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dizcologyJon Wayne Parrott
dizcology
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Jon Wayne Parrott
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Natural Language GAPIC client library (#1018)
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‎language/cloud-client/.DS_Store

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‎language/cloud-client/v1/README.rst

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@@ -5,6 +5,10 @@ Google Cloud Natural Language API Python Samples
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This directory contains samples for Google Cloud Natural Language API. The `Google Cloud Natural Language API`_ provides natural language understanding technologies to developers, including sentiment analysis, entity recognition, and syntax analysis. This API is part of the larger Cloud Machine Learning API.
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- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
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.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
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‎language/cloud-client/v1/README.rst.in

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entity recognition, and syntax analysis. This API is part of the larger
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Cloud Machine Learning API.
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- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
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.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
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setup:
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- auth
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- install_deps

‎language/cloud-client/v1/quickstart.py

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def run_quickstart():
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# [START language_quickstart]
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# Imports the Google Cloud client library
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# [START migration_import]
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from google.cloud import language
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from google.cloud.language import enums
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from google.cloud.language import types
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# [END migration_import]
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# Instantiates a client
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language_client = language.Client()
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# [START migration_client]
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client = language.LanguageServiceClient()
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# [END migration_client]
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# The text to analyze
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text = 'Hello, world!'
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document = language_client.document_from_text(text)
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text = u'Hello, world!'
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document = types.Document(
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content=text,
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type=enums.Document.Type.PLAIN_TEXT)
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# Detects the sentiment of the text
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sentiment = document.analyze_sentiment().sentiment
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sentiment = client.analyze_sentiment(document=document).document_sentiment
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print('Text: {}'.format(text))
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print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))

‎language/cloud-client/v1/requirements.txt

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google-cloud-language==0.25.0
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google-cloud-language==0.26.1

‎language/cloud-client/v1/snippets.py

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@@ -24,118 +24,164 @@
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import argparse
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from google.cloud import language
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from google.cloud.language import enums
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from google.cloud.language import types
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import six
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# [START def_sentiment_text]
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def sentiment_text(text):
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"""Detects sentiment in the text."""
32-
language_client = language.Client()
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client = language.LanguageServiceClient()
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if isinstance(text, six.binary_type):
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text = text.decode('utf-8')
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# Instantiates a plain text document.
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document = language_client.document_from_text(text)
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# [START migration_document_text]
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# [START migration_analyze_sentiment]
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document = types.Document(
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content=text,
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type=enums.Document.Type.PLAIN_TEXT)
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# [END migration_document_text]
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# Detects sentiment in the document. You can also analyze HTML with:
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# document.doc_type == language.Document.HTML
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sentiment = document.analyze_sentiment().sentiment
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# document.type == enums.Document.Type.HTML
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sentiment = client.analyze_sentiment(document).document_sentiment
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print('Score: {}'.format(sentiment.score))
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print('Magnitude: {}'.format(sentiment.magnitude))
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# [END migration_analyze_sentiment]
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# [END def_sentiment_text]
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# [START def_sentiment_file]
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def sentiment_file(gcs_uri):
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"""Detects sentiment in the file located in Google Cloud Storage."""
50-
language_client = language.Client()
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client = language.LanguageServiceClient()
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# Instantiates a plain text document.
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document = language_client.document_from_url(gcs_uri)
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# [START migration_document_gcs_uri]
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document = types.Document(
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gcs_content_uri=gcs_uri,
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type=enums.Document.Type.PLAIN_TEXT)
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# [END migration_document_gcs_uri]
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# Detects sentiment in the document. You can also analyze HTML with:
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# document.doc_type == language.Document.HTML
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sentiment = document.analyze_sentiment().sentiment
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# document.type == enums.Document.Type.HTML
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sentiment = client.analyze_sentiment(document).document_sentiment
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print('Score: {}'.format(sentiment.score))
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print('Magnitude: {}'.format(sentiment.magnitude))
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# [END def_sentiment_file]
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# [START def_entities_text]
6380
def entities_text(text):
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"""Detects entities in the text."""
65-
language_client = language.Client()
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client = language.LanguageServiceClient()
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if isinstance(text, six.binary_type):
6885
text = text.decode('utf-8')
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# Instantiates a plain text document.
71-
document = language_client.document_from_text(text)
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# [START migration_analyze_entities]
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document = types.Document(
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content=text,
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type=enums.Document.Type.PLAIN_TEXT)
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# Detects entities in the document. You can also analyze HTML with:
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# document.doc_type == language.Document.HTML
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entities = document.analyze_entities().entities
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# document.type == enums.Document.Type.HTML
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entities = client.analyze_entities(document).entities
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for entity in entities:
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print('=' * 20)
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print(u'{:<16}: {}'.format('name', entity.name))
80-
print(u'{:<16}: {}'.format('type', entity.entity_type))
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print(u'{:<16}: {}'.format('type', entity.type))
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print(u'{:<16}: {}'.format('metadata', entity.metadata))
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print(u'{:<16}: {}'.format('salience', entity.salience))
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print(u'{:<16}: {}'.format('wikipedia_url',
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entity.metadata.get('wikipedia_url', '-')))
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# [END migration_analyze_entities]
106+
# [END def_entities_text]
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# [START def_entities_file]
87110
def entities_file(gcs_uri):
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"""Detects entities in the file located in Google Cloud Storage."""
89-
language_client = language.Client()
112+
client = language.LanguageServiceClient()
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# Instantiates a plain text document.
92-
document = language_client.document_from_url(gcs_uri)
115+
document = types.Document(
116+
gcs_content_uri=gcs_uri,
117+
type=enums.Document.Type.PLAIN_TEXT)
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# Detects sentiment in the document. You can also analyze HTML with:
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# document.doc_type == language.Document.HTML
96-
entities = document.analyze_entities().entities
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# document.type == enums.Document.Type.HTML
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entities = client.analyze_entities(document).entities
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for entity in entities:
99124
print('=' * 20)
100125
print(u'{:<16}: {}'.format('name', entity.name))
101-
print(u'{:<16}: {}'.format('type', entity.entity_type))
126+
print(u'{:<16}: {}'.format('type', entity.type))
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print(u'{:<16}: {}'.format('metadata', entity.metadata))
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print(u'{:<16}: {}'.format('salience', entity.salience))
104129
print(u'{:<16}: {}'.format('wikipedia_url',
105130
entity.metadata.get('wikipedia_url', '-')))
131+
# [END def_entities_file]
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134+
# [START def_syntax_text]
108135
def syntax_text(text):
109136
"""Detects syntax in the text."""
110-
language_client = language.Client()
137+
client = language.LanguageServiceClient()
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112139
if isinstance(text, six.binary_type):
113140
text = text.decode('utf-8')
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115142
# Instantiates a plain text document.
116-
document = language_client.document_from_text(text)
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# [START migration_analyze_syntax]
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document = types.Document(
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content=text,
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type=enums.Document.Type.PLAIN_TEXT)
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# Detects syntax in the document. You can also analyze HTML with:
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# document.doc_type == language.Document.HTML
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tokens = document.analyze_syntax().tokens
149+
# document.type == enums.Document.Type.HTML
150+
tokens = client.analyze_syntax(document).tokens
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152+
# part-of-speech tags from enums.PartOfSpeech.Tag
153+
pos_tag = ('UNKNOWN', 'ADJ', 'ADP', 'ADV', 'CONJ', 'DET', 'NOUN', 'NUM',
154+
'PRON', 'PRT', 'PUNCT', 'VERB', 'X', 'AFFIX')
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for token in tokens:
123-
print(u'{}: {}'.format(token.part_of_speech.tag, token.text_content))
157+
print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
158+
token.text.content))
159+
# [END migration_analyze_syntax]
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# [END def_syntax_text]
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125162

163+
# [START def_syntax_file]
126164
def syntax_file(gcs_uri):
127165
"""Detects syntax in the file located in Google Cloud Storage."""
128-
language_client = language.Client()
166+
client = language.LanguageServiceClient()
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130168
# Instantiates a plain text document.
131-
document = language_client.document_from_url(gcs_uri)
169+
document = types.Document(
170+
gcs_content_uri=gcs_uri,
171+
type=enums.Document.Type.PLAIN_TEXT)
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133173
# Detects syntax in the document. You can also analyze HTML with:
134-
# document.doc_type == language.Document.HTML
135-
tokens = document.analyze_syntax().tokens
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# document.type == enums.Document.Type.HTML
175+
tokens = client.analyze_syntax(document).tokens
176+
177+
# part-of-speech tags from enums.PartOfSpeech.Tag
178+
pos_tag = ('UNKNOWN', 'ADJ', 'ADP', 'ADV', 'CONJ', 'DET', 'NOUN', 'NUM',
179+
'PRON', 'PRT', 'PUNCT', 'VERB', 'X', 'AFFIX')
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137181
for token in tokens:
138-
print(u'{}: {}'.format(token.part_of_speech.tag, token.text_content))
182+
print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
183+
token.text.content))
184+
# [END def_syntax_file]
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if __name__ == '__main__':

‎language/cloud-client/v1beta2/README.rst

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This directory contains samples for Google Cloud Natural Language API. The `Google Cloud Natural Language API`_ provides natural language understanding technologies to developers, including sentiment analysis, entity recognition, and syntax analysis. This API is part of the larger Cloud Machine Learning API.
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8+
- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
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10+
.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
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‎language/cloud-client/v1beta2/README.rst.in

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entity recognition, and syntax analysis. This API is part of the larger
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Cloud Machine Learning API.
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- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
15+
16+
17+
.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
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setup:
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- auth
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- install_deps

‎language/cloud-client/v1beta2/quickstart.py

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1818
def run_quickstart():
1919
# [START language_quickstart]
2020
# Imports the Google Cloud client library
21-
from google.cloud import language
21+
# [START beta_import_client]
22+
# [START beta_import]
23+
from google.cloud import language_v1beta2
24+
from google.cloud.language_v1beta2 import enums
25+
from google.cloud.language_v1beta2 import types
26+
# [END beta_import]
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23-
# Instantiates a client with they v1beta2 version
24-
language_client = language.Client(api_version='v1beta2')
28+
# Instantiates a client with the v1beta2 version
29+
client = language_v1beta2.LanguageServiceClient()
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# [END beta_import_client]
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2632
# The text to analyze
27-
text = 'Hallo Welt!'
28-
document = language_client.document_from_text(text, language='DE')
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33+
text = u'Hallo Welt!'
34+
document = types.Document(
35+
content=text,
36+
type=enums.Document.Type.PLAIN_TEXT,
37+
language='de')
3038
# Detects the sentiment of the text
31-
sentiment = document.analyze_sentiment().sentiment
39+
sentiment = client.analyze_sentiment(document).document_sentiment
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3341
print('Text: {}'.format(text))
3442
print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))

‎language/cloud-client/v1beta2/requirements.txt

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gapic-google-cloud-language-v1beta2==0.15.3
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google-cloud-language==0.25.0
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google-cloud-language==0.26.1

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