Add generated code samples. (#9153)

packages/google-cloud-language/noxfile.py

@@ -125,6 +125,26 @@ def system(session):
         session.run("py.test", "--quiet", system_test_folder_path, *session.posargs)
 
 
+@nox.session(python=["3.7"])
+def samples(session):
+    """Run the samples test suite."""
+    # Sanity check: Only run tests if the environment variable is set.
+    if not os.environ.get("GOOGLE_APPLICATION_CREDENTIALS", ""):
+        session.skip("Credentials must be set via environment variable")
+
+    samples_path = "samples"
+    if not os.path.exists(samples_path):
+        session.skip("Samples not found.")
+
+    session.install("pyyaml")
+    session.install("sample-tester")
+    for local_dep in LOCAL_DEPS:
+        session.install("-e", local_dep)
+    session.install("-e", ".")
+
+    session.run("sample-tester", samples_path, *session.posargs)
+
+
 @nox.session(python="3.7")
 def cover(session):
     """Run the final coverage report.

packages/google-cloud-language/samples/v1/language_classify_gcs.py

@@ -0,0 +1,85 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("Request",  "language_classify_gcs")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-language
+
+# sample-metadata
+#   title: Classify Content (GCS)
+#   description: Classifying Content in text file stored in Cloud Storage
+#   usage: python3 samples/v1/language_classify_gcs.py [--gcs_content_uri "gs://cloud-samples-data/language/classify-entertainment.txt"]
+
+# [START language_classify_gcs]
+from google.cloud import language_v1
+from google.cloud.language_v1 import enums
+
+
+def sample_classify_text(gcs_content_uri):
+    """
+    Classifying Content in text file stored in Cloud Storage
+
+    Args:
+      gcs_content_uri Google Cloud Storage URI where the file content is located.
+      e.g. gs://[Your Bucket]/[Path to File]
+      The text file must include at least 20 words.
+    """
+
+    client = language_v1.LanguageServiceClient()
+
+    # gcs_content_uri = 'gs://cloud-samples-data/language/classify-entertainment.txt'
+
+    # Available types: PLAIN_TEXT, HTML
+    type_ = enums.Document.Type.PLAIN_TEXT
+
+    # Optional. If not specified, the language is automatically detected.
+    # For list of supported languages:
+    # https://cloud.google.com/natural-language/docs/languages
+    language = "en"
+    document = {"gcs_content_uri": gcs_content_uri, "type": type_, "language": language}
+
+    response = client.classify_text(document)
+    # Loop through classified categories returned from the API
+    for category in response.categories:
+        # Get the name of the category representing the document.
+        # See the predefined taxonomy of categories:
+        # https://cloud.google.com/natural-language/docs/categories
+        print(u"Category name: {}".format(category.name))
+        # Get the confidence. Number representing how certain the classifier
+        # is that this category represents the provided text.
+        print(u"Confidence: {}".format(category.confidence))
+
+
+# [END language_classify_gcs]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--gcs_content_uri",
+        type=str,
+        default="gs://cloud-samples-data/language/classify-entertainment.txt",
+    )
+    args = parser.parse_args()
+
+    sample_classify_text(args.gcs_content_uri)
+
+
+if __name__ == "__main__":
+    main()

packages/google-cloud-language/samples/v1/language_classify_text.py

@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("Request",  "language_classify_text")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-language
+
+# sample-metadata
+#   title: Classify Content
+#   description: Classifying Content in a String
+#   usage: python3 samples/v1/language_classify_text.py [--text_content "That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows."]
+
+# [START language_classify_text]
+from google.cloud import language_v1
+from google.cloud.language_v1 import enums
+
+
+def sample_classify_text(text_content):
+    """
+    Classifying Content in a String
+
+    Args:
+      text_content The text content to analyze. Must include at least 20 words.
+    """
+
+    client = language_v1.LanguageServiceClient()
+
+    # text_content = 'That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows.'
+
+    # Available types: PLAIN_TEXT, HTML
+    type_ = enums.Document.Type.PLAIN_TEXT
+
+    # Optional. If not specified, the language is automatically detected.
+    # For list of supported languages:
+    # https://cloud.google.com/natural-language/docs/languages
+    language = "en"
+    document = {"content": text_content, "type": type_, "language": language}
+
+    response = client.classify_text(document)
+    # Loop through classified categories returned from the API
+    for category in response.categories:
+        # Get the name of the category representing the document.
+        # See the predefined taxonomy of categories:
+        # https://cloud.google.com/natural-language/docs/categories
+        print(u"Category name: {}".format(category.name))
+        # Get the confidence. Number representing how certain the classifier
+        # is that this category represents the provided text.
+        print(u"Confidence: {}".format(category.confidence))
+
+
+# [END language_classify_text]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--text_content",
+        type=str,
+        default="That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows.",
+    )
+    args = parser.parse_args()
+
+    sample_classify_text(args.text_content)
+
+
+if __name__ == "__main__":
+    main()

packages/google-cloud-language/samples/v1/language_entities_gcs.py

@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2019 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DO NOT EDIT! This is a generated sample ("Request",  "language_entities_gcs")
+
+# To install the latest published package dependency, execute the following:
+#   pip install google-cloud-language
+
+# sample-metadata
+#   title: Analyzing Entities (GCS)
+#   description: Analyzing Entities in text file stored in Cloud Storage
+#   usage: python3 samples/v1/language_entities_gcs.py [--gcs_content_uri "gs://cloud-samples-data/language/entity.txt"]
+
+# [START language_entities_gcs]
+from google.cloud import language_v1
+from google.cloud.language_v1 import enums
+
+
+def sample_analyze_entities(gcs_content_uri):
+    """
+    Analyzing Entities in text file stored in Cloud Storage
+
+    Args:
+      gcs_content_uri Google Cloud Storage URI where the file content is located.
+      e.g. gs://[Your Bucket]/[Path to File]
+    """
+
+    client = language_v1.LanguageServiceClient()
+
+    # gcs_content_uri = 'gs://cloud-samples-data/language/entity.txt'
+
+    # Available types: PLAIN_TEXT, HTML
+    type_ = enums.Document.Type.PLAIN_TEXT
+
+    # Optional. If not specified, the language is automatically detected.
+    # For list of supported languages:
+    # https://cloud.google.com/natural-language/docs/languages
+    language = "en"
+    document = {"gcs_content_uri": gcs_content_uri, "type": type_, "language": language}
+
+    # Available values: NONE, UTF8, UTF16, UTF32
+    encoding_type = enums.EncodingType.UTF8
+
+    response = client.analyze_entities(document, encoding_type=encoding_type)
+    # Loop through entitites returned from the API
+    for entity in response.entities:
+        print(u"Representative name for the entity: {}".format(entity.name))
+        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
+        print(u"Entity type: {}".format(enums.Entity.Type(entity.type).name))
+        # Get the salience score associated with the entity in the [0, 1.0] range
+        print(u"Salience score: {}".format(entity.salience))
+        # Loop over the metadata associated with entity. For many known entities,
+        # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).
+        # Some entity types may have additional metadata, e.g. ADDRESS entities
+        # may have metadata for the address street_name, postal_code, et al.
+        for metadata_name, metadata_value in entity.metadata.items():
+            print(u"{}: {}".format(metadata_name, metadata_value))
+
+        # Loop over the mentions of this entity in the input document.
+        # The API currently supports proper noun mentions.
+        for mention in entity.mentions:
+            print(u"Mention text: {}".format(mention.text.content))
+            # Get the mention type, e.g. PROPER for proper noun
+            print(
+                u"Mention type: {}".format(enums.EntityMention.Type(mention.type).name)
+            )
+
+    # Get the language of the text, which will be the same as
+    # the language specified in the request or, if not specified,
+    # the automatically-detected language.
+    print(u"Language of the text: {}".format(response.language))
+
+
+# [END language_entities_gcs]
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--gcs_content_uri",
+        type=str,
+        default="gs://cloud-samples-data/language/entity.txt",
+    )
+    args = parser.parse_args()
+
+    sample_analyze_entities(args.gcs_content_uri)
+
+
+if __name__ == "__main__":
+    main()