このシリーズの 3 回目で最終回へようこそ。パート 2 では、会議と文字起こしを保存できるように、バックエンドを作成して Strapi に接続しました。シリーズのこのパートでは、ChatGPT と Strapi を使用して、ボタンをクリックするだけで転写されたテキストについての洞察を取得します。また、いくつかのテストと、アプリケーションを Strapi クラウドにデプロイする方法についても説明します。
このシリーズの概要は以下でご覧いただけます:
ChatGPT に接続するには、Strapi CMS のカスタム エンドポイントが必要なので、ターミナルに移動し、ディレクトリを Strapi-transcribe-api に変更し、以下のコマンドを実行します。
yarn strapi generate
これを実行すると、カスタム API を生成するプロセスが開始されます。 API オプションを選択し、transcribe-insight-gpt という名前を付け、プラグイン用かどうかを尋ねられたら 「いいえ」 を選択します。
src ディレクトリ内で、コード エディターで API ディレクトリを確認すると、routes、controllers を含む、transcribe-insight-gpt 用に新しく作成された API が表示されるはずです。および サービス ディレクトリ。
各ファイル内のコードのコメントを外し、サーバーを再起動し、管理ダッシュボードに移動して、機能するかどうかを確認してみましょう。このルートへのアクセスを公開したいので、設定 > をクリックします。ユーザーと権限プラグイン >役割 > [公開]をクリックし、transcribe-insight-gpt API で すべて選択 までスクロールして権限を公開し、右上の 保存 をクリックします。
ブラウザに次のように入力して Enter をクリックすると、「ok」 というメッセージが表示されます。
http://localhost:1337/api/transcribe-insight-gpt
API エンドポイントが動作していることを確認しました。まず OpenAI に接続し、OpenAI パッケージをインストールし、ルート ディレクトリに移動して、ターミナルで以下のコマンドを実行します
yarn add openai
次に、.env ファイルで、API キーを OPENAI 環境変数に追加します。
OPENAI=<OpenAI api key here>
ここで、transcribe-insight-gpt ディレクトリの下で、routes ディレクトリのコードを次のように変更します。
module.exports = {
routes: [
{
method: "POST",
path: "/transcribe-insight-gpt/exampleAction",
handler: "transcribe-insight-gpt.exampleAction",
config: {
policies: [],
middlewares: [],
},
},
],
};
コントローラー ディレクトリのコードを次のように変更します。
"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);
}
},
};
サービス ディレクトリ内のコードは次のようになります:
"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} ${input}` }],
model: "gpt-3.5-turbo",
});
const answer = completion.choices[0].message.content;
return answer;
}
ここでは、2 つのパラメーターを API に渡します。文字起こしとなる入力テキストと、実行する操作に応じて分析または応答となる操作です。各操作には、ChatGPT の異なるプロンプトが表示されます。
以下のコードをターミナルに貼り付けることで、POST ルートへの接続を確認できます。
curl -X POST \
http://localhost:1337/api/transcribe-insight-gpt/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "Comparatively, four-dimensional space has an extra coordinate axis, orthogonal to the other three, which is usually labeled w. To describe the two additional cardinal directions",
"operation": "analysis"
}
}'
また、応答操作を確認するには、以下のコマンドを使用できます:
curl -X POST \
http://localhost:1337/api/transcribe-insight-gpt/exampleAction \
-H 'Content-Type: application/json' \
-d '{
"data": {
"input": "I speak without a mouth and hear without ears. I have no body, but I come alive with the wind. What am I?",
"operation": "answer"
}
}'
それは素晴らしいですね。 Strapi API ルート内に分析機能と回答機能が備わったので、これをフロントエンド コードに接続し、会議や文字起こし用にこの情報を確実に保存できるようにする必要があります。
懸念事項を明確に分離するために、アプリの分析機能用に別の API ファイルを作成しましょう。
API ディレクトリの下の Transcribe-frontend で、analytics.js という新しいファイルを作成し、次のコードを貼り付けます。
const baseUrl = 'http://localhost:1337';
const url = `${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);
}
}
上記のコードは、Insight API を呼び出し、ChatGPT から分析を取得するための POST リクエストです。
分析と回答で文字起こしを更新する方法を追加しましょう。次のコードをtranscriptions.jsファイルに貼り付けます。
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;
}
}
上記のコードは、各文字起こしの分析または回答フィールドの更新を処理する PUT リクエストです。
それでは、このメソッドを使用できるフックを作成しましょう。 useInsightGpt という名前のファイルをフック ディレクトリに作成し、次のコードを貼り付けます:
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,
};
};
このフックは、会議が終了したときにその概要を取得して保存するロジックを処理します。また、文字起こしに対する分析や回答の取得と保存も処理します。どの文字起こしの分析をリクエストしているかを追跡するので、特定の読み込み状態を表示できます。
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.
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.
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.
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}
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.
Here is what our application looks like.
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"
}
}'
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.
그래서 됐어요! 이제 처음부터 끝까지 전사 앱을 구축했습니다. 우리는 여러 가지 최첨단 기술을 사용하여 이를 달성하는 방법을 살펴보았습니다. 우리는 백엔드 CMS 및 사용자 정의 ChatGPT 통합을 위해 Strapi를 사용하여 이 기술이 얼마나 빠르고 쉽게 복잡한 웹 앱을 구축할 수 있는지 보여주었습니다. 또한 Next.js에서 오류 처리 및 테스트를 통해 일부 아키텍처 패턴을 다루고 마지막으로 백엔드를 Strapi 클라우드에 배포했습니다. 이 시리즈를 통해 여러분의 아이디어를 실현할 수 있는 계기가 되기를 바랍니다.
위 내용은 Strapi, ChatGPT 및 Whisper를 사용하여 전사 앱 구축: 3부의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!
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