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 2024 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
import os
import re
from typing import (
Callable,
Dict,
Mapping,
MutableMapping,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import warnings
from google.api_core import client_options as client_options_lib
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry as retries
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.exceptions import MutualTLSChannelError # type: ignore
from google.auth.transport import mtls # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.cloud.automl_v1beta1 import gapic_version as package_version
try:
OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None]
except AttributeError: # pragma: NO COVER
OptionalRetry = Union[retries.Retry, object, None] # 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,
data_items,
io,
operations,
prediction_service,
)
from .transports.base import DEFAULT_CLIENT_INFO, PredictionServiceTransport
from .transports.grpc import PredictionServiceGrpcTransport
from .transports.grpc_asyncio import PredictionServiceGrpcAsyncIOTransport
from .transports.rest import PredictionServiceRestTransport
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
_transport_registry["rest"] = PredictionServiceRestTransport
def get_transport_class(
cls,
label: Optional[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")
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = "automl.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
_DEFAULT_ENDPOINT_TEMPLATE = "automl.{UNIVERSE_DOMAIN}"
_DEFAULT_UNIVERSE = "googleapis.com"
[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 {}
[docs] @classmethod
def get_mtls_endpoint_and_cert_source(
cls, client_options: Optional[client_options_lib.ClientOptions] = None
):
"""Deprecated. Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
client cert source is None.
(2) if `client_options.client_cert_source` is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if `client_options.api_endpoint` if provided, use the provided one.
(2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Args:
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. Only the `api_endpoint` and `client_cert_source` properties may be used
in this method.
Returns:
Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
client cert source to use.
Raises:
google.auth.exceptions.MutualTLSChannelError: If any errors happen.
"""
warnings.warn(
"get_mtls_endpoint_and_cert_source is deprecated. Use the api_endpoint property instead.",
DeprecationWarning,
)
if client_options is None:
client_options = client_options_lib.ClientOptions()
use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
# Figure out the client cert source to use.
client_cert_source = None
if use_client_cert == "true":
if client_options.client_cert_source:
client_cert_source = client_options.client_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
api_endpoint = cls.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = cls.DEFAULT_ENDPOINT
return api_endpoint, client_cert_source
@staticmethod
def _read_environment_variables():
"""Returns the environment variables used by the client.
Returns:
Tuple[bool, str, str]: returns the GOOGLE_API_USE_CLIENT_CERTIFICATE,
GOOGLE_API_USE_MTLS_ENDPOINT, and GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variables.
Raises:
ValueError: If GOOGLE_API_USE_CLIENT_CERTIFICATE is not
any of ["true", "false"].
google.auth.exceptions.MutualTLSChannelError: If GOOGLE_API_USE_MTLS_ENDPOINT
is not any of ["auto", "never", "always"].
"""
use_client_cert = os.getenv(
"GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"
).lower()
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto").lower()
universe_domain_env = os.getenv("GOOGLE_CLOUD_UNIVERSE_DOMAIN")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
return use_client_cert == "true", use_mtls_endpoint, universe_domain_env
@staticmethod
def _get_client_cert_source(provided_cert_source, use_cert_flag):
"""Return the client cert source to be used by the client.
Args:
provided_cert_source (bytes): The client certificate source provided.
use_cert_flag (bool): A flag indicating whether to use the client certificate.
Returns:
bytes or None: The client cert source to be used by the client.
"""
client_cert_source = None
if use_cert_flag:
if provided_cert_source:
client_cert_source = provided_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
return client_cert_source
@staticmethod
def _get_api_endpoint(
api_override, client_cert_source, universe_domain, use_mtls_endpoint
):
"""Return the API endpoint used by the client.
Args:
api_override (str): The API endpoint override. If specified, this is always
the return value of this function and the other arguments are not used.
client_cert_source (bytes): The client certificate source used by the client.
universe_domain (str): The universe domain used by the client.
use_mtls_endpoint (str): How to use the mTLS endpoint, which depends also on the other parameters.
Possible values are "always", "auto", or "never".
Returns:
str: The API endpoint to be used by the client.
"""
if api_override is not None:
api_endpoint = api_override
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
_default_universe = PredictionServiceClient._DEFAULT_UNIVERSE
if universe_domain != _default_universe:
raise MutualTLSChannelError(
f"mTLS is not supported in any universe other than {_default_universe}."
)
api_endpoint = PredictionServiceClient.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = PredictionServiceClient._DEFAULT_ENDPOINT_TEMPLATE.format(
UNIVERSE_DOMAIN=universe_domain
)
return api_endpoint
@staticmethod
def _get_universe_domain(
client_universe_domain: Optional[str], universe_domain_env: Optional[str]
) -> str:
"""Return the universe domain used by the client.
Args:
client_universe_domain (Optional[str]): The universe domain configured via the client options.
universe_domain_env (Optional[str]): The universe domain configured via the "GOOGLE_CLOUD_UNIVERSE_DOMAIN" environment variable.
Returns:
str: The universe domain to be used by the client.
Raises:
ValueError: If the universe domain is an empty string.
"""
universe_domain = PredictionServiceClient._DEFAULT_UNIVERSE
if client_universe_domain is not None:
universe_domain = client_universe_domain
elif universe_domain_env is not None:
universe_domain = universe_domain_env
if len(universe_domain.strip()) == 0:
raise ValueError("Universe Domain cannot be an empty string.")
return universe_domain
def _validate_universe_domain(self):
"""Validates client's and credentials' universe domains are consistent.
Returns:
bool: True iff the configured universe domain is valid.
Raises:
ValueError: If the configured universe domain is not valid.
"""
# NOTE (b/349488459): universe validation is disabled until further notice.
return True
@property
def api_endpoint(self):
"""Return the API endpoint used by the client instance.
Returns:
str: The API endpoint used by the client instance.
"""
return self._api_endpoint
@property
def universe_domain(self) -> str:
"""Return the universe domain used by the client instance.
Returns:
str: The universe domain used by the client instance.
"""
return self._universe_domain
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[
str,
PredictionServiceTransport,
Callable[..., PredictionServiceTransport],
]
] = None,
client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = 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 (Optional[Union[str,PredictionServiceTransport,Callable[..., PredictionServiceTransport]]]):
The transport to use, or a Callable that constructs and returns a new transport.
If a Callable is given, it will be called with the same set of initialization
arguments as used in the PredictionServiceTransport constructor.
If set to None, a transport is chosen automatically.
client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]):
Custom options for the client.
1. The ``api_endpoint`` property can be used to override the
default endpoint provided by the client when ``transport`` is
not explicitly provided. Only if this property is not set and
``transport`` was not explicitly provided, the endpoint is
determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment
variable, which have one of the following values:
"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).
2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide a client certificate for mTLS 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.
3. The ``universe_domain`` property can be used to override the
default "googleapis.com" universe. Note that the ``api_endpoint``
property still takes precedence; and ``universe_domain`` is
currently not supported for mTLS.
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.
"""
self._client_options = client_options
if isinstance(self._client_options, dict):
self._client_options = client_options_lib.from_dict(self._client_options)
if self._client_options is None:
self._client_options = client_options_lib.ClientOptions()
self._client_options = cast(
client_options_lib.ClientOptions, self._client_options
)
universe_domain_opt = getattr(self._client_options, "universe_domain", None)
(
self._use_client_cert,
self._use_mtls_endpoint,
self._universe_domain_env,
) = PredictionServiceClient._read_environment_variables()
self._client_cert_source = PredictionServiceClient._get_client_cert_source(
self._client_options.client_cert_source, self._use_client_cert
)
self._universe_domain = PredictionServiceClient._get_universe_domain(
universe_domain_opt, self._universe_domain_env
)
self._api_endpoint = None # updated below, depending on `transport`
# Initialize the universe domain validation.
self._is_universe_domain_valid = False
api_key_value = getattr(self._client_options, "api_key", None)
if api_key_value and credentials:
raise ValueError(
"client_options.api_key and credentials are mutually exclusive"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
transport_provided = isinstance(transport, PredictionServiceTransport)
if transport_provided:
# transport is a PredictionServiceTransport instance.
if credentials or self._client_options.credentials_file or api_key_value:
raise ValueError(
"When providing a transport instance, "
"provide its credentials directly."
)
if self._client_options.scopes:
raise ValueError(
"When providing a transport instance, provide its scopes "
"directly."
)
self._transport = cast(PredictionServiceTransport, transport)
self._api_endpoint = self._transport.host
self._api_endpoint = (
self._api_endpoint
or PredictionServiceClient._get_api_endpoint(
self._client_options.api_endpoint,
self._client_cert_source,
self._universe_domain,
self._use_mtls_endpoint,
)
)
if not transport_provided:
import google.auth._default # type: ignore
if api_key_value and hasattr(
google.auth._default, "get_api_key_credentials"
):
credentials = google.auth._default.get_api_key_credentials(
api_key_value
)
transport_init: Union[
Type[PredictionServiceTransport],
Callable[..., PredictionServiceTransport],
] = (
PredictionServiceClient.get_transport_class(transport)
if isinstance(transport, str) or transport is None
else cast(Callable[..., PredictionServiceTransport], transport)
)
# initialize with the provided callable or the passed in class
self._transport = transport_init(
credentials=credentials,
credentials_file=self._client_options.credentials_file,
host=self._api_endpoint,
scopes=self._client_options.scopes,
client_cert_source_for_mtls=self._client_cert_source,
quota_project_id=self._client_options.quota_project_id,
client_info=client_info,
always_use_jwt_access=True,
api_audience=self._client_options.api_audience,
)
[docs] def predict(
self,
request: Optional[Union[prediction_service.PredictRequest, dict]] = None,
*,
name: Optional[str] = None,
payload: Optional[data_items.ExamplePayload] = None,
params: Optional[MutableMapping[str, str]] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
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.
.. code-block:: python
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_predict():
# Create a client
client = automl_v1beta1.PredictionServiceClient()
# Initialize request argument(s)
payload = automl_v1beta1.ExamplePayload()
payload.image.image_bytes = b'image_bytes_blob'
request = automl_v1beta1.PredictRequest(
name="name_value",
payload=payload,
)
# Make the request
response = client.predict(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.PredictRequest, dict]):
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 (MutableMapping[str, str]):
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.
# - Quick 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."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def batch_predict(
self,
request: Optional[Union[prediction_service.BatchPredictRequest, dict]] = None,
*,
name: Optional[str] = None,
input_config: Optional[io.BatchPredictInputConfig] = None,
output_config: Optional[io.BatchPredictOutputConfig] = None,
params: Optional[MutableMapping[str, str]] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
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
.. code-block:: python
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1beta1
def sample_batch_predict():
# Create a client
client = automl_v1beta1.PredictionServiceClient()
# Initialize request argument(s)
request = automl_v1beta1.BatchPredictRequest(
name="name_value",
)
# Make the request
operation = client.batch_predict(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.BatchPredictRequest, dict]):
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 (MutableMapping[str, str]):
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.
# - Quick 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."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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
def __enter__(self) -> "PredictionServiceClient":
return self
[docs] def __exit__(self, type, value, traceback):
"""Releases underlying transport's resources.
.. warning::
ONLY use as a context manager if the transport is NOT shared
with other clients! Exiting the with block will CLOSE the transport
and may cause errors in other clients!
"""
self.transport.close()
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
__all__ = ("PredictionServiceClient",)
