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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_compose_model.py
DESCRIPTION:
Model compose allows multiple models to be composed and called with a single model ID.
This is useful when you have built different models and want to aggregate a group of
them into a single model that you (or a user) could use to analyze a document. When doing
so, you can let the service decide which model more accurately represents the document to
analyze, instead of manually trying each built model against the document and selecting
the most accurate one.
In our case, we will be writing an application that collects the expenses a company is making.
There are 4 main areas where we get purchase orders from (office supplies, office equipment,
furniture, and cleaning supplies). Because each area has its own document with its own structure,
we need to build a model per document. Note that you can substitute your own models or container
SAS URLs for this sample.
USAGE:
python sample_compose_model.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) PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL - a container SAS URL to your Azure Storage blob container.
4) PURCHASE_ORDER_OFFICE_EQUIPMENT_SAS_URL - a container SAS URL to your Azure Storage blob container.
5) PURCHASE_ORDER_OFFICE_FURNITURE_SAS_URL - a container SAS URL to your Azure Storage blob container.
6) PURCHASE_ORDER_OFFICE_CLEANING_SUPPLIES_SAS_URL - a container SAS URL to your Azure Storage blob container.
"""
import os
def sample_compose_model():
# [START composed_model]
from azure.core.credentials import AzureKeyCredential
from azure.ai.formrecognizer import DocumentModelAdministrationClient, ModelBuildMode
endpoint = os.environ["AZURE_FORM_RECOGNIZER_ENDPOINT"]
key = os.environ["AZURE_FORM_RECOGNIZER_KEY"]
po_supplies = os.environ['PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL']
po_equipment = os.environ['PURCHASE_ORDER_OFFICE_EQUIPMENT_SAS_URL']
po_furniture = os.environ['PURCHASE_ORDER_OFFICE_FURNITURE_SAS_URL']
po_cleaning_supplies = os.environ['PURCHASE_ORDER_OFFICE_CLEANING_SUPPLIES_SAS_URL']
document_model_admin_client = DocumentModelAdministrationClient(endpoint=endpoint, credential=AzureKeyCredential(key))
supplies_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE, blob_container_url=po_supplies, description="Purchase order-Office supplies"
)
equipment_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE, blob_container_url=po_equipment, description="Purchase order-Office Equipment"
)
furniture_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE, blob_container_url=po_furniture, description="Purchase order-Furniture"
)
cleaning_supplies_poller = document_model_admin_client.begin_build_document_model(
ModelBuildMode.TEMPLATE, blob_container_url=po_cleaning_supplies, description="Purchase order-Cleaning Supplies"
)
supplies_model = supplies_poller.result()
equipment_model = equipment_poller.result()
furniture_model = furniture_poller.result()
cleaning_supplies_model = cleaning_supplies_poller.result()
purchase_order_models = [
supplies_model.model_id,
equipment_model.model_id,
furniture_model.model_id,
cleaning_supplies_model.model_id
]
poller = document_model_admin_client.begin_compose_document_model(
purchase_order_models, description="Office Supplies Composed Model"
)
model = poller.result()
print("Office Supplies Composed Model Info:")
print("Model ID: {}".format(model.model_id))
print("Description: {}".format(model.description))
print("Model created on: {}\n".format(model.created_on))
print("Doc types the model can recognize:")
for name, doc_type in model.doc_types.items():
print("\nDoc Type: '{}' which has the following fields:".format(name))
for field_name, field in doc_type.field_schema.items():
print("Field: '{}' has type '{}' and confidence score {}".format(
field_name, field["type"], doc_type.field_confidence[field_name]
))
# [END composed_model]
if __name__ == '__main__':
sample_compose_model()
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