# duplicate_checker.py (v2.1 - mit Match-Basis-Anzeige)

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

# --- Konfiguration ---
CRM_SHEET_NAME = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD = 80 # Wird jetzt nur noch zur Hervorhebung genutzt, angezeigt werden alle

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}
    
    # 1. Website-Domain (stärkstes Signal)
    if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']:
        scores['domain'] = 100
    
    # 2. Firmenname (Fuzzy-Signal)
    if record1['normalized_name'] and record2['normalized_name']:
        scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.7)

    # 3. Standort (Bestätigungs-Signal)
    if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
        if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
            scores['location'] = 20

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

def main():
    """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
    logging.info("Starte den Duplikats-Check (v2.1 mit Match-Basis)...")
    
    try:
        sheet_handler = GoogleSheetHandler()
    except Exception as e:
        logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {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

    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()
        df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)

    logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
    crm_index = crm_df.groupby('block_key').apply(lambda x: x.to_dict('records')).to_dict()
    
    logging.info("Starte Matching-Prozess...")
    results = []
    
    for index, match_row in matching_df.iterrows():
        best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
        best_match_name = ""
        
        logging.info(f"Prüfe {index + 1}/{len(matching_df)}: {match_row['CRM Name']}...")

        block_key = match_row['block_key']
        candidates = crm_index.get(block_key, [])
        
        for crm_row in candidates:
            score_info = calculate_similarity_details(match_row, crm_row)
            if score_info['total'] > best_score_info['total']:
                best_score_info = score_info
                best_match_name = crm_row['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)
    
    # Originale Spalten aus matching_df für die Ausgabe nehmen
    original_cols = [col for col in ['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land'] if col in matching_df.columns]
    output_df = pd.concat([matching_df[original_cols].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.")

if __name__ == "__main__":
    main()