Language region tag update [(#1643)](GoogleCloudPlatform/python-docs-samples#1643)

Author: alixhami

samples/snippets/classify_text/classify_text_tutorial.py

@@ -13,15 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START classify_text_tutorial]
+# [START language_classify_text_tutorial]
 """Using the classify_text method to find content categories of text files,
 Then use the content category labels to compare text similarity.
 
 For more information, see the tutorial page at
 https://cloud.google.com/natural-language/docs/classify-text-tutorial.
 """
 
-# [START classify_text_tutorial_import]
+# [START language_classify_text_tutorial_imports]
 import argparse
 import io
 import json
@@ -30,10 +30,10 @@
 from google.cloud import language
 import numpy
 import six
-# [END classify_text_tutorial_import]
+# [END language_classify_text_tutorial_imports]
 
 
-# [START def_classify]
+# [START language_classify_text_tutorial_classify]
 def classify(text, verbose=True):
     """Classify the input text into categories. """
 
@@ -61,10 +61,10 @@ def classify(text, verbose=True):
             print(u'{:<16}: {}'.format('confidence', category.confidence))
 
     return result
-# [END def_classify]
+# [END language_classify_text_tutorial_classify]
 
 
-# [START def_index]
+# [START language_classify_text_tutorial_index]
 def index(path, index_file):
     """Classify each text file in a directory and write
     the results to the index_file.
@@ -91,10 +91,10 @@ def index(path, index_file):
 
     print('Texts indexed in file: {}'.format(index_file))
     return result
-# [END def_index]
+# [END language_classify_text_tutorial_index]
 
 
-# [START def_split_labels]
+# [START language_classify_text_tutorial_split_labels]
 def split_labels(categories):
     """The category labels are of the form "/a/b/c" up to three levels,
     for example "/Computers & Electronics/Software", and these labels
@@ -121,10 +121,10 @@ def split_labels(categories):
             _categories[label] = confidence
 
     return _categories
-# [END def_split_labels]
+# [END language_classify_text_tutorial_split_labels]
 
 
-# [START def_similarity]
+# [START language_classify_text_tutorial_similarity]
 def similarity(categories1, categories2):
     """Cosine similarity of the categories treated as sparse vectors."""
     categories1 = split_labels(categories1)
@@ -143,10 +143,10 @@ def similarity(categories1, categories2):
         dot += confidence * categories2.get(label, 0.0)
 
     return dot / (norm1 * norm2)
-# [END def_similarity]
+# [END language_classify_text_tutorial_similarity]
 
 
-# [START def_query]
+# [START language_classify_text_tutorial_query]
 def query(index_file, text, n_top=3):
     """Find the indexed files that are the most similar to
     the query text.
@@ -176,10 +176,10 @@ def query(index_file, text, n_top=3):
         print('\n')
 
     return similarities
-# [END def_query]
+# [END language_classify_text_tutorial_query]
 
 
-# [START def_query_category]
+# [START language_classify_text_tutorial_query_category]
 def query_category(index_file, category_string, n_top=3):
     """Find the indexed files that are the most similar to
     the query label.
@@ -211,7 +211,7 @@ def query_category(index_file, category_string, n_top=3):
         print('\n')
 
     return similarities
-# [END def_query_category]
+# [END language_classify_text_tutorial_query_category]
 
 
 if __name__ == '__main__':
@@ -255,4 +255,4 @@ def query_category(index_file, category_string, n_top=3):
         query(args.index_file, args.text)
     if args.command == 'query-category':
         query_category(args.index_file, args.category)
-# [END classify_text_tutorial]
+# [END language_classify_text_tutorial]

samples/snippets/cloud-client/v1/quickstart.py

@@ -18,16 +18,16 @@
 def run_quickstart():
     # [START language_quickstart]
     # Imports the Google Cloud client library
-    # [START migration_import]
+    # [START language_python_migration_imports]
     from google.cloud import language
     from google.cloud.language import enums
     from google.cloud.language import types
-    # [END migration_import]
+    # [END language_python_migration_imports]
 
     # Instantiates a client
-    # [START migration_client]
+    # [START language_python_migration_client]
     client = language.LanguageServiceClient()
-    # [END migration_client]
+    # [END language_python_migration_client]
 
     # The text to analyze
     text = u'Hello, world!'

samples/snippets/cloud-client/v1/snippets.py

@@ -30,7 +30,7 @@
 import six
 
 
-# [START def_sentiment_text]
+# [START language_sentiment_text]
 def sentiment_text(text):
     """Detects sentiment in the text."""
     client = language.LanguageServiceClient()
@@ -39,45 +39,45 @@ def sentiment_text(text):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    # [START migration_document_text]
-    # [START migration_analyze_sentiment]
+    # [START language_python_migration_document_text]
+    # [START language_python_migration_sentiment_text]
     document = types.Document(
         content=text,
         type=enums.Document.Type.PLAIN_TEXT)
-    # [END migration_document_text]
+    # [END language_python_migration_document_text]
 
     # Detects sentiment in the document. You can also analyze HTML with:
     #   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]
+    # [END language_python_migration_sentiment_text]
+# [END language_sentiment_text]
 
 
-# [START def_sentiment_file]
+# [START language_sentiment_gcs]
 def sentiment_file(gcs_uri):
     """Detects sentiment in the file located in Google Cloud Storage."""
     client = language.LanguageServiceClient()
 
     # Instantiates a plain text document.
-    # [START migration_document_gcs_uri]
+    # [START language_python_migration_document_gcs]
     document = types.Document(
         gcs_content_uri=gcs_uri,
         type=enums.Document.Type.PLAIN_TEXT)
-    # [END migration_document_gcs_uri]
+    # [END language_python_migration_document_gcs]
 
     # Detects sentiment in the document. You can also analyze HTML with:
     #   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]
+# [END language_sentiment_gcs]
 
 
-# [START def_entities_text]
+# [START language_entities_text]
 def entities_text(text):
     """Detects entities in the text."""
     client = language.LanguageServiceClient()
@@ -86,7 +86,7 @@ def entities_text(text):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    # [START migration_analyze_entities]
+    # [START language_python_migration_entities_text]
     document = types.Document(
         content=text,
         type=enums.Document.Type.PLAIN_TEXT)
@@ -107,11 +107,11 @@ def entities_text(text):
         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]
+    # [END language_python_migration_entities_text]
+# [END language_entities_text]
 
 
-# [START def_entities_file]
+# [START language_entities_gcs]
 def entities_file(gcs_uri):
     """Detects entities in the file located in Google Cloud Storage."""
     client = language.LanguageServiceClient()
@@ -137,10 +137,10 @@ def entities_file(gcs_uri):
         print(u'{:<16}: {}'.format('salience', entity.salience))
         print(u'{:<16}: {}'.format('wikipedia_url',
               entity.metadata.get('wikipedia_url', '-')))
-# [END def_entities_file]
+# [END language_entities_gcs]
 
 
-# [START def_syntax_text]
+# [START language_syntax_text]
 def syntax_text(text):
     """Detects syntax in the text."""
     client = language.LanguageServiceClient()
@@ -149,7 +149,7 @@ def syntax_text(text):
         text = text.decode('utf-8')
 
     # Instantiates a plain text document.
-    # [START migration_analyze_syntax]
+    # [START language_python_migration_syntax_text]
     document = types.Document(
         content=text,
         type=enums.Document.Type.PLAIN_TEXT)
@@ -165,11 +165,11 @@ def syntax_text(text):
     for token in tokens:
         print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
                                token.text.content))
-    # [END migration_analyze_syntax]
-# [END def_syntax_text]
+    # [END language_python_migration_syntax_text]
+# [END language_syntax_text]
 
 
-# [START def_syntax_file]
+# [START language_syntax_gcs]
 def syntax_file(gcs_uri):
     """Detects syntax in the file located in Google Cloud Storage."""
     client = language.LanguageServiceClient()
@@ -190,10 +190,10 @@ def syntax_file(gcs_uri):
     for token in tokens:
         print(u'{}: {}'.format(pos_tag[token.part_of_speech.tag],
                                token.text.content))
-# [END def_syntax_file]
+# [END language_syntax_gcs]
 
 
-# [START def_entity_sentiment_text]
+# [START language_entity_sentiment_text]
 def entity_sentiment_text(text):
     """Detects entity sentiment in the provided text."""
     client = language.LanguageServiceClient()
@@ -223,9 +223,10 @@ def entity_sentiment_text(text):
             print(u'  Type : {}'.format(mention.type))
         print(u'Salience: {}'.format(entity.salience))
         print(u'Sentiment: {}\n'.format(entity.sentiment))
-# [END def_entity_sentiment_text]
+# [END language_entity_sentiment_text]
 
 
+# [START language_entity_sentiment_gcs]
 def entity_sentiment_file(gcs_uri):
     """Detects entity sentiment in a Google Cloud Storage file."""
     client = language.LanguageServiceClient()
@@ -251,9 +252,10 @@ def entity_sentiment_file(gcs_uri):
             print(u'  Type : {}'.format(mention.type))
         print(u'Salience: {}'.format(entity.salience))
         print(u'Sentiment: {}\n'.format(entity.sentiment))
+# [END language_entity_sentiment_gcs]
 
 
-# [START def_classify_text]
+# [START language_classify_text]
 def classify_text(text):
     """Classifies content categories of the provided text."""
     client = language.LanguageServiceClient()
@@ -271,10 +273,10 @@ def classify_text(text):
         print(u'=' * 20)
         print(u'{:<16}: {}'.format('name', category.name))
         print(u'{:<16}: {}'.format('confidence', category.confidence))
-# [END def_classify_text]
+# [END language_classify_text]
 
 
-# [START def_classify_file]
+# [START language_classify_gcs]
 def classify_file(gcs_uri):
     """Classifies content categories of the text in a Google Cloud Storage
     file.
@@ -291,7 +293,7 @@ def classify_file(gcs_uri):
         print(u'=' * 20)
         print(u'{:<16}: {}'.format('name', category.name))
         print(u'{:<16}: {}'.format('confidence', category.confidence))
-# [END def_classify_file]
+# [END language_classify_gcs]
 
 
 if __name__ == '__main__':

samples/snippets/sentiment/sentiment_analysis.py

@@ -11,19 +11,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START sentiment_tutorial]
+# [START language_sentiment_tutorial]
 """Demonstrates how to make a simple call to the Natural Language API."""
 
-# [START sentiment_tutorial_import]
+# [START language_sentiment_tutorial_imports]
 import argparse
 
 from google.cloud import language
 from google.cloud.language import enums
 from google.cloud.language import types
-# [END sentiment_tutorial_import]
+# [END language_sentiment_tutorial_imports]
 
 
-# [START def_print_result]
+# [START language_sentiment_tutorial_print_result]
 def print_result(annotations):
     score = annotations.document_sentiment.score
     magnitude = annotations.document_sentiment.magnitude
@@ -36,10 +36,10 @@ def print_result(annotations):
     print('Overall Sentiment: score of {} with magnitude of {}'.format(
         score, magnitude))
     return 0
-# [END def_print_result]
+# [END language_sentiment_tutorial_print_result]
 
 
-# [START def_analyze]
+# [START language_sentiment_tutorial_analyze_sentiment]
 def analyze(movie_review_filename):
     """Run a sentiment analysis request on text within a passed filename."""
     client = language.LanguageServiceClient()
@@ -55,9 +55,10 @@ def analyze(movie_review_filename):
 
     # Print the results
     print_result(annotations)
-# [END def_analyze]
+# [END language_sentiment_tutorial_analyze_sentiment]
 
 
+# [START language_sentiment_tutorial_run_application]
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         description=__doc__,
@@ -68,4 +69,5 @@ def analyze(movie_review_filename):
     args = parser.parse_args()
 
     analyze(args.movie_review_filename)
-# [END sentiment_tutorial]
+# [END language_sentiment_tutorial_run_application]
+# [END language_sentiment_tutorial]