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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_text_custom_multi_label_classification_async.py
DESCRIPTION:
This sample demonstrates how to run a **custom multi-label classification** action over text (async LRO).
USAGE:
python sample_text_custom_multi_label_classification_async.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_TEXT_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_TEXT_ENDPOINT
- AZURE_TEXT_KEY
OPTIONAL ENV VARS:
PROJECT_NAME # defaults to "<project-name>"
DEPLOYMENT_NAME # defaults to "<deployment-name>"
"""
# [START text_custom_multi_label_classification_async]
import os
import asyncio
from azure.identity.aio import DefaultAzureCredential
from azure.ai.textanalytics.aio import TextAnalysisClient
from azure.ai.textanalytics.models import (
MultiLanguageTextInput,
MultiLanguageInput,
CustomMultiLabelClassificationActionContent,
CustomMultiLabelClassificationOperationAction,
CustomMultiLabelClassificationOperationResult,
)
async def sample_text_custom_multi_label_classification_async():
# get settings
endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
project_name = os.environ.get("PROJECT_NAME", "<project-name>")
deployment_name = os.environ.get("DEPLOYMENT_NAME", "<deployment-name>")
credential = DefaultAzureCredential()
async with TextAnalysisClient(endpoint, credential=credential) as client:
# Build input
text_a = (
"I need a reservation for an indoor restaurant in China. Please don't stop the music. "
"Play music and add it to my playlist."
)
text_input = MultiLanguageTextInput(
multi_language_inputs=[MultiLanguageInput(id="A", text=text_a, language="en")]
)
action = CustomMultiLabelClassificationOperationAction(
name="Custom Multi-Label Classification",
action_content=CustomMultiLabelClassificationActionContent(
project_name=project_name,
deployment_name=deployment_name,
),
)
# Start long-running operation (async)
poller = await client.begin_analyze_text_job(
text_input=text_input,
actions=[action],
)
# Operation metadata (pre-final)
print(f"Operation ID: {poller.details.get('operation_id')}")
# Wait for completion and get AsyncItemPaged of TextActions
paged_actions = await poller.result()
# Final-state metadata
d = poller.details
print(f"Job ID: {d.get('job_id')}")
print(f"Status: {d.get('status')}")
print(f"Created: {d.get('created_date_time')}")
print(f"Last Updated: {d.get('last_updated_date_time')}")
if d.get("expiration_date_time"):
print(f"Expires: {d.get('expiration_date_time')}")
if d.get("display_name"):
print(f"Display Name: {d.get('display_name')}")
if d.get("errors"):
print("\nErrors:")
for err in d["errors"]:
print(f" Code: {err.code} - {err.message}")
# Iterate results (async pageable)
async for actions_page in paged_actions:
# Page-level counts if available
print(
f"Completed: {actions_page.completed}, "
f"In Progress: {actions_page.in_progress}, "
f"Failed: {actions_page.failed}, "
f"Total: {actions_page.total}"
)
# Items are the individual operation results
for op_result in actions_page.items_property or []:
if isinstance(op_result, CustomMultiLabelClassificationOperationResult):
print(f"\nAction Name: {op_result.task_name}")
print(f"Action Status: {op_result.status}")
print(f"Kind: {op_result.kind}")
results = op_result.results
for doc in results.documents or []:
print(f"\nDocument ID: {doc.id}")
print("Predicted Labels:")
for cls_item in doc.class_property or []:
print(f" Category: {cls_item.category}")
print(f" Confidence score: {cls_item.confidence_score}")
else:
try:
print(
f"\n[Other action] name={op_result.task_name}, "
f"status={op_result.status}, kind={op_result.kind}"
)
except Exception:
print("\n[Other action present]")
# [END text_custom_multi_label_classification_async]
async def main():
await sample_text_custom_multi_label_classification_async()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
|
