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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_classify_document_async.py
DESCRIPTION:
This sample demonstrates how to classify a document using a trained document classifier.
To learn how to build your custom classifier, see sample_build_classifier.py.
More details on building a classifier and labeling your data can be found here:
https://aka.ms/azsdk/documentintelligence/buildclassifiermodel
USAGE:
python sample_classify_document_async.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) CLASSIFIER_ID - the ID of your trained document classifier
-OR-
DOCUMENTINTELLIGENCE_TRAINING_DATA_CLASSIFIER_SAS_URL - The shared access signature (SAS) Url of your Azure Blob Storage container
with your training files. A document classifier will be built and used to run the sample.
"""
import asyncio
import os
async def classify_document(classifier_id):
path_to_sample_documents = os.path.abspath(
os.path.join(
os.path.abspath(__file__),
"..",
"..",
"./sample_forms/forms/IRS-1040.pdf",
)
)
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence.aio import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
classifier_id = os.getenv("CLASSIFIER_ID", classifier_id)
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
async with document_intelligence_client:
with open(path_to_sample_documents, "rb") as f:
poller = await document_intelligence_client.begin_classify_document(classifier_id, body=f)
result: AnalyzeResult = await poller.result()
print("----Classified documents----")
if result.documents:
for doc in result.documents:
if doc.bounding_regions:
print(
f"Found document of type '{doc.doc_type or 'N/A'}' with a confidence of {doc.confidence} contained on "
f"the following pages: {[region.page_number for region in doc.bounding_regions]}"
)
async def main():
classifier_id = None
if os.getenv("DOCUMENTINTELLIGENCE_TRAINING_DATA_CLASSIFIER_SAS_URL") and not os.getenv("CUSTOM_BUILT_MODEL_ID"):
import uuid
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence.aio import DocumentIntelligenceAdministrationClient
from azure.ai.documentintelligence.models import (
AzureBlobContentSource,
ClassifierDocumentTypeDetails,
BuildDocumentClassifierRequest,
)
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
blob_container_sas_url = os.environ["DOCUMENTINTELLIGENCE_TRAINING_DATA_CLASSIFIER_SAS_URL"]
document_intelligence_admin_client = DocumentIntelligenceAdministrationClient(
endpoint=endpoint, credential=AzureKeyCredential(key)
)
async with document_intelligence_admin_client:
poller = await document_intelligence_admin_client.begin_build_classifier(
BuildDocumentClassifierRequest(
classifier_id=str(uuid.uuid4()),
doc_types={
"IRS-1040-A": ClassifierDocumentTypeDetails(
azure_blob_source=AzureBlobContentSource(
container_url=blob_container_sas_url, prefix="IRS-1040-A/train"
)
),
"IRS-1040-B": ClassifierDocumentTypeDetails(
azure_blob_source=AzureBlobContentSource(
container_url=blob_container_sas_url, prefix="IRS-1040-B/train"
)
),
},
)
)
classifier = await poller.result()
classifier_id = classifier.classifier_id
await classify_document(classifier_id)
if __name__ == "__main__":
from azure.core.exceptions import HttpResponseError
from dotenv import find_dotenv, load_dotenv
try:
load_dotenv(find_dotenv())
asyncio.run(main())
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
|
