import os
import sys
import logging
import pandas as pd
from datetime import datetime
from thefuzz import fuzz
from helpers import normalize_company_name, simple_normalize_url
from google_sheet_handler import GoogleSheetHandler

# duplicate_checker.py v2.3 (Rückkehr zum v2.0-Scoring, erweitert mit Logging)
# Version: 2025-08-06_16-00
# --- Konfiguration ---
CRM_SHEET_NAME      = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD     = 80   # v2.0 Schwelle (0–190 Skala)
LOG_DIR             = "Log"

# --- Logging Setup mit Datum im Dateinamen ---
if not os.path.exists(LOG_DIR):
    os.makedirs(LOG_DIR, exist_ok=True)
now = datetime.now().strftime('%Y-%m-%d_%H-%M')
log_path = os.path.join(LOG_DIR, f"{now}_Duplicate_v2.3.log")
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# Console Handler (INFO+)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s"))
logger.addHandler(ch)
# File Handler (DEBUG+)
fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
fh.setLevel(logging.DEBUG)
fh.setFormatter(logging.Formatter("%(asctime)s - %(levelname)-8s - %(name)s - %(message)s"))
logger.addHandler(fh)
logger.info(f"Logging in Datei: {log_path}")
logger.info("Version: duplicate_checker.py v2.3 (v2.0-Scoring mit Logging) | Build: 2025-08-06_16-00")


def calculate_similarity(record1, record2):
    """Berechnet den v2.0-Score: Domain=100, Name*0.7, Ort+Land=20."""
    total_score = 0
    # Domain(exakt)
    if record1.get('normalized_domain') and record1['normalized_domain'] == record2.get('normalized_domain'):
        total_score += 100
    # Name fuzzy
    name1 = record1.get('normalized_name','')
    name2 = record2.get('normalized_name','')
    if name1 and name2:
        sim = fuzz.token_set_ratio(name1, name2)
        total_score += sim * 0.7
    # Ort+Land exakt
    if record1.get('CRM Ort') == record2.get('CRM Ort') and record1.get('CRM Land') == record2.get('CRM Land'):
        total_score += 20
    return round(total_score)


def main():
    logger.info("Starte Duplikats-Check v2.3 (v2.0-Scoring mit Logging)")
    try:
        sheet = GoogleSheetHandler()
        logger.info("GoogleSheetHandler initialisiert")
    except Exception as e:
        logger.critical(f"FEHLER Init GoogleSheetHandler: {e}")
        sys.exit(1)

    # Daten laden
    crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
    match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
    if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
        logger.critical("Daten fehlen. Abbruch.")
        return
    logger.info(f"{len(crm_df)} CRM- und {len(match_df)} Matching-Zeilen geladen")

    # Normalisierung & Blocking-Key
    for df, label in [(crm_df,'CRM'), (match_df,'Matching')]:
        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)
        logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")

    # Build Blocking-Index
    crm_index = {}
    for idx, row in crm_df.iterrows():
        key = row['block_key']
        if key:
            crm_index.setdefault(key, []).append(row)
    logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")

    # Matching mit relevanten Kandidaten im Log
    results = []
    total = len(match_df)
    for i, mrow in match_df.iterrows():
        key = mrow['block_key']
        candidates = crm_index.get(key, [])
        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(candidates)} Kandidaten")
        if not candidates:
            results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':0})
            continue
        # Score for each candidate
        scored = [(crow['CRM Name'], calculate_similarity(mrow,crow)) for crow in candidates]
        # Top 3 candidates logged
        top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
        logger.debug(f" Top3 Kandidaten: {top3}")
        best_name, best_score = max(scored, key=lambda x: x[1])
        if best_score >= SCORE_THRESHOLD:
            results.append({'Potenzieller Treffer im CRM':best_name,'Ähnlichkeits-Score':best_score})
            logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
        else:
            results.append({'Potenzieller Treffer im CRM':'','Ähnlichkeits-Score':best_score})
            logger.info(f" --> Kein Match (höchster Score {best_score})")

    # Write results back
    out = pd.DataFrame(results)
    output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out], axis=1)
    data = [output.columns.tolist()] + output.values.tolist()
    ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data)
    if ok:
        logger.info("Ergebnisse erfolgreich geschrieben")
    else:
        logger.error("Fehler beim Schreiben ins Google Sheet")

if __name__=='__main__':
    main()