import os 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, } LOG_DIR = '/log' LOG_FILENAME = 'duplicate_check.log' # --- Logging Setup --- if not os.path.exists(LOG_DIR): try: os.makedirs(LOG_DIR) except Exception as e: print(f"Warnung: Konnte Log-Ordner nicht anlegen: {e}") log_path = os.path.join(LOG_DIR, LOG_FILENAME) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(levelname)-8s - %(name)s - %(message)s') # Console handler console_handler = logging.StreamHandler() console_handler.setLevel(logging.INFO) console_handler.setFormatter(formatter) logger.addHandler(console_handler) # File handler try: file_handler = logging.FileHandler(log_path, mode='a', encoding='utf-8') file_handler.setLevel(logging.DEBUG) file_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.info(f"Logging auch in Datei: {log_path}") except Exception as e: logger.warning(f"Konnte keine Log-Datei schreiben: {e}") # --- 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 den Duplikats-Check (v2.0 mit Kandidaten-Logging)...") try: sheet_handler = GoogleSheetHandler() logger.info("GoogleSheetHandler initialisiert") except Exception as e: logger.critical(f"FEHLER bei Initialisierung des 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 or match_df is None or match_df.empty: logger.critical("CRM- oder Matching-Daten leer. Abbruch.") 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['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"{label}-Daten normalisiert: Beispiel: {df.iloc[0][['norm_name','norm_domain','city']].to_dict()}") # Blocking indexer = recordlinkage.Index() indexer.block('norm_domain') candidate_pairs = indexer.index(crm_df, match_df) logger.info(f"Blocking abgeschlossen: {len(candidate_pairs)} Kandidatenpaare") # Compare 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"Features berechnet: {features.head()}\n...") # Score features['score'] = (WEIGHTS['domain']*features['domain'] + WEIGHTS['name']*features['name_sim'] + WEIGHTS['city']*features['city']) logger.info("Scores berechnet") # Per Match Logging results = [] crm_df_idx = crm_df.reset_index() for match_idx, group in features.reset_index().groupby('level_1'): logger.info(f"--- Prüfe Matching-Zeile {match_idx} ---") df_block = group.sort_values('score', ascending=False).copy() # Enrich with CRM fields df_block['CRM Name'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Name']) df_block['CRM Website'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Website']) df_block['CRM Ort'] = df_block['level_0'].map(crm_df_idx.set_index('index')['CRM Ort']) # Log top candidates logger.debug("Kandidaten (CRM_Index, Score, Domain, Name_sim, City, CRM Name):") for _, row in df_block.iterrows(): logger.debug(f" [{int(row['level_0'])}] score={row['score']:.3f} dom={row['domain']} name_sim={row['name_sim']:.3f} city={row['city']} => {row['CRM Name']}") top = df_block.iloc[0] crm_idx = top['level_0'] if top['score'] >= SCORE_THRESHOLD else None if crm_idx is not None: logger.info(f" --> Match: CRM-Index {int(crm_idx)} ({top['CRM Name']}) mit Score {top['score']:.2f}") else: logger.info(f" --> Kein Match (höchster Score {top['score']:.2f})") results.append((crm_idx, match_idx, top['score'])) # Prepare output match_df_idx = match_df.reset_index() output = match_df_idx[['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: crm_row = crm_df_idx[crm_df_idx['index']==crm_idx].iloc[0] output.at[match_idx, 'Matched CRM Name'] = crm_row['CRM Name'] output.at[match_idx, 'Matched CRM Website'] = crm_row['CRM Website'] output.at[match_idx, 'Matched CRM Ort'] = crm_row['CRM Ort'] output.at[match_idx, 'Matched CRM Land'] = crm_row['CRM Land'] output.at[match_idx, 'Score'] = round(score,3) # Write back data = [output.columns.tolist()] + output.values.tolist() success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data) if success: logger.info(f"Erfolgreich geschrieben: {len([r for r in results if r[0] is not None])} Matches") else: logger.error("Fehler beim Schreiben ins Google Sheet.") if __name__ == '__main__': main()