriptionAnalysis("analysis", input, transcriptionId);
// re-fetch meeting details
await getMeetingDetails(meetingId);
};
const handleGetAnswer = async (input, transcriptionId) => {
await getAndSaveTranscriptionAnalysis("answer", input, transcriptionId);
// re-fetch meeting details
await getMeetingDetails(meetingId);
};
const handleStopMeeting = async () => {
// provide meeting overview and save it
// getMeetingOverview(transcribed_chunks)
await updateMeetingDetails(
{
title: meetingTitle,
ended: true,
},
meetingId,
);
// re-fetch meeting details
await getMeetingDetails(meetingId);
setTranscribed("");
};
const stopAndSaveTranscription = async () => {
// save transcription first
let {
data: { id: transcriptionId },
} = await createNewTranscription(transcribed);
// make a call to save the transcription chunk here
await saveTranscriptionToMeeting(meetingId, meetingTitle, transcriptionId);
// re-fetch current meeting which should have updated transcriptions
await getMeetingDetails(meetingId);
// Stop and clear the current transcription as it's now saved
await handleStopRecording();
};
const handleGoBack = () => {
router.back();
};
if (loading) return <p>Loading...</p>;
return (
<div style={{ margin: "20px" }}>
{ended && (
<button onClick={handleGoBack} className={styles.goBackButton}>
Go Back
</button>
)}
{!ended && (
<button
className={styles["end-meeting-button"]}
onClick={handleStopMeeting}
>
End Meeting
</button>
)}
{ended ? (
<p className={styles.title}>{meetingTitle}</p>
) : (
<input
onChange={(e) => setMeetingTitle(e.target.value)}
value={meetingTitle}
type="text"
placeholder="Meeting title here..."
className={styles["custom-input"]}
/>
)}
<div>
{!ended && (
<div>
<RecordingControls
handleStartRecording={handleStartRecording}
handleStopRecording={stopAndSaveTranscription}
/>
{recording ? (
<p className={styles["primary-text"]}>Recording</p>
) : (
<p>Not recording</p>
)}
</div>
)}
{/*Current transcription*/}
{transcribed && <h1>Current transcription</h1>}
<TranscribedText transcribed={transcribed} current={true} />
{/*Transcribed history*/}
<h1>History</h1>
{transcribedHistory
?.slice()
.reverse()
.map((val, i) => {
const transcribedChunk = val.attributes;
const text = transcribedChunk.text;
const transcriptionId = val.id;
return (
<TranscribedText
key={transcriptionId}
transcribed={text}
answer={transcribedChunk.answer}
analysis={transcribedChunk.analysis}
handleGetAnalysis={() =>
handleGetAnalysis(text, transcriptionId)
}
handleGetAnswer={() => handleGetAnswer(text, transcriptionId)}
loading={transcriptionIdLoading === transcriptionId}
/>
);
})}
</div>
</div>
);
};
export default TranscribeContainer;
Here, depending on your need, we use the useInsightGpt hook to get the analysis or answer. We also display a loading indicator beside the transcribed text.
Display Answers and Analysis of Transcriptions in Real Time
Paste the following code into TranscribedText.jsx to update the UI accordingly.
import styles from '../../styles/Transcribe.module.css';
function TranscribedText({
transcribed,
answer,
analysis,
handleGetAnalysis,
handleGetAnswer,
loading,
current,
}) {
return (
<div className={styles['transcribed-text-container']}>
<div className={styles['speech-bubble-container']}>
{transcribed && (
<div className={styles['speech-bubble']}>
<div className={styles['speech-pointer']}></div>
<div className={styles['speech-text-question']}>{transcribed}</div>
{!current && (
<div className={styles['button-container']}>
<button
className={styles['primary-button-analysis']}
onClick={handleGetAnalysis}
>
Get analysis
</button>
<button
className={styles['primary-button-answer']}
onClick={handleGetAnswer}
>
Get answer
</button>
</div>
)}
</div>
)}
</div>
<div>
<div className={styles['speech-bubble-container']}>
{loading && (
<div className={styles['analysis-bubble']}>
<div className={styles['analysis-pointer']}></div>
<div className={styles['speech-text-answer']}>Loading...</div>
</div>
)}
{analysis && (
<div className={styles['analysis-bubble']}>
<div className={styles['analysis-pointer']}></div>
<p style={{ margin: 0 }}>Analysis</p>
<div className={styles['speech-text-answer']}>{analysis}</div>
</div>
)}
</div>
<div className={styles['speech-bubble-container']}>
{answer && (
<div className={styles['speech-bubble-right']}>
<div className={styles['speech-pointer-right']}></div>
<p style={{ margin: 0 }}>Answer</p>
<div className={styles['speech-text-answer']}>{answer}</div>
</div>
)}
</div>
</div>
</div>
);
}
export default TranscribedText;
We can now request analysis and get answers to questions in real-time straight after they have been transcribed.
Implement Meeting Overview functionality
When the user ends the meeting, we want to provide an overview of everything discussed. Let's add this functionality to the TranscribeContainer component.
In the function handleStopMeeting we can use the method getAndSaveOverviewAnalysis from the useInsightGpt hook:
const handleStopMeeting = async () => {
// provide meeting overview and save it
const transcribedHistoryText = transcribedHistory
.map((val) => `transcribed_chunk: ${val.attributes.text}`)
.join(', ');
await getAndSaveOverviewAnalysis(
'analysis',
transcribedHistoryText,
meetingId
);
await updateMeetingDetails(
{
title: meetingTitle,
ended: true,
},
meetingId
);
// re-fetch meeting details
await getMeetingDetails(meetingId);
setTranscribed('');
};
Here, we are joining all of the transcribed chunks from the meeting and then sending them to our ChatGPT API for analysis, where they will be saved for our meeting.
Now, let's display the overview once it has been loaded. Add the following code above the RecordingControls:
{loadingAnalysis && <p>Loading Overview...</p>}
{overview && (
<div>
<h1>Overview</h1>
<p>{overview}</p>
</div>
)}
Then, destructure the overview from the meeting details by adding the following line below our hook declarations:
const { ended, overview } = meetingDetails;
To summarise, we listen to the loading indicator from useInsightGpt and check if overview is present from the meeting; if it is, we display it.
Error handling in Next.js
We have a couple of errors that could be caused by one of our hooks; let's create a component to handle them.
Create a file called ErrorToast.js under the components directory:
import { useEffect, useState } from 'react';
const ErrorToast = ({ message, duration }) => {
const [visible, setVisible] = useState(true);
useEffect(() => {
const timer = setTimeout(() => {
setVisible(false);
}, duration);
return () => clearTimeout(timer);
}, [duration]);
if (!visible) return null;
return <div className="toast">{message}</div>;
};
export default ErrorToast;
And add the following css code to globals.css under the style directory:
.toast {
position: fixed;
top: 20px;
left: 50%;
transform: translateX(-50%);
background-color: rgba(255, 0, 0, 0.8);
color: white;
padding: 16px;
border-radius: 8px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
z-index: 1000;
transition: opacity 0.5s ease-out;
opacity: 1;
display: flex;
align-items: center;
justify-content: center;
text-align: center;
}
.toast-hide {
opacity: 0;
}
Now, we can use this error component in TranscribeContainer; whenever we encounter an unexpected error from the API, we will show this error toast briefly to notify the user that something went wrong.
Import the ErrorToast at the top of the file and then paste the following code above the Go Back button in the return statement of our component:
{error || analysisError ? (
<ErrorToast message={error || analysisError} duration={5000} />
) : null}
Testing with Next.js using Jest
Now, let's add a test to ensure our hooks are working as we expect them to and to alert us to any breaking changes in the code that might be introduced later. First, add the packages below so we can use jest in our project.
yarn add -D jest jest-environment-jsdom @testing-library/react @testing-library/jest-dom @testing-library/react-hooks
Then create a jest.config.js file in the route of the frontend project and add the following code:
const nextJest = require('next/jest');
const createJestConfig = nextJest({
dir: './',
});
const customJestConfig = {
moduleDirectories: ['node_modules', '<rootDir>/'],
testEnvironment: 'jest-environment-jsdom',
};
module.exports = createJestConfig(customJestConfig);
This just sets up Jest ready to be used in Next.js.
Create a test directory and an index.test.js file with the following code:
import { renderHook, act } from '@testing-library/react-hooks';
import { useInsightGpt } from '../hooks/useInsightGpt';
import { callInsightGpt } from '../api/analysis';
import { updateMeeting } from '../api/meetings';
import { updateTranscription } from '../api/transcriptions';
jest.mock('../api/analysis');
jest.mock('../api/meetings');
jest.mock('../api/transcriptions');
describe('useInsightGpt', () => {
beforeEach(() => {
jest.clearAllMocks();
});
it('should handle transcription analysis successfully', async () => {
const mockData = { data: { message: 'Test analysis message' } };
callInsightGpt.mockResolvedValueOnce(mockData);
updateTranscription.mockResolvedValueOnce({});
const { result } = renderHook(() => useInsightGpt());
await act(async () => {
await result.current.getAndSaveTranscriptionAnalysis(
'analysis',
'input',
'transcriptionId'
);
});
expect(callInsightGpt).toHaveBeenCalledWith('analysis', 'input');
expect(updateTranscription).toHaveBeenCalledWith(
{ analysis: 'Test analysis message' },
'transcriptionId'
);
expect(result.current.transcriptionIdLoading).toBe('');
expect(result.current.analysisError).toBe(null);
});
it('should handle overview analysis successfully', async () => {
const mockData = { data: { message: 'Test overview message' } };
callInsightGpt.mockResolvedValueOnce(mockData);
updateMeeting.mockResolvedValueOnce({});
const { result } = renderHook(() => useInsightGpt());
await act(async () => {
await result.current.getAndSaveOverviewAnalysis(
'overview',
'input',
'meetingId'
);
});
expect(callInsightGpt).toHaveBeenCalledWith('overview', 'input');
expect(updateMeeting).toHaveBeenCalledWith(
{ overview: 'Test overview message' },
'meetingId'
);
expect(result.current.loadingAnalysis).toBe(false);
expect(result.current.analysisError).toBe(null);
});
it('should handle errors in transcription analysis', async () => {
const mockError = new Error('Test error');
callInsightGpt.mockRejectedValueOnce(mockError);
const { result } = renderHook(() => useInsightGpt());
await act(async () => {
await result.current.getAndSaveTranscriptionAnalysis(
'analysis',
'input',
'transcriptionId'
);
});
expect(result.current.transcriptionIdLoading).toBe('');
expect(result.current.analysisError).toBe(
'Error getting analysis',
mockError
);
});
it('should handle errors in overview analysis', async () => {
const mockError = new Error('Test error');
callInsightGpt.mockRejectedValueOnce(mockError);
const { result } = renderHook(() => useInsightGpt());
await act(async () => {
await result.current.getAndSaveOverviewAnalysis(
'overview',
'input',
'meetingId'
);
});
expect(result.current.loadingAnalysis).toBe(false);
expect(result.current.analysisError).toBe(
'Error getting overview',
mockError
);
});
});
Because the hooks use our Strapi API, we need a way to replace the data we're getting back from the API calls. We're using jest.mock to intercept the APIs and send back mock data. This way, we can test our hooks' internal logic without calling the API.
In the first two tests, we mock the API call and return some data, then render our hook and call the correct function. We then check if the correct functions have been called with the correct data from inside the hook. The last two tests just test that errors are handled correctly.
Add the following under scripts in the package.json file:
"test": "jest --watch"
Now open the terminal, navigate to the route directory of the frontend project, and run the following command to check if the tests are passing:
yarn test
You should see a success message like the one below:
As an optional challenge, let's see if you can apply what we did with testing useInsightGpt to testing the other hooks.
Application Demo
Here is what our application looks like.
Deployment with Strapi cloud
Finally, we have the finished application up and running correctly with some tests. The time has come to deploy our project to Strapi cloud.
First, navigate to Strapi and click on "cloud" at the top right.
Connect with GitHub.
From the dashboard, click on Create project.
Choose your GitHub account and the correct repo, fill out the display name, and choose the region.
Now, if you have the same file structure as me, which you should do if you've been following along, then you will just need to add the base directory, so click on Show advanced settings and enter the base directory of /strapi-transcribe-api, then you will need to add all of the environment variables that can be found in the .env file in the route of the strapi project.
Once you have added all of these, click on "create project." This will bring you to a loading screen, and then you will be redirected to the build logs; here, you can just wait for the build to finish.
Once it has finished building, you can click on Overview from the top left. This should direct you to the dashboard, where you will find the details of your deployment and the app URL under Overview on the right.
First, click on your app URL, which will open a new tab and direct you to the welcome page of your Strapi app. Then, create a new admin user, which will log you into the dashboard.
This is a new deployment, and as such, it won't have any of the data we had saved locally; it also won't have carried across the public settings we had on the API, so click on Settings>Users & Permissions Plugin>Roles>Public, expand and select all on Meeting, Transcribe-insight-gpt, and Transcribed-chunk, and then click save in the top right.
Once again, let's just check that our deployment was successful by running the below command in the terminal. Please replace https://yourDeployedUrlHere.com with the URL in the Strapi cloud dashboard.
curl -X POST \
https://yourDeployedUrlHere.com/api/transcribe-insight-gpt/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?",
"operation": "answer"
}
}'
Deplying Next.js with Vercel
Now we have the API deployed and ready to use, let's deploy our frontend with Vercel.
First, we will need to change the baseUrl in our API files to link to our newly deployed Strapi instance,
Add the following variable to .env.local
NEXT_PUBLIC_STRAPI_URL="your strapi cloud url"
Now go ahead and replace the current value of baseUrl with the following in all three API files:
const baseUrl =
process.env.NODE_ENV == 'production'
? process.env.NEXT_PUBLIC_STRAPI_URL
: 'http://localhost:1337';
This will just check if the app is running in production. If so, it will use our deployed strap instance. If not, it will revert to localhost. Make sure to push these changes to Github.
Now navigate to Vercel and sign up if you don't already have an account.
Now, let's create a new project by continuing with GitHub.
Once you have verified your account, import the correct GitHub repo
Now we will fill out some configuration details, give the project a name, change the framework preset to Next.js, change the root directory to 'transcribe-frontend', and add the two environment variables from the .env.local file in the Next.js project.
Now click deploy and wait for it to finish. Once deployed, it should redirect you to a success page with a preview of the app.
Now click continue to the dashboard, where you can find information about the app, such as the domain and the deployment logs.
From here, you can click visit to be directed to the app's frontend deployment.
Conclusion
So there you have it! You have now built your transcription app from start to finish. We have gone over how to achieve this with several cutting-edge technologies. We used Strapi for the backend CMS and custom ChatGPT integration, demonstrating how quickly and easily this technology can make building complex web apps. We also covered some architectural patterns with error handling and testing in Next.js, and finally, we deployed the backend to the Strapi cloud. I hope that you have found this series eye-opening and that it will encourage you to bring your ideas to life.
Additional Resources
- Github link to the complete code.
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