import re
import logging
import pandas as pd
import recordlinkage
from rapidfuzz import fuzz
from google_sheet_handler import GoogleSheetHandler

# --- Konfiguration ---
CRM_SHEET_NAME      = "CRM_Accounts"
MATCHING_SHEET_NAME = "Matching_Accounts"
SCORE_THRESHOLD     = 0.8
WEIGHTS = {
    'domain': 0.5,
    'name':   0.4,
    'city':   0.1,
}

# --- Logging Setup ---
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)-8s - %(message)s'
)
logger = logging.getLogger(__name__)

# --- Hilfsfunktionen ---
def normalize_company_name(name: str) -> str:
    s = str(name).casefold()
    for src, dst in [('ä','ae'), ('ö','oe'), ('ü','ue'), ('ß','ss')]:
        s = s.replace(src, dst)
    s = re.sub(r'[^a-z0-9\s]', ' ', s)
    stops = ['gmbh','ag','kg','ug','ohg','holding','group','international']
    tokens = [t for t in s.split() if t and t not in stops]
    return ' '.join(tokens)


def normalize_domain(url: str) -> str:
    s = str(url).casefold().strip()
    s = re.sub(r'^https?://', '', s)
    s = s.split('/')[0]
    if s.startswith('www.'):
        s = s[4:]
    return s


def main():
    logger.info("Starte Duplikat-Check mit ausführlichem Logging...")
    sheet_handler = GoogleSheetHandler()
    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 or match_df is None or match_df.empty:
        logger.error("Leere Daten in CRM oder Matching Tab. Abbruch.")
        return

    # Normalisierung
    for df, name in [(crm_df, 'CRM'), (match_df, 'Matching')]:
        df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
        df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
        df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
        logger.debug(f"{name}-Daten nach Normalisierung. Erste Zeile: {df.iloc[0].to_dict()}")

    # Blocking per Domain
    indexer = recordlinkage.Index()
    indexer.block('norm_domain')
    candidate_pairs = indexer.index(crm_df, match_df)
    logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare gefunden")

    # Vergleichsregeln
    compare = recordlinkage.Compare()
    compare.exact('norm_domain', 'norm_domain', label='domain')
    compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
    compare.exact('city', 'city', label='city')
    features = compare.compute(candidate_pairs, crm_df, match_df)
    logger.debug(f"Feature-DataFrame Vorschau:\n{features.head()}" )

    # Score berechnen
    features['score'] = (
        WEIGHTS['domain'] * features['domain'] +
        WEIGHTS['name']   * features['name_sim'] +
        WEIGHTS['city']   * features['city']
    )
    logger.info("Scores berechnet")

    # Best Match pro neuer Zeile mit Logging der Kandidaten
    results = []
    crm_idx_col = []
    match_idx_col = []
    for match_idx, group in features.reset_index().groupby('level_1'):
        crm_idx_col.append(match_idx)
        # sortiere Kandidaten nach Score
        sorted_group = group.sort_values('score', ascending=False)
        logger.debug(f"Matching-Index {match_idx}: untersuchte Kandidaten:\n{sorted_group[['level_0','score','domain','name_sim','city']]}" )
        top = sorted_group.iloc[0]
        if top['score'] >= SCORE_THRESHOLD:
            results.append((top['level_0'], match_idx, top['score']))
            logger.info(f"Zeile {match_idx}: Match mit CRM-Index {top['level_0']} Score {top['score']:.2f}")
        else:
            results.append((None, match_idx, top['score']))
            logger.info(f"Zeile {match_idx}: Kein ausreichender Score (top {top['score']:.2f})")

    # Ausgabe DataFrame
    crm_df = crm_df.reset_index()
    match_df = match_df.reset_index()
    output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
    output['Matched CRM Name'] = ''
    output['Matched CRM Website'] = ''
    output['Matched CRM Ort'] = ''
    output['Matched CRM Land'] = ''
    output['Score'] = 0.0

    for crm_idx, match_idx, score in results:
        if crm_idx is not None:
            row_crm = crm_df.loc[crm_df['index'] == crm_idx].iloc[0]
            output.at[match_idx, 'Matched CRM Name'] = row_crm['CRM Name']
            output.at[match_idx, 'Matched CRM Website'] = row_crm['CRM Website']
            output.at[match_idx, 'Matched CRM Ort'] = row_crm['CRM Ort']
            output.at[match_idx, 'Matched CRM Land'] = row_crm['CRM Land']
        output.at[match_idx, 'Score'] = round(score, 3)

    # Zurückschreiben ins Google Sheet
    data = [output.columns.tolist()] + output.values.tolist()
    success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
    if success:
        logger.info("Erfolgreich geschrieben ins Google Sheet")
    else:
        logger.error("Fehler beim Schreiben ins Google Sheet.")

if __name__ == '__main__':
    main()