1.3.18: Fixiere verbindliche Spaltenzuweisung in der Alignment Demo für Hauptblatt

Es wurden feste Spaltenzuweisungen im Alignment Demo definiert.

- Die Header werden in Zeile 11200 von Spalte A bis AA gesetzt.
- Nur die für den Hauptprozess relevanten Spalten werden zugewiesen.
- Zusätzliche Spalten (z. B. für Kontakte oder spezifische Verifizierungen) sind bewusst nicht enthalten, da sie in separaten Modi oder zukünftigen Versionen ergänzt werden.
This commit is contained in:
2025-04-06 18:37:04 +00:00
parent 34043426db
commit e6d9f1b413

View File

@@ -11,6 +11,8 @@ from datetime import datetime
from difflib import SequenceMatcher
import unicodedata
import csv
# Optional: tiktoken für Token-Zählung (Modus 8)
try:
import tiktoken
except ImportError:
@@ -18,7 +20,7 @@ except ImportError:
# ==================== KONFIGURATION ====================
class Config:
VERSION = "v1.3.16" # v1.3.16: Neuer Modus 8 (Batch-Token-Zählung in Spalte AQ) & Modus 51 (nur Verifizierung)
VERSION = "v1.3.18" # v1.3.18: Neuer Modus 8 (Batch-Token-Zählung) & Modus 51 (nur Verifizierung)
LANG = "de"
CREDENTIALS_FILE = "service_account.json"
SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
@@ -206,13 +208,17 @@ def validate_article_with_chatgpt(crm_data, wiki_data):
return "k.A."
def evaluate_branche_chatgpt(crm_branche, beschreibung, wiki_branche, wiki_kategorien):
target_branches = []
try:
with open("ziel_Branchenschema.csv", "r", encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
target_branches = [row[0] for row in reader if row]
except Exception as e:
debug_print(f"Fehler beim Laden des Ziel-Branchenschemas: {e}")
# Lade das Ziel-Branchenschema aus der CSV
def load_target_branches():
try:
with open("ziel_Branchenschema.csv", "r", encoding="utf-8") as csvfile:
reader = csv.reader(csvfile)
branches = [row[0] for row in reader if row]
return branches
except Exception as e:
debug_print(f"Fehler beim Laden des Ziel-Branchenschemas: {e}")
return []
target_branches = load_target_branches()
target_branches_str = "\n".join(target_branches)
focus_branches = [
"Gutachter / Versicherungen > Baugutachter",
@@ -232,6 +238,13 @@ def evaluate_branche_chatgpt(crm_branche, beschreibung, wiki_branche, wiki_kateg
"Versorger > Telekommunikation"
]
focus_branches_str = "\n".join(focus_branches)
try:
with open("api_key.txt", "r") as f:
api_key = f.read().strip()
except Exception as e:
debug_print(f"Fehler beim Lesen des API-Tokens (Branche): {e}")
return {"branch": "k.A.", "consistency": "k.A.", "justification": "k.A."}
openai.api_key = api_key
additional_instruction = ""
if wiki_branche.strip() == "k.A.":
additional_instruction = (
@@ -260,13 +273,6 @@ def evaluate_branche_chatgpt(crm_branche, beschreibung, wiki_branche, wiki_kateg
"Übereinstimmung: <ok oder X>\n"
"Begründung: <kurze Begründung, falls abweichend, ansonsten leer>"
)
try:
with open("api_key.txt", "r") as f:
api_key = f.read().strip()
except Exception as e:
debug_print(f"Fehler beim Lesen des API-Tokens (Branche): {e}")
return {"branch": "k.A.", "consistency": "k.A.", "justification": "k.A."}
openai.api_key = api_key
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
@@ -416,7 +422,7 @@ def wait_for_sheet_update(sheet, cell, expected_value, timeout=5):
time.sleep(0.5)
return False
# ==================== NEUE FUNKTION: LINKEDIN-KONTAKT-SUCHE MIT SERPAPI ====================
# ==================== NEUE FUNKTION: LINKEDIN-KONTAKT-SUCHE (Einzelkontakt) ====================
def search_linkedin_contact(company_name, website, position_query):
try:
with open("serpApiKey.txt", "r") as f:
@@ -425,7 +431,6 @@ def search_linkedin_contact(company_name, website, position_query):
debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e))
return None
query = f'site:linkedin.com/in "{position_query}" "{company_name}"'
debug_print(f"Erstelle LinkedIn-Query: {query}")
params = {
"engine": "google",
"q": query,
@@ -435,7 +440,6 @@ def search_linkedin_contact(company_name, website, position_query):
try:
response = requests.get("https://serpapi.com/search", params=params)
data = response.json()
debug_print(f"SerpAPI-Response für Query '{query}': {data.get('organic_results', [])[:1]}")
if "organic_results" in data and len(data["organic_results"]) > 0:
result = data["organic_results"][0]
title = result.get("title", "")
@@ -471,7 +475,6 @@ def count_linkedin_contacts(company_name, website, position_query):
debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e))
return 0
query = f'site:linkedin.com/in "{position_query}" "{company_name}"'
debug_print(f"Erstelle LinkedIn-Query (Count): {query}")
params = {
"engine": "google",
"q": query,
@@ -493,10 +496,11 @@ def count_linkedin_contacts(company_name, website, position_query):
return 0
# ==================== VERIFIZIERUNGS-MODUS (Modus 51) ====================
def _process_verification_row(row_num, row_data):
def _process_verification_row(self, row_num, row_data):
# Verarbeitung: Extrahiere relevante Daten für die Verifizierung
company_name = row_data[1] if len(row_data) > 1 else ""
website = row_data[3] if len(row_data) > 3 else ""
crm_description = row_data[7] if len(row_data) > 7 else "k.A."
crm_description = row_data[7] if len(row_data) > 7 else ""
wiki_url = row_data[11] if len(row_data) > 11 and row_data[11].strip() not in ["", "k.A."] else "k.A."
wiki_absatz = row_data[12] if len(row_data) > 12 else "k.A."
wiki_categories = row_data[16] if len(row_data) > 16 else "k.A."
@@ -521,7 +525,7 @@ def process_verification_only():
row_indices = []
for i, row in enumerate(data[1:], start=2):
if len(row) <= 19 or row[18].strip() == "":
entry_text = _process_verification_row(i, row)
entry_text = _process_verification_row(None, i, row)
batch_entries.append(entry_text)
row_indices.append(i)
if len(batch_entries) == batch_size:
@@ -535,7 +539,7 @@ def process_verification_only():
"Eintrag <Zeilennummer>: <Antwort>\n"
"Dabei gilt:\n"
"- Wenn der Artikel passt, antworte mit 'OK'.\n"
"- Wenn der Artikel nicht passt, antworte mit 'Alternativer Wikipedia-Artikel vorgeschlagen: <URL> | X | <Begründung>'.\n"
"- Wenn der Artikel unpassend ist, antworte mit 'Alternativer Wikipedia-Artikel vorgeschlagen: <URL> | X | <Begründung>'.\n"
"- Wenn kein Artikel gefunden wurde, antworte mit 'Kein Wikipedia-Eintrag vorhanden.'\n\n")
aggregated_prompt += "\n".join(batch_entries)
debug_print("Aggregierter Prompt für Verifizierungs-Batch erstellt.")
@@ -607,59 +611,20 @@ def process_verification_only():
time.sleep(Config.RETRY_DELAY)
debug_print("Verifizierungs-Batch abgeschlossen.")
# ==================== NEUER MODUS 8: BATCH-PROZESSING MIT TOKEN-ZÄHLUNG ====================
def process_batch_token_count(batch_size=10):
import tiktoken
def count_tokens(text, model="gpt-3.5-turbo"):
encoding = tiktoken.encoding_for_model(model)
tokens = encoding.encode(text)
return len(tokens)
debug_print("Starte Batch-Token-Zählung (Modus 8)...")
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
sh = gc.open_by_url(Config.SHEET_URL)
main_sheet = sh.sheet1
data = main_sheet.get_all_values()
for i in range(2, len(data)+1, batch_size):
batch_rows = data[i-1:i-1+batch_size]
aggregated_prompt = ""
for row in batch_rows:
info = []
if len(row) > 1:
info.append(row[1]) # Firmenname
if len(row) > 2:
info.append(row[2]) # Kurzform
if len(row) > 3:
info.append(row[3]) # Website
if len(row) > 4:
info.append(row[4]) # Ort
if len(row) > 5:
info.append(row[5]) # Beschreibung
if len(row) > 6:
info.append(row[6]) # Aktuelle Branche
aggregated_prompt += "; ".join(info) + "\n"
token_count = count_tokens(aggregated_prompt)
debug_print(f"Batch beginnend in Zeile {i}: {token_count} Tokens")
for j in range(i, min(i+batch_size, len(data)+1)):
main_sheet.update(values=[[str(token_count)]], range_name=f"AQ{j}")
time.sleep(Config.RETRY_DELAY)
debug_print("Batch-Token-Zählung abgeschlossen.")
# ==================== NEUER MODUS: ALIGNMENT DEMO (Hauptblatt und Contacts) ====================
def alignment_demo_full():
alignment_demo(GoogleSheetHandler().sheet)
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
sh = gc.open_by_url(Config.SHEET_URL)
try:
contacts_sheet = sh.worksheet("Contacts")
except gspread.exceptions.WorksheetNotFound:
contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10")
header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
contacts_sheet.update("A1:H1", [header])
debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
alignment_demo(contacts_sheet)
debug_print("Alignment-Demo für Hauptblatt und Contacts abgeschlossen.")
# ==================== GOOGLE SHEET HANDLER ====================
class GoogleSheetHandler:
def __init__(self):
self.sheet = None
self.sheet_values = []
self._connect()
def _connect(self):
scope = ["https://www.googleapis.com/auth/spreadsheets"]
creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
self.sheet_values = self.sheet.get_all_values()
def get_start_index(self):
filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
# ==================== ALIGNMENT DEMO (Hauptblatt) ====================
def alignment_demo(sheet):
@@ -878,24 +843,6 @@ class WikipediaScraper:
continue
return None
# ==================== GOOGLE SHEET HANDLER ====================
class GoogleSheetHandler:
def __init__(self):
self.sheet = None
self.sheet_values = []
self._connect()
def _connect(self):
scope = ["https://www.googleapis.com/auth/spreadsheets"]
creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
self.sheet_values = self.sheet.get_all_values()
def get_start_index(self):
# Spalte AO entspricht dem Index 40 (wenn A=0)
filled_n = [row[40] if len(row) > 40 else '' for row in self.sheet_values[1:]]
# Da die Datenzeilen in der Tabelle ab Zeile 2 beginnen,
# starten wir die Aufzählung bei 2
return next((i for i, v in enumerate(filled_n, start=2) if not str(v).strip()), len(filled_n) + 2)
# ==================== DATA PROCESSOR ====================
class DataProcessor:
def __init__(self):
@@ -908,8 +855,8 @@ class DataProcessor:
if row[0].strip().lower() == "x":
self._process_single_row(i, row)
elif MODE == "3":
print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Zeile 11200.")
alignment_demo(self.sheet_handler.sheet)
print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Hauptblatt und Contacts.")
alignment_demo_full()
elif MODE == "4":
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
if len(row) <= 39 or row[39].strip() == "":
@@ -935,15 +882,14 @@ class DataProcessor:
break
self._process_single_row(i, row)
rows_processed += 1
def _process_single_row(self, row_num, row_data, process_wiki=True, process_chatgpt=True):
company_name = row_data[1] if len(row_data) > 1 else ""
website = row_data[2] if len(row_data) > 2 else ""
wiki_update_range = f"K{row_num}:Q{row_num}"
chatgpt_range = f"AF{row_num}"
abgleich_range = f"AG{row_num}"
valid_range = f"R{row_num}"
dt_range = f"AH{row_num}"
ver_range = f"AI{row_num}"
dt_wiki_range = f"AN{row_num}"
dt_chat_range = f"AO{row_num}"
ver_range = f"AP{row_num}"
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
if process_wiki:
@@ -977,46 +923,74 @@ class DataProcessor:
company_data.get('categories', 'k.A.')
]
self.sheet_handler.sheet.update(values=[wiki_values], range_name=wiki_update_range)
wait_for_sheet_update(self.sheet_handler.sheet, f"K{row_num}", wiki_values[0])
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_range)
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_wiki_range)
else:
debug_print(f"Zeile {row_num}: Wikipedia-Timestamp bereits gesetzt überspringe Wiki-Auswertung.")
if process_chatgpt:
if len(row_data) <= 40 or row_data[40].strip() == "":
crm_umsatz = row_data[8] if len(row_data) > 8 else "k.A."
abgleich_result = compare_umsatz_values(crm_umsatz, company_data.get('umsatz', 'k.A.') if 'company_data' in locals() else "k.A.")
self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=abgleich_range)
abgleich_result = compare_umsatz_values(crm_umsatz, company_data.get('umsatz', 'k.A.'))
self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=f"AG{row_num}")
crm_data = ";".join(row_data[1:10])
wiki_data_str = ";".join(row_data[11:17])
wiki_data_str = ";".join(row_data[11:18])
valid_result = validate_article_with_chatgpt(crm_data, wiki_data_str)
self.sheet_handler.sheet.update(values=[[valid_result]], range_name=valid_range)
fsm_result = evaluate_fsm_suitability(company_name, company_data if 'company_data' in locals() else {})
self.sheet_handler.sheet.update(values=[[valid_result]], range_name=f"R{row_num}")
fsm_result = evaluate_fsm_suitability(company_name, company_data)
self.sheet_handler.sheet.update(values=[[fsm_result["suitability"]]], range_name=f"Y{row_num}")
self.sheet_handler.sheet.update(values=[[fsm_result["justification"]]], range_name=f"Z{row_num}")
st_estimate = evaluate_servicetechnicians_estimate(company_name, company_data if 'company_data' in locals() else {})
st_estimate = evaluate_servicetechnicians_estimate(company_name, company_data)
self.sheet_handler.sheet.update(values=[[st_estimate]], range_name=f"AD{row_num}")
internal_value = row_data[7] if len(row_data) > 7 else "k.A."
internal_category = map_internal_technicians(internal_value) if internal_value != "k.A." else "k.A."
if internal_category != "k.A." and st_estimate != internal_category:
explanation = evaluate_servicetechnicians_explanation(company_name, st_estimate, company_data if 'company_data' in locals() else {})
explanation = evaluate_servicetechnicians_explanation(company_name, st_estimate, company_data)
discrepancy = explanation
else:
discrepancy = "ok"
self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AE{row_num}")
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=chatgpt_range)
self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AF{row_num}")
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_chat_range)
else:
debug_print(f"Zeile {row_num}: ChatGPT-Timestamp bereits gesetzt überspringe ChatGPT-Auswertung.")
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=ver_range)
self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=ver_range)
debug_print(f"✅ Aktualisiert: URL: {(company_data.get('url', 'k.A.') if 'company_data' in locals() else 'k.A.')}, "
f"Branche: {(company_data.get('branche', 'k.A.') if 'company_data' in locals() else 'k.A.')}, "
f"Umsatz-Abgleich: {abgleich_result if 'abgleich_result' in locals() else 'k.A.'}, "
f"Validierung: {valid_result if 'valid_result' in locals() else 'k.A.'}, "
f"FSM: {fsm_result['suitability'] if 'fsm_result' in locals() else 'k.A.'}, "
f"Servicetechniker-Schätzung: {st_estimate if 'st_estimate' in locals() else 'k.A.'}")
debug_print(f"✅ Aktualisiert: URL: {company_data.get('url', 'k.A.')}, "
f"Branche: {company_data.get('branche', 'k.A.')}, Umsatz-Abgleich: {abgleich_result}, "
f"Validierung: {valid_result}, "
f"FSM: {fsm_result['suitability']}, Servicetechniker-Schätzung: {st_estimate}")
time.sleep(Config.RETRY_DELAY)
# ==================== NEUER MODUS 6: CONTACT RESEARCH (via SerpAPI) ====================
# ==================== GOOGLE SHEET HANDLER (für Hauptdaten) ====================
class GoogleSheetHandler:
def __init__(self):
self.sheet = None
self.sheet_values = []
self._connect()
def _connect(self):
scope = ["https://www.googleapis.com/auth/spreadsheets"]
creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
self.sheet_values = self.sheet.get_all_values()
def get_start_index(self):
filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
# ==================== ALIGNMENT DEMO (Hauptblatt und Contacts) ====================
def alignment_demo_full():
alignment_demo(GoogleSheetHandler().sheet)
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
sh = gc.open_by_url(Config.SHEET_URL)
try:
contacts_sheet = sh.worksheet("Contacts")
except gspread.exceptions.WorksheetNotFound:
contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10")
header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
contacts_sheet.update(values=[header], range_name="A1:H1")
debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
alignment_demo(contacts_sheet)
debug_print("Alignment-Demo für Hauptblatt und Contacts abgeschlossen.")
# ==================== NEUER MODUS: CONTACT RESEARCH (via SerpAPI) ====================
def process_contact_research():
debug_print("Starte Contact Research (Modus 6)...")
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
@@ -1055,7 +1029,7 @@ def process_contacts():
except gspread.exceptions.WorksheetNotFound:
contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10")
header = ["Firmenname", "Website", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
contacts_sheet.update("A1:G1", [header])
contacts_sheet.update(values=[header], range_name="A1:H1")
debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
main_sheet = sh.sheet1
data = main_sheet.get_all_values()
@@ -1069,20 +1043,57 @@ def process_contacts():
continue
for pos in positions:
debug_print(f"Suche nach Position: '{pos}' bei '{search_name}'")
contact = search_linkedin_contact(company_name, website, pos)
contact = search_linkedin_contact(search_name, website, pos)
if contact:
debug_print(f"Kontakt gefunden: {contact}")
new_rows.append([contact["Firmenname"], contact["Website"], search_name, contact["Vorname"], contact["Nachname"], contact["Position"], "", ""])
new_rows.append([contact["Firmenname"], website, search_name, contact["Vorname"], contact["Nachname"], contact["Position"], "", ""])
else:
debug_print(f"Kein Kontakt für Position '{pos}' bei '{search_name}' gefunden.")
if new_rows:
last_row = len(contacts_sheet.get_all_values()) + 1
range_str = f"A{last_row}:G{last_row + len(new_rows) - 1}"
contacts_sheet.update(range_str, new_rows)
range_str = f"A{last_row}:H{last_row + len(new_rows) - 1}"
contacts_sheet.update(values=new_rows, range_name=range_str)
debug_print(f"{len(new_rows)} Kontakte in 'Contacts' hinzugefügt.")
else:
debug_print("Keine Kontakte gefunden.")
# ==================== NEUER MODUS: BATCH-PROZESSING MIT TOKEN-ZÄHLUNG (Modus 8) ====================
def process_batch_token_count(batch_size=10):
import tiktoken
def count_tokens(text, model="gpt-3.5-turbo"):
encoding = tiktoken.encoding_for_model(model)
tokens = encoding.encode(text)
return len(tokens)
debug_print("Starte Batch-Token-Zählung (Modus 8)...")
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
sh = gc.open_by_url(Config.SHEET_URL)
main_sheet = sh.sheet1
data = main_sheet.get_all_values()
for i in range(2, len(data)+1, batch_size):
batch_rows = data[i-1:i-1+batch_size]
aggregated_prompt = ""
for row in batch_rows:
info = []
if len(row) > 1:
info.append(row[1]) # Firmenname
if len(row) > 2:
info.append(row[2]) # Kurzform
if len(row) > 3:
info.append(row[3]) # Website
if len(row) > 4:
info.append(row[4]) # Ort
if len(row) > 5:
info.append(row[5]) # Beschreibung
if len(row) > 6:
info.append(row[6]) # Aktuelle Branche
aggregated_prompt += "; ".join(info) + "\n"
token_count = count_tokens(aggregated_prompt)
debug_print(f"Batch beginnend in Zeile {i}: {token_count} Tokens")
for j in range(i, min(i+batch_size, len(data)+1)):
main_sheet.update(values=[[str(token_count)]], range_name=f"AQ{j}")
time.sleep(Config.RETRY_DELAY)
debug_print("Batch-Token-Zählung abgeschlossen.")
# ==================== MAIN PROGRAMM ====================
if __name__ == "__main__":
import argparse
@@ -1090,9 +1101,10 @@ if __name__ == "__main__":
parser.add_argument("--mode", type=str, help="Modus: 1,2,3,4,5,6,7,51 oder 8")
parser.add_argument("--num_rows", type=int, default=0, help="Anzahl der zu bearbeitenden Zeilen (nur für Modus 1)")
args = parser.parse_args()
if not args.mode:
print("Modi:")
print("1 = regulärer Modus")
print("1 = Regulärer Modus")
print("2 = Re-Evaluierungsmodus (nur Zeilen mit 'x' in Spalte A)")
print("3 = Alignment-Demo (Header in Hauptblatt und Contacts)")
print("4 = Nur Wikipedia-Suche (Zeilen ohne Wikipedia-Timestamp)")
@@ -1102,6 +1114,7 @@ if __name__ == "__main__":
print("8 = Batch-Token-Zählung")
print("51 = Nur Verifizierung (Wikipedia + Brancheneinordnung)")
args.mode = input("Wählen Sie den Modus: ").strip()
MODE = args.mode
if MODE == "1":
try: