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

# duplicate_checker.py v2.11 (SerpAPI nur für Matching-Accounts)
# Version: 2025-08-08_10-00

# --- Konfiguration ---
CRM_SHEET_NAME      = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD     = 80   # Score-Schwelle
LOG_DIR             = "Log"
LOG_FILE            = "duplicate_check_v2.11.txt"

# --- 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)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
for h in list(root.handlers): root.removeHandler(h)
formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
root.addHandler(ch)
fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
root.addHandler(fh)
logger = logging.getLogger(__name__)
logger.info(f"Logging to console and file: {log_path}")
logger.info("Starting duplicate_checker.py v2.11 | Version: 2025-08-08_10-00")

# --- SerpAPI Key laden ---
try:
    Config.load_api_keys()
    serp_key = Config.API_KEYS.get('serpapi')
    if not serp_key:
        logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
except Exception as e:
    logger.warning(f"Fehler beim Laden API-Keys: {e}")
    serp_key = None

# --- Ähnlichkeitsberechnung ---
def calculate_similarity(record1, record2):
    dom1 = record1.get('normalized_domain','')
    dom2 = record2.get('normalized_domain','')
    domain_flag = 1 if dom1 and dom1 == dom2 else 0
    loc_flag = 1 if (record1.get('CRM Ort')==record2.get('CRM Ort') and record1.get('CRM Land')==record2.get('CRM Land')) else 0
    n1, n2 = record1.get('normalized_name',''), record2.get('normalized_name','')
    if n1 and n2:
        ts = fuzz.token_set_ratio(n1,n2)
        pr = fuzz.partial_ratio(n1,n2)
        ss = fuzz.token_sort_ratio(n1,n2)
        name_score = max(ts,pr,ss)
    else:
        name_score = 0
    bonus_flag = 1 if domain_flag==0 and loc_flag==0 and name_score>=85 else 0
    total = domain_flag*100 + name_score*1.0 + loc_flag*20 + bonus_flag*20
    return round(total), domain_flag, name_score, loc_flag, bonus_flag

# --- Hauptfunktion ---
def main():
    logger.info("Starte Duplikats-Check v2.11 mit SerpAPI-Fallback (nur Matching)")
    try:
        sheet = GoogleSheetHandler()
        logger.info("GoogleSheetHandler initialisiert")
    except Exception as e:
        logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
        sys.exit(1)

    logger.info(f"Lade CRM-Daten aus '{CRM_SHEET_NAME}'...")
    crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
    logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze geladen")
    logger.info(f"Lade Matching-Daten aus '{MATCHING_SHEET_NAME}'...")
    match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
    logger.info(f"{0 if match_df is None else len(match_df)} Matching-Datensätze geladen")
    if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
        logger.critical("Leere Daten in einem der Sheets. Abbruch.")
        return

    # --- SerpAPI-Fallback für leere Domains (nur MATCHING) ---
    if serp_key:
        empty_mask = match_df['CRM Website'].fillna('').astype(str).str.strip() == ''
        empty_count = int(empty_mask.sum())
        if empty_count > 0:
            logger.info(f"Serp-Fallback für Matching: {empty_count} Firmen ohne URL")
            found_cnt = 0
            for idx, row in match_df[empty_mask].iterrows():
                company = row['CRM Name']
                try:
                    url = serp_website_lookup(company)
                    if url and 'http' in url and 'k.A.' not in url:
                        match_df.at[idx, 'CRM Website'] = url
                        logger.info(f"  ✓ URL gefunden: '{company}' -> {url}")
                        found_cnt += 1
                    else:
                        logger.debug(f"  ✗ Keine eindeutige URL: '{company}' -> {url}")
                except Exception as e:
                    logger.warning(f"  ! Serp-Fehler für '{company}': {e}")
            logger.info(f"Serp-Fallback beendet: {found_cnt}/{empty_count} URLs ergänzt")
        else:
            logger.info("Serp-Fallback übersprungen: keine fehlenden Matching-URLs")

    # 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()}")

    # Blocking-Index erstellen
    crm_index = {}
    for _, 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
    results=[]
    total=len(match_df)
    logger.info("Starte Matching-Prozess...")
    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']}' -> {len(cands)} Kandidaten")
        if not cands:
            results.append({'Match':'','Score':0}); continue
        scored=[]
        for crow in cands:
            sc,dm,ns,lm,bf=calculate_similarity(mrow,crow)
            scored.append((crow['CRM Name'],sc,dm,ns,lm,bf))
        for name,sc,dm,ns,lm,bf in sorted(scored,key=lambda x:x[1],reverse=True)[:3]:
            logger.debug(f"  Kandidat: {name}, Score={sc}, Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}")
        best_name,best_score,dm,ns,lm,bf=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}' ({best_score}) [Dom={dm},Name={ns},Ort={lm},Bonus={bf}]")
        else:
            results.append({'Match':'','Score':best_score})
            logger.info(f" --> Kein Match (Score={best_score}) [Dom={dm},Name={ns},Ort={lm},Bonus={bf}]")

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

if __name__=='__main__':
    main()