Predice para el análisis de opiniones de texto

Obtiene la predicción para el análisis de opiniones de texto con el método de predicción.

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Para obtener documentación en la que se incluye esta muestra de código, consulta lo siguiente:

Obtén predicciones a partir de un modelo de análisis de opiniones en texto

Muestra de código

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Java.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.


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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Node.js incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Node.js.

/**
 * 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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Python incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Python.

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))

¿Qué sigue?

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