As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1beta1.services.prediction_service.client

# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
from distutils import util
import os
import re
from typing import Callable, Dict, Optional, Sequence, Tuple, Type, Union
import pkg_resources

from google.api_core import client_options as client_options_lib  # type: ignore
from google.api_core import exceptions as core_exceptions  # type: ignore
from google.api_core import gapic_v1  # type: ignore
from google.api_core import retry as retries  # type: ignore
from google.auth import credentials as ga_credentials  # type: ignore
from google.auth.transport import mtls  # type: ignore
from google.auth.transport.grpc import SslCredentials  # type: ignore
from google.auth.exceptions import MutualTLSChannelError  # type: ignore
from google.oauth2 import service_account  # type: ignore

from google.api_core import operation  # type: ignore
from google.api_core import operation_async  # type: ignore
from google.cloud.automl_v1beta1.types import annotation_payload
from google.cloud.automl_v1beta1.types import data_items
from google.cloud.automl_v1beta1.types import io
from google.cloud.automl_v1beta1.types import operations
from google.cloud.automl_v1beta1.types import prediction_service
from .transports.base import PredictionServiceTransport, DEFAULT_CLIENT_INFO
from .transports.grpc import PredictionServiceGrpcTransport
from .transports.grpc_asyncio import PredictionServiceGrpcAsyncIOTransport


class PredictionServiceClientMeta(type):
    """Metaclass for the PredictionService client.

    This provides class-level methods for building and retrieving
    support objects (e.g. transport) without polluting the client instance
    objects.
    """

    _transport_registry = (
        OrderedDict()
    )  # type: Dict[str, Type[PredictionServiceTransport]]
    _transport_registry["grpc"] = PredictionServiceGrpcTransport
    _transport_registry["grpc_asyncio"] = PredictionServiceGrpcAsyncIOTransport

    def get_transport_class(
        cls, label: str = None,
    ) -> Type[PredictionServiceTransport]:
        """Returns an appropriate transport class.

        Args:
            label: The name of the desired transport. If none is
                provided, then the first transport in the registry is used.

        Returns:
            The transport class to use.
        """
        # If a specific transport is requested, return that one.
        if label:
            return cls._transport_registry[label]

        # No transport is requested; return the default (that is, the first one
        # in the dictionary).
        return next(iter(cls._transport_registry.values()))


[docs]class PredictionServiceClient(metaclass=PredictionServiceClientMeta): """AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted. """ @staticmethod def _get_default_mtls_endpoint(api_endpoint): """Converts api endpoint to mTLS endpoint. Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively. Args: api_endpoint (Optional[str]): the api endpoint to convert. Returns: str: converted mTLS api endpoint. """ if not api_endpoint: return api_endpoint mtls_endpoint_re = re.compile( r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?" ) m = mtls_endpoint_re.match(api_endpoint) name, mtls, sandbox, googledomain = m.groups() if mtls or not googledomain: return api_endpoint if sandbox: return api_endpoint.replace( "sandbox.googleapis.com", "mtls.sandbox.googleapis.com" ) return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com") DEFAULT_ENDPOINT = "automl.googleapis.com" DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore DEFAULT_ENDPOINT )
[docs] @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: PredictionServiceClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_info(info) kwargs["credentials"] = credentials return cls(*args, **kwargs)
[docs] @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: PredictionServiceClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs)
from_service_account_json = from_service_account_file @property def transport(self) -> PredictionServiceTransport: """Returns the transport used by the client instance. Returns: PredictionServiceTransport: The transport used by the client instance. """ return self._transport
[docs] @staticmethod def model_path(project: str, location: str, model: str,) -> str: """Returns a fully-qualified model string.""" return "projects/{project}/locations/{location}/models/{model}".format( project=project, location=location, model=model, )
[docs] @staticmethod def parse_model_path(path: str) -> Dict[str, str]: """Parses a model path into its component segments.""" m = re.match( r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)$", path, ) return m.groupdict() if m else {}
[docs] @staticmethod def common_billing_account_path(billing_account: str,) -> str: """Returns a fully-qualified billing_account string.""" return "billingAccounts/{billing_account}".format( billing_account=billing_account, )
[docs] @staticmethod def parse_common_billing_account_path(path: str) -> Dict[str, str]: """Parse a billing_account path into its component segments.""" m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_folder_path(folder: str,) -> str: """Returns a fully-qualified folder string.""" return "folders/{folder}".format(folder=folder,)
[docs] @staticmethod def parse_common_folder_path(path: str) -> Dict[str, str]: """Parse a folder path into its component segments.""" m = re.match(r"^folders/(?P<folder>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_organization_path(organization: str,) -> str: """Returns a fully-qualified organization string.""" return "organizations/{organization}".format(organization=organization,)
[docs] @staticmethod def parse_common_organization_path(path: str) -> Dict[str, str]: """Parse a organization path into its component segments.""" m = re.match(r"^organizations/(?P<organization>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_project_path(project: str,) -> str: """Returns a fully-qualified project string.""" return "projects/{project}".format(project=project,)
[docs] @staticmethod def parse_common_project_path(path: str) -> Dict[str, str]: """Parse a project path into its component segments.""" m = re.match(r"^projects/(?P<project>.+?)$", path) return m.groupdict() if m else {}
[docs] @staticmethod def common_location_path(project: str, location: str,) -> str: """Returns a fully-qualified location string.""" return "projects/{project}/locations/{location}".format( project=project, location=location, )
[docs] @staticmethod def parse_common_location_path(path: str) -> Dict[str, str]: """Parse a location path into its component segments.""" m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path) return m.groupdict() if m else {}
def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Union[str, PredictionServiceTransport, None] = None, client_options: Optional[client_options_lib.ClientOptions] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the prediction service client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Union[str, PredictionServiceTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (google.api_core.client_options.ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. Raises: google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport creation failed for any reason. """ if isinstance(client_options, dict): client_options = client_options_lib.from_dict(client_options) if client_options is None: client_options = client_options_lib.ClientOptions() # Create SSL credentials for mutual TLS if needed. use_client_cert = bool( util.strtobool(os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")) ) client_cert_source_func = None is_mtls = False if use_client_cert: if client_options.client_cert_source: is_mtls = True client_cert_source_func = client_options.client_cert_source else: is_mtls = mtls.has_default_client_cert_source() if is_mtls: client_cert_source_func = mtls.default_client_cert_source() else: client_cert_source_func = None # Figure out which api endpoint to use. if client_options.api_endpoint is not None: api_endpoint = client_options.api_endpoint else: use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto") if use_mtls_env == "never": api_endpoint = self.DEFAULT_ENDPOINT elif use_mtls_env == "always": api_endpoint = self.DEFAULT_MTLS_ENDPOINT elif use_mtls_env == "auto": if is_mtls: api_endpoint = self.DEFAULT_MTLS_ENDPOINT else: api_endpoint = self.DEFAULT_ENDPOINT else: raise MutualTLSChannelError( "Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted " "values: never, auto, always" ) # Save or instantiate the transport. # Ordinarily, we provide the transport, but allowing a custom transport # instance provides an extensibility point for unusual situations. if isinstance(transport, PredictionServiceTransport): # transport is a PredictionServiceTransport instance. if credentials or client_options.credentials_file: raise ValueError( "When providing a transport instance, " "provide its credentials directly." ) if client_options.scopes: raise ValueError( "When providing a transport instance, provide its scopes " "directly." ) self._transport = transport else: Transport = type(self).get_transport_class(transport) self._transport = Transport( credentials=credentials, credentials_file=client_options.credentials_file, host=api_endpoint, scopes=client_options.scopes, client_cert_source_for_mtls=client_cert_source_func, quota_project_id=client_options.quota_project_id, client_info=client_info, always_use_jwt_access=( Transport == type(self).get_transport_class("grpc") or Transport == type(self).get_transport_class("grpc_asyncio") ), )
[docs] 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 will be directly returned in the response. Available for following ML problems, and their expected request payloads: - Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. - Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. - Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded. - Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded. - Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded. - Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]. - Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded. Args: request (google.cloud.automl_v1beta1.types.PredictRequest): The request object. Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. name (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 (google.cloud.automl_v1beta1.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 (Sequence[google.cloud.automl_v1beta1.types.PredictRequest.ParamsEntry]): Additional domain-specific parameters, any string must be up to 25000 characters long. - For Image 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. - For Image 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) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server. - For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. 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_v1beta1.types.PredictResponse: Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.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." ) # Minor optimization to avoid making a copy if the user passes # in a prediction_service.PredictRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, prediction_service.PredictRequest): 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 is not None: request.params = params # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.predict] # 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 = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
[docs] def batch_predict( self, request: prediction_service.BatchPredictRequest = None, *, name: str = None, input_config: io.BatchPredictInputConfig = None, output_config: io.BatchPredictOutputConfig = None, params: Sequence[prediction_service.BatchPredictRequest.ParamsEntry] = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> operation.Operation: r"""Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1beta1.PredictionService.Predict], batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via [GetOperation][google.longrunning.Operations.GetOperation] method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1beta1.BatchPredictResult] is returned in the [response][google.longrunning.Operation.response] field. Available for following ML problems: - Image Classification - Image Object Detection - Video Classification - Video Object Tracking \* Text Extraction - Tables Args: request (google.cloud.automl_v1beta1.types.BatchPredictRequest): The request object. Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict]. name (str): Required. Name of the model requested to serve the batch prediction. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. input_config (google.cloud.automl_v1beta1.types.BatchPredictInputConfig): Required. The input configuration for batch prediction. This corresponds to the ``input_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. output_config (google.cloud.automl_v1beta1.types.BatchPredictOutputConfig): Required. The Configuration specifying where output predictions should be written. This corresponds to the ``output_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. params (Sequence[google.cloud.automl_v1beta1.types.BatchPredictRequest.ParamsEntry]): Required. Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. - For Text Classification: ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. - For Image 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. - For Image 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) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. - For Video Classification : ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5. ``segment_classification`` - (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is "true". ``shot_classification`` - (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". ``1s_interval_classification`` - (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". - For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. The default is false. - For Video Object Tracking: ``score_threshold`` - (float) When Model detects objects on video frames, 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) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. ``min_bounding_box_size`` - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be returned. Value in 0 to 1 range. Default is 0. 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.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.automl_v1beta1.types.BatchPredictResult` Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict]. """ # 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, input_config, output_config, 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." ) # Minor optimization to avoid making a copy if the user passes # in a prediction_service.BatchPredictRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, prediction_service.BatchPredictRequest): request = prediction_service.BatchPredictRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if input_config is not None: request.input_config = input_config if output_config is not None: request.output_config = output_config if params is not None: request.params = params # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.batch_predict] # 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 = rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Wrap the response in an operation future. response = operation.from_gapic( response, self._transport.operations_client, prediction_service.BatchPredictResult, metadata_type=operations.OperationMetadata, ) # Done; return the response. return response
try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution("google-cloud-automl",).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() __all__ = ("PredictionServiceClient",)