1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
|
# 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_analyze_text.py
DESCRIPTION:
This sample demonstrates how to submit a collection of text documents for analysis, which consists of a variety
of text analysis tasks, such as Entity Recognition, PII Entity Recognition, Entity Linking, Sentiment Analysis,
or Key Phrase Extraction. The response will contain results from each of the individual tasks specified in the request.
USAGE:
python sample_analyze_text.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 asyncio
class AnalyzeSampleAsync(object):
async def analyze_async(self):
# [START analyze_async]
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.aio import TextAnalyticsClient
from azure.ai.textanalytics import EntitiesRecognitionTask, \
PiiEntitiesRecognitionTask, \
KeyPhraseExtractionTask
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=AzureKeyCredential(key),
api_version="v3.1-preview.3"
)
documents = [
"We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore the spot! \
They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) \
and he is super nice, coming out of the kitchen and greeted us all. We enjoyed very much dining in the place! \
The Sirloin steak I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their \
online menu at www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! \
The only complaint I have is the food didn't come fast enough. Overall I highly recommend it!"
]
async with text_analytics_client:
poller = await text_analytics_client.begin_analyze(
documents,
display_name="Sample Text Analysis",
entities_recognition_tasks=[EntitiesRecognitionTask()],
pii_entities_recognition_tasks=[PiiEntitiesRecognitionTask()],
key_phrase_extraction_tasks=[KeyPhraseExtractionTask()]
)
result = await poller.result()
async for page in result:
for task in page.entities_recognition_results:
print("Results of Entities Recognition task:")
docs = [doc for doc in task.results if not doc.is_error]
for idx, doc in enumerate(docs):
print("\nDocument text: {}".format(documents[idx]))
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Category: {}".format(entity.category))
print("...Confidence Score: {}".format(entity.confidence_score))
print("...Offset: {}".format(entity.offset))
print("------------------------------------------")
for task in page.pii_entities_recognition_results:
print("Results of PII Entities Recognition task:")
docs = [doc for doc in task.results if not doc.is_error]
for idx, doc in enumerate(docs):
print("Document text: {}".format(documents[idx]))
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("Category: {}".format(entity.category))
print("Confidence Score: {}\n".format(entity.confidence_score))
print("------------------------------------------")
for task in page.key_phrase_extraction_results:
print("Results of Key Phrase Extraction task:")
docs = [doc for doc in task.results if not doc.is_error]
for idx, doc in enumerate(docs):
print("Document text: {}\n".format(documents[idx]))
print("Key Phrases: {}\n".format(doc.key_phrases))
print("------------------------------------------")
# [END analyze_async]
async def main():
sample = AnalyzeSampleAsync()
await sample.analyze_async()
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
|
