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sample_analyze_addon_formulas.py
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sample_analyze_addon_formulas.py
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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_analyze_addon_formulas.py
DESCRIPTION:
This sample demonstrates how to extract all identified formulas, such as mathematical
equations, using the add-on 'FORMULAS' capability.
This sample uses Layout model to demonstrate.
Add-on capabilities accept a list of strings containing values from the `DocumentAnalysisFeature`
enum class. For more information, see:
https://aka.ms/azsdk/python/documentintelligence/analysisfeature.
The following capabilities are free:
- BARCODES
- LANGUAGES
The following capabilities will incur additional charges:
- FORMULAS
- OCR_HIGH_RESOLUTION
- STYLE_FONT
- QUERY_FIELDS
See pricing: https://azure.microsoft.com/pricing/details/ai-document-intelligence/.
PREREQUISITES:
The following prerequisites are necessary to run the code. For more details, please visit the "How-to guides" link: https://aka.ms/how-to-guide
-------Python and IDE------
1) Install Python 3.7 or later (https://www.python.org/), which should include pip (https://pip.pypa.io/en/stable/).
2) Install the latest version of Visual Studio Code (https://code.visualstudio.com/) or your preferred IDE.
------Azure AI services or Document Intelligence resource------
Create a single-service (https://aka.ms/single-service) or multi-service (https://aka.ms/multi-service) resource.
You can use the free pricing tier (F0) to try the service and upgrade to a paid tier for production later.
------Get the key and endpoint------
1) After your resource is deployed, select "Go to resource".
2) In the left navigation menu, select "Keys and Endpoint".
3) Copy one of the keys and the Endpoint for use in this sample.
------Set your environment variables------
At a command prompt, run the following commands, replacing <yourKey> and <yourEndpoint> with the values from your resource in the Azure portal.
1) For Windows:
setx DOCUMENTINTELLIGENCE_API_KEY <yourKey>
setx DOCUMENTINTELLIGENCE_ENDPOINT <yourEndpoint>
• You need to restart any running programs that read the environment variable.
2) For macOS:
export key=<yourKey>
export endpoint=<yourEndpoint>
• This is a temporary environment variable setting method that only lasts until you close the terminal session.
• To set an environment variable permanently, visit: https://aka.ms/set-environment-variables-for-macOS
3) For Linux:
export DOCUMENTINTELLIGENCE_API_KEY=<yourKey>
export DOCUMENTINTELLIGENCE_ENDPOINT=<yourEndpoint>
• This is a temporary environment variable setting method that only lasts until you close the terminal session.
• To set an environment variable permanently, visit: https://aka.ms/set-environment-variables-for-Linux
------Set up your programming environment------
At a command prompt,run the following code to install the Azure AI Document Intelligence client library for Python with pip:
pip install azure-ai-documentintelligence --pre
------Create your Python application------
1) Create a new Python file called "sample_analyze_addon_formulas.py" in an editor or IDE.
2) Open the "sample_analyze_addon_formulas.py" file and insert the provided code sample into your application.
3) At a command prompt, use the following code to run the Python code:
python sample_analyze_addon_formulas.py
"""
import os
def analyze_formulas():
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import DocumentAnalysisFeature, AnalyzeResult, AnalyzeDocumentRequest
# For how to obtain the endpoint and key, please see PREREQUISITES above.
endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
# Analyze a document at a URL:
formUrl = "https://github.com/microsoft/Form-Recognizer-Toolkit/blob/main/SampleCode/DotNet/Quickstarts/Assets/layout-formulas.png?raw=true"
# Replace with your actual formUrl:
# If you use the URL of a public website, to find more URLs, please visit: https://aka.ms/more-URLs
# If you analyze a document in Blob Storage, you need to generate Public SAS URL, please visit: https://aka.ms/create-sas-tokens
poller = document_intelligence_client.begin_analyze_document(
"prebuilt-layout",
AnalyzeDocumentRequest(url_source=formUrl),
features=[DocumentAnalysisFeature.FORMULAS], # Specify which add-on capabilities to enable
)
# # If analyzing a local document, remove the comment markers (#) at the beginning of these 11 lines.
# # Delete or comment out the part of "Analyze a document at a URL" above.
# # Replace <path to your sample file> with your actual file path.
# path_to_sample_document = "<path to your sample file>"
# with open(path_to_sample_document, "rb") as f:
# poller = document_intelligence_client.begin_analyze_document(
# "prebuilt-layout",
# analyze_request=f,
# features=[DocumentAnalysisFeature.FORMULAS], # Specify which add-on capabilities to enable
# content_type="application/octet-stream",
# )
result: AnalyzeResult = poller.result()
# [START analyze_formulas]
# Iterate over extracted formulas on each page and print inline and display formulas
# separately.
for page in result.pages:
print(f"----Formulas detected from page #{page.page_number}----")
if page.formulas:
inline_formulas = [f for f in page.formulas if f.kind == "inline"]
display_formulas = [f for f in page.formulas if f.kind == "display"]
# To learn the detailed concept of "polygon" in the following content, visit: https://aka.ms/bounding-region
print(f"Detected {len(inline_formulas)} inline formulas.")
for formula_idx, formula in enumerate(inline_formulas):
print(f"- Inline #{formula_idx}: {formula.value}")
print(f" Confidence: {formula.confidence}")
print(f" Bounding regions: {formula.polygon}")
print(f"\nDetected {len(display_formulas)} display formulas.")
for formula_idx, formula in enumerate(display_formulas):
print(f"- Display #{formula_idx}: {formula.value}")
print(f" Confidence: {formula.confidence}")
print(f" Bounding regions: {formula.polygon}")
print("----------------------------------------")
# [END analyze_formulas]
if __name__ == "__main__":
from azure.core.exceptions import HttpResponseError
from dotenv import find_dotenv, load_dotenv
try:
load_dotenv(find_dotenv())
analyze_formulas()
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
# Next steps:
# Learn more about Add-on capabilities (Formula extraction): https://aka.ms/formula-extraction
# Find more sample code: https://aka.ms/doc-intelligence-samples