创建用于分析客户反馈和合成音频的应用程序

此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤：

完整的应用程序可使用 Amazon CDK 进行部署。有关源代码和部署说明，请参阅 GitHub 中的项目。

此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤：

完整的应用程序可使用 Amazon CDK 进行部署。有关源代码和部署说明，请参阅 GitHub 中的项目。

此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤：

完整的应用程序可使用 Amazon CDK 进行部署。有关源代码和部署说明，请参阅 GitHub 中的项目。以下摘录显示了 Amazon SDK for JavaScript 在 Lambda 函数中是如何使用的。

import {
  ComprehendClient,
  DetectDominantLanguageCommand,
  DetectSentimentCommand,
} from "@aws-sdk/client-comprehend";

/**
 * Determine the language and sentiment of the extracted text.
 *
 * @param {{ source_text: string}} extractTextOutput
 */
export const handler = async (extractTextOutput) => {
  const comprehendClient = new ComprehendClient({});

  const detectDominantLanguageCommand = new DetectDominantLanguageCommand({
    Text: extractTextOutput.source_text,
  });

  // The source language is required for sentiment analysis and
  // translation in the next step.
  const { Languages } = await comprehendClient.send(
    detectDominantLanguageCommand,
  );

  const languageCode = Languages[0].LanguageCode;

  const detectSentimentCommand = new DetectSentimentCommand({
    Text: extractTextOutput.source_text,
    LanguageCode: languageCode,
  });

  const { Sentiment } = await comprehendClient.send(detectSentimentCommand);

  return {
    sentiment: Sentiment,
    language_code: languageCode,
  };
};

import {
  DetectDocumentTextCommand,
  TextractClient,
} from "@aws-sdk/client-textract";

/**
 * Fetch the S3 object from the event and analyze it using Amazon Textract.
 *
 * @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event
 */
export const handler = async (eventBridgeS3Event) => {
  const textractClient = new TextractClient();

  const detectDocumentTextCommand = new DetectDocumentTextCommand({
    Document: {
      S3Object: {
        Bucket: eventBridgeS3Event.bucket,
        Name: eventBridgeS3Event.object,
      },
    },
  });

  // Textract returns a list of blocks. A block can be a line, a page, word, etc.
  // Each block also contains geometry of the detected text.
  // For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html.
  const { Blocks } = await textractClient.send(detectDocumentTextCommand);

  // For the purpose of this example, we are only interested in words.
  const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map(
    (b) => b.Text,
  );

  return extractedWords.join(" ");
};

import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly";
import { S3Client } from "@aws-sdk/client-s3";
import { Upload } from "@aws-sdk/lib-storage";

/**
 * Synthesize an audio file from text.
 *
 * @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig
 */
export const handler = async (sourceDestinationConfig) => {
  const pollyClient = new PollyClient({});

  const synthesizeSpeechCommand = new SynthesizeSpeechCommand({
    Engine: "neural",
    Text: sourceDestinationConfig.translated_text,
    VoiceId: "Ruth",
    OutputFormat: "mp3",
  });

  const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand);

  const audioKey = `${sourceDestinationConfig.object}.mp3`;

  // Store the audio file in S3.
  const s3Client = new S3Client();
  const upload = new Upload({
    client: s3Client,
    params: {
      Bucket: sourceDestinationConfig.bucket,
      Key: audioKey,
      Body: AudioStream,
      ContentType: "audio/mp3",
    },
  });

  await upload.done();
  return audioKey;
};

import {
  TranslateClient,
  TranslateTextCommand,
} from "@aws-sdk/client-translate";

/**
 * Translate the extracted text to English.
 *
 * @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage
 */
export const handler = async (textAndSourceLanguage) => {
  const translateClient = new TranslateClient({});

  const translateCommand = new TranslateTextCommand({
    SourceLanguageCode: textAndSourceLanguage.source_language_code,
    TargetLanguageCode: "en",
    Text: textAndSourceLanguage.extracted_text,
  });

  const { TranslatedText } = await translateClient.send(translateCommand);

  return { translated_text: TranslatedText };
};

此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤：

完整的应用程序可使用 Amazon CDK 进行部署。有关源代码和部署说明，请参阅 GitHub 中的项目。