Predict for text sentiment analysis

  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 
 ); 
  
 } 
  
 } 
  
 } 
 } 
 

  /** 
 * 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 
 (); 
 

  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 
 ))