language_classify_text.py

# -*- coding: utf-8 -*-
#
# Copyright 2020 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

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_ = language_v1.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(request = {'document': 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()