chat GPT version
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@@ -1,173 +1,117 @@
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# duplicate_checker.py (v2.0 + Transparenz)
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import logging
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import re
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import pandas as pd
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from thefuzz import fuzz
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from config import Config
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from helpers import normalize_company_name, simple_normalize_url, create_log_filename
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import recordlinkage
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from rapidfuzz import fuzz
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from google_sheet_handler import GoogleSheetHandler
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import time
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# --- Konfiguration ---
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CRM_SHEET_NAME = "CRM_Accounts"
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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SCORE_THRESHOLD = 80
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SCORE_THRESHOLD = 0.8
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WEIGHTS = {
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'domain': 0.5,
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'name': 0.4,
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'city': 0.1,
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}
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# --- VOLLSTÄNDIGES LOGGING SETUP ---
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LOG_LEVEL = logging.DEBUG if Config.DEBUG else logging.INFO
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LOG_FORMAT = '%(asctime)s - %(levelname)-8s - %(name)s - %(message)s'
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root_logger = logging.getLogger()
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root_logger.setLevel(LOG_LEVEL)
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# Handler nur hinzufügen, wenn noch keine konfiguriert sind, um Dopplung zu vermeiden
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if not root_logger.handlers:
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stream_handler = logging.StreamHandler()
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stream_handler.setFormatter(logging.Formatter(LOG_FORMAT))
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root_logger.addHandler(stream_handler)
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log_file_path = create_log_filename("duplicate_check_v2_final")
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if log_file_path:
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file_handler = logging.FileHandler(log_file_path, mode='a', encoding='utf-8')
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file_handler.setFormatter(logging.Formatter(LOG_FORMAT))
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root_logger.addHandler(file_handler)
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else:
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log_file_path = next((h.baseFilename for h in root_logger.handlers if isinstance(h, logging.FileHandler)), None)
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logger = logging.getLogger(__name__)
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def calculate_similarity_with_details(record1, record2):
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# --- Hilfsfunktionen ---
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def normalize_company_name(name: str) -> str:
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"""
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Berechnet einen gewichteten Ähnlichkeits-Score und gibt den Score und den Grund zurück.
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Basierend auf der v2.0 Scoring-Logik.
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Vereinfachte Normalisierung von Firmennamen:
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- Unicode‑safe Kleinschreibung
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- Umlaute in ae/oe/ue, ß in ss
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- Entfernen von Rechtsformen und Stop-Wörtern
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"""
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scores = {'name': 0, 'location': 0, 'domain': 0}
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domain1 = record1.get('normalized_domain')
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domain2 = record2.get('normalized_domain')
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if domain1 and domain1 != 'k.a.' and domain1 == domain2:
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scores['domain'] = 100
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name1 = record1.get('normalized_name')
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name2 = record2.get('normalized_name')
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if name1 and name2:
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name_similarity = fuzz.token_set_ratio(name1, name2)
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scores['name'] = round(name_similarity * 0.7)
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s = str(name).casefold()
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for src, dst in [('ä','ae'), ('ö','oe'), ('ü','ue'), ('ß','ss')]:
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s = s.replace(src, dst)
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# Nur alphanumerisch und Leerzeichen
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s = re.sub(r'[^a-z0-9\s]', ' ', s)
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stops = ['gmbh','ag','kg','ug','ohg','holding','group','international']
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tokens = [t for t in s.split() if t and t not in stops]
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return ' '.join(tokens)
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ort1 = record1.get('CRM Ort')
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ort2 = record2.get('CRM Ort')
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land1 = record1.get('CRM Land')
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land2 = record2.get('CRM Land')
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if ort1 and ort1 == ort2 and land1 and land1 == land2:
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scores['location'] = 20
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total_score = sum(scores.values())
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reasons = []
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if scores['domain'] > 0: reasons.append(f"Domain({scores['domain']})")
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if scores['name'] > 0: reasons.append(f"Name({scores['name']})")
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if scores['location'] > 0: reasons.append(f"Ort({scores['location']})")
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reason_text = " + ".join(reasons) if reasons else "Keine Übereinstimmung"
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return round(total_score), reason_text
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def normalize_domain(url: str) -> str:
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"""Root-Domain extrahieren, Protokoll und www entfernen"""
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s = str(url).casefold().strip()
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s = re.sub(r'^https?://', '', s)
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s = s.split('/')[0]
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return s.removeprefix('www.')
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def main():
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"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
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start_time = time.time()
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logger.info("Starte den Duplikats-Check (v2.0 mit Blocking und Maximum Logging)...")
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logger.info(f"Logdatei: {log_file_path}")
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try:
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sheet_handler = GoogleSheetHandler()
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except Exception as e:
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logger.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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# Google Sheets laden
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sheet_handler = GoogleSheetHandler()
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
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print("Fehler: Leere Daten in einem der Tabs. Abbruch.")
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return
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logger.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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if crm_df is None or crm_df.empty: return
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# Normalisierung
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for df in (crm_df, match_df):
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df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
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df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
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df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
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logger.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if matching_df is None or matching_df.empty: return
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original_matching_df = matching_df.copy()
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# Blocking per Domain
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indexer = recordlinkage.Index()
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indexer.block('norm_domain')
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candidate_pairs = indexer.index(crm_df, match_df)
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logger.info("Normalisiere Daten für den Vergleich...")
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for df in [crm_df, matching_df]:
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df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x and x.split() else None)
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# Vergleichsregeln definieren
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compare = recordlinkage.Compare()
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compare.exact('norm_domain', 'norm_domain', label='domain')
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compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
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compare.exact('city', 'city', label='city')
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features = compare.compute(candidate_pairs, crm_df, match_df)
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logger.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
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crm_index = {}
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crm_records = crm_df.to_dict('records')
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for record in crm_records:
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key = record['block_key']
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if key:
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if key not in crm_index:
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crm_index[key] = []
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crm_index[key].append(record)
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# Gewichte und Score
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features['score'] = (
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WEIGHTS['domain'] * features['domain'] +
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WEIGHTS['name'] * features['name_sim'] +
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WEIGHTS['city'] * features['city']
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)
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logger.info("Starte Matching-Prozess...")
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results = []
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for match_record in matching_df.to_dict('records'):
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best_score = -1
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best_match_name = ""
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best_reason = ""
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logger.info(f"--- Prüfe: '{match_record.get('CRM Name', 'N/A')}' ---")
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logger.debug(f" [Normalisiert: '{match_record.get('normalized_name')}', Domain: '{match_record.get('normalized_domain')}', Key: '{match_record.get('block_key')}']")
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# Bestes Match pro neuer Zeile
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matches = features.reset_index()
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best = matches.sort_values(['level_1','score'], ascending=[True, False]) \
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.drop_duplicates('level_1')
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best = best[best['score'] >= SCORE_THRESHOLD] \
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.rename(columns={'level_0':'crm_idx','level_1':'match_idx'})
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block_key = match_record.get('block_key')
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candidates = crm_index.get(block_key, [])
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if not candidates:
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logger.debug(" -> Keine Kandidaten im Index gefunden. Überspringe Vergleich.")
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results.append({
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'Potenzieller Treffer im CRM': "", 'Ähnlichkeits-Score': 0, 'Matching-Grund': "Keine Kandidaten"
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})
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continue
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# Merges
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crm_df = crm_df.reset_index()
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match_df = match_df.reset_index()
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merged = (best
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.merge(crm_df, left_on='crm_idx', right_on='index')
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.merge(match_df, left_on='match_idx', right_on='index', suffixes=('_CRM','_NEW'))
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)
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logger.debug(f" -> Vergleiche mit {len(candidates)} Kandidaten aus Block '{block_key}'.")
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for crm_row in candidates:
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score, reason = calculate_similarity_with_details(match_record, crm_row)
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if score > 0:
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logger.debug(f" - Kandidat: '{crm_row.get('CRM Name', 'N/A')}' -> Score: {score} (Grund: {reason})")
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if score > best_score:
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best_score = score
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best_match_name = crm_row.get('CRM Name', 'N/A')
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best_reason = reason
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logger.info(f" --> Bester Treffer: '{best_match_name}' mit Score {best_score} (Grund: {best_reason})")
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results.append({
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'Potenzieller Treffer im CRM': best_match_name if best_score >= SCORE_THRESHOLD else "",
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'Ähnlichkeits-Score': best_score,
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'Matching-Grund': best_reason
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})
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# Ausgabe aufbauen
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output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
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output['Matched CRM Name'] = ''
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output['Matched CRM Website'] = ''
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output['Matched CRM Ort'] = ''
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output['Matched CRM Land'] = ''
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output['Score'] = 0.0
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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result_df = pd.DataFrame(results)
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output_df = pd.concat([original_matching_df.reset_index(drop=True), result_df], axis=1)
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data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
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for _, row in merged.iterrows():
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i = int(row['match_idx'])
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output.at[i, 'Matched CRM Name'] = row['CRM Name_CRM']
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output.at[i, 'Matched CRM Website'] = row['CRM Website_CRM']
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output.at[i, 'Matched CRM Ort'] = row['CRM Ort_CRM']
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output.at[i, 'Matched CRM Land'] = row['CRM Land_CRM']
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output.at[i, 'Score'] = row['score']
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# Zurückschreiben ins Google Sheet
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data = [output.columns.tolist()] + output.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
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if success:
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logger.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
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print(f"Erfolgreich: {len(best)} Matches mit Score ≥ {SCORE_THRESHOLD}")
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else:
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logger.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
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print("Fehler beim Schreiben ins Google Sheet.")
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end_time = time.time()
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logger.info(f"Gesamtdauer des Duplikats-Checks: {end_time - start_time:.2f} Sekunden.")
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logger.info(f"===== Skript beendet =====")
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if __name__ == "__main__":
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main()
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if __name__ == '__main__':
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main()
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