# """ # Batch processor for large-scale PDF extraction # Includes progress tracking, error handling, and resumability # """ # from pathlib import Path # from typing import List, Dict # import logging # from tqdm import tqdm # import json # from datetime import datetime # from pdf_extractor import LegalJudgmentExtractor # logging.basicConfig( # level=logging.INFO, # format='%(asctime)s - %(levelname)s - %(message)s' # ) # logger = logging.getLogger(__name__) # class BatchProcessor: # """ # Batch processor for extracting text from thousands of legal judgments # Features: Progress tracking, resumability, comprehensive reporting # """ # def __init__(self, # input_dir: Path, # output_dir: Path, # num_workers: int = 1, # enable_ocr: bool = True): # self.input_dir = Path(input_dir) # self.output_dir = Path(output_dir) # self.num_workers = num_workers # # Create extractor # self.extractor = LegalJudgmentExtractor(output_dir, enable_ocr=enable_ocr) # # Progress tracking # self.progress_file = output_dir / "processing_progress.json" # self.stats_file = output_dir / "processing_stats.json" # def get_all_pdfs(self) -> List[Path]: # """Get all PDF files from input directory""" # return sorted(self.input_dir.rglob("*.pdf")) # def load_progress(self) -> set: # """Load already processed files""" # if self.progress_file.exists(): # with open(self.progress_file, 'r') as f: # data = json.load(f) # return set(data.get('processed_files', [])) # return set() # def save_progress(self, processed_files: set): # """Save processing progress""" # with open(self.progress_file, 'w') as f: # json.dump({ # 'processed_files': list(processed_files), # 'last_updated': datetime.now().isoformat(), # 'total_processed': len(processed_files) # }, f, indent=2) # def process_single_pdf(self, pdf_path: Path) -> Dict: # """Process a single PDF""" # try: # success = self.extractor.process_pdf(pdf_path) # return { # 'filename': pdf_path.name, # 'year': pdf_path.parent.name, # 'success': success, # 'error': None # } # except Exception as e: # return { # 'filename': pdf_path.name, # 'year': pdf_path.parent.name, # 'success': False, # 'error': str(e) # } # def process_batch(self, # start_year: int = None, # end_year: int = None, # limit: int = None, # resume: bool = True): # """ # Process PDFs in batch with progress tracking # Args: # start_year: Start from this year (inclusive) # end_year: Process until this year (inclusive) # limit: Maximum number of PDFs to process # resume: Continue from last checkpoint # """ # logger.info("Starting batch processing...") # logger.info(f"Workers: {self.num_workers}") # # Get all PDFs # all_pdfs = self.get_all_pdfs() # logger.info(f"Found {len(all_pdfs):,} PDFs") # # Filter by year if specified # if start_year or end_year: # all_pdfs = [ # p for p in all_pdfs # if (not start_year or int(p.parent.name) >= start_year) and # (not end_year or int(p.parent.name) <= end_year) # ] # logger.info(f"Filtered to {len(all_pdfs):,} PDFs (years {start_year}-{end_year})") # # Load progress and filter already processed # if resume: # processed = self.load_progress() # all_pdfs = [p for p in all_pdfs if str(p) not in processed] # logger.info(f"Resuming: {len(all_pdfs):,} PDFs remaining") # else: # processed = set() # # Apply limit # if limit: # all_pdfs = all_pdfs[:limit] # logger.info(f"Limited to {len(all_pdfs):,} PDFs") # if not all_pdfs: # logger.info("No PDFs to process!") # return # # Initialize stats # stats = { # 'total': len(all_pdfs), # 'successful': 0, # 'failed': 0, # 'start_time': datetime.now().isoformat(), # 'failed_files': [] # } # # Process with progress bar # with tqdm(total=len(all_pdfs), desc="Processing PDFs") as pbar: # for pdf_path in all_pdfs: # result = self.process_single_pdf(pdf_path) # if result['success']: # stats['successful'] += 1 # else: # stats['failed'] += 1 # stats['failed_files'].append({ # 'file': result['filename'], # 'error': result['error'] # }) # logger.warning(f"Failed: {result['filename']} - {result['error']}") # # Update progress # processed.add(str(pdf_path)) # # Save progress every 50 files # if len(processed) % 50 == 0: # self.save_progress(processed) # pbar.update(1) # pbar.set_postfix({ # 'Success': stats['successful'], # 'Failed': stats['failed'] # }) # # Final save # self.save_progress(processed) # # Save statistics # stats['end_time'] = datetime.now().isoformat() # stats['success_rate'] = (stats['successful'] / stats['total'] * 100) if stats['total'] > 0 else 0 # with open(self.stats_file, 'w') as f: # json.dump(stats, f, indent=2) # # Summary # logger.info("\n" + "="*60) # logger.info("PROCESSING COMPLETE") # logger.info("="*60) # logger.info(f"Total processed: {stats['total']:,}") # logger.info(f"Successful: {stats['successful']:,}") # logger.info(f"Failed: {stats['failed']:,}") # logger.info(f"Success rate: {stats['success_rate']:.2f}%") # logger.info("="*60) # def main(): # """Main execution with CLI arguments""" # import argparse # parser = argparse.ArgumentParser(description='Batch process legal judgment PDFs') # parser.add_argument('--start-year', type=int, help='Start year (inclusive)') # parser.add_argument('--end-year', type=int, help='End year (inclusive)') # parser.add_argument('--limit', type=int, help='Maximum PDFs to process') # parser.add_argument('--no-resume', action='store_true', help='Start fresh') # parser.add_argument('--no-ocr', action='store_true', help='Disable OCR fallback') # args = parser.parse_args() # # Configuration # INPUT_DIR = Path("data/raw") # OUTPUT_DIR = Path("data/processed/extracted") # processor = BatchProcessor( # input_dir=INPUT_DIR, # output_dir=OUTPUT_DIR, # enable_ocr=not args.no_ocr # ) # processor.process_batch( # start_year=args.start_year, # end_year=args.end_year, # limit=args.limit, # resume=not args.no_resume # ) # if __name__ == "__main__": # main() """ Batch processor for large-scale PDF extraction Includes progress tracking, error handling, and resumability """ from pathlib import Path from typing import List, Dict import logging from tqdm import tqdm import json from datetime import datetime from pdf_extractor import LegalJudgmentExtractor logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) class BatchProcessor: """ Batch processor for extracting text from thousands of legal judgments Features: Progress tracking, resumability, comprehensive reporting """ def __init__(self, input_dir: Path, output_dir: Path, num_workers: int = 1, enable_ocr: bool = True): self.input_dir = Path(input_dir) self.output_dir = Path(output_dir) self.num_workers = num_workers # Create extractor self.extractor = LegalJudgmentExtractor(output_dir, enable_ocr=enable_ocr) # Progress tracking self.progress_file = output_dir / "processing_progress.json" self.stats_file = output_dir / "processing_stats.json" def get_all_pdfs(self) -> List[Path]: """Get all PDF files (case-insensitive)""" # FIX: Search for both .pdf and .PDF pdfs_lower = list(self.input_dir.rglob("*.pdf")) pdfs_upper = list(self.input_dir.rglob("*.PDF")) all_pdfs = pdfs_lower + pdfs_upper return sorted(set(all_pdfs)) # Remove duplicates and sort def load_progress(self) -> set: """Load already processed files""" if self.progress_file.exists(): with open(self.progress_file, 'r') as f: data = json.load(f) return set(data.get('processed_files', [])) return set() def save_progress(self, processed_files: set): """Save processing progress""" with open(self.progress_file, 'w') as f: json.dump({ 'processed_files': list(processed_files), 'last_updated': datetime.now().isoformat(), 'total_processed': len(processed_files) }, f, indent=2) def process_single_pdf(self, pdf_path: Path) -> Dict: """Process a single PDF""" try: success = self.extractor.process_pdf(pdf_path) return { 'filename': pdf_path.name, 'year': pdf_path.parent.name, 'success': success, 'error': None } except Exception as e: return { 'filename': pdf_path.name, 'year': pdf_path.parent.name, 'success': False, 'error': str(e) } def process_batch(self, start_year: int = None, end_year: int = None, limit: int = None, resume: bool = True): """ Process PDFs in batch with progress tracking Args: start_year: Start from this year (inclusive) end_year: Process until this year (inclusive) limit: Maximum number of PDFs to process resume: Continue from last checkpoint """ logger.info("Starting batch processing...") logger.info(f"Input directory: {self.input_dir}") logger.info(f"Output directory: {self.output_dir}") # Get all PDFs all_pdfs = self.get_all_pdfs() logger.info(f"Found {len(all_pdfs):,} PDFs") if len(all_pdfs) == 0: logger.error("❌ No PDFs found! Check your data/raw directory.") logger.error(f"Looking in: {self.input_dir}") logger.error("Make sure PDFs are in year folders like: data/raw/1950/*.PDF") return # Filter by year if specified if start_year or end_year: filtered_pdfs = [] for p in all_pdfs: try: year = int(p.parent.name) if (not start_year or year >= start_year) and (not end_year or year <= end_year): filtered_pdfs.append(p) except ValueError: logger.warning(f"Skipping non-year folder: {p.parent.name}") all_pdfs = filtered_pdfs logger.info(f"Filtered to {len(all_pdfs):,} PDFs (years {start_year}-{end_year})") # Load progress and filter already processed if resume: processed = self.load_progress() all_pdfs = [p for p in all_pdfs if str(p) not in processed] logger.info(f"Resuming: {len(all_pdfs):,} PDFs remaining") else: processed = set() # Apply limit if limit: all_pdfs = all_pdfs[:limit] logger.info(f"Limited to {len(all_pdfs):,} PDFs") if not all_pdfs: logger.info("No PDFs to process!") return # Initialize stats stats = { 'total': len(all_pdfs), 'successful': 0, 'failed': 0, 'start_time': datetime.now().isoformat(), 'failed_files': [] } # Process with progress bar with tqdm(total=len(all_pdfs), desc="Processing PDFs") as pbar: for pdf_path in all_pdfs: result = self.process_single_pdf(pdf_path) if result['success']: stats['successful'] += 1 else: stats['failed'] += 1 stats['failed_files'].append({ 'file': result['filename'], 'year': result['year'], 'error': result['error'] }) logger.warning(f"Failed: {result['filename']} - {result['error']}") # Update progress processed.add(str(pdf_path)) # Save progress every 50 files if len(processed) % 50 == 0: self.save_progress(processed) pbar.update(1) pbar.set_postfix({ 'Success': stats['successful'], 'Failed': stats['failed'] }) # Final save self.save_progress(processed) # Save statistics stats['end_time'] = datetime.now().isoformat() stats['success_rate'] = (stats['successful'] / stats['total'] * 100) if stats['total'] > 0 else 0 with open(self.stats_file, 'w') as f: json.dump(stats, f, indent=2) # Summary logger.info("\n" + "="*60) logger.info("PROCESSING COMPLETE") logger.info("="*60) logger.info(f"Total processed: {stats['total']:,}") logger.info(f"Successful: {stats['successful']:,}") logger.info(f"Failed: {stats['failed']:,}") logger.info(f"Success rate: {stats['success_rate']:.2f}%") logger.info("="*60) def main(): """Main execution with CLI arguments""" import argparse parser = argparse.ArgumentParser(description='Batch process legal judgment PDFs') parser.add_argument('--start-year', type=int, help='Start year (inclusive)') parser.add_argument('--end-year', type=int, help='End year (inclusive)') parser.add_argument('--limit', type=int, help='Maximum PDFs to process') parser.add_argument('--no-resume', action='store_true', help='Start fresh') parser.add_argument('--no-ocr', action='store_true', help='Disable OCR fallback') args = parser.parse_args() # Configuration INPUT_DIR = Path("data/raw") OUTPUT_DIR = Path("data/processed/extracted") processor = BatchProcessor( input_dir=INPUT_DIR, output_dir=OUTPUT_DIR, enable_ocr=not args.no_ocr ) processor.process_batch( start_year=args.start_year, end_year=args.end_year, limit=args.limit, resume=not args.no_resume ) if __name__ == "__main__": main()