# duplicate_checker.py (v2.4 - Optimized Brute-Force)

import logging
import pandas as pd
from thefuzz import fuzz
from config import Config
from helpers import normalize_company_name, simple_normalize_url
from google_sheet_handler import GoogleSheetHandler
import time

# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 85 # Treffer unter diesem Wert werden nicht als "potenzieller Treffer" angezeigt

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

def calculate_similarity_details(record1, record2):
    """Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück."""
    scores = {'name': 0, 'location': 0, 'domain': 0}
    
    # Domain-Match gibt 100 Punkte
    if record1.get('normalized_domain') and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2.get('normalized_domain'):
        scores['domain'] = 100
    
    # Namensähnlichkeit (85% Gewichtung)
    if record1.get('normalized_name') and record2.get('normalized_name'):
        scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.85)

    # Standort-Bonus (20 Punkte)
    if record1.get('CRM Ort') and record1['CRM Ort'] == record2.get('CRM Ort'):
        if record1.get('CRM Land') and record1['CRM Land'] == record2.get('CRM Land'):
            scores['location'] = 20

    total_score = sum(scores.values())
    return {'total': total_score, 'details': scores}

def main():
    start_time = time.time()
    logging.info("Starte den Duplikats-Check (v2.4 - Optimized Brute-Force)...")
    
    try:
        sheet_handler = GoogleSheetHandler()
    except Exception as e:
        logging.critical(f"FEHLER bei Initialisierung: {e}")
        return

    logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
    crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
    if crm_df is None or crm_df.empty: return

    logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
    matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
    if matching_df is None or matching_df.empty: return
    original_matching_df = matching_df.copy()

    logging.info("Normalisiere Daten für den Vergleich...")
    for df in [crm_df, matching_df]:
        df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
        df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
        df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
        df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()

    # Konvertiere DataFrames in Listen von Dictionaries für schnelleren Zugriff in der Schleife
    crm_records = crm_df.to_dict('records')
    matching_records = matching_df.to_dict('records')
    
    logging.info(f"Starte Matching-Prozess: {len(matching_records)} Einträge werden mit {len(crm_records)} CRM-Einträgen verglichen...")
    results = []
    
    for i, match_record in enumerate(matching_records):
        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
        best_match_name = ""
        
        logging.info(f"Prüfe {i + 1}/{len(matching_records)}: {match_record['CRM Name']}...")
        
        # Der Brute-Force-Ansatz: Vergleiche mit jedem einzelnen CRM-Eintrag
        for crm_record in crm_records:
            score_info = calculate_similarity_details(match_record, crm_record)
            if score_info['total'] > best_score_info['total']:
                best_score_info = score_info
                best_match_name = crm_record['CRM Name']
        
        results.append({
            'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "",
            'Score (Gesamt)': best_score_info['total'],
            'Score (Name)': best_score_info['details']['name'],
            'Bonus (Standort)': best_score_info['details']['location'],
            'Bonus (Domain)': best_score_info['details']['domain']
        })

    logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
    result_df = pd.DataFrame(results)
    
    output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
    
    data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
    
    success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
    if success:
        logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
    else:
        logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")

    end_time = time.time()
    logging.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")

if __name__ == "__main__":
    main()