Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
| # 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 | |
| ) | |