sample_manage_models.py

# 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_models.py

DESCRIPTION:
    This sample demonstrates how to manage the models on your account. To learn
    how to build a model, look at sample_build_model.py.

USAGE:
    python sample_manage_models.py

    Set the environment variables with your own values before running the sample:
    1) DOCUMENTINTELLIGENCE_ENDPOINT - the endpoint to your Document Intelligence resource.
    2) DOCUMENTINTELLIGENCE_API_KEY - your Document Intelligence API key.
    3) DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL - The shared access signature (SAS) Url of your Azure Blob Storage container
"""

import os


def sample_manage_models():
    # [START build_model]
    # Let's build a model to use for this sample
    import uuid
    from azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient
    from azure.ai.documentintelligence.models import (
        DocumentBuildMode,
        BuildDocumentModelRequest,
        AzureBlobContentSource,
        DocumentModelDetails,
    )
    from azure.core.credentials import AzureKeyCredential

    endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
    key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
    container_sas_url = os.environ["DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL"]

    document_intelligence_admin_client = DocumentIntelligenceAdministrationClient(endpoint, AzureKeyCredential(key))
    poller = document_intelligence_admin_client.begin_build_document_model(
        BuildDocumentModelRequest(
            model_id=str(uuid.uuid4()),
            build_mode=DocumentBuildMode.TEMPLATE,
            azure_blob_source=AzureBlobContentSource(container_url=container_sas_url),
            description="my model description",
        )
    )
    model: DocumentModelDetails = poller.result()

    print(f"Model ID: {model.model_id}")
    print(f"Description: {model.description}")
    print(f"Model created on: {model.created_date_time}")
    print(f"Model expires on: {model.expiration_date_time}")
    if model.doc_types:
        print("Doc types the model can recognize:")
        for name, doc_type in model.doc_types.items():
            print(f"Doc Type: '{name}' built with '{doc_type.build_mode}' mode which has the following fields:")
            for field_name, field in doc_type.field_schema.items():
                if doc_type.field_confidence:
                    print(
                        f"Field: '{field_name}' has type '{field['type']}' and confidence score "
                        f"{doc_type.field_confidence[field_name]}"
                    )
    # [END build_model]

    # [START get_resource_info]
    account_details = document_intelligence_admin_client.get_resource_info()
    print(
        f"Our resource has {account_details.custom_document_models.count} custom models, "
        f"and we can have at most {account_details.custom_document_models.limit} custom models"
    )
    neural_models = account_details.custom_neural_document_model_builds
    print(
        f"The quota limit for custom neural document models is {neural_models.quota} and the resource has"
        f"used {neural_models.used}. The resource quota will reset on {neural_models.quota_reset_date_time}"
    )
    # [END get_resource_info]

    # [START list_models]
    # Next, we get a paged list of all of our custom models
    models = document_intelligence_admin_client.list_models()

    print("We have the following 'ready' models with IDs and descriptions:")
    for model in models:
        print(f"{model.model_id} | {model.description}")
    # [END list_models]

    # [START get_model]
    my_model = document_intelligence_admin_client.get_model(model_id=model.model_id)
    print(f"\nModel ID: {my_model.model_id}")
    print(f"Description: {my_model.description}")
    print(f"Model created on: {my_model.created_date_time}")
    print(f"Model expires on: {my_model.expiration_date_time}")
    if my_model.warnings:
        print("Warnings encountered while building the model:")
        for warning in my_model.warnings:
            print(f"warning code: {warning.code}, message: {warning.message}, target of the error: {warning.target}")
    # [END get_model]

    # [START delete_model]
    # Finally, we will delete this model by ID
    document_intelligence_admin_client.delete_model(model_id=my_model.model_id)

    from azure.core.exceptions import ResourceNotFoundError

    try:
        document_intelligence_admin_client.get_model(model_id=my_model.model_id)
    except ResourceNotFoundError:
        print(f"Successfully deleted model with ID {my_model.model_id}")
    # [END delete_model]


if __name__ == "__main__":
    from azure.core.exceptions import HttpResponseError
    from dotenv import find_dotenv, load_dotenv

    try:
        load_dotenv(find_dotenv())
        sample_manage_models()
    except HttpResponseError as error:
        # Examples of how to check an HttpResponseError
        # Check by error code:
        if error.error is not None:
            if error.error.code == "InvalidImage":
                print(f"Received an invalid image error: {error.error}")
            if error.error.code == "InvalidRequest":
                print(f"Received an invalid request error: {error.error}")
            # Raise the error again after printing it
            raise
        # If the inner error is None and then it is possible to check the message to get more information:
        if "Invalid request".casefold() in error.message.casefold():
            print(f"Uh-oh! Seems there was an invalid request: {error}")
        # Raise the error again
        raise