Classify text with a large language model (Generative AI)

  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.protobuf. Value 
 
 ; 
 import 
  
 com.google.protobuf.util. JsonFormat 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 // Text Classification with a Large Language Model 
 public 
  
 class 
 PredictTextClassificationSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 instance 
  
 = 
  
 "{ \"content\": \"What is the topic for a given news headline?\n" 
  
 + 
  
 "- business\n" 
  
 + 
  
 "- entertainment\n" 
  
 + 
  
 "- health\n" 
  
 + 
  
 "- sports\n" 
  
 + 
  
 "- technology\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: Pixel 7 Pro Expert Hands On Review, the Most Helpful Google Phones.\n" 
  
 + 
  
 "The answer is: technology\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: Quit smoking?\n" 
  
 + 
  
 "The answer is: health\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: Roger Federer reveals why he touched Rafael Nadals hand while they were" 
  
 + 
  
 " crying\n" 
  
 + 
  
 "The answer is: sports\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: Business relief from Arizona minimum-wage hike looking more remote\n" 
  
 + 
  
 "The answer is: business\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: #TomCruise has arrived in Bari, Italy for #MissionImpossible.\n" 
  
 + 
  
 "The answer is: entertainment\n" 
  
 + 
  
 "\n" 
  
 + 
  
 "Text: CNBC Reports Rising Digital Profit as Print Advertising Falls\n" 
  
 + 
  
 "The answer is:\"}" 
 ; 
  
 String 
  
 parameters 
  
 = 
  
 "{\n" 
  
 + 
  
 "  \"temperature\": 0,\n" 
  
 + 
  
 "  \"maxDecodeSteps\": 5,\n" 
  
 + 
  
 "  \"topP\": 0,\n" 
  
 + 
  
 "  \"topK\": 1\n" 
  
 + 
  
 "}" 
 ; 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 publisher 
  
 = 
  
 "google" 
 ; 
  
 String 
  
 model 
  
 = 
  
 "text-bison@001" 
 ; 
  
 predictTextClassification 
 ( 
 instance 
 , 
  
 parameters 
 , 
  
 project 
 , 
  
 publisher 
 , 
  
 model 
 ); 
  
 } 
  
 static 
  
 void 
  
 predictTextClassification 
 ( 
  
 String 
  
 instance 
 , 
  
 String 
  
 parameters 
 , 
  
 String 
  
 project 
 , 
  
 String 
  
 publisher 
 , 
  
 String 
  
 model 
 ) 
  
 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. 
  
 try 
  
 ( 
  PredictionServiceClient 
 
  
 predictionServiceClient 
  
 = 
  
  PredictionServiceClient 
 
 . 
 create 
 ( 
 predictionServiceSettings 
 )) 
  
 { 
  
 String 
  
 location 
  
 = 
  
 "us-central1" 
 ; 
  
 final 
  
  EndpointName 
 
  
 endpointName 
  
 = 
  
  EndpointName 
 
 . 
  ofProjectLocationPublisherModelName 
 
 ( 
 project 
 , 
  
 location 
 , 
  
 publisher 
 , 
  
 model 
 ); 
  
  Value 
 
 . 
 Builder 
  
 instanceValue 
  
 = 
  
  Value 
 
 . 
 newBuilder 
 (); 
  
  JsonFormat 
 
 . 
 parser 
 (). 
 merge 
 ( 
 instance 
 , 
  
 instanceValue 
 ); 
  
  List<Value> 
 
  
 instances 
  
 = 
  
 new 
  
 ArrayList 
<> (); 
  
 instances 
 . 
 add 
 ( 
 instanceValue 
 . 
 build 
 ()); 
  
  Value 
 
 . 
 Builder 
  
 parameterValueBuilder 
  
 = 
  
  Value 
 
 . 
 newBuilder 
 (); 
  
  JsonFormat 
 
 . 
 parser 
 (). 
 merge 
 ( 
 parameters 
 , 
  
 parameterValueBuilder 
 ); 
  
  Value 
 
  
 parameterValue 
  
 = 
  
 parameterValueBuilder 
 . 
 build 
 (); 
  
  PredictResponse 
 
  
 predictResponse 
  
 = 
  
 predictionServiceClient 
 . 
 predict 
 ( 
 endpointName 
 , 
  
 instances 
 , 
  
 parameterValue 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Predict Response" 
 ); 
  
 } 
  
 } 
 }