File: sample_copy_model_to.py

package info (click to toggle)
python-azure 20250603%2Bgit-1
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 851,724 kB
  • sloc: python: 7,362,925; ansic: 804; javascript: 287; makefile: 195; sh: 145; xml: 109
file content (137 lines) | stat: -rw-r--r-- 6,424 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
# 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_copy_model_to.py

DESCRIPTION:
    This sample demonstrates how to copy a custom model from a source Document Intelligence resource
    to a target Document Intelligence resource.

USAGE:
    python sample_copy_model_to.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_TARGET_ENDPOINT - the endpoint to your target Document Intelligence resource.
    4) DOCUMENTINTELLIGENCE_TARGET_API_KEY - your target Document Intelligence API key
    5) AZURE_SOURCE_MODEL_ID - the model ID from the source resource to be copied over to the target resource.
        - OR -
       DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL - The shared access signature (SAS) Url of your Azure Blob Storage container with your training files.
       A model will be built and used to run the sample.
"""

import os


def sample_copy_model_to(custom_model_id):
    # [START begin_copy_document_model_to]
    import uuid
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient
    from azure.ai.documentintelligence.models import AuthorizeCopyRequest, DocumentModelDetails

    source_endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
    source_key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
    target_endpoint = os.environ["DOCUMENTINTELLIGENCE_TARGET_ENDPOINT"]
    target_key = os.environ["DOCUMENTINTELLIGENCE_TARGET_API_KEY"]
    source_model_id = os.getenv("AZURE_SOURCE_MODEL_ID", custom_model_id)

    target_client = DocumentIntelligenceAdministrationClient(
        endpoint=target_endpoint, credential=AzureKeyCredential(target_key)
    )
    target_auth = target_client.authorize_model_copy(
        AuthorizeCopyRequest(
            model_id=str(uuid.uuid4()),  # target model ID
            description="copied model",
        )
    )
    source_client = DocumentIntelligenceAdministrationClient(
        endpoint=source_endpoint, credential=AzureKeyCredential(source_key)
    )

    poller = source_client.begin_copy_model_to(
        model_id=source_model_id,
        body=target_auth,
    )
    copied_over_model: DocumentModelDetails = poller.result()

    print(f"Model ID: {copied_over_model.model_id}")
    print(f"Description: {copied_over_model.description}")
    print(f"Model created on: {copied_over_model.created_date_time}")
    print(f"Model expires on: {copied_over_model.expiration_date_time}")
    if copied_over_model.doc_types:
        print("Doc types the model can recognize:")
        for name, doc_type in copied_over_model.doc_types.items():
            print(f"Doc Type: '{name}' which has the following fields:")
            if doc_type.field_schema:
                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]}"
                        )
    if copied_over_model.warnings:
        print("Warnings encountered while building the model:")
        for warning in copied_over_model.warnings:
            print(f"warning code: {warning.code}, message: {warning.message}, target of the error: {warning.target}")
    # [END begin_copy_document_model_to]


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

    try:
        load_dotenv(find_dotenv())
        model_id = None
        if os.getenv("DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL") and not os.getenv("AZURE_SOURCE_MODEL_ID"):
            import uuid
            from azure.core.credentials import AzureKeyCredential
            from azure.ai.documentintelligence import DocumentIntelligenceAdministrationClient
            from azure.ai.documentintelligence.models import (
                DocumentBuildMode,
                BuildDocumentModelRequest,
                AzureBlobContentSource,
            )

            endpoint = os.getenv("DOCUMENTINTELLIGENCE_ENDPOINT")
            key = os.getenv("DOCUMENTINTELLIGENCE_API_KEY")

            if not endpoint or not key:
                raise ValueError("Please provide endpoint and API key to run the samples.")

            document_intelligence_admin_client = DocumentIntelligenceAdministrationClient(
                endpoint=endpoint, credential=AzureKeyCredential(key)
            )
            blob_container_sas_url = os.getenv("DOCUMENTINTELLIGENCE_STORAGE_CONTAINER_SAS_URL")
            if blob_container_sas_url is not None:
                request = BuildDocumentModelRequest(
                    model_id=str(uuid.uuid4()),
                    build_mode=DocumentBuildMode.TEMPLATE,
                    azure_blob_source=AzureBlobContentSource(container_url=blob_container_sas_url),
                )
                model = document_intelligence_admin_client.begin_build_document_model(request).result()
                model_id = model.model_id
        sample_copy_model_to(model_id)
    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