text: # -*- 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()
  