# duplicate_checker.py

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 # Mindest-Score, um als "wahrscheinlicher Treffer" zu gelten

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

def calculate_similarity(record1, record2):
    """Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
    total_score = 0

    # 1. Website-Domain (stärkstes Signal)
    if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
        total_score += 100
    
    # 2. Firmenname (Fuzzy-Signal)
    if record1['normalized_name'] and record2['normalized_name']:
        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
        total_score += name_similarity * 0.7 # Gewichtung: 70%

    # 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']:
            total_score += 20 # Bonus für vollen Standort-Match

    return round(total_score)

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

    # 1. Daten laden
    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:
        logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
        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:
        logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
        return

    # 2. Daten normalisieren
    logging.info("Normalisiere Daten für den Vergleich...")
    crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
    crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url)
    crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip()
    crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip()

    matching_df['normalized_name'] = matching_df['CRM Name'].astype(str).apply(normalize_company_name)
    matching_df['normalized_domain'] = matching_df['CRM Website'].astype(str).apply(simple_normalize_url)
    matching_df['CRM Ort'] = matching_df['CRM Ort'].astype(str).str.lower().str.strip()
    matching_df['CRM Land'] = matching_df['CRM Land'].astype(str).str.lower().str.strip()

    # 3. Matching-Prozess
    logging.info("Starte Matching-Prozess... Dies kann einige Zeit dauern.")
    results = []
    
    for index, match_row in matching_df.iterrows():
        best_score = 0
        best_match_name = ""
        
        logging.info(f"Prüfe: {match_row['CRM Name']}...")

        for _, crm_row in crm_df.iterrows():
            score = calculate_similarity(match_row, crm_row)
            if score > best_score:
                best_score = score
                best_match_name = crm_row['CRM Name']
        
        if best_score >= SCORE_THRESHOLD:
            results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
        else:
            results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': best_score})

    # 4. Ergebnisse zusammenführen und schreiben
    logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
    result_df = pd.DataFrame(results)
    output_df = pd.concat([matching_df.reset_index(drop=True), result_df], axis=1)
    
    # Entferne die temporären normalisierten Spalten für eine saubere Ausgabe
    output_df = output_df.drop(columns=['normalized_name', 'normalized_domain'])
    
    # Konvertiere DataFrame in Liste von Listen für den Upload
    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 das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
    else:
        logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")

if __name__ == "__main__":
    main()