import os
import logging
import pandas as pd
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  # Score ab hier gilt als Match
LOG_DIR = "Log"
LOG_FILE = "duplicate_check.log"

# --- Logging Setup ---
if not os.path.exists(LOG_DIR):
    os.makedirs(LOG_DIR, exist_ok=True)
log_path = os.path.join(LOG_DIR, LOG_FILE)

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 zwischen zwei Datensätzen."""
    total_score = 0
    # Domain exact match
    if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
        total_score += 100
    # Name fuzzy
    name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
    total_score += name_similarity * 0.7
    # Ort+Land exact
    if record1['CRM Ort'] == record2['CRM Ort'] and record1['CRM Land'] == record2['CRM Land']:
        total_score += 20
    return round(total_score)


def main():
    logger.info("Starte Duplikats-Check (v2.0 - mit Blocking & relevantem Kandidaten-Log)")
    try:
        sheet_handler = GoogleSheetHandler()
        logger.info("GoogleSheetHandler initialisiert")
    except Exception as e:
        logger.critical(f"FEHLER Init GoogleSheetHandler: {e}")
        return

    # Daten laden
    crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
    match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
    if crm_df is None or crm_df.empty:
        logger.critical(f"Keine Daten in '{CRM_SHEET_NAME}'")
        return
    if match_df is None or match_df.empty:
        logger.critical(f"Keine Daten in '{MATCHING_SHEET_NAME}'")
        return
    logger.info(f"{len(crm_df)} CRM-Zeilen, {len(match_df)} Matching-Zeilen geladen")

    # Normalisierung
    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()
        # Blocking Key
        df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
        logger.debug(f"{label}-Sample nach Norm: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")

    # Blocking Index erstellen
    crm_index = {}
    for idx, row in crm_df.iterrows():
        key = row['block_key']
        if not key: continue
        crm_index.setdefault(key, []).append(row)
    logger.info(f"Blocking-Index erstellt: {len(crm_index)} Keys")

    # Matching
    results = []
    total = len(match_df)
    for i, match_row in match_df.iterrows():
        key = match_row['block_key']
        candidates = crm_index.get(key, [])
        logger.info(f"Prüfe {i+1}/{total}: {match_row['CRM Name']} (Key='{key}') -> {len(candidates)} Kandidaten")

        if not candidates:
            results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': 0})
            continue

        # Scores für Kandidaten sammeln
        scored = []
        for crm_row in candidates:
            score = calculate_similarity(match_row, crm_row)
            scored.append((crm_row['CRM Name'], score))
        # Top 3 loggen
        top3 = sorted(scored, key=lambda x: x[1], reverse=True)[:3]
        logger.debug(f" Top 3 Kandidaten: {top3}")

        # Besten Treffer wählen
        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})")

    # Ergebnisse zurückschreiben
    out_df = pd.DataFrame(results)
    output = pd.concat([match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].reset_index(drop=True), out_df], axis=1)
    data = [output.columns.tolist()] + output.values.tolist()
    ok = sheet_handler.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()