use of com.google.cloud.redis.v1beta1.InputConfig in project java-automl by googleapis.
the class ImportDataset method importDataset.
// Import a dataset
static void importDataset(String projectId, String datasetId, String path) throws IOException, ExecutionException, InterruptedException, TimeoutException {
Duration totalTimeout = Duration.ofMinutes(45);
RetrySettings retrySettings = RetrySettings.newBuilder().setTotalTimeout(totalTimeout).build();
AutoMlSettings.Builder builder = AutoMlSettings.newBuilder();
builder.importDataSettings().setRetrySettings(retrySettings).build();
AutoMlSettings settings = builder.build();
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create(settings)) {
// Get the complete path of the dataset.
DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
// Get multiple Google Cloud Storage URIs to import data from
GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
// Import data from the input URI
InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
System.out.println("Processing import...");
// Start the import job
OperationFuture<Empty, OperationMetadata> operation = client.importDataAsync(datasetFullId, inputConfig);
System.out.format("Operation name: %s%n", operation.getName());
// If you want to wait for the operation to finish, adjust the timeout appropriately. The
// operation will still run if you choose not to wait for it to complete. You can check the
// status of your operation using the operation's name.
Empty response = operation.get(45, TimeUnit.MINUTES);
System.out.format("Dataset imported. %s%n", response);
} catch (TimeoutException e) {
System.out.println("The operation's polling period was not long enough.");
System.out.println("You can use the Operation's name to get the current status.");
System.out.println("The import job is still running and will complete as expected.");
throw e;
}
}
use of com.google.cloud.redis.v1beta1.InputConfig in project java-automl by googleapis.
the class ImportDataset method importDataset.
// Import a dataset
static void importDataset(String projectId, String datasetId, String path) throws IOException, ExecutionException, InterruptedException, TimeoutException {
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// Get the complete path of the dataset.
DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
// Get multiple Google Cloud Storage URIs to import data from
GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
// Import data from the input URI
InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
System.out.println("Processing import...");
// Start the import job
OperationFuture<Empty, OperationMetadata> operation = client.importDataAsync(datasetFullId, inputConfig);
System.out.format("Operation name: %s%n", operation.getName());
// If you want to wait for the operation to finish, adjust the timeout appropriately. The
// operation will still run if you choose not to wait for it to complete. You can check the
// status of your operation using the operation's name.
Empty response = operation.get(45, TimeUnit.MINUTES);
System.out.format("Dataset imported. %s%n", response);
} catch (TimeoutException e) {
System.out.println("The operation's polling period was not long enough.");
System.out.println("You can use the Operation's name to get the current status.");
System.out.println("The import job is still running and will complete as expected.");
throw e;
}
}
use of com.google.cloud.redis.v1beta1.InputConfig in project spring-cloud-gcp by GoogleCloudPlatform.
the class CloudVisionTemplate method analyzeFile.
/**
* Analyze a file and extract the features of the image specified by {@code featureTypes}.
*
* <p>A feature describes the kind of Cloud Vision analysis one wishes to perform on a file, such
* as text detection, image labelling, facial detection, etc. A full list of feature types can be
* found in {@link Feature.Type}.
*
* @param fileResource the file one wishes to analyze. The Cloud Vision APIs support image formats
* described here: https://cloud.google.com/vision/docs/supported-files. Documents with more
* than 5 pages are not supported.
* @param mimeType the mime type of the fileResource. Currently, only "application/pdf",
* "image/tiff" and "image/gif" are supported.
* @param featureTypes the types of image analysis to perform on the image
* @return the results of file analyse
* @throws CloudVisionException if the file could not be read or if a malformed response is
* received from the Cloud Vision APIs
*/
public AnnotateFileResponse analyzeFile(Resource fileResource, String mimeType, Feature.Type... featureTypes) {
ByteString imgBytes;
try {
imgBytes = ByteString.readFrom(fileResource.getInputStream());
} catch (IOException ex) {
throw new CloudVisionException(READ_BYTES_ERROR_MESSAGE, ex);
}
InputConfig inputConfig = InputConfig.newBuilder().setMimeType(mimeType).setContent(imgBytes).build();
List<Feature> featureList = Arrays.stream(featureTypes).map(featureType -> Feature.newBuilder().setType(featureType).build()).collect(Collectors.toList());
BatchAnnotateFilesRequest request = BatchAnnotateFilesRequest.newBuilder().addRequests(AnnotateFileRequest.newBuilder().addAllFeatures(featureList).setInputConfig(inputConfig).build()).build();
BatchAnnotateFilesResponse response = this.imageAnnotatorClient.batchAnnotateFiles(request);
List<AnnotateFileResponse> annotateFileResponses = response.getResponsesList();
if (!annotateFileResponses.isEmpty()) {
return annotateFileResponses.get(0);
} else {
throw new CloudVisionException(EMPTY_RESPONSE_ERROR_MESSAGE);
}
}
use of com.google.cloud.redis.v1beta1.InputConfig in project java-automl by googleapis.
the class DatasetApi method importData.
// [START automl_translate_import_data]
/**
* Import sentence pairs to the dataset.
*
* @param projectId the Google Cloud Project ID.
* @param computeRegion the Region name. (e.g., "us-central1").
* @param datasetId the Id of the dataset.
* @param path the remote Path of the training data csv file.
*/
public static void importData(String projectId, String computeRegion, String datasetId, String path) throws IOException, InterruptedException, ExecutionException {
// Instantiates a client
try (AutoMlClient client = AutoMlClient.create()) {
// Get the complete path of the dataset.
DatasetName datasetFullId = DatasetName.of(projectId, computeRegion, datasetId);
GcsSource.Builder gcsSource = GcsSource.newBuilder();
// Get multiple Google Cloud Storage URIs to import data from
String[] inputUris = path.split(",");
for (String inputUri : inputUris) {
gcsSource.addInputUris(inputUri);
}
// Import data from the input URI
InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
System.out.println("Processing import...");
Empty response = client.importDataAsync(datasetFullId, inputConfig).get();
System.out.println(String.format("Dataset imported. %s", response));
}
}
use of com.google.cloud.redis.v1beta1.InputConfig in project java-translate by googleapis.
the class BatchTranslateTextWithModel method batchTranslateTextWithModel.
// Batch translate text using AutoML Translation model
public static void batchTranslateTextWithModel(String projectId, String sourceLanguage, String targetLanguage, String inputUri, String outputUri, String modelId) throws IOException, ExecutionException, InterruptedException, TimeoutException {
// the "close" method on the client to safely clean up any remaining background resources.
try (TranslationServiceClient client = TranslationServiceClient.create()) {
// Supported Locations: `global`, [glossary location], or [model location]
// Glossaries must be hosted in `us-central1`
// Custom Models must use the same location as your model. (us-central1)
String location = "us-central1";
LocationName parent = LocationName.of(projectId, location);
// Configure the source of the file from a GCS bucket
GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
// Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();
// Configure where to store the output in a GCS bucket
GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
OutputConfig outputConfig = OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
// Configure the model used in the request
String modelPath = String.format("projects/%s/locations/%s/models/%s", projectId, location, modelId);
// Build the request that will be sent to the API
BatchTranslateTextRequest request = BatchTranslateTextRequest.newBuilder().setParent(parent.toString()).setSourceLanguageCode(sourceLanguage).addTargetLanguageCodes(targetLanguage).addInputConfigs(inputConfig).setOutputConfig(outputConfig).putModels(targetLanguage, modelPath).build();
// Start an asynchronous request
OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future = client.batchTranslateTextAsync(request);
System.out.println("Waiting for operation to complete...");
// random number between 300 - 450 (maximum allowed seconds)
long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);
// Display the translation for each input text provided
System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
}
}
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