Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
File size: 3,947 Bytes
165da3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: CC-BY-NC-4.0
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Optional
from loguru import logger
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
from models import Document
from services.textract_ocr import TextractOcr
from services.deepseek_ocr import DeepSeekOcr
from services.asset_writer import AssetWriter
class AssetCreator:
"""Creates assets from PDFs: extracts images and OCR text."""
def __init__(self, writer: AssetWriter, ocr):
"""Initialize AssetCreator.
Args:
writer: AssetWriter instance
ocr: OCR service (TextractOcr or DeepSeekOcr)
"""
self.writer = writer
self.ocr = ocr
self.is_deepseek = isinstance(ocr, DeepSeekOcr)
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=retry_if_exception_type((Exception,)),
reraise=True,
)
def create_assets(self, doc: Document) -> None:
"""Create assets for a single document with retry logic."""
try:
logger.trace(f"Processing: {doc.doc_name}")
# Read PDF
with open(doc.absolute_filepath, 'rb') as f:
pdf_bytes = f.read()
# Extract text via OCR
if self.is_deepseek:
# DeepSeek needs images, extract them first
from src.idp_labs.core.util.pdf_util import PdfUtil
images = PdfUtil.get_pages_images_from_pdf(pdf_path=doc.absolute_filepath)
text_pages = self.ocr.extract_text_from_images(images)
else:
# Textract uses PDF bytes
text_pages = self.ocr.extract_text_from_pdf(pdf_bytes)
# Write all assets
self.writer.save_document_assets(
doc_type=doc.doc_type,
doc_name=doc.doc_name,
filename=doc.filename,
pdf_bytes=pdf_bytes,
text_pages=text_pages
)
logger.trace(f"Completed: {doc.doc_name}")
except Exception as e:
logger.exception(f"Error processing {doc.doc_name}: {e}")
raise
def create_all(
self,
documents: List[Document],
workers: Optional[int] = None,
limit: Optional[int] = None
) -> dict:
"""Create assets for all documents with concurrent execution.
Returns:
Dict with 'successful', 'failed', and 'failed_docs' keys.
"""
if workers is None:
workers = os.cpu_count() or 1
docs_to_process = documents[:limit] if limit and limit > 0 else documents
logger.info(f"Processing {len(docs_to_process)} documents with {workers} workers")
successful = 0
failed = 0
failed_docs = []
with ThreadPoolExecutor(max_workers=workers) as executor:
future_to_doc = {
executor.submit(self.create_assets, doc): doc
for doc in docs_to_process
}
for future in as_completed(future_to_doc):
doc = future_to_doc[future]
try:
future.result()
successful += 1
except Exception as e:
failed += 1
failed_docs.append(doc.doc_name)
logger.error(f"Failed: {doc.doc_name} - {e}")
logger.info(f"Completed: {successful} successful, {failed} failed")
return {
'successful': successful,
'failed': failed,
'failed_docs': failed_docs
}
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