def RunLanguageCommand(feature,
content_file=None,
content=None,
language=None,
content_type=None,
encoding_type=None):
"""Runs a gcloud ml language command.
Args:
feature: str, the name of the feature to request (e.g. 'extractEntities')
content_file: str, the file to be used to analyze text.
content: str, the text to be analyzed.
language: str, the language of the input text.
content_type: str, the format of the input text - 'PLAIN_TEXT' or 'HTML'.
encoding_type: str, the encoding type to be used for calculating word
offsets - 'UTF8', 'UTF16', 'UTF32', 'NONE'.
Raises:
ContentFileError: if content file can't be found and is not a GCS URL.
ContentError: if content is given but empty.
googlecloudsdk.api_lib.util.exceptions.HttpException: if the API returns
an error.
Returns:
messages.AnnotateTextResponse: the response from the API.
"""
client = util.LanguageClient()
source = util.GetContentSource(content, content_file)
return client.Annotate(feature,
source=source,
language=language,
content_type=content_type,
encoding_type=encoding_type)
def RunLanguageCommand(feature, content_file=None, content=None,
language=None, content_type=None,
encoding_type=None,
api_version=util.LANGUAGE_GA_VERSION):
"""Runs a gcloud ml language command.
Args:
feature: str, the name of the feature being used, such as analyzeEntities.
content_file: str, the file to be used to analyze text.
content: str, the text to be analyzed.
language: str, the language of the input text.
content_type: str, the format of the input text - 'PLAIN_TEXT' or 'HTML'.
encoding_type: str, the encoding type to be used for calculating word
offsets - 'UTF8', 'UTF16', 'UTF32', 'NONE'.
api_version: str, the API version to use.
Raises:
ContentFileError: if content file can't be found and is not a GCS URL.
ContentError: if content is given but empty.
googlecloudsdk.api_lib.util.exceptions.HttpException: if the API returns
an error.
Returns:
the response from the API (type depends on feature, for example
if feature is analyzeEntities, response would be
messages.AnalyzeEntitiesResponse).
"""
entity_sentiment = True if feature == 'analyzeEntitySentiment' else False
client = util.LanguageClient(version=api_version,
entity_sentiment_enabled=entity_sentiment)
source = util.GetContentSource(content, content_file)
return client.SingleFeatureAnnotate(feature, source=source, language=language,
content_type=content_type,
encoding_type=encoding_type)
def RunLanguageCommand(feature, content_file=None, content=None,
language=None, content_type=None,
encoding_type=None,
api_version=util.LANGUAGE_GA_VERSION):
"""Runs a gcloud ml language command.
Args:
feature: str, the name of the feature being used, such as analyzeEntities.
content_file: str, the file to be used to analyze text.
content: str, the text to be analyzed.
language: str, the language of the input text.
content_type: str, the format of the input text - 'PLAIN_TEXT' or 'HTML'.
encoding_type: str, the encoding type to be used for calculating word
offsets - 'UTF8', 'UTF16', 'UTF32', 'NONE'.
api_version: str, the API version to use.
Raises:
ContentFileError: if content file can't be found and is not a GCS URL.
ContentError: if content is given but empty.
googlecloudsdk.api_lib.util.exceptions.HttpException: if the API returns
an error.
Returns:
the response from the API (type depends on feature, for example
if feature is analyzeEntities, response would be
messages.AnalyzeEntitiesResponse).
"""
entity_sentiment = True if feature == 'analyzeEntitySentiment' else False
classify_text = True if feature == 'classifyText' else False
client = util.LanguageClient(version=api_version,
entity_sentiment_enabled=entity_sentiment,
classify_text_enabled=classify_text)
source = util.GetContentSource(content, content_file)
try:
return client.SingleFeatureAnnotate(
feature,
source=source,
language=language,
content_type=content_type,
encoding_type=encoding_type)
except HttpError as e:
# Print Service Account Help on Access Denied errors
# as this is most likely cause
if e.status_code == 403:
log.warn('Please Note: {}\n'.format(SERVICE_ACCOUNT_HELP))
raise e
def AddContentToRequest(unused_ref, args, request):
"""The Python hook for yaml commands to inject content into the request."""
source = util.GetContentSource(args.content, args.content_file)
source.UpdateContent(request.document)
return request
