async def test_predict_field_headers_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Any value that is part of the HTTP/1.1 URI should be sent as
# a field header. Set these to a non-empty value.
request = prediction_service.PredictRequest()
request.name = "name/value"
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.predict),
"__call__") as call:
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
prediction_service.PredictResponse())
await client.predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the field header was sent.
_, _, kw = call.mock_calls[0]
assert (
"x-goog-request-params",
"name=name/value",
) in kw["metadata"]
async def test_predict_async(transport: str = "grpc_asyncio"):
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(),
transport=transport,
)
# Everything is optional in proto3 as far as the runtime is concerned,
# and we are mocking out the actual API, so just send an empty request.
request = prediction_service.PredictRequest()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client._client._transport.predict),
"__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
prediction_service.PredictResponse())
response = await client.predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls)
_, args, _ = call.mock_calls[0]
assert args[0] == request
# Establish that the response is the type that we expect.
assert isinstance(response, prediction_service.PredictResponse)
async def test_predict_flattened_error_async():
client = PredictionServiceAsyncClient(
credentials=credentials.AnonymousCredentials(), )
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
await client.predict(
prediction_service.PredictRequest(),
name="name_value",
payload=data_items.ExamplePayload(image=data_items.Image(
image_bytes=b"image_bytes_blob")),
params={"key_value": "value_value"},
)
async def predict(
self,
request: prediction_service.PredictRequest = None,
*,
name: str = None,
payload: data_items.ExamplePayload = None,
params: Sequence[prediction_service.PredictRequest.ParamsEntry] = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> prediction_service.PredictResponse:
r"""Perform an online prediction. The prediction result is directly
returned in the response. Available for following ML scenarios,
and their expected request payloads:
AutoML Vision Classification
- An image in .JPEG, .GIF or .PNG format, image_bytes up to
30MB.
AutoML Vision Object Detection
- An image in .JPEG, .GIF or .PNG format, image_bytes up to
30MB.
AutoML Natural Language Classification
- A TextSnippet up to 60,000 characters, UTF-8 encoded or a
document in .PDF, .TIF or .TIFF format with size upto 2MB.
AutoML Natural Language Entity Extraction
- A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a
document in .PDF, .TIF or .TIFF format with size upto 20MB.
AutoML Natural Language Sentiment Analysis
- A TextSnippet up to 60,000 characters, UTF-8 encoded or a
document in .PDF, .TIF or .TIFF format with size upto 2MB.
AutoML Translation
- A TextSnippet up to 25,000 characters, UTF-8 encoded.
AutoML Tables
- A row with column values matching the columns of the model,
up to 5MB. Not available for FORECASTING ``prediction_type``.
Args:
request (:class:`google.cloud.automl_v1.types.PredictRequest`):
The request object. Request message for
[PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
name (:class:`str`):
Required. Name of the model requested
to serve the prediction.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
payload (:class:`google.cloud.automl_v1.types.ExamplePayload`):
Required. Payload to perform a
prediction on. The payload must match
the problem type that the model was
trained to solve.
This corresponds to the ``payload`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
params (:class:`Sequence[google.cloud.automl_v1.types.PredictRequest.ParamsEntry]`):
Additional domain-specific parameters, any string must
be up to 25000 characters long.
AutoML Vision Classification
``score_threshold`` : (float) A value from 0.0 to 1.0.
When the model makes predictions for an image, it will
only produce results that have at least this confidence
score. The default is 0.5.
AutoML Vision Object Detection
``score_threshold`` : (float) When Model detects objects
on the image, it will only produce bounding boxes which
have at least this confidence score. Value in 0 to 1
range, default is 0.5.
``max_bounding_box_count`` : (int64) The maximum number
of bounding boxes returned. The default is 100. The
number of returned bounding boxes might be limited by
the server.
AutoML Tables
``feature_importance`` : (boolean) Whether
[feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
is populated in the returned list of
[TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]
objects. The default is false.
This corresponds to the ``params`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.PredictResponse:
Response message for
[PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name, payload, params])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
request = prediction_service.PredictRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if payload is not None:
request.payload = payload
if params:
request.params.update(params)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.predict,
default_timeout=60.0,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("name", request.name), )), )
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
