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NL-tagging.py
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NL-tagging.py
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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.
"""
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}
content_categories_version = (
language_v1.ClassificationModelOptions.V2Model.ContentCategoriesVersion.V2
)
response = client.classify_text(
request={
"document": document,
"classification_model_options": {
"v2_model": {"content_categories_version": content_categories_version}
},
}
)
return response.categories[0].name.split("/")[1]
# 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(f"Category name: {category.name}")
# Get the confidence. Number representing how certain the classifier
# is that this category represents the provided text.
print(f"Confidence: {category.confidence}")
input=[
"Who was the first President of the United States?"
]
#print(sample_classify_text("How Many formats are there in Cricket ?"))
for i in input:
print(i)
print(sample_classify_text(i))