import os
import sys
import logging
import pandas as pd
from datetime import datetime
import tldextract
from thefuzz import fuzz
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  # ab hier automatisches Match
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.txt")

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}")


def calculate_similarity(record1, record2):
    """Berechnet gewichteten Ähnlichkeits-Score (0–190) zwischen zwei Datensätzen."""
    total = 0
    # Domain-Check über registered domain
    url1 = record1.get('CRM Website','')
    url2 = record2.get('CRM Website','')
    dom1 = tldextract.extract(url1).registered_domain or ''
    dom2 = tldextract.extract(url2).registered_domain or ''
    if dom1 and dom1 == dom2:
        total += 100
    # Name-Fuzzy
    name1 = record1['normalized_name']
    name2 = record2['normalized_name']
    if name1 and name2:
        total += fuzz.token_set_ratio(name1, name2) * 0.7
    # Ort+Land exakt
    if record1['CRM Ort'] == record2['CRM Ort'] and record1['CRM Land'] == record2['CRM Land']:
        total += 20
    return round(total)


def main():
    logger.info("Starte Duplikats-Check (v2.0) mit Datum im Lognamen und verbessertem Domain-Match")
    try:
        sheet = GoogleSheetHandler()
        logger.info("GoogleSheetHandler initialisiert")
    except Exception as e:
        logger.critical(f"FEHLER beim Init GoogleSheetHandler: {e}")
        sys.exit(1)

    # Daten einlesen
    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("CRM- oder Matching-Daten fehlen. Abbruch.")
        return
    logger.info(f"{len(crm_df)} CRM-Datensätze, {len(match_df)} Matching-Datensätze geladen")

    # Normalisierung und 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}-Normierung Beispiel: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")

    # 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 erstellt: {len(crm_index)} Keys")

    # Matching
    results = []
    total = len(match_df)
    for i, mrow in match_df.iterrows():
        key = mrow['block_key']
        cands = crm_index.get(key, [])
        logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' (Key='{key}') -> {len(cands)} Kandidaten")
        if not cands:
            results.append({'Match': '', 'Score': 0})
            continue
        scored = []
        for crow in cands:
            score = calculate_similarity(mrow, crow)
            scored.append((crow['CRM Name'], score))
        # Log relevante Kandidaten mit Score>=SCORE_THRESHOLD-20
        relevant = [(n,s) for n,s in scored if s >= SCORE_THRESHOLD-20]
        logger.debug(f" Relevante Kandidaten (>= {SCORE_THRESHOLD-20}): {relevant}")
        best_name, best_score = max(scored, key=lambda x: x[1])
        if best_score >= SCORE_THRESHOLD:
            results.append({'Match': best_name, 'Score': best_score})
            logger.info(f" --> Match: '{best_name}' mit Score {best_score}")
        else:
            results.append({'Match': '', 'Score': best_score})
            logger.info(f" --> Kein Match (höchster Score {best_score})")

    # Ergebnis zurück in Sheet
    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()