#!/usr/bin/env python3
"""
Chilean GLAM Wikidata Enrichment - Batch 14 (Manual WebFetch Results)

Apply manually verified Wikidata identifier found via WebFetch tool:
- Museo Rudolph Philippi (Valdivia) → Q6940547

Target: 62/90 institutions (68.9% coverage)
Previous: 61/90 (67.8%)

Manual verification performed Nov 9, 2025 via WebFetch after API rate limits.
See: scripts/batch14_manual_results.json
"""

import sys
from pathlib import Path
from datetime import datetime, timezone
import yaml


def load_institutions(filepath: Path) -> list[dict]:
    """Load YAML file as list of dictionaries."""
    with open(filepath, 'r', encoding='utf-8') as f:
        return yaml.safe_load(f)


def save_institutions(institutions: list[dict], filepath: Path):
    """Save institutions to YAML file."""
    with open(filepath, 'w', encoding='utf-8') as f:
        yaml.dump(institutions, f, allow_unicode=True, sort_keys=False, width=120)


def enrich_batch14(institutions: list[dict]) -> tuple[list[dict], int]:
    """
    Apply Batch 14 Wikidata enrichments (manual WebFetch verification).
    
    Returns:
        Tuple of (enriched_institutions, count_enriched)
    """
    enrichment_date = datetime.now(timezone.utc).isoformat()
    enriched_count = 0
    
    # Batch 14: Manual WebFetch verification (1 institution)
    # Q6940547 - Museo de la Exploración Rudolph Amandus Philippi
    valdivia_philippi_matches = [
        ("Museo Rudolph Philippi", "Valdivia", "Q6940547", 
         "Museo de la Exploración Rudolph Amandus Philippi, museum in Valdivia, founded 1914, reopened 2006")
    ]
    
    for inst in institutions:
        name = inst.get('name', '')
        city = inst.get('locations', [{}])[0].get('city', '') if inst.get('locations') else ''
        
        # Check if institution already has Wikidata identifier
        existing_wikidata = any(
            id_item.get('identifier_scheme') == 'Wikidata' 
            for id_item in inst.get('identifiers', [])
        )
        
        if existing_wikidata:
            continue  # Skip if already enriched
        
        # Check against Batch 14 matches
        for match_name, match_city, q_number, verification in valdivia_philippi_matches:
            if match_name.lower() in name.lower() and match_city.lower() == city.lower():
                # Add Wikidata identifier
                if 'identifiers' not in inst:
                    inst['identifiers'] = []
                
                inst['identifiers'].append({
                    'identifier_scheme': 'Wikidata',
                    'identifier_value': q_number,
                    'identifier_url': f'https://www.wikidata.org/wiki/{q_number}'
                })
                
                # Update provenance
                if 'provenance' not in inst:
                    inst['provenance'] = {}
                
                inst['provenance']['data_tier'] = 'TIER_3_CROWD_SOURCED'
                inst['provenance']['last_updated'] = enrichment_date
                inst['provenance']['enrichment_batch'] = 14
                inst['provenance']['wikidata_match_confidence'] = 'HIGH'
                inst['provenance']['wikidata_match_reason'] = 'Manual verification via WebFetch after API rate limits'
                inst['provenance']['wikidata_name'] = verification
                
                enriched_count += 1
                print(f"✓ Enriched: {name} ({city}) → {q_number}")
                print(f"  Wikidata: {verification}")
                break
    
    return institutions, enriched_count


def main():
    """Main enrichment workflow."""
    # Paths
    input_file = Path("data/instances/chile/chilean_institutions_batch13_enriched.yaml")
    output_file = Path("data/instances/chile/chilean_institutions_batch14_enriched.yaml")
    
    print("=" * 80)
    print("Chilean GLAM Wikidata Enrichment - Batch 14")
    print("Manual WebFetch Verification Results")
    print("=" * 80)
    print()
    
    # Load data
    print(f"Loading: {input_file}")
    institutions = load_institutions(input_file)
    print(f"  Total institutions: {len(institutions)}")
    
    # Count current Wikidata coverage
    current_with_wikidata = sum(
        1 for inst in institutions
        if any(id_item.get('identifier_scheme') == 'Wikidata' 
               for id_item in inst.get('identifiers', []))
    )
    print(f"  Current Wikidata coverage: {current_with_wikidata}/{len(institutions)} ({current_with_wikidata/len(institutions)*100:.1f}%)")
    print()
    
    # Apply Batch 14 enrichments
    print("Applying Batch 14 enrichments...")
    institutions, enriched_count = enrich_batch14(institutions)
    print()
    
    # Calculate new coverage
    new_with_wikidata = sum(
        1 for inst in institutions
        if any(id_item.get('identifier_scheme') == 'Wikidata' 
               for id_item in inst.get('identifiers', []))
    )
    
    print("=" * 80)
    print("Enrichment Summary")
    print("=" * 80)
    print(f"Institutions enriched: {enriched_count}")
    print(f"New Wikidata coverage: {new_with_wikidata}/{len(institutions)} ({new_with_wikidata/len(institutions)*100:.1f}%)")
    print(f"Coverage change: +{new_with_wikidata - current_with_wikidata} ({(new_with_wikidata - current_with_wikidata)/len(institutions)*100:.1f}%)")
    print()
    
    # Save enriched data
    print(f"Saving: {output_file}")
    save_institutions(institutions, output_file)
    print("✓ Done!")
    print()
    
    # Progress towards 70% target
    target = 63  # 70% of 90
    remaining = target - new_with_wikidata
    print(f"Progress to 70% target: {new_with_wikidata}/{target}")
    if remaining > 0:
        print(f"  Still need: {remaining} more institution(s)")
    else:
        print("  🎉 Target reached!")


if __name__ == '__main__':
    main()