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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: sample_get_detailed_diagnostics_information.py
DESCRIPTION:
This sample demonstrates how to retrieve batch statistics, the
model version used, and the raw response in JSON format returned from the service.
USAGE:
python sample_get_detailed_diagnostics_information.py
Set the environment variables with your own values before running the sample:
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your Cognitive Services resource.
2) AZURE_TEXT_ANALYTICS_KEY - your Text Analytics subscription key
"""
import os
import logging
import json
_LOGGER = logging.getLogger(__name__)
class GetDetailedDiagnosticsInformationSample(object):
def get_detailed_diagnostics_information(self):
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]
# This client will log detailed information about its HTTP sessions, at DEBUG level
text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key), logging_enable=True)
documents = [
"""I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more.
I developed a deep-connection with my instructor as well.
""",
"This was a waste of my time. The speaker put me to sleep.",
"No tengo dinero ni nada que dar...",
"L'hôtel n'était pas très confortable. L'éclairage était trop sombre."
]
json_responses = []
def callback(resp):
_LOGGER.debug("document_count: {}".format(resp.statistics["document_count"]))
_LOGGER.debug("valid_document_count: {}".format(resp.statistics["valid_document_count"]))
_LOGGER.debug("erroneous_document_count: {}".format(resp.statistics["erroneous_document_count"]))
_LOGGER.debug("transaction_count: {}".format(resp.statistics["transaction_count"]))
_LOGGER.debug("model_version: {}".format(resp.model_version))
json_response = json.dumps(resp.raw_response)
json_responses.append(json_response)
result = text_analytics_client.extract_key_phrases(
documents,
show_stats=True,
model_version="latest",
raw_response_hook=callback
)
for doc in result:
_LOGGER.warning("Doc with id {} has these warnings: {}".format(doc.id, doc.warnings))
_LOGGER.debug("json response: {}".format(json_responses[0]))
if __name__ == '__main__':
sample = GetDetailedDiagnosticsInformationSample()
sample.get_detailed_diagnostics_information()
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