Natural Language GAPIC client library

language/cloud-client/v1/README.rst

@@ -5,6 +5,10 @@ Google Cloud Natural Language API Python Samples
 
 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.
 
+- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
+
+.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
+
 
 
 

language/cloud-client/v1/README.rst.in

@@ -10,6 +10,12 @@ product:
     entity recognition, and syntax analysis. This API is part of the larger
     Cloud Machine Learning API.
 
+
+    - See the `migration guide`_ for information about migrating to Python client library v0.26.1.
+
+
+    .. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
+
 setup:
 - auth
 - install_deps

language/cloud-client/v1/quickstart.py

@@ -18,17 +18,25 @@
 def run_quickstart():
     # [START language_quickstart]
     # Imports the Google Cloud client library
+    # [START migration_import]
     from google.cloud import language
+    from google.cloud.language import enums
+    from google.cloud.language import types
+    # [END migration_import]
 
     # Instantiates a client
-    language_client = language.Client()
+    # [START migration_client]
+    client = language.LanguageServiceClient()
+    # [END migration_client]
 
     # The text to analyze
-    text = 'Hello, world!'
-    document = language_client.document_from_text(text)
+    text = u'Hello, world!'
+    document = types.Document(
+        content=text,
+        type=enums.Document.Type.PLAIN_TEXT)
 
     # Detects the sentiment of the text
-    sentiment = document.analyze_sentiment().sentiment
+    sentiment = client.analyze_sentiment(document=document).document_sentiment
 
     print('Text: {}'.format(text))
     print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))

language/cloud-client/v1/requirements.txt

@@ -1 +1 @@
-google-cloud-language==0.25.0
+google-cloud-language==0.26.1

language/cloud-client/v1/snippets.py

@@ -24,118 +24,164 @@
 import argparse
 
 from google.cloud import language
+from google.cloud.language import enums
+from google.cloud.language import types
 import six
 
 
+# [START def_sentiment_text]
 def sentiment_text(text):
     """Detects sentiment in the text."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     if isinstance(text, six.binary_type):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    document = language_client.document_from_text(text)
+    # [START migration_document_text]
+    # [START migration_analyze_sentiment]
+    document = types.Document(
+        content=text,
+        type=enums.Document.Type.PLAIN_TEXT)
+    # [END migration_document_text]
 
     # Detects sentiment in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    sentiment = document.analyze_sentiment().sentiment
+    #   document.type == enums.Document.Type.HTML
+    sentiment = client.analyze_sentiment(document).document_sentiment
 
     print('Score: {}'.format(sentiment.score))
     print('Magnitude: {}'.format(sentiment.magnitude))
+    # [END migration_analyze_sentiment]
+# [END def_sentiment_text]
 
 
+# [START def_sentiment_file]
 def sentiment_file(gcs_uri):
     """Detects sentiment in the file located in Google Cloud Storage."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     # Instantiates a plain text document.
-    document = language_client.document_from_url(gcs_uri)
+    # [START migration_document_gcs_uri]
+    document = types.Document(
+        gcs_content_uri=gcs_uri,
+        type=enums.Document.Type.PLAIN_TEXT)
+    # [END migration_document_gcs_uri]
 
     # Detects sentiment in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    sentiment = document.analyze_sentiment().sentiment
+    #   document.type == enums.Document.Type.HTML
+    sentiment = client.analyze_sentiment(document).document_sentiment
 
     print('Score: {}'.format(sentiment.score))
     print('Magnitude: {}'.format(sentiment.magnitude))
+# [END def_sentiment_file]
 
 
+# [START def_entities_text]
 def entities_text(text):
     """Detects entities in the text."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     if isinstance(text, six.binary_type):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    document = language_client.document_from_text(text)
+    # [START migration_analyze_entities]
+    document = types.Document(
+        content=text,
+        type=enums.Document.Type.PLAIN_TEXT)
 
     # Detects entities in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    entities = document.analyze_entities().entities
+    #   document.type == enums.Document.Type.HTML
+    entities = client.analyze_entities(document).entities
 
     for entity in entities:
         print('=' * 20)
         print(u'{:<16}: {}'.format('name', entity.name))
-        print(u'{:<16}: {}'.format('type', entity.entity_type))
+        print(u'{:<16}: {}'.format('type', entity.type))
         print(u'{:<16}: {}'.format('metadata', entity.metadata))
         print(u'{:<16}: {}'.format('salience', entity.salience))
         print(u'{:<16}: {}'.format('wikipedia_url',
               entity.metadata.get('wikipedia_url', '-')))
+    # [END migration_analyze_entities]
+# [END def_entities_text]
 
 
+# [START def_entities_file]
 def entities_file(gcs_uri):
     """Detects entities in the file located in Google Cloud Storage."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     # Instantiates a plain text document.
-    document = language_client.document_from_url(gcs_uri)
+    document = types.Document(
+        gcs_content_uri=gcs_uri,
+        type=enums.Document.Type.PLAIN_TEXT)
 
     # Detects sentiment in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    entities = document.analyze_entities().entities
+    #   document.type == enums.Document.Type.HTML
+    entities = client.analyze_entities(document).entities
 
     for entity in entities:
         print('=' * 20)
         print(u'{:<16}: {}'.format('name', entity.name))
-        print(u'{:<16}: {}'.format('type', entity.entity_type))
+        print(u'{:<16}: {}'.format('type', entity.type))
         print(u'{:<16}: {}'.format('metadata', entity.metadata))
         print(u'{:<16}: {}'.format('salience', entity.salience))
         print(u'{:<16}: {}'.format('wikipedia_url',
               entity.metadata.get('wikipedia_url', '-')))
+# [END def_entities_file]
 
 
+# [START def_syntax_text]
 def syntax_text(text):
     """Detects syntax in the text."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     if isinstance(text, six.binary_type):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    document = language_client.document_from_text(text)
+    # [START migration_analyze_syntax]
+    document = types.Document(
+        content=text,
+        type=enums.Document.Type.PLAIN_TEXT)
 
     # Detects syntax in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    tokens = document.analyze_syntax().tokens
+    #   document.type == enums.Document.Type.HTML
+    tokens = client.analyze_syntax(document).tokens
+
+    # part-of-speech tags from enums.PartOfSpeech.Tag
+    pos_tag = ('UNKNOWN', 'ADJ', 'ADP', 'ADV', 'CONJ', 'DET', 'NOUN', 'NUM',
+               'PRON', 'PRT', 'PUNCT', 'VERB', 'X', 'AFFIX')
 
     for token in tokens:
-        print(u'{}: {}'.format(token.part_of_speech.tag, token.text_content))
+        print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
+                               token.text.content))
+    # [END migration_analyze_syntax]
+# [END def_syntax_text]
 
 
+# [START def_syntax_file]
 def syntax_file(gcs_uri):
     """Detects syntax in the file located in Google Cloud Storage."""
-    language_client = language.Client()
+    client = language.LanguageServiceClient()
 
     # Instantiates a plain text document.
-    document = language_client.document_from_url(gcs_uri)
+    document = types.Document(
+        gcs_content_uri=gcs_uri,
+        type=enums.Document.Type.PLAIN_TEXT)
 
     # Detects syntax in the document. You can also analyze HTML with:
-    #   document.doc_type == language.Document.HTML
-    tokens = document.analyze_syntax().tokens
+    #   document.type == enums.Document.Type.HTML
+    tokens = client.analyze_syntax(document).tokens
+
+    # part-of-speech tags from enums.PartOfSpeech.Tag
+    pos_tag = ('UNKNOWN', 'ADJ', 'ADP', 'ADV', 'CONJ', 'DET', 'NOUN', 'NUM',
+               'PRON', 'PRT', 'PUNCT', 'VERB', 'X', 'AFFIX')
 
     for token in tokens:
-        print(u'{}: {}'.format(token.part_of_speech.tag, token.text_content))
+        print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
+                               token.text.content))
+# [END def_syntax_file]
 
 
 if __name__ == '__main__':

language/cloud-client/v1beta2/README.rst

@@ -5,6 +5,10 @@ Google Cloud Natural Language API Python Samples
 
 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.
 
+- See the `migration guide`_ for information about migrating to Python client library v0.26.1.
+
+.. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
+
 
 
 

language/cloud-client/v1beta2/README.rst.in

@@ -10,6 +10,12 @@ product:
     entity recognition, and syntax analysis. This API is part of the larger
     Cloud Machine Learning API.
 
+
+    - See the `migration guide`_ for information about migrating to Python client library v0.26.1.
+
+
+    .. _migration guide: https://cloud.google.com/natural-language/docs/python-client-migration
+
 setup:
 - auth
 - install_deps

language/cloud-client/v1beta2/quickstart.py

@@ -18,17 +18,25 @@
 def run_quickstart():
     # [START language_quickstart]
     # Imports the Google Cloud client library
-    from google.cloud import language
+    # [START beta_import_client]
+    # [START beta_import]
+    from google.cloud import language_v1beta2
+    from google.cloud.language_v1beta2 import enums
+    from google.cloud.language_v1beta2 import types
+    # [END beta_import]
 
-    # Instantiates a client with they v1beta2 version
-    language_client = language.Client(api_version='v1beta2')
+    # Instantiates a client with the v1beta2 version
+    client = language_v1beta2.LanguageServiceClient()
+    # [END beta_import_client]
 
     # The text to analyze
-    text = 'Hallo Welt!'
-    document = language_client.document_from_text(text, language='DE')
-
+    text = u'Hallo Welt!'
+    document = types.Document(
+        content=text,
+        type=enums.Document.Type.PLAIN_TEXT,
+        language='de')
     # Detects the sentiment of the text
-    sentiment = document.analyze_sentiment().sentiment
+    sentiment = client.analyze_sentiment(document).document_sentiment
 
     print('Text: {}'.format(text))
     print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))

language/cloud-client/v1beta2/requirements.txt

@@ -1,2 +1 @@
-gapic-google-cloud-language-v1beta2==0.15.3
-google-cloud-language==0.25.0
+google-cloud-language==0.26.1