# duplicate_checker.py (v2.0 - Enhanced Transparency)

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

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

def calculate_similarity_with_details(record1, record2):
    """
    Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen
    und gibt die Details für die Begründung 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']:
        name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
        scores['name'] = round(name_similarity * 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())
    
    # Erstelle den Begründungstext
    reasons = []
    if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
    if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
    if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
    reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"

    return round(total_score), reason_text

def main():
    """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
    logging.info("Starte den Duplikats-Check (v2.0 mit erweiterter Ausgabe)...")
    
    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:
        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
    # Kopie für die finale Ausgabe sichern
    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()
        df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)

    logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
    crm_index = {}
    # Konvertiere in Records für schnelleren Zugriff
    crm_records = crm_df.to_dict('records')
    for record in crm_records:
        key = record['block_key']
        if key:
            if key not in crm_index:
                crm_index[key] = []
            crm_index[key].append(record)

    logging.info("Starte Matching-Prozess...")
    results = []
    
    for match_record in matching_df.to_dict('records'):
        best_score = -1
        best_match_name = ""
        best_reason = ""
        
        logging.info(f"Prüfe: {match_record.get('CRM Name', 'N/A')}...")

        block_key = match_record.get('block_key')
        candidates = crm_index.get(block_key, [])
        
        for crm_row in candidates:
            score, reason = calculate_similarity_with_details(match_record, crm_row)
            if score > best_score:
                best_score = score
                best_match_name = crm_row.get('CRM Name', 'N/A')
                best_reason = reason
        
        results.append({
            'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
            'Ähnlichkeits-Score': best_score,
            'Matching-Grund': best_reason
        })

    logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
    result_df = pd.DataFrame(results)
    
    # Füge die neuen Spalten zu den Originaldaten hinzu
    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 das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
    else:
        logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")

if __name__ == "__main__":
    main()