use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.
the class TablesBatchPredictBigQuery method batchPredict.
static void batchPredict(String projectId, String modelId, String inputUri, String outputUri) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
// Configure the source of the file from BigQuery
BigQuerySource bigQuerySource = BigQuerySource.newBuilder().setInputUri(inputUri).build();
BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().setBigquerySource(bigQuerySource).build();
// Configure where to store the output in BigQuery
BigQueryDestination bigQueryDestination = BigQueryDestination.newBuilder().setOutputUri(outputUri).build();
BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().setBigqueryDestination(bigQueryDestination).build();
// Build the request that will be sent to the API
BatchPredictRequest request = BatchPredictRequest.newBuilder().setName(name.toString()).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
// Start an asynchronous request
OperationFuture<BatchPredictResult, OperationMetadata> future = client.batchPredictAsync(request);
System.out.println("Waiting for operation to complete...");
BatchPredictResult response = future.get();
System.out.println("Batch Prediction results saved to BigQuery.");
}
}
use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.
the class UndeployModel method undeployModel.
// Undeploy a model from prediction
static void undeployModel(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// Get the full path of the model.
ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
UndeployModelRequest request = UndeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(request);
future.get();
System.out.println("Model undeployment finished");
}
}
use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.
the class LanguageSentimentAnalysisPredict method predict.
static void predict(String projectId, String modelId, String content) throws IOException {
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
// For available mime types, see:
// https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/predict#textsnippet
TextSnippet textSnippet = TextSnippet.newBuilder().setContent(content).setMimeType(// Types: text/plain, text/html
"text/plain").build();
ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
PredictRequest predictRequest = PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();
PredictResponse response = client.predict(predictRequest);
for (AnnotationPayload annotationPayload : response.getPayloadList()) {
System.out.format("Predicted class name: %s\n", annotationPayload.getDisplayName());
System.out.format("Predicted sentiment score: %d\n", annotationPayload.getTextSentiment().getSentiment());
}
}
}
use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.
the class BatchPredict method batchPredict.
static void batchPredict(String projectId, String modelId, String inputUri, String outputUri) throws IOException, ExecutionException, InterruptedException {
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
GcsSource gcsSource = GcsSource.newBuilder().addInputUris(inputUri).build();
BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().setGcsSource(gcsSource).build();
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
BatchPredictRequest request = BatchPredictRequest.newBuilder().setName(name.toString()).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
OperationFuture<BatchPredictResult, OperationMetadata> future = client.batchPredictAsync(request);
System.out.println("Waiting for operation to complete...");
BatchPredictResult response = future.get();
System.out.println("Batch Prediction results saved to specified Cloud Storage bucket.");
}
}
use of com.google.cloud.automl.v1beta1.ModelName in project java-automl by googleapis.
the class LanguageEntityExtractionPredict method predict.
static void predict(String projectId, String modelId, String content) throws IOException {
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
// For available mime types, see:
// https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/predict#textsnippet
TextSnippet textSnippet = TextSnippet.newBuilder().setContent(content).setMimeType(// Types: text/plain, text/html
"text/plain").build();
ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
PredictRequest predictRequest = PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();
PredictResponse response = client.predict(predictRequest);
for (AnnotationPayload annotationPayload : response.getPayloadList()) {
System.out.format("Text Extract Entity Type: %s\n", annotationPayload.getDisplayName());
System.out.format("Text score: %.2f\n", annotationPayload.getTextExtraction().getScore());
TextSegment textSegment = annotationPayload.getTextExtraction().getTextSegment();
System.out.format("Text Extract Entity Content: %s\n", textSegment.getContent());
System.out.format("Text Start Offset: %s\n", textSegment.getStartOffset());
System.out.format("Text End Offset: %s\n\n", textSegment.getEndOffset());
}
}
}
Aggregations