# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.service_client import SDKClient
from msrest import Configuration, Serializer, Deserializer
from .version import VERSION
from msrest.pipeline import ClientRawResponse
from msrest.exceptions import HttpOperationError
from . import models
class TextAnalyticsClientConfiguration(Configuration):
"""Configuration for TextAnalyticsClient
Note that all parameters used to create this instance are saved as instance
attributes.
:param endpoint: Supported Cognitive Services endpoints (protocol and
hostname, for example: https://westus.api.cognitive.microsoft.com).
:type endpoint: str
:param credentials: Subscription credentials which uniquely identify
client subscription.
:type credentials: None
"""
def __init__(
self, endpoint, credentials):
if endpoint is None:
raise ValueError("Parameter 'endpoint' must not be None.")
if credentials is None:
raise ValueError("Parameter 'credentials' must not be None.")
base_url = '{Endpoint}/text/analytics/v2.1'
super(TextAnalyticsClientConfiguration, self).__init__(base_url)
self.add_user_agent('azure-cognitiveservices-language-textanalytics/{}'.format(VERSION))
self.endpoint = endpoint
self.credentials = credentials
[docs]class TextAnalyticsClient(SDKClient):
"""The Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction and language detection. No training data is needed to use this API; just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions. Further documentation can be found in https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
:ivar config: Configuration for client.
:vartype config: TextAnalyticsClientConfiguration
:param endpoint: Supported Cognitive Services endpoints (protocol and
hostname, for example: https://westus.api.cognitive.microsoft.com).
:type endpoint: str
:param credentials: Subscription credentials which uniquely identify
client subscription.
:type credentials: None
"""
def __init__(
self, endpoint, credentials):
self.config = TextAnalyticsClientConfiguration(endpoint, credentials)
super(TextAnalyticsClient, self).__init__(self.config.credentials, self.config)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self.api_version = 'v2.1'
self._serialize = Serializer(client_models)
self._deserialize = Deserializer(client_models)
[docs] def detect_language(
self, show_stats=None, documents=None, custom_headers=None, raw=False, **operation_config):
"""The API returns the detected language and a numeric score between 0 and
1.
Scores close to 1 indicate 100% certainty that the identified language
is true. A total of 120 languages are supported.
:param show_stats: (optional) if set to true, response will contain
input and document level statistics.
:type show_stats: bool
:param documents:
:type documents:
list[~azure.cognitiveservices.language.textanalytics.models.LanguageInput]
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: LanguageBatchResult or ClientRawResponse if raw=true
:rtype:
~azure.cognitiveservices.language.textanalytics.models.LanguageBatchResult
or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`ErrorResponseException<azure.cognitiveservices.language.textanalytics.models.ErrorResponseException>`
"""
language_batch_input = None
if documents is not None:
language_batch_input = models.LanguageBatchInput(documents=documents)
# Construct URL
url = self.detect_language.metadata['url']
path_format_arguments = {
'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if show_stats is not None:
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
# Construct headers
header_parameters = {}
header_parameters['Accept'] = 'application/json'
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
if language_batch_input is not None:
body_content = self._serialize.body(language_batch_input, 'LanguageBatchInput')
else:
body_content = None
# Construct and send request
request = self._client.post(url, query_parameters, header_parameters, body_content)
response = self._client.send(request, stream=False, **operation_config)
if response.status_code not in [200]:
raise models.ErrorResponseException(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('LanguageBatchResult', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
detect_language.metadata = {'url': '/languages'}
[docs] def entities(
self, show_stats=None, documents=None, custom_headers=None, raw=False, **operation_config):
"""The API returns a list of recognized entities in a given document.
To get even more information on each recognized entity we recommend
using the Bing Entity Search API by querying for the recognized
entities names. See the <a
href="https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/text-analytics-supported-languages">Supported
languages in Text Analytics API</a> for the list of enabled languages.
:param show_stats: (optional) if set to true, response will contain
input and document level statistics.
:type show_stats: bool
:param documents:
:type documents:
list[~azure.cognitiveservices.language.textanalytics.models.MultiLanguageInput]
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: EntitiesBatchResult or ClientRawResponse if raw=true
:rtype:
~azure.cognitiveservices.language.textanalytics.models.EntitiesBatchResult
or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`ErrorResponseException<azure.cognitiveservices.language.textanalytics.models.ErrorResponseException>`
"""
multi_language_batch_input = None
if documents is not None:
multi_language_batch_input = models.MultiLanguageBatchInput(documents=documents)
# Construct URL
url = self.entities.metadata['url']
path_format_arguments = {
'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if show_stats is not None:
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
# Construct headers
header_parameters = {}
header_parameters['Accept'] = 'application/json'
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
if multi_language_batch_input is not None:
body_content = self._serialize.body(multi_language_batch_input, 'MultiLanguageBatchInput')
else:
body_content = None
# Construct and send request
request = self._client.post(url, query_parameters, header_parameters, body_content)
response = self._client.send(request, stream=False, **operation_config)
if response.status_code not in [200]:
raise models.ErrorResponseException(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('EntitiesBatchResult', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
entities.metadata = {'url': '/entities'}
[docs] def key_phrases(
self, show_stats=None, documents=None, custom_headers=None, raw=False, **operation_config):
"""The API returns a list of strings denoting the key talking points in
the input text.
See the <a
href="https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview#supported-languages">Text
Analytics Documentation</a> for details about the languages that are
supported by key phrase extraction.
:param show_stats: (optional) if set to true, response will contain
input and document level statistics.
:type show_stats: bool
:param documents:
:type documents:
list[~azure.cognitiveservices.language.textanalytics.models.MultiLanguageInput]
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: KeyPhraseBatchResult or ClientRawResponse if raw=true
:rtype:
~azure.cognitiveservices.language.textanalytics.models.KeyPhraseBatchResult
or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`ErrorResponseException<azure.cognitiveservices.language.textanalytics.models.ErrorResponseException>`
"""
multi_language_batch_input = None
if documents is not None:
multi_language_batch_input = models.MultiLanguageBatchInput(documents=documents)
# Construct URL
url = self.key_phrases.metadata['url']
path_format_arguments = {
'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if show_stats is not None:
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
# Construct headers
header_parameters = {}
header_parameters['Accept'] = 'application/json'
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
if multi_language_batch_input is not None:
body_content = self._serialize.body(multi_language_batch_input, 'MultiLanguageBatchInput')
else:
body_content = None
# Construct and send request
request = self._client.post(url, query_parameters, header_parameters, body_content)
response = self._client.send(request, stream=False, **operation_config)
if response.status_code not in [200]:
raise models.ErrorResponseException(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('KeyPhraseBatchResult', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
key_phrases.metadata = {'url': '/keyPhrases'}
[docs] def sentiment(
self, show_stats=None, documents=None, custom_headers=None, raw=False, **operation_config):
"""The API returns a numeric score between 0 and 1.
Scores close to 1 indicate positive sentiment, while scores close to 0
indicate negative sentiment. A score of 0.5 indicates the lack of
sentiment (e.g. a factoid statement). See the <a
href="https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview#supported-languages">Text
Analytics Documentation</a> for details about the languages that are
supported by sentiment analysis.
:param show_stats: (optional) if set to true, response will contain
input and document level statistics.
:type show_stats: bool
:param documents:
:type documents:
list[~azure.cognitiveservices.language.textanalytics.models.MultiLanguageInput]
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: object or ClientRawResponse if raw=true
:rtype: object or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`HttpOperationError<msrest.exceptions.HttpOperationError>`
"""
multi_language_batch_input = None
if documents is not None:
multi_language_batch_input = models.MultiLanguageBatchInput(documents=documents)
# Construct URL
url = self.sentiment.metadata['url']
path_format_arguments = {
'Endpoint': self._serialize.url("self.config.endpoint", self.config.endpoint, 'str', skip_quote=True)
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if show_stats is not None:
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
# Construct headers
header_parameters = {}
header_parameters['Accept'] = 'application/json'
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
if multi_language_batch_input is not None:
body_content = self._serialize.body(multi_language_batch_input, 'MultiLanguageBatchInput')
else:
body_content = None
# Construct and send request
request = self._client.post(url, query_parameters, header_parameters, body_content)
response = self._client.send(request, stream=False, **operation_config)
if response.status_code not in [200, 500]:
raise HttpOperationError(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('SentimentBatchResult', response)
if response.status_code == 500:
deserialized = self._deserialize('ErrorResponse', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
sentiment.metadata = {'url': '/sentiment'}