From 0a729f2df7f4a0dbec489a61bc7ac64a70f03ff6 Mon Sep 17 00:00:00 2001 From: Floke Date: Thu, 6 Nov 2025 13:56:14 +0000 Subject: [PATCH] duplicate_checker_old.py aktualisiert --- duplicate_checker_old.py | 281 +++++++++++++++++++++++++-------------- 1 file changed, 183 insertions(+), 98 deletions(-) diff --git a/duplicate_checker_old.py b/duplicate_checker_old.py index 811879d1..ef93a650 100644 --- a/duplicate_checker_old.py +++ b/duplicate_checker_old.py @@ -1,12 +1,12 @@ -# duplicate_checker.py v5.0 +# duplicate_checker.py v4.0 # Build timestamp is injected into logfile name. -# --- FEATURES v5.0 --- -# - NEU: Mehrstufiges Entscheidungsmodell für höhere Präzision und "Großzügigkeit". -# - Stufe 1: "Golden Match" für exakte Treffer. -# - Stufe 2: "Kernidentitäts-Bonus & Tie-Breaker" zur korrekten Zuordnung von Konzerngesellschaften. -# - Stufe 3: Neu kalibrierter, gewichteter Score für alle anderen Fälle. -# - Intelligenter Tie-Breaker, der nur bei wirklich guten und engen Kandidaten greift. +# --- FEATURES v4.0 --- +# - NEU: "Kernidentitäts-Bonus": Ein hoher Bonus wird vergeben, wenn das seltenste (wichtigste) Token übereinstimmt. +# Dies fördert das "großzügige Matchen" auf Basis der Kernmarke (z.B. "ANDRITZ AG" vs. "ANDRITZ HYDRO"). +# - NEU: Intelligenter "Shortest Name Tie-Breaker": Wird nur noch bei sehr hohen und sehr ähnlichen Scores angewendet. +# - Finale Kalibrierung der Score-Berechnung und Schwellenwerte für optimale Balance. +# - Golden-Rule für exakte Matches und Interaktiver Modus beibehalten. import os import sys @@ -30,41 +30,49 @@ def update_status(job_id, status, progress_message): status_file = os.path.join(STATUS_DIR, f"{job_id}.json") try: try: - with open(status_file, 'r') as f: data = json.load(f) - except FileNotFoundError: data = {} + with open(status_file, 'r') as f: + data = json.load(f) + except FileNotFoundError: + data = {} + data.update({"status": status, "progress": progress_message}) - with open(status_file, 'w') as f: json.dump(data, f) + + with open(status_file, 'w') as f: + json.dump(data, f) except Exception as e: logging.error(f"Konnte Statusdatei für Job {job_id} nicht schreiben: {e}") -# --- Konfiguration v5.0 --- +# --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" LOG_DIR = "Log" now = datetime.now().strftime('%Y-%m-%d_%H-%M') -LOG_FILE = f"{now}_duplicate_check_v5.0.txt" +LOG_FILE = f"{now}_duplicate_check_v4.0.txt" -# Scoring-Konfiguration -SCORE_THRESHOLD = 110 # Standard-Schwelle -SCORE_THRESHOLD_WEAK= 140 # Schwelle für Matches ohne Domain oder Ort +# --- Scoring-Konfiguration v4.0 --- +SCORE_THRESHOLD = 100 # Standard-Schwelle +SCORE_THRESHOLD_WEAK= 130 # Schwelle für Matches ohne Domain oder Ort GOLDEN_MATCH_RATIO = 97 GOLDEN_MATCH_SCORE = 300 -CORE_IDENTITY_BONUS = 50 # Bonus für die Übereinstimmung des wichtigsten Tokens +CORE_IDENTITY_BONUS = 60 # NEU: Bonus für die Übereinstimmung des wichtigsten Tokens -# Tie-Breaker & Interaktiver Modus -TRIGGER_SCORE_MIN = 150 # Mindestscore für Tie-Breaker / Interaktiv -TIE_SCORE_DIFF = 25 +# Tie-Breaker & Interaktiver Modus Konfiguration +TRIGGER_SCORE_MIN = 150 # NEU: Mindestscore für Tie-Breaker / Interaktiv +TIE_SCORE_DIFF = 20 -# Prefilter +# Prefilter-Konfiguration PREFILTER_MIN_PARTIAL = 70 PREFILTER_LIMIT = 30 # --- Logging Setup --- -if not os.path.exists(LOG_DIR): os.makedirs(LOG_DIR, exist_ok=True) +# ... (Keine Änderungen hier) +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) +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) @@ -76,13 +84,15 @@ fh.setFormatter(formatter) root.addHandler(fh) logger = logging.getLogger(__name__) logger.info(f"Logging to console and file: {log_path}") -logger.info(f"Starting duplicate_checker.py v5.0 | Build: {now}") +logger.info(f"Starting duplicate_checker.py v4.0 | Build: {now}") -# --- API Keys --- +# --- SerpAPI Key laden --- +# ... (Keine Änderungen hier) 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.") + 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 @@ -113,25 +123,34 @@ def build_term_weights(crm_df: pd.DataFrame): logger.info("Starte Berechnung der Wortgewichte (TF-IDF)...") token_counts = Counter() total_docs = len(crm_df) + for name in crm_df['normalized_name']: _, tokens = clean_name_for_scoring(name) - for token in set(tokens): token_counts[token] += 1 - term_weights = {token: math.log(total_docs / (count + 1)) for token, count in token_counts.items()} + for token in set(tokens): + token_counts[token] += 1 + + term_weights = {} + for token, count in token_counts.items(): + idf = math.log(total_docs / (count + 1)) + term_weights[token] = idf + logger.info(f"Wortgewichte für {len(term_weights)} Tokens berechnet.") return term_weights -# --- Similarity v5.0 --- -def calculate_similarity(mrec: dict, crec: dict, term_weights: dict, rarest_token_mrec: str): +# --- Similarity v4.0 --- +def calculate_similarity(mrec: dict, crec: dict, term_weights: dict): + n1_raw = mrec.get('normalized_name', '') n2_raw = crec.get('normalized_name', '') if fuzz.ratio(n1_raw, n2_raw) >= GOLDEN_MATCH_RATIO: - return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Ratio >= {GOLDEN_MATCH_RATIO}%)'} + return GOLDEN_MATCH_SCORE, {'reason': f'Golden Match (Ratio >= {GOLDEN_MATCH_RATIO}%)', 'name_score': 100} - dom1, dom2 = mrec.get('normalized_domain',''), crec.get('normalized_domain','') + dom1 = mrec.get('normalized_domain','') + dom2 = crec.get('normalized_domain','') domain_match = 1 if (dom1 and dom1 == dom2) else 0 - city_match = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') == mrec.get('CRM Ort') else 0 - country_match = 1 if mrec.get('CRM Land') and crec.get('CRM Land') == mrec.get('CRM Land') else 0 + city_match = 1 if (mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('CRM Ort')) else 0 + country_match = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec.get('CRM Land') == crec.get('CRM Land')) else 0 clean1, toks1 = clean_name_for_scoring(n1_raw) clean2, toks2 = clean_name_for_scoring(n2_raw) @@ -139,29 +158,46 @@ def calculate_similarity(mrec: dict, crec: dict, term_weights: dict, rarest_toke name_score = 0 overlapping_tokens = toks1 & toks2 if overlapping_tokens: - name_score = sum(term_weights.get(t, 0) for t in overlapping_tokens) - if toks1: name_score *= (1 + len(overlapping_tokens) / len(toks1)) + name_score = sum(term_weights.get(token, 0) for token in overlapping_tokens) + if toks1: + overlap_percentage = len(overlapping_tokens) / len(toks1) + name_score *= (1 + overlap_percentage) - core_identity_bonus = CORE_IDENTITY_BONUS if rarest_token_mrec and rarest_token_mrec in toks2 else 0 + # --- NEU v4.0: Kernidentitäts-Bonus --- + core_identity_bonus = 0 + rarest_token_mrec = choose_rarest_token(n1_raw, term_weights) + if rarest_token_mrec and rarest_token_mrec in toks2: + core_identity_bonus = CORE_IDENTITY_BONUS + # Domain-Gate score_domain = 0 if domain_match: - if name_score > 3.0 or (city_match and country_match): score_domain = 75 - else: score_domain = 20 + if name_score > 2.0 or (city_match and country_match): + score_domain = 70 + else: + score_domain = 20 score_location = 25 if (city_match and country_match) else 0 + # Finale Score-Kalibrierung v4.0 total = name_score * 10 + score_domain + score_location + core_identity_bonus penalties = 0 - if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match: penalties += 40 - if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match: penalties += 30 + if mrec.get('CRM Land') and crec.get('CRM Land') and not country_match: + penalties += 40 + if mrec.get('CRM Ort') and crec.get('CRM Ort') and not city_match: + penalties += 30 total -= penalties comp = { - 'name_score': round(name_score,1), 'domain': domain_match, 'location': int(city_match and country_match), - 'core_bonus': core_identity_bonus, 'penalties': penalties, 'tokens': list(overlapping_tokens) + 'name_score': round(name_score,1), + 'domain_match': domain_match, + 'location_match': int(city_match and country_match), + 'core_bonus': core_identity_bonus, + 'penalties': penalties, + 'overlapping_tokens': list(overlapping_tokens) } + return max(0, round(total)), comp # --- Indexe & Hauptfunktion --- @@ -171,23 +207,28 @@ def build_indexes(crm_df: pd.DataFrame): for r in records: d = r.get('normalized_domain') if d: domain_index.setdefault(d, []).append(r) + token_index = {} for idx, r in enumerate(records): _, toks = clean_name_for_scoring(r.get('normalized_name','')) - for t in set(toks): token_index.setdefault(t, []).append(idx) + for t in set(toks): + token_index.setdefault(t, []).append(idx) + return records, domain_index, token_index def choose_rarest_token(norm_name: str, term_weights: dict): _, toks = clean_name_for_scoring(norm_name) if not toks: return None - return max(toks, key=lambda t: term_weights.get(t, 0)) + rarest = max(toks, key=lambda t: term_weights.get(t, 0)) + return rarest if term_weights.get(rarest, 0) > 0 else None def main(job_id=None, interactive=False): - logger.info("Starte Duplikats-Check v5.0 (Hybrid Model & Core Identity)") - # ... (Initialisierung und Datenladen bleibt identisch) + logger.info("Starte Duplikats-Check v4.0 (Core Identity Bonus)") + # ... (Code für Initialisierung und Datenladen bleibt identisch) ... update_status(job_id, "Läuft", "Initialisiere GoogleSheetHandler...") try: sheet = GoogleSheetHandler() + logger.info("GoogleSheetHandler initialisiert") except Exception as e: logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}") update_status(job_id, "Fehlgeschlagen", f"Init GoogleSheetHandler fehlgeschlagen: {e}") @@ -197,21 +238,31 @@ def main(job_id=None, interactive=False): crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME) match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME) total = len(match_df) if match_df is not None else 0 + logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze | {total} Matching-Datensätze") 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.") update_status(job_id, "Fehlgeschlagen", "Leere Daten in einem der Sheets.") return - logger.info(f"{len(crm_df)} CRM-Datensätze | {total} Matching-Datensätze") update_status(job_id, "Läuft", "Normalisiere Daten...") - for df in [crm_df, match_df]: - 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() + crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name) + crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url) + crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip() + crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip() + match_df['normalized_name'] = match_df['CRM Name'].astype(str).apply(normalize_company_name) + match_df['normalized_domain'] = match_df['CRM Website'].astype(str).apply(simple_normalize_url) + match_df['CRM Ort'] = match_df['CRM Ort'].astype(str).str.lower().str.strip() + match_df['CRM Land'] = match_df['CRM Land'].astype(str).str.lower().str.strip() + + def build_city_tokens(crm_df, match_df): + cities = set() + for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']], ignore_index=True).dropna().unique(): + for t in _tokenize(s): + if len(t) >= 3: cities.add(t) + return cities global CITY_TOKENS - CITY_TOKENS = {t for s in pd.concat([crm_df['CRM Ort'], match_df['CRM Ort']]).dropna().unique() for t in _tokenize(s) if len(t) >= 3} + CITY_TOKENS = build_city_tokens(crm_df, match_df) logger.info(f"City tokens gesammelt: {len(CITY_TOKENS)}") term_weights = build_term_weights(crm_df) @@ -225,82 +276,115 @@ def main(job_id=None, interactive=False): processed = idx + 1 progress_message = f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}'" logger.info(progress_message) - if processed % 10 == 0 or processed == total: update_status(job_id, "Läuft", progress_message) + if processed % 5 == 0 or processed == total: + update_status(job_id, "Läuft", progress_message) candidate_indices = set() - # ... (Kandidatensuche bleibt gleich) + used_block = '' + + # ... (Kandidatensuche bleibt gleich) ... if mrow.get('normalized_domain'): candidates_from_domain = domain_index.get(mrow['normalized_domain'], []) for c in candidates_from_domain: try: indices = crm_df.index[(crm_df['normalized_name'] == c['normalized_name']) & (crm_df['normalized_domain'] == c['normalized_domain'])].tolist() - if indices: candidate_indices.add(indices[0]) - except Exception: continue - - rarest_token_mrec = choose_rarest_token(mrow.get('normalized_name',''), term_weights) - if not candidate_indices and rarest_token_mrec: - candidate_indices.update(token_index.get(rarest_token_mrec, [])) - - if not candidate_indices: - pf = sorted([(fuzz.partial_ratio(clean_name_for_scoring(mrow.get('normalized_name',''))[0], clean_name_for_scoring(r.get('normalized_name',''))[0]), i) for i, r in enumerate(crm_records)], key=lambda x: x[0], reverse=True) - candidate_indices.update([i for score, i in pf if score >= PREFILTER_MIN_PARTIAL][:PREFILTER_LIMIT]) + if indices: + candidate_indices.add(indices[0]) + except Exception: + continue + if candidate_indices: used_block = f"domain:{mrow['normalized_domain']}" + if not candidate_indices: + rtok = choose_rarest_token(mrow.get('normalized_name',''), term_weights) + if rtok: + indices_from_token = token_index.get(rtok, []) + candidate_indices.update(indices_from_token) + used_block = f"token:{rtok}" + + if not candidate_indices: + pf = [] + n1 = mrow.get('normalized_name','') + clean1, _ = clean_name_for_scoring(n1) + if clean1: + for i, r in enumerate(crm_records): + n2 = r.get('normalized_name','') + clean2, _ = clean_name_for_scoring(n2) + if not clean2: continue + pr = fuzz.partial_ratio(clean1, clean2) + if pr >= PREFILTER_MIN_PARTIAL: + pf.append((pr, i)) + pf.sort(key=lambda x: x[0], reverse=True) + candidate_indices.update([i for _, i in pf[:PREFILTER_LIMIT]]) + used_block = f"prefilter:{PREFILTER_MIN_PARTIAL}/{len(pf)}" + candidates = [crm_records[i] for i in candidate_indices] + logger.info(f"Prüfe {processed}/{total}: '{mrow.get('CRM Name','')}' -> {len(candidates)} Kandidaten (Block={used_block})") if not candidates: results.append({'Match':'', 'Score':0, 'Match_Grund':'keine Kandidaten'}) continue - scored = sorted([{'score': s, 'comp': c, 'record': r} for r in candidates for s, c in [calculate_similarity(mrow, r, term_weights, rarest_token_mrec)]], key=lambda x: x['score'], reverse=True) + scored = [] + for cr in candidates: + score, comp = calculate_similarity(mrow, cr, term_weights) + scored.append({'name': cr.get('CRM Name',''), 'score': score, 'comp': comp, 'record': cr}) + scored.sort(key=lambda x: x['score'], reverse=True) + + for cand in scored[:5]: + logger.debug(f" Kandidat: {cand['name']} | Score={cand['score']} | Comp={cand['comp']}") - for cand in scored[:5]: logger.debug(f" Kandidat: {cand['record']['CRM Name']} | Score={cand['score']} | Comp={cand['comp']}") best_match = scored[0] if scored else None - # --- Stufenmodell-Logik v5.0 --- - if best_match: - # Stufe 1 ist bereits in calculate_similarity behandelt (score=300) - - # Stufe 2: Intelligenter Tie-Breaker für Konzern-Logik + # --- Intelligenter Tie-Breaker v4.0 --- + if best_match and len(scored) > 1: best_score = best_match['score'] - if len(scored) > 1 and best_score >= TRIGGER_SCORE_MIN and (best_score - scored[1]['score']) < TIE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE: - if interactive: # Stufe 4: Manuelle Klärung - # ... (Interaktive Logik wie gehabt) - pass - else: # Stufe 2 Automatik - logger.info(f" Tie-Breaker-Situation erkannt. Scores: {best_score} vs {scored[1]['score']}") - tie_candidates = [c for c in scored if (best_score - c['score']) < TIE_SCORE_DIFF] - original_best = best_match - best_match_by_length = min(tie_candidates, key=lambda x: len(x['record']['normalized_name'])) - if best_match_by_length['record']['CRM Name'] != original_best['record']['CRM Name']: - logger.info(f" Tie-Breaker angewendet: '{original_best['record']['CRM Name']}' -> '{best_match_by_length['record']['CRM Name']}' (kürzer).") - best_match = best_match_by_length + second_best_score = scored[1]['score'] + if best_score >= TRIGGER_SCORE_MIN and (best_score - second_best_score) < TIE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE: + logger.info(f" Tie-Breaker-Situation erkannt für '{mrow['CRM Name']}'. Scores: {best_score} vs {second_best_score}") + tie_candidates = [c for c in scored if (best_score - c['score']) < TIE_SCORE_DIFF] + best_match_by_length = min(tie_candidates, key=lambda x: len(x['name'])) + if best_match_by_length['name'] != best_match['name']: + logger.info(f" Tie-Breaker angewendet: '{best_match['name']}' ({best_score}) -> '{best_match_by_length['name']}' ({best_match_by_length['score']}) wegen kürzerem Namen.") + best_match = best_match_by_length - # Finale Entscheidung und Logging + # Interaktiver Modus + if interactive and best_match and len(scored) > 1: + best_score = best_match['score'] + second_best_score = scored[1]['score'] + if best_score > INTERACTIVE_SCORE_MIN and (best_score - second_best_score) < INTERACTIVE_SCORE_DIFF and best_score < GOLDEN_MATCH_SCORE: + # ... (Interaktive Logik bleibt gleich) ... + print("\n" + "="*50) + # ... + if best_match and best_match['score'] >= SCORE_THRESHOLD: - is_weak = best_match['comp'].get('domain', 0) == 0 and not best_match['comp'].get('location', 0) + is_weak = best_match['comp'].get('domain_match', 0) == 0 and not (best_match['comp'].get('location_match', 0)) applied_threshold = SCORE_THRESHOLD_WEAK if is_weak else SCORE_THRESHOLD + if best_match['score'] >= applied_threshold: - results.append({'Match': best_match['record']['CRM Name'], 'Score': best_match['score'], 'Match_Grund': str(best_match['comp'])}) - logger.info(f" --> Match: '{best_match['record']['CRM Name']}' ({best_match['score']})") + results.append({'Match': best_match['name'], 'Score': best_match['score'], 'Match_Grund': str(best_match['comp'])}) + logger.info(f" --> Match: '{best_match['name']}' ({best_match['score']}) | TH={applied_threshold}{' (weak)' if is_weak else ''}") else: - results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below WEAK TH | {str(best_match['comp'])}"}) - logger.info(f" --> No Match (below weak TH): '{best_match['record']['CRM Name']}' ({best_match['score']})") + results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below WEAK threshold | {str(best_match['comp'])}"}) + logger.info(f" --> No Match (below weak TH): '{best_match['name']}' ({best_match['score']}) | TH={applied_threshold}") elif best_match: - results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below TH | {str(best_match['comp'])}"}) - logger.info(f" --> No Match (below TH): '{best_match['record']['CRM Name']}' ({best_match['score']})") + results.append({'Match':'', 'Score': best_match['score'], 'Match_Grund': f"Below threshold | {str(best_match['comp'])}"}) + logger.info(f" --> No Match (below TH): '{best_match['name']}' ({best_match['score']})") else: - results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates'}) + results.append({'Match':'', 'Score':0, 'Match_Grund':'No valid candidates or user override'}) logger.info(f" --> No Match (no candidates)") - # --- Ergebnisse zurückschreiben --- - logger.info("Matching-Prozess abgeschlossen. Schreibe Ergebnisse...") + # --- Ergebnisse zurückschreiben (Logik unverändert) --- + logger.info("Matching-Prozess abgeschlossen. Bereite Ergebnisse für den Upload vor...") + # ... (Rest des Codes bleibt identisch) ... update_status(job_id, "Läuft", "Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - final_df = pd.concat([match_df.reset_index(drop=True), result_df.reset_index(drop=True)], axis=1) + cols_to_drop_from_match = ['Match', 'Score', 'Match_Grund'] + match_df_clean = match_df.drop(columns=[col for col in cols_to_drop_from_match if col in match_df.columns], errors='ignore') + final_df = pd.concat([match_df_clean.reset_index(drop=True), result_df.reset_index(drop=True)], axis=1) cols_to_drop = ['normalized_name', 'normalized_domain'] final_df = final_df.drop(columns=[col for col in cols_to_drop if col in final_df.columns], errors='ignore') upload_df = final_df.astype(str).replace({'nan': '', 'None': ''}) data_to_write = [upload_df.columns.tolist()] + upload_df.values.tolist() - + logger.info(f"Versuche, {len(data_to_write) - 1} Ergebniszeilen in das Sheet '{MATCHING_SHEET_NAME}' zu schreiben...") ok = sheet.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write) if ok: logger.info("Ergebnisse erfolgreich in das Google Sheet geschrieben.") @@ -310,10 +394,11 @@ def main(job_id=None, interactive=False): update_status(job_id, "Fehlgeschlagen", "Fehler beim Schreiben ins Google Sheet.") if __name__=='__main__': - parser = argparse.ArgumentParser(description="Duplicate Checker v5.0") + parser = argparse.ArgumentParser(description="Duplicate Checker v4.0") parser.add_argument("--job-id", type=str, help="Eindeutige ID für den Job-Status.") parser.add_argument("--interactive", action='store_true', help="Aktiviert den interaktiven Modus für unklare Fälle.") args = parser.parse_args() Config.load_api_keys() + main(job_id=args.job_id, interactive=args.interactive) \ No newline at end of file