Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
File size: 4,006 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: CC-BY-NC-4.0
import os
import csv
from pathlib import Path
from typing import List, Dict, Optional
from loguru import logger
from models import DocumentAsset, PageAsset
class AssetLoader:
"""Loads document assets from create_assets output using document_mapping.csv."""
def __init__(self, assets_path: str, mapping_csv: str = None):
"""Initialize asset loader.
Args:
assets_path: Path to assets directory
mapping_csv: Path to document_mapping.csv (default: data/metadata/document_mapping.csv)
"""
self.assets_path = Path(assets_path)
if mapping_csv is None:
mapping_csv = "data/metadata/document_mapping.csv"
self.mapping_csv = Path(mapping_csv)
if not self.mapping_csv.exists():
raise FileNotFoundError(f"Document mapping not found: {self.mapping_csv}")
def load_all_documents(self, doc_types: List[str] = None) -> Dict[str, List[DocumentAsset]]:
"""Load all document assets grouped by type from CSV.
Args:
doc_types: Optional list of document types to load. If None, loads all.
Returns:
Dict mapping doc_type to list of DocumentAsset objects.
"""
documents_by_type = {}
# Read document mapping CSV
with open(self.mapping_csv, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
doc_type = row['type']
doc_name = row['doc_name']
filename = row['filename']
page_count = int(row['pages'])
# Filter by doc_types if specified
if doc_types and doc_type not in doc_types:
continue
# Build document asset
doc_asset = self._create_document_asset(
doc_type=doc_type,
doc_name=doc_name,
filename=filename,
page_count=page_count
)
if doc_type not in documents_by_type:
documents_by_type[doc_type] = []
documents_by_type[doc_type].append(doc_asset)
total = sum(len(docs) for docs in documents_by_type.values())
logger.info(f"Loaded {total} documents across {len(documents_by_type)} types from CSV")
return documents_by_type
def _create_document_asset(
self,
doc_type: str,
doc_name: str,
filename: str,
page_count: int
) -> DocumentAsset:
"""Create DocumentAsset with page information.
Args:
doc_type: Document category
doc_name: Document identifier
filename: PDF filename
page_count: Number of pages
Returns:
DocumentAsset with populated pages
"""
pages = []
for page_num in range(1, page_count + 1):
page_num_str = f"{page_num:04d}"
# Construct paths based on known structure
page_dir = self.assets_path / doc_type / filename / "pages" / page_num_str
image_path = page_dir / f"page-{page_num_str}.png"
text_path = page_dir / f"page-{page_num_str}-textract.md"
page_asset = PageAsset(
page_num=page_num,
image_path=str(image_path),
text_path=str(text_path),
text_content=None # Loaded on demand
)
pages.append(page_asset)
return DocumentAsset(
doc_type=doc_type,
doc_name=doc_name,
filename=filename,
page_count=page_count,
pages=pages
)
|