Erstellen Sie eine Transkriptions-App mit Strapi, ChatGPT und Whisper: Teil 3

Willkommen zum dritten und letzten Teil dieser Serie. In Teil 2 haben wir das Backend erstellt und mit Strapi verbunden, um unsere Meetings und Transkriptionen zu speichern. In diesem Teil der Serie verwenden wir ChatGPT mit Strapi, um auf Knopfdruck Einblicke in den transkribierten Text zu gewinnen. Wir werden uns auch einige Tests und die Bereitstellung der Anwendung in der Strapi-Cloud ansehen.

Gliederung

Die Gliederung für diese Serie finden Sie unten:

• Teil 1: Audioaufzeichnung und Benutzeroberfläche implementieren
• Teil 2: Strapi CMS integrieren und Transkriptionen speichern
• Teil 3: Verbindung zu chatGPT implementieren und in der Strapi-Cloud bereitstellen

Erstellen Sie benutzerdefinierte API-Endpunkte in Strapi

Wir benötigen unsere benutzerdefinierten Endpunkte in Strapi CMS, um eine Verbindung mit ChatGPT herzustellen. Navigieren Sie also zum Terminal, ändern Sie das Verzeichnis in strapi-trancribe-api und führen Sie den folgenden Befehl aus:

yarn strapi generate

Damit beginnt der Prozess der Generierung unserer benutzerdefinierten API. Wählen Sie die API-Option, geben Sie ihr den Namen trancribe-insight-gpt und wählen Sie „Nein“, wenn wir gefragt werden, ob es sich um ein Plugin handelt.

Machen Sie die Strapi-API öffentlich

Wenn wir im src-Verzeichnis das API-Verzeichnis in unserem Code-Editor überprüfen, sollten wir die neu erstellte API für trancribe-insight-gpt mit ihren Routen, Controllern sehen. und Dienstleistungsverzeichnisse.

Überprüfen wir, ob es funktioniert, indem wir den Code in jeder Datei auskommentieren, den Server neu starten und zum Admin-Dashboard navigieren. Wir möchten den Zugriff auf diese Route öffentlich machen, klicken Sie also auf Einstellungen > Benutzer- und Berechtigungen-Plugin > Rollen > Öffentlich, scrollen Sie dann nach unten zu Alle auswählen in der trancribe-insight-gpt-API, um die Berechtigungen öffentlich zu machen, und klicken Sie oben rechts auf Speichern.

Wenn wir Folgendes in unseren Browser eingeben und auf die Eingabetaste klicken, sollten wir eine „OK“-Meldung erhalten.

http://localhost:1337/api/transcribe-insight-gpt

Verwenden von ChatGPT mit Strapi

Wir haben bestätigt, dass der API-Endpunkt funktioniert. Verbinden wir ihn zunächst mit OpenAI, installieren Sie das OpenAI-Paket, navigieren Sie zum Routenverzeichnis und führen Sie den folgenden Befehl im Terminal aus

yarn add openai

Dann fügen Sie in der .env-Datei den API-Schlüssel zur OPENAI-Umgebungsvariablen hinzu:

OPENAI=<OpenAI api key here>

Ändern Sie nun im Verzeichnis „trancribe-insight-gpt“ den Code im Verzeichnis „routes“ wie folgt:

module.exports = {
  routes: [
    {
      method: "POST",
      path: "/transcribe-insight-gpt/exampleAction",
      handler: "transcribe-insight-gpt.exampleAction",
      config: {
        policies: [],
        middlewares: [],
      },
    },
  ],
};

Ändern Sie den Code im Controller-Verzeichnis wie folgt:

"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);
    }
  },
};

Und der Code im Diensteverzeichnis lautet wie folgt:

"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;
}

Hier übergeben wir zwei Parameter an unsere API: den Eingabetext, bei dem es sich um unsere Transkriptionen handelt, und die Operation, bei der es sich entweder um eine Analyse oder eine Antwort handelt, je nachdem, welche Operation wir ausführen möchten. Bei jedem Vorgang gibt es eine andere Eingabeaufforderung für ChatGPT.

Bestätigen Sie, dass ChatGPT funktioniert

Wir können die Verbindung zu unserer POST-Route überprüfen, indem wir den folgenden Code in unser Terminal einfügen:

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"
    }
}'

Und um den Antwortvorgang zu überprüfen, können Sie den folgenden Befehl verwenden:

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"
    }
}'

Das ist großartig. Da wir nun über unsere Analyse- und Antwortfunktionen innerhalb einer Strapi-API-Route verfügen, müssen wir diese mit unserem Front-End-Code verbinden und sicherstellen, dass wir diese Informationen für unsere Besprechungen und Transkriptionen speichern können.

Verbinden Sie eine benutzerdefinierte API für die Analyse in Next.js

Um eine klare Trennung der Anliegen zu gewährleisten, erstellen wir eine separate API-Datei für die Analysefunktionalität unserer App.

Erhalten Sie eine Analyse von ChatGPT

Erstellen Sie im Transcribe-Frontend im API-Verzeichnis eine neue Datei mit dem Namen „analysis.js“ und fügen Sie den folgenden Code ein:

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);
  }
}

Der obige Code ist eine POST-Anfrage, um die Insight-API aufzurufen und die Analyse von ChatGPT zurückzubekommen.

Aktualisieren Sie Transkriptionen mit Analysen und Antworten

Fügen wir eine Möglichkeit hinzu, unsere Transkriptionen mit Analysen und Antworten zu aktualisieren. Fügen Sie den folgenden Code in die Datei transcriptions.js ein.

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;
  }
}

Der obige Code ist eine PUT-Anfrage, um eine Aktualisierung des Analyse- oder Antwortfelds bei jeder Transkription durchzuführen.

Erstellen Sie einen benutzerdefinierten Hook, um die Übersicht und Analyse von Transkriptionen zu verwalten

Jetzt erstellen wir einen Hook, in dem wir diese Methode verwenden können. Erstellen Sie eine Datei mit dem Namen useInsightGpt im Hooks-Verzeichnis und fügen Sie den folgenden Code ein:

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,
  };
};

Dieser Hook verwaltet die Logik, um die Übersicht für unser Meeting abzurufen und zu speichern, wenn es beendet ist. Es übernimmt auch das Abrufen der Analysen oder Antworten auf unsere Transkriptionen und deren Speicherung. Es verfolgt, für welche Transkription wir eine Analyse anfordern, damit wir bestimmte Ladezustände anzeigen können.

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.

Abschluss

Da haben Sie es also! Sie haben Ihre Transkriptions-App nun von Anfang bis Ende erstellt. Wir haben besprochen, wie wir dies mit mehreren Spitzentechnologien erreichen können. Wir haben Strapi für das Backend-CMS und die benutzerdefinierte ChatGPT-Integration verwendet und gezeigt, wie schnell und einfach diese Technologie die Erstellung komplexer Web-Apps ermöglichen kann. Wir haben auch einige Architekturmuster mit Fehlerbehandlung und Tests in Next.js abgedeckt und schließlich das Backend in der Strapi-Cloud bereitgestellt. Ich hoffe, dass Ihnen diese Serie die Augen geöffnet hat und dass sie Sie dazu ermutigt, Ihre Ideen zum Leben zu erwecken.

Zusätzliche Ressourcen

• Github-Link zum vollständigen Code.

Das obige ist der detaillierte Inhalt vonErstellen Sie eine Transkriptions-App mit Strapi, ChatGPT und Whisper: Teil 3. Für weitere Informationen folgen Sie bitte anderen verwandten Artikeln auf der PHP chinesischen Website!