def test_predict_flattened():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client.transport.predict), "__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = prediction_service.PredictResponse()
# Call the method with a truthy value for each flattened field,
# using the keyword arguments to the method.
client.predict(
endpoint="endpoint_value",
instances=[struct.Value(null_value=struct.NullValue.NULL_VALUE)],
parameters=struct.Value(null_value=struct.NullValue.NULL_VALUE),
)
# Establish that the underlying call was made with the expected
# request object values.
assert len(call.mock_calls) == 1
_, args, _ = call.mock_calls[0]
assert args[0].endpoint == "endpoint_value"
assert args[0].instances == [
struct.Value(null_value=struct.NullValue.NULL_VALUE)
]
def test_predict_flattened_error():
client = PredictionServiceClient(credentials=credentials.AnonymousCredentials(),)
# Attempting to call a method with both a request object and flattened
# fields is an error.
with pytest.raises(ValueError):
client.predict(
prediction_service.PredictRequest(),
endpoint="endpoint_value",
instances=[struct.Value(null_value=struct.NullValue.NULL_VALUE)],
parameters=struct.Value(null_value=struct.NullValue.NULL_VALUE),
)
def test_predict(
transport: str = "grpc", request_type=prediction_service.PredictRequest
):
client = PredictionServiceClient(
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 = request_type()
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client.transport.predict), "__call__") as call:
# Designate an appropriate return value for the call.
call.return_value = prediction_service.PredictResponse(
deployed_model_id="deployed_model_id_value",
)
response = client.predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls) == 1
_, args, _ = call.mock_calls[0]
assert args[0] == prediction_service.PredictRequest()
# Establish that the response is the type that we expect.
assert isinstance(response, prediction_service.PredictResponse)
assert response.deployed_model_id == "deployed_model_id_value"
def test_predict_field_headers():
client = PredictionServiceClient(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.endpoint = "endpoint/value"
# Mock the actual call within the gRPC stub, and fake the request.
with mock.patch.object(type(client.transport.predict), "__call__") as call:
call.return_value = prediction_service.PredictResponse()
client.predict(request)
# Establish that the underlying gRPC stub method was called.
assert len(call.mock_calls) == 1
_, 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", "endpoint=endpoint/value",) in kw["metadata"]