使用预测方法获取文本情感分析的预测。
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代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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))
后续步骤
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