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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: sample_single_label_classify_async.py
DESCRIPTION:
This sample demonstrates how to classify documents into a single custom category. For example,
movie plot summaries can be categorized into a single movie genre like "Mystery", "Drama", "Thriller",
"Comedy", "Action", etc. Classifying documents is also available as an action type through
the begin_analyze_actions API.
For information on regional support of custom features and how to train a model to
classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities
USAGE:
python sample_single_label_classify_async.py
Set the environment variables with your own values before running the sample:
1) AZURE_LANGUAGE_ENDPOINT - the endpoint to your Language resource.
2) AZURE_LANGUAGE_KEY - your Language subscription key
3) SINGLE_LABEL_CLASSIFY_PROJECT_NAME - your Language Studio project name
4) SINGLE_LABEL_CLASSIFY_DEPLOYMENT_NAME - your Language Studio deployment name
"""
import asyncio
async def sample_classify_document_single_label_async() -> None:
# [START single_label_classify_async]
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.aio import TextAnalyticsClient
endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
key = os.environ["AZURE_LANGUAGE_KEY"]
project_name = os.environ["SINGLE_LABEL_CLASSIFY_PROJECT_NAME"]
deployment_name = os.environ["SINGLE_LABEL_CLASSIFY_DEPLOYMENT_NAME"]
path_to_sample_document = os.path.abspath(
os.path.join(
os.path.abspath(__file__),
"..",
"..",
"./text_samples/custom_classify_sample.txt",
)
)
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
)
with open(path_to_sample_document) as fd:
document = [fd.read()]
async with text_analytics_client:
poller = await text_analytics_client.begin_single_label_classify(
document,
project_name=project_name,
deployment_name=deployment_name
)
pages = await poller.result()
document_results = []
async for page in pages:
document_results.append(page)
for doc, classification_result in zip(document, document_results):
if classification_result.kind == "CustomDocumentClassification":
classification = classification_result.classifications[0]
print("The document text '{}' was classified as '{}' with confidence score {}.".format(
doc, classification.category, classification.confidence_score)
)
elif classification_result.is_error is True:
print("Document text '{}' has an error with code '{}' and message '{}'".format(
doc, classification_result.error.code, classification_result.error.message
))
# [END single_label_classify_async]
async def main():
await sample_classify_document_single_label_async()
if __name__ == '__main__':
asyncio.run(main())
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