Examples with OperationMetadata com.google.cloud.documentai.v1beta2.OperationMetadata used on opensource projects

Example 21 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project spring-cloud-gcp by GoogleCloudPlatform.

the class DocumentOcrTemplate method runOcrForDocument.

/**
 * Runs OCR processing for a specified {@code document} and generates OCR output files under the
 * path specified by {@code outputFilePathPrefix}.
 *
 * <p>For example, if you specify an {@code outputFilePathPrefix} of
 * "gs://bucket_name/ocr_results/myDoc_", all the output files of OCR processing will be saved
 * under prefix, such as:
 *
 * <ul>
 *   <li>gs://bucket_name/ocr_results/myDoc_output-1-to-5.json
 *   <li>gs://bucket_name/ocr_results/myDoc_output-6-to-10.json
 *   <li>gs://bucket_name/ocr_results/myDoc_output-11-to-15.json
 * </ul>
 *
 * <p>Note: OCR processing operations may take several minutes to complete, so it may not be
 * advisable to block on the completion of the operation. One may use the returned {@link
 * ListenableFuture} to register callbacks or track the status of the operation.
 *
 * @param document The {@link GoogleStorageLocation} of the document to run OCR processing
 * @param outputFilePathPrefix The {@link GoogleStorageLocation} of a file, folder, or a bucket
 *     describing the path for which all output files shall be saved under
 * @return A {@link ListenableFuture} allowing you to register callbacks or wait for the
 *     completion of the operation.
 */
public ListenableFuture<DocumentOcrResultSet> runOcrForDocument(GoogleStorageLocation document, GoogleStorageLocation outputFilePathPrefix) {
    Assert.isTrue(document.isFile(), "Provided document location is not a valid file location: " + document);
    GcsSource gcsSource = GcsSource.newBuilder().setUri(document.uriString()).build();
    String contentType = extractContentType(document);
    InputConfig inputConfig = InputConfig.newBuilder().setMimeType(contentType).setGcsSource(gcsSource).build();
    GcsDestination gcsDestination = GcsDestination.newBuilder().setUri(outputFilePathPrefix.uriString()).build();
    OutputConfig outputConfig = OutputConfig.newBuilder().setGcsDestination(gcsDestination).setBatchSize(this.jsonOutputBatchSize).build();
    AsyncAnnotateFileRequest request = AsyncAnnotateFileRequest.newBuilder().addFeatures(DOCUMENT_OCR_FEATURE).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
    OperationFuture<AsyncBatchAnnotateFilesResponse, OperationMetadata> result = imageAnnotatorClient.asyncBatchAnnotateFilesAsync(Collections.singletonList(request));
    return extractOcrResultFuture(result);
}

Example 22 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class LanguageTextClassificationCreateDataset method createDataset.

// Create a dataset
static void createDataset(String projectId, String displayName) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // A resource that represents Google Cloud Platform location.
        LocationName projectLocation = LocationName.of(projectId, "us-central1");
        // Specify the classification type
        // Types:
        // MultiLabel: Multiple labels are allowed for one example.
        // MultiClass: At most one label is allowed per example.
        ClassificationType classificationType = ClassificationType.MULTILABEL;
        // Specify the text classification type for the dataset.
        TextClassificationDatasetMetadata metadata = TextClassificationDatasetMetadata.newBuilder().setClassificationType(classificationType).build();
        Dataset dataset = Dataset.newBuilder().setDisplayName(displayName).setTextClassificationDatasetMetadata(metadata).build();
        OperationFuture<Dataset, OperationMetadata> future = client.createDatasetAsync(projectLocation, dataset);
        Dataset createdDataset = future.get();
        // Display the dataset information.
        System.out.format("Dataset name: %s\n", createdDataset.getName());
        // To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
        // required for other methods.
        // Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
        String[] names = createdDataset.getName().split("/");
        String datasetId = names[names.length - 1];
        System.out.format("Dataset id: %s\n", datasetId);
    }
}

Example 23 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata 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");
    }
}

Example 24 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class VisionClassificationCreateModel method createModel.

// Create a model
static void createModel(String projectId, String datasetId, String displayName) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // A resource that represents Google Cloud Platform location.
        LocationName projectLocation = LocationName.of(projectId, "us-central1");
        // Set model metadata.
        ImageClassificationModelMetadata metadata = ImageClassificationModelMetadata.newBuilder().setTrainBudgetMilliNodeHours(24000).build();
        Model model = Model.newBuilder().setDisplayName(displayName).setDatasetId(datasetId).setImageClassificationModelMetadata(metadata).build();
        // Create a model with the model metadata in the region.
        OperationFuture<Model, OperationMetadata> future = client.createModelAsync(projectLocation, model);
        // OperationFuture.get() will block until the model is created, which may take several hours.
        // You can use OperationFuture.getInitialFuture to get a future representing the initial
        // response to the request, which contains information while the operation is in progress.
        System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
        System.out.println("Training started...");
    }
}

Example 25 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class ClassificationDeployModelNodeCount method classificationDeployModelNodeCount.

// Deploy a model with a specified node count
static void classificationDeployModelNodeCount(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);
        // Set how many nodes the model is deployed on
        ImageClassificationModelDeploymentMetadata deploymentMetadata = ImageClassificationModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
        DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).setImageClassificationModelDeploymentMetadata(deploymentMetadata).build();
        // Deploy the model
        OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
        future.get();
        System.out.println("Model deployment on 2 nodes finished");
    }
}