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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_load_snapshot_async.py
DESCRIPTION:
This sample demonstrates how to load a snapshot of a trained model in a Conversation Authoring project (async).
USAGE:
python sample_load_snapshot_async.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
- AZURE_CONVERSATIONS_AUTHORING_KEY
OPTIONAL ENV VARS:
PROJECT_NAME # defaults to "<project-name>"
TRAINED_MODEL # defaults to "<trained-model-label>"
"""
# [START conversation_authoring_load_snapshot_async]
import os
import asyncio
from azure.identity import DefaultAzureCredential
from azure.core.exceptions import HttpResponseError
from azure.ai.language.conversations.authoring.aio import ConversationAuthoringClient
async def sample_load_snapshot_async():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_AUTHORING_ENDPOINT"]
project_name = os.environ.get("PROJECT_NAME", "<project-name>")
trained_model_label = os.environ.get("TRAINED_MODEL", "<trained-model-label>")
credential = DefaultAzureCredential()
async with ConversationAuthoringClient(endpoint, credential=credential) as client:
project_client = client.get_project_client(project_name)
# start load snapshot operation (async long-running operation)
poller = await project_client.trained_model.begin_load_snapshot(trained_model_label)
try:
await poller.result()
print("Load completed.")
print(f"done: {poller.done()}")
print(f"status: {poller.status()}")
except HttpResponseError as e:
print(f"Operation failed: {e.message}")
print(e.error)
# [END conversation_authoring_load_snapshot_async]
async def main():
await sample_load_snapshot_async()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
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