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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_conversation_prediction.py
DESCRIPTION:
This sample demonstrates how to analyze an utterance using a standard
CLU Conversation project (sync).
USAGE:
python sample_conversation_prediction.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
AZURE_CONVERSATIONS_PROJECT_NAME
AZURE_CONVERSATIONS_DEPLOYMENT_NAME
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_CONVERSATIONS_ENDPOINT
- AZURE_CONVERSATIONS_KEY
"""
# [START conversation_prediction]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
ConversationLanguageUnderstandingInput,
ConversationAnalysisInput,
TextConversationItem,
ConversationActionContent,
StringIndexType,
ConversationActionResult,
ConversationPrediction,
DateTimeResolution,
)
def sample_conversation_prediction():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT_NAME"]
credential = DefaultAzureCredential()
client = ConversationAnalysisClient(endpoint, credential=credential)
# build request
data = ConversationLanguageUnderstandingInput(
conversation_input=ConversationAnalysisInput(
conversation_item=TextConversationItem(
id="1",
participant_id="participant1",
text="Send an email to Carol about tomorrow's demo",
)
),
action_content=ConversationActionContent(
project_name=project_name,
deployment_name=deployment_name,
string_index_type=StringIndexType.UTF16_CODE_UNIT,
),
)
# call sync API
response = client.analyze_conversation(data)
if isinstance(response, ConversationActionResult):
pred = response.result.prediction
if isinstance(pred, ConversationPrediction):
# top intent
print(f"Top intent: {pred.top_intent}\n")
# intents
print("Intents:")
for intent in pred.intents or []:
print(f" Category: {intent.category}")
print(f" Confidence: {intent.confidence}")
print()
# entities
print("Entities:")
for entity in pred.entities or []:
print(f" Category: {entity.category}")
print(f" Text: {entity.text}")
print(f" Offset: {entity.offset}")
print(f" Length: {entity.length}")
print(f" Confidence: {entity.confidence}")
for res in entity.resolutions or []:
if isinstance(res, DateTimeResolution):
print(" DateTime Resolution:")
print(f" Sub Kind: {res.date_time_sub_kind}")
print(f" Timex: {res.timex}")
print(f" Value: {res.value}")
print()
else:
print("Unexpected result type from analyze_conversation.")
# [END conversation_prediction]
def main():
sample_conversation_prediction()
if __name__ == "__main__":
main()
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