텍스트 감정 분석을 위한 예측

predict 메서드를 사용하여 텍스트 감정 분석을 위한 예측을 가져옵니다.

더 살펴보기

이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.

• 텍스트 감정 분석 모델에서 예측 가져오기

코드 샘플

Java

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.gson.JsonObject;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public class PredictTextSentimentAnalysisSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String content = "YOUR_TEXT_CONTENT";
    String endpointId = "YOUR_ENDPOINT_ID";

    predictTextSentimentAnalysis(project, content, endpointId);
  }

  static void predictTextSentimentAnalysis(String project, String content, String endpointId)
      throws IOException {
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {
      String location = "us-central1";

      // Use JsonObject to ensure safe serialization of the content; handles characters like `"`.
      JsonObject contentJsonObject = new JsonObject();
      contentJsonObject.addProperty("content", content);

      EndpointName endpointName = EndpointName.of(project, location, endpointId);

      Value parameter = Value.newBuilder().setNumberValue(0).setNumberValue(5).build();
      Value.Builder instance = Value.newBuilder();
      JsonFormat.parser().merge(contentJsonObject.toString(), instance);

      List<Value> instances = new ArrayList<>();
      instances.add(instance.build());

      PredictResponse predictResponse =
          predictionServiceClient.predict(endpointName, instances, parameter);
      System.out.println("Predict Text Sentiment Analysis Response");
      System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());

      System.out.println("Predictions");
      for (Value prediction : predictResponse.getPredictionsList()) {
        System.out.format("\tPrediction: %s\n", prediction);
      }
    }
  }
}

Node.js

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vertex AI Node.js API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const text = "YOUR_PREDICTION_TEXT";
// const endpointId = "YOUR_ENDPOINT_ID";
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {instance, prediction} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;

// Imports the Google Cloud Model Service Client library
const {PredictionServiceClient} = aiplatform.v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function predictTextSentimentAnalysis() {
  // Configure the endpoint resource
  const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;

  const instanceObj = new instance.TextSentimentPredictionInstance({
    content: text,
  });
  const instanceVal = instanceObj.toValue();

  const instances = [instanceVal];
  const request = {
    endpoint,
    instances,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);

  console.log('Predict text sentiment analysis response:');
  console.log(`\tDeployed model id : ${response.deployedModelId}`);

  console.log('\nPredictions :');
  for (const predictionResultValue of response.predictions) {
    const predictionResult =
      prediction.TextSentimentPredictionResult.fromValue(
        predictionResultValue
      );
    console.log(`\tSentiment measure: ${predictionResult.sentiment}`);
  }
}
predictTextSentimentAnalysis();

Python

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

def predict_text_sentiment_analysis_sample(
    project: str,
    endpoint_id: str,
    content: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
    instance = predict.instance.TextSentimentPredictionInstance(
        content=content,
    ).to_value()
    instances = [instance]
    parameters_dict = {}
    parameters = json_format.ParseDict(parameters_dict, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.predict(
        endpoint=endpoint, instances=instances, parameters=parameters
    )
    print("response")
    print(" deployed_model_id:", response.deployed_model_id)
    # See gs://google-cloud-aiplatform/schema/predict/prediction/text_sentiment_1.0.0.yaml for the format of the predictions.
    predictions = response.predictions
    for prediction in predictions:
        print(" prediction:", dict(prediction))

다음 단계

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