sample_analyze_custom_documents.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_analyze_custom_documents.py

DESCRIPTION:
    This sample demonstrates how to analyze a document with a custom
    built model. The document must be of the same type as the documents the custom model
    was built on. To learn how to build your own models, look at
    sample_build_model.py.

USAGE:
    python sample_analyze_custom_documents.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) CUSTOM_BUILT_MODEL_ID - the ID of your custom built model.
        -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 analyze_custom_documents(custom_model_id):
    path_to_sample_documents = os.path.abspath(
        os.path.join(os.path.abspath(__file__), "..", "./sample_forms/forms/Form_1.jpg")
    )
    # [START analyze_custom_documents]
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.documentintelligence import DocumentIntelligenceClient
    from azure.ai.documentintelligence.models import AnalyzeResult

    endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
    key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
    model_id = os.getenv("CUSTOM_BUILT_MODEL_ID", custom_model_id)

    document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))

    # Make sure your document's type is included in the list of document types the custom model can analyze
    with open(path_to_sample_documents, "rb") as f:
        poller = document_intelligence_client.begin_analyze_document(
            model_id=model_id, analyze_request=f, content_type="application/octet-stream"
        )
    result: AnalyzeResult = poller.result()

    if result.documents:
        for idx, document in enumerate(result.documents):
            print(f"--------Analyzing document #{idx + 1}--------")
            print(f"Document has type {document.doc_type}")
            print(f"Document has document type confidence {document.confidence}")
            print(f"Document was analyzed with model with ID {result.model_id}")
            if document.fields:
                for name, field in document.fields.items():
                    field_value = field.get("valueString") if field.get("valueString") else field.content
                    print(
                        f"......found field of type '{field.type}' with value '{field_value}' and with confidence {field.confidence}"
                    )

    # iterate over tables, lines, and selection marks on each page
    for page in result.pages:
        print(f"\nLines found on page {page.page_number}")
        if page.lines:
            for line in page.lines:
                print(f"...Line '{line.content}'")
        if page.words:
            for word in page.words:
                print(f"...Word '{word.content}' has a confidence of {word.confidence}")
        if page.selection_marks:
            print(f"\nSelection marks found on page {page.page_number}")
            for selection_mark in page.selection_marks:
                print(
                    f"...Selection mark is '{selection_mark.state}' and has a confidence of {selection_mark.confidence}"
                )

    if result.tables:
        for i, table in enumerate(result.tables):
            print(f"\nTable {i + 1} can be found on page:")
            if table.bounding_regions:
                for region in table.bounding_regions:
                    print(f"...{region.page_number}")
            for cell in table.cells:
                print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
    print("-----------------------------------")
    # [END analyze_custom_documents]


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("CUSTOM_BUILT_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
        analyze_custom_documents(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