classification.py

# mypy: disable-error-code="1"
"""
Sends a request to a Document AI Specialized Splitter/Classifier Processor
"""
from google.cloud import documentai_v1 as documentai
import pandas as pd


def online_process(
    project_id: str,
    location: str,
    processor_id: str,
    file_path: str,
    mime_type: str,
) -> documentai.Document:
    """
    Processes a document using the Document AI Online Processing API.
    """

    opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}

    # Instantiates a client
    documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)

    # The full resource name of the processor, e.g.:
    # projects/project-id/locations/location/processor/processor-id
    # You must create new processors in the Cloud Console first
    resource_name = documentai_client.processor_path(project_id, location, processor_id)

    # Read the file into memory
    with open(file_path, "rb") as file:
        file_content = file.read()

    # Load Binary Data into Document AI RawDocument Object
    raw_document = documentai.RawDocument(content=file_content, mime_type=mime_type)

    # Configure the process request
    request = documentai.ProcessRequest(name=resource_name, raw_document=raw_document)

    # Use the Document AI client to process the sample form
    result = documentai_client.process_document(request=request)

    return result.document


PROJECT_ID = "YOUR_PROJECT_ID"
LOCATION = "YOUR_PROJECT_LOCATION"  # Format is 'us' or 'eu'
PROCESSOR_ID = "PROCUREMENT_SPLITTER_ID"  # Create processor in Cloud Console

# The local file in your current working directory
FILE_PATH = "procurement_multi_document.pdf"
# Refer to https://cloud.google.com/document-ai/docs/processors-list
# for supported file types
MIME_TYPE = "application/pdf"

document = online_process(
    project_id=PROJECT_ID,
    location=LOCATION,
    processor_id=PROCESSOR_ID,
    file_path=FILE_PATH,
    mime_type=MIME_TYPE,
)

print("Document processing complete.")

types = []
confidence = []
pages = []

# Each Document.entity is a classification
for entity in document.entities:
    classification = entity.type_
    types.append(classification)
    confidence.append(f"{entity.confidence:.0%}")

    # entity.page_ref contains the pages that match the classification
    pages_list = []
    for page_ref in entity.page_anchor.page_refs:
        pages_list.append(page_ref.page)
    pages.append(pages_list)

# Create a Pandas Dataframe to print the values in tabular format.
df = pd.DataFrame({"Classification": types, "Confidence": confidence, "Pages": pages})

print(df)