How to modify the main.py to process all .pdf extension file in specified bucket

[noopur100]

Hi Team, Can someone help me to modify the code to process all the document with .pdf extension and process it through docAi and load into BQ:

I tried below but when I run #python main.py, nothing happens and no error appears:

import json
import logging
import os
import traceback
from datetime import datetime
from google.api_core import retry
from google.cloud import bigquery
from google.cloud import storage


# Get ENV variables 
PROCESSOR_PROJECT_ID="dev-amer-agcg-scfc-svc-6b"    # The project id for the processor to be used
PROCESSOR_ID="dev-amer-agcg-scfc-svc-6b"           # The id of the processor to be used  BSC
bucket_name="docai_bucket10"             # The Google Cloud Storage bucket name for the source document. Example: 'split-docs'
FILE_NAME="$file_name"           # The file name for the source document within the bucket. Example: 'my-document-12.pdf'
DESTINATION_PROJECT_ID="dev-amer-agcg-scfc-svc-6b"    # The BigQuery project id for the destination
DESTINATION_DATASET_ID="docai"   # The BigQuery dataset id for the destination
DESTINATION_TABLE_ID="doc_reference"     # The BigQuery table id for the destination
CONTENT_TYPE="application/pdf"          # The MIME type of the document to be processed
PROCESSOR_LOCATION="us"                 # The location of the processor to be used
PARSING_METHODOLOGY="entities"          # entities,form,normalized_values   default="entities"
EXTRACTION_OUTPUT_BUCKET="docai-output"
ASYNC_OUTPUT_FOLDER_GCS_URI="docai-output10"


def bq_load(data, context):
    '''This function is executed whenever a file is added to Cloud Storage Landing bucket'''
    
    file_name = data['name']
    table_name = file_name.split(".")[0] 
    file_extension = str(file_name.split(".")[1])
    
    
    # Check for file extension 
    if(file_extension.lower() == "pdf"):
        message = 'PDF existing, file : \'%s\'' % (file_name)
        logging.info(message)
        _load_data_from_pdf(file_name)
        
    else: 
        message = 'Not supported file format, file : \'%s\'' % (file_name)
        logging.info(message)
        




def _load_data_from_pdf(file_name):

    arg_parser = argparse.ArgumentParser(
        description="Document AI BQ Connector process input arguments",
        allow_abbrev=False,
    )

    doc_options_group = arg_parser.add_argument_group("document arguments")
    doc_options_group.add_argument(
        "--bucket_name",
        type=str,
        help="The Google Cloud Storage bucket name for the "
        "source document. Example: 'split-docs'",
    )
    doc_options_group.add_argument(
        "--file_name",
        type=str,
        help="The file name for the source document within the bucket. Example: "
        "'my-document-12.pdf'",
    )
    doc_options_group.add_argument(
        "--content_type", type=str, help="The MIME type of the document to be processed"
    )
    doc_options_group.add_argument(
        "--processing_type_override",
        choices=["sync", "async"],
        default=None,
        help="If specified, overrides the default async/sync processing logic",
    )
    doc_options_group.add_argument(
        "--processor_project_id",
        type=str,
        help="The project id for the processor to be used",
    )
    doc_options_group.add_argument(
        "--processor_location",
        type=str,
        help="The location of the processor to be used",
    )
    doc_options_group.add_argument(
        "--processor_id", type=str, help="The id of the processor to be used"
    )
    doc_options_group.add_argument("--async_output_folder_gcs_uri", type=str, help="")
    doc_options_group.add_argument(
        "--max_sync_page_count",
        type=int,
        default=5,
        help="The maximum number of pages "
        "that will be supported for "
        "sync processing. If page count "
        "is larger, async processing "
        "will be used.",
    )
    doc_options_group.add_argument(
        "--write_extraction_result",
        action="store_true",
        help="Indicates if raw results of " "extraction should be " "written " "to GCS",
    )
    doc_options_group.add_argument("--extraction_output_bucket", type=str, help="")
    doc_options_group.add_argument(
        "--custom_fields",
        type=json.loads,
        help="Custom field json dictionary to union "
        "with the "
        "resulting dictionary for BigQuery. "
        'Example: \'{"event_id": 1, '
        '"document_type": "my_document"}\'',
    )
    doc_options_group.add_argument(
        "--metadata_mapping_info",
        type=json.loads,
        help="Json object holding information on how to map document "
        "metadata to BigQuery. If column name or value not provided, "
        "defaults will be used if possible. "
        'Example: \'{"file_name": {"bq_column_name": "doc_file_name", '
        '               "metadata_value": "my_file.pdf", '
        '               "skip_map": "false"  }\'',
    )
    doc_options_group.add_argument(
        "--should_async_wait",
        type=bool,
        default=True,
        help="Specifies if the CLI should "
        "block and wait until async "
        "document operation is "
        "completed and process result "
        "into BigQuery",
    )
    doc_options_group.add_argument(
        "--operation_id",
        type=str,
        help="An existing operation id for which to complete " "BQ processing",
    )
    doc_options_group.add_argument(
        "--parsing_methodology",
        choices=["entities", "form", "normalized_values"],
        default="entities",
        help="The parsing methodology",
    )

    timeout_filter_group = doc_options_group.add_mutually_exclusive_group()
    timeout_filter_group.add_argument(
        "--doc_ai_sync_timeout",
        type=int,
        default=900,
        help="The sync processor timeout",
    )
    timeout_filter_group.add_argument(
        "--doc_ai_async_timeout",
        type=int,
        default=900,
        help="The async processor timeout",
    )

    bigquery_options_group = arg_parser.add_argument_group("bigquery arguments")
    bigquery_options_group.add_argument(
        "--destination_project_id", help="The BigQuery project id for the destination"
    )
    bigquery_options_group.add_argument(
        "--destination_dataset_id", help="The BigQuery dataset id for the destination"
    )
    bigquery_options_group.add_argument(
        "--destination_table_id", help="The BigQuery table id for the destination"
    )
    bigquery_options_group.add_argument(
        "--include_raw_entities",
        action="store_true",
        help="If raw_entities field should be outputted to the specified table",
    )
    bigquery_options_group.add_argument(
        "--include_error_fields",
        action="store_true",
        help="If 'has_errors' and 'errors' fields should be outputted to the "
        "specified table",
    )

    arg_parser.add_argument(
        "--retry_count",
        type=int,
        default=1,
        help="The retry attempt count if continue_on_error "
        "is True. Default is 1. If "
        "there are no retries, a final insert attempt "
        "will still be made excluding the parsed "
        "document fields",
    )
    arg_parser.add_argument(
        "--continue_on_error",
        action="store_true",
        help="Indicates if processing should continue " "upon errors",
    )
    arg_parser.add_argument(
        "--log",
        choices=["notset", "debug", "info", "warning", "error", "critical"],
        default="info",
        help="The default logging level.",
    )
    arg_parser.add_argument(
        "-q", "--quiet", action="store_true", help="Suppress message output to console."
    )
    arg_parser.add_argument(
        "-v", "--version", action="version", version="Document AI BQ Connector 1.0.0"
    )

    args = arg_parser.parse_args()

    logging.basicConfig(level=args.log.upper())
    logging.debug(args)

    bucket_name = args.bucket_name
    file_name = args.file_name
    content_type = args.content_type
    processing_type_override = args.processing_type_override
    processor_project_id = args.processor_project_id
    processor_location = args.processor_location
    processor_id = args.processor_id
    async_output_folder_gcs_uri = args.async_output_folder_gcs_uri
    should_async_wait = args.should_async_wait
    should_write_extraction_result = args.write_extraction_result
    extraction_result_output_bucket = args.extraction_output_bucket
    operation_id = args.operation_id
    doc_ai_sync_timeout = args.doc_ai_sync_timeout
    doc_ai_async_timeout = args.doc_ai_async_timeout
    destination_project_id = args.destination_project_id
    destination_dataset_id = args.destination_dataset_id
    destination_table_id = args.destination_table_id
    include_raw_entities = args.include_raw_entities
    include_error_fields = args.include_error_fields
    retry_count = args.retry_count
    continue_on_error = args.continue_on_error
    custom_fields = args.custom_fields
    max_sync_page_count = args.max_sync_page_count
    parsing_methodology = args.parsing_methodology

    my_metadata_mapping_info = None
    if args.metadata_mapping_info is not None:
        my_metadata_mapping_info = {}
        for (
            cur_metadata_name,
            cur_metadata_mapping_info,
        ) in args.metadata_mapping_info.items():
            my_metadata_mapping_info[cur_metadata_name] = BqMetadataMappingInfo(
                bq_column_name=cur_metadata_mapping_info.get("bq_column_name"),
                metadata_value=cur_metadata_mapping_info.get("metadata_value"),
                skip_map=cur_metadata_mapping_info.get("skip_map"),
            )

    connector = DocAIBQConnector(
        bucket_name=bucket_name,
        file_name=file_name,
        content_type=content_type,
        processing_type_override=processing_type_override,
        processor_project_id=processor_project_id,
        processor_location=processor_location,
        processor_id=processor_id,
        async_output_folder_gcs_uri=async_output_folder_gcs_uri,
        should_async_wait=should_async_wait,
        extraction_result_output_bucket=extraction_result_output_bucket,
        should_write_extraction_result=should_write_extraction_result,
        operation_id=operation_id,
        destination_project_id=destination_project_id,
        destination_dataset_id=destination_dataset_id,
        destination_table_id=destination_table_id,
        doc_ai_sync_timeout=doc_ai_sync_timeout,
        doc_ai_async_timeout=doc_ai_async_timeout,
        custom_fields=custom_fields,
        metadata_mapping_info=my_metadata_mapping_info,
        include_raw_entities=include_raw_entities,
        include_error_fields=include_error_fields,
        retry_count=retry_count,
        continue_on_error=continue_on_error,
        max_sync_page_count=max_sync_page_count,
        parsing_methodology=parsing_methodology,
    )

    connector.run()
    print(
        f"Finished processing document - Extracted fields using parsing methodology '{parsing_methodology}' "
        "and saved results to BigQuery"
        ""
    )  # noqa: E127


if __name__ == "___load_data_from_pdf__":
    _load_data_from_pdf()

    print("Job finished.")

[anguillanneuf]

friendly ping to both @holtskinner @mservidio

cc: @gericdong or @telpirion for DocAI triaging?