#!/usr/bin/env python3
"""
Batch parse all LinkedIn HTML files in the manual directory.

Extracts custodian name from filename, generates slug, and parses to JSON.
"""

import json
import re
import sys
import unicodedata
from datetime import datetime, timezone
from pathlib import Path

# Add scripts directory to path for import
sys.path.insert(0, str(Path(__file__).parent))
from parse_linkedin_html import parse_html_file


def extract_custodian_name(filename: str) -> str:
    """Extract custodian name from LinkedIn filename.
    
    Filename format: "(N) Custodian Name_ People _ LinkedIn.html"
    """
    # Remove the "(N) " prefix
    name = re.sub(r'^\(\d+\)\s*', '', filename)
    # Remove "_ People _ LinkedIn.html" suffix
    name = re.sub(r'_\s*People\s*_\s*LinkedIn\.html$', '', name)
    # Clean up underscores that LinkedIn uses instead of colons
    name = name.replace('_ ', ': ').replace(' _', ':')
    # Remove trailing/leading whitespace
    return name.strip()


def generate_slug(name: str) -> str:
    """Generate URL-safe slug from custodian name."""
    # Normalize unicode
    normalized = unicodedata.normalize('NFD', name.lower())
    # Remove diacritics
    ascii_name = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn')
    # Replace special chars with hyphens
    slug = re.sub(r'[^a-z0-9]+', '-', ascii_name)
    # Clean up multiple hyphens
    slug = re.sub(r'-+', '-', slug).strip('-')
    return slug


def main():
    manual_dir = Path('/Users/kempersc/apps/glam/data/custodian/person/affiliated/manual')
    output_dir = Path('/Users/kempersc/apps/glam/data/custodian/person/affiliated/parsed')
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # Get timestamp for this batch
    timestamp = datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')
    
    # Find all HTML files
    html_files = sorted(manual_dir.glob('*.html'))
    
    print(f"Found {len(html_files)} HTML files to process")
    print(f"Output directory: {output_dir}")
    print(f"Timestamp: {timestamp}")
    print("-" * 60)
    
    results = []
    errors = []
    
    for html_file in html_files:
        filename = html_file.name
        
        # Skip non-standard files
        if 'People' not in filename:
            print(f"SKIP: {filename} (not a People page)")
            continue
        
        custodian_name = extract_custodian_name(filename)
        custodian_slug = generate_slug(custodian_name)
        
        # Generate output filename
        output_file = output_dir / f"{custodian_slug}_staff_{timestamp}.json"
        
        # Skip if already exists
        if output_file.exists():
            print(f"SKIP: {custodian_name} (already exists)")
            continue
        
        try:
            print(f"Parsing: {custodian_name}")
            result = parse_html_file(html_file, custodian_name, custodian_slug)
            
            # Save output
            with open(output_file, 'w', encoding='utf-8') as f:
                json.dump(result, f, indent=2, ensure_ascii=False)
            
            staff_count = result['staff_analysis']['total_staff_extracted']
            heritage_count = result['staff_analysis']['heritage_relevant_count']
            print(f"  -> {staff_count} staff ({heritage_count} heritage-relevant)")
            
            results.append({
                'custodian_name': custodian_name,
                'custodian_slug': custodian_slug,
                'staff_count': staff_count,
                'heritage_relevant': heritage_count,
                'output_file': str(output_file.name)
            })
            
        except Exception as e:
            print(f"  ERROR: {e}")
            errors.append({
                'custodian_name': custodian_name,
                'error': str(e)
            })
    
    # Summary
    print("\n" + "=" * 60)
    print(f"BATCH COMPLETE")
    print(f"  Processed: {len(results)}")
    print(f"  Errors: {len(errors)}")
    print(f"  Total staff: {sum(r['staff_count'] for r in results)}")
    print(f"  Total heritage-relevant: {sum(r['heritage_relevant'] for r in results)}")
    
    # Save batch summary
    summary_file = output_dir / f"batch_results_{timestamp}.json"
    with open(summary_file, 'w', encoding='utf-8') as f:
        json.dump({
            'timestamp': timestamp,
            'processed': len(results),
            'errors': len(errors),
            'total_staff': sum(r['staff_count'] for r in results),
            'total_heritage_relevant': sum(r['heritage_relevant'] for r in results),
            'results': results,
            'errors_list': errors
        }, f, indent=2, ensure_ascii=False)
    
    print(f"\nBatch summary saved to: {summary_file}")
    
    if errors:
        print("\nErrors:")
        for err in errors:
            print(f"  - {err['custodian_name']}: {err['error']}")
    
    return 0 if not errors else 1


if __name__ == '__main__':
    sys.exit(main())