When I use videointelligence_v1p3beta1, I got "403 The caller does not have permission" error

Question

I'm trying to use the videointelligence_v1p3beta1 API, but a 403 error occurs when I use this API.

403 The caller does not have permission

Using videointelligence API works fine, but v1p3beta1 asks for permission. I'm not sure how I can solve this problem.

I am using the discover_labels_streaming function of videointelligence_v1p3beta1 and would appreciate any help.

This code working well as for now.

def analyze_explicit_content_file(self, path):
    from google.cloud import videointelligence

    # [START video_analyze_explicit_content]
    """Detects explicit content from the GCS path to a video."""
    video_client = videointelligence.VideoIntelligenceServiceClient()
    features = [videointelligence.Feature.EXPLICIT_CONTENT_DETECTION]

    with io.open(path, "rb") as movie:
        input_content = movie.read()

    operation = video_client.annotate_video(
        request={"features": features, "input_content": input_content}
    )
    print("\nProcessing video for explicit content annotations:")

    result = operation.result(timeout=90)
    print("\nFinished processing.")

    # Retrieve first result because a single video was processed
    for frame in result.annotation_results[0].explicit_annotation.frames:
        likelihood = videointelligence.Likelihood(frame.pornography_likelihood)
        frame_time = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
        print("Time: {}s".format(frame_time))
        print("\tpornography: {}".format(likelihood.name))
    # [END video_analyze_explicit_content]

But this code not working now.

def detect_labels_streaming_file(self, path):
    # [START video_streaming_label_detection_beta]
    from google.cloud import videointelligence_v1p3beta1 as videointelligence

    # path = 'path_to_file'

    client = videointelligence.StreamingVideoIntelligenceServiceClient()

    # Set streaming config.
    config = videointelligence.StreamingVideoConfig(
        feature=(videointelligence.StreamingFeature.STREAMING_LABEL_DETECTION)
    )

    # config_request should be the first in the stream of requests.
    config_request = videointelligence.StreamingAnnotateVideoRequest(
        video_config=config
    )

    # Set the chunk size to 5MB (recommended less than 10MB).
    chunk_size = 5 * 1024 * 1024

    # Load file content.
    stream = []
    with io.open(path, "rb") as video_file:
        while True:
            data = video_file.read(chunk_size)
            if not data:
                break
            stream.append(data)

    def stream_generator():
        yield config_request
        for chunk in stream:
            yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)

    requests = stream_generator()

    # streaming_annotate_video returns a generator.
    # The default timeout is about 300 seconds.
    # To process longer videos it should be set to
    # larger than the length (in seconds) of the stream.
    responses = client.streaming_annotate_video(requests, timeout=600)

    # Each response corresponds to about 1 second of video.
    for response in responses:
        # Check for errors.
        if response.error.message:
            print(response.error.message)
            break

        label_annotations = response.annotation_results.label_annotations

        # label_annotations could be empty
        if not label_annotations:
            continue

        for annotation in label_annotations:
            # Each annotation has one frame, which has a timeoffset.
            frame = annotation.frames[0]
            time_offset = (
                frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
            )

            description = annotation.entity.description
            confidence = annotation.frames[0].confidence
            # description is in Unicode
            print(
                "{}s: {} (confidence: {})".format(time_offset, description, confidence)
            )
    # [END video_streaming_label_detection_beta]

Thank you. Warm regards.