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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_manage_custom_models.py
DESCRIPTION:
This sample demonstrates how to manage the custom models on your account. To learn
how to create and train a custom model, look at sample_train_unlabeled_model.py and
sample_train_labeled_model.py.
USAGE:
python sample_manage_custom_models.py
Set the environment variables with your own values before running the sample:
1) AZURE_FORM_RECOGNIZER_ENDPOINT - the endpoint to your Form Recognizer resource.
2) AZURE_FORM_RECOGNIZER_KEY - your Form Recognizer API key
3) CONTAINER_SAS_URL_V2 - The shared access signature (SAS) Url of your Azure Blob Storage container
"""
import os
class ManageCustomModelsSample(object):
def manage_custom_models(self):
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import ResourceNotFoundError
from azure.ai.formrecognizer import FormTrainingClient
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
container_sas_url = os.environ["CONTAINER_SAS_URL_V2"]
# [START get_account_properties]
form_training_client = FormTrainingClient(endpoint=endpoint, credential=AzureKeyCredential(key))
# First, we see how many custom models we have, and what our limit is
account_properties = form_training_client.get_account_properties()
print("Our account has {} custom models, and we can have at most {} custom models\n".format(
account_properties.custom_model_count, account_properties.custom_model_limit
))
# [END get_account_properties]
# Next, we get a paged list of all of our custom models
# [START list_custom_models]
custom_models = form_training_client.list_custom_models()
print("We have models with the following IDs:")
for model_info in custom_models:
print(model_info.model_id)
# [END list_custom_models]
# let's train a model to use for this sample
poller = form_training_client.begin_training(container_sas_url, use_training_labels=False)
model = poller.result()
# Now we'll get information for the model we just trained
# [START get_custom_model]
custom_model = form_training_client.get_custom_model(model_id=model.model_id)
print("\nModel ID: {}".format(custom_model.model_id))
print("Status: {}".format(custom_model.status))
print("Model name: {}".format(custom_model.model_name))
print("Is this a composed model?: {}".format(custom_model.properties.is_composed_model))
print("Training started on: {}".format(custom_model.training_started_on))
print("Training completed on: {}".format(custom_model.training_completed_on))
# [END get_custom_model]
# Finally, we will delete this model by ID
# [START delete_model]
form_training_client.delete_model(model_id=custom_model.model_id)
try:
form_training_client.get_custom_model(model_id=custom_model.model_id)
except ResourceNotFoundError:
print("Successfully deleted model with id {}".format(custom_model.model_id))
# [END delete_model]
if __name__ == '__main__':
sample = ManageCustomModelsSample()
sample.manage_custom_models()
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