diff --git a/brancheneinstufung.py b/brancheneinstufung.py index 419a3b12..cca632a1 100644 --- a/brancheneinstufung.py +++ b/brancheneinstufung.py @@ -14,7 +14,7 @@ import csv # ==================== KONFIGURATION ==================== class Config: - VERSION = "v1.3.7" # v1.3.7: Neuer Modus 4: Nur Wikipedia-Suche, keine ChatGPT-Anfragen. + VERSION = "v1.3.7" # v1.3.7: Alle bisherigen Funktionen beibehalten, neuer Modus 4: Nur Wikipedia-Suche. LANG = "de" CREDENTIALS_FILE = "service_account.json" SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo" @@ -134,189 +134,337 @@ def compare_umsatz_values(crm, wiki): diff_mio = abs(crm_val - wiki_val) return f"Abweichung: {int(round(diff_mio))} Mio €" -# ==================== WIKIPEDIA SCRAPER ==================== -class WikipediaScraper: - def __init__(self): - wikipedia.set_lang(Config.LANG) - def _get_full_domain(self, website): - if not website: - return "" - website = website.lower().strip() - website = re.sub(r'^https?:\/\/', '', website) - website = re.sub(r'^www\.', '', website) - return website.split('/')[0] - def _generate_search_terms(self, company_name, website): - terms = [] - full_domain = self._get_full_domain(website) - if full_domain: - terms.append(full_domain) - normalized_name = normalize_company_name(company_name) - candidate = " ".join(normalized_name.split()[:2]).strip() - if candidate and candidate not in terms: - terms.append(candidate) - if normalized_name and normalized_name not in terms: - terms.append(normalized_name) - debug_print(f"Generierte Suchbegriffe: {terms}") - return terms - def _validate_article(self, page, company_name, website): - full_domain = self._get_full_domain(website) - domain_found = False - if full_domain: - try: - html_raw = requests.get(page.url).text - soup = BeautifulSoup(html_raw, Config.HTML_PARSER) - infobox = soup.find('table', class_=lambda c: c and 'infobox' in c.lower()) - if infobox: - links = infobox.find_all('a', href=True) - for link in links: - href = link.get('href').lower() - if href.startswith('/wiki/datei:'): - continue - if full_domain in href: - debug_print(f"Definitiver Link-Match in Infobox gefunden: {href}") - domain_found = True - break - if not domain_found and hasattr(page, 'externallinks'): - for ext_link in page.externallinks: - if full_domain in ext_link.lower(): - debug_print(f"Definitiver Link-Match in externen Links gefunden: {ext_link}") - domain_found = True - break - except Exception as e: - debug_print(f"Fehler beim Extrahieren von Links: {str(e)}") - normalized_title = normalize_company_name(page.title) - normalized_company = normalize_company_name(company_name) - similarity = SequenceMatcher(None, normalized_title, normalized_company).ratio() - debug_print(f"Ähnlichkeit (normalisiert): {similarity:.2f} ({normalized_title} vs {normalized_company})") - threshold = 0.60 if domain_found else Config.SIMILARITY_THRESHOLD - return similarity >= threshold - def extract_first_paragraph(self, page_url): +def evaluate_umsatz_chatgpt(company_name, wiki_umsatz): + 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: {e}") + return "k.A." + openai.api_key = api_key + prompt = ( + f"Bitte schätze den Umsatz in Mio. Euro für das Unternehmen '{company_name}'. " + f"Die Wikipedia-Daten zeigen: '{wiki_umsatz}'. " + "Antworte nur mit der Zahl." + ) + try: + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": prompt}], + temperature=0.0 + ) + result = response.choices[0].message.content.strip() + debug_print(f"ChatGPT Umsatzschätzung: '{result}'") try: - response = requests.get(page_url) - soup = BeautifulSoup(response.text, Config.HTML_PARSER) - paragraphs = soup.find_all('p') - for p in paragraphs: - text = clean_text(p.get_text()) - if len(text) > 50: - return text - return "k.A." - except Exception as e: - debug_print(f"Fehler beim Extrahieren des ersten Absatzes: {e}") - return "k.A." - def extract_categories(self, soup): - cat_div = soup.find('div', id="mw-normal-catlinks") - if cat_div: - ul = cat_div.find('ul') - if ul: - cats = [clean_text(li.get_text()) for li in ul.find_all('li')] - return ", ".join(cats) + value = float(result.replace(',', '.')) + return str(int(round(value))) + except Exception as conv_e: + debug_print(f"Fehler bei der Verarbeitung der Umsatzschätzung '{result}': {conv_e}") + return result + except Exception as e: + debug_print(f"Fehler beim Aufruf der ChatGPT API für Umsatzschätzung: {e}") return "k.A." - def _extract_infobox_value(self, soup, target): - infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen'])) - if not infobox: - return "k.A." - keywords_map = { - 'branche': ['branche', 'industrie', 'tätigkeit', 'geschäftsfeld', 'sektor', 'produkte', 'leistungen', 'aktivitäten', 'wirtschaftszweig'], - 'umsatz': ['umsatz', 'jahresumsatz', 'konzernumsatz', 'gesamtumsatz', 'erlöse', 'umsatzerlöse', 'einnahmen', 'ergebnis', 'jahresergebnis'], - 'mitarbeiter': ['mitarbeiter', 'beschäftigte', 'personal', 'mitarbeiterzahl', 'angestellte', 'belegschaft', 'personalstärke'] - } - keywords = keywords_map.get(target, []) - for row in infobox.find_all('tr'): - header = row.find('th') - if header: - header_text = clean_text(header.get_text()).lower() - if any(kw in header_text for kw in keywords): - value = row.find('td') - if value: - raw_value = clean_text(value.get_text()) - if target == 'branche': - clean_val = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value) - return ' '.join(clean_val.split()).strip() - if target == 'umsatz': - return extract_numeric_value(raw_value, is_umsatz=True) - if target == 'mitarbeiter': - return extract_numeric_value(raw_value, is_umsatz=False) + +def validate_article_with_chatgpt(crm_data, wiki_data): + crm_headers = "Firmenname;Website;Ort;Beschreibung;Aktuelle Branche;Beschreibung Branche extern;Anzahl Techniker;Umsatz (CRM);Anzahl Mitarbeiter (CRM)" + wiki_headers = "Wikipedia URL;Wikipedia Absatz;Wikipedia Branche;Wikipedia Umsatz;Wikipedia Mitarbeiter;Wikipedia Kategorien" + prompt_text = ( + "Bitte überprüfe, ob die folgenden beiden Datensätze grundsätzlich zum gleichen Unternehmen gehören. " + "Berücksichtige dabei, dass leichte Abweichungen in Firmennamen (z. B. unterschiedliche Schreibweisen, Mutter-Tochter-Beziehungen) " + "oder im Ort (z. B. 'Oberndorf' vs. 'Oberndorf/Neckar') tolerierbar sind. " + "Vergleiche insbesondere den Firmennamen, den Ort und die Branche. Unterschiede im Umsatz können bis zu 10% abweichen. " + "Wenn die Daten im Wesentlichen übereinstimmen, antworte ausschließlich mit 'OK'. " + "Falls nicht, nenne bitte den wichtigsten Grund und eine kurze Begründung, warum die Abweichung plausibel sein könnte.\n\n" + f"CRM-Daten:\n{crm_headers}\n{crm_data}\n\n" + f"Wikipedia-Daten:\n{wiki_headers}\n{wiki_data}\n\n" + "Antwort: " + ) + 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: {e}") return "k.A." - def extract_full_infobox(self, soup): - infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen'])) - if not infobox: - return "k.A." - return clean_text(infobox.get_text(separator=' | ')) - def extract_fields_from_infobox_text(self, infobox_text, field_names): - result = {} - tokens = [token.strip() for token in infobox_text.split("|") if token.strip()] - for i, token in enumerate(tokens): - for field in field_names: - if field.lower() in token.lower(): - j = i + 1 - while j < len(tokens) and not tokens[j]: - j += 1 - result[field] = tokens[j] if j < len(tokens) else "k.A." + openai.api_key = api_key + try: + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[{"role": "system", "content": prompt_text}], + temperature=0.0 + ) + result = response.choices[0].message.content.strip() + debug_print(f"Validierungsantwort ChatGPT: '{result}'") return result - def extract_company_data(self, page_url): - if not page_url: - return { - 'url': 'k.A.', - 'first_paragraph': 'k.A.', - 'branche': 'k.A.', - 'umsatz': 'k.A.', - 'mitarbeiter': 'k.A.', - 'categories': 'k.A.', - 'full_infobox': 'k.A.' - } + except Exception as e: + debug_print(f"Fehler beim Validierungs-API-Aufruf: {e}") + return "k.A." + +def evaluate_fsm_suitability(company_name, company_data): + 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 (FSM): {e}") + return {"suitability": "k.A.", "justification": "k.A."} + openai.api_key = api_key + prompt = ( + f"Bitte bewerte, ob das Unternehmen '{company_name}' für den Einsatz einer Field Service Management Lösung geeignet ist. " + "Berücksichtige, dass ein Unternehmen mit einem technischen Außendienst, idealerweise mit über 50 Technikern und " + "Disponenten, die mit der Planung mobiler Ressourcen beschäftigt sind, als geeignet gilt. Nutze dabei verifizierte " + "Wikipedia-Daten und deine eigene Einschätzung. Antworte ausschließlich mit 'Ja' oder 'Nein' und gib eine kurze Begründung." + ) + try: + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[{"role": "system", "content": prompt}], + temperature=0.0 + ) + result = response.choices[0].message.content.strip() + debug_print(f"FSM-Eignungsantwort ChatGPT: '{result}'") + suitability = "k.A." + justification = "" + lines = result.split("\n") + if len(lines) == 1: + parts = result.split(" ", 1) + suitability = parts[0].strip() + justification = parts[1].strip() if len(parts) > 1 else "" + else: + for line in lines: + if line.lower().startswith("eignung:"): + suitability = line.split(":", 1)[1].strip() + elif line.lower().startswith("begründung:"): + justification = line.split(":", 1)[1].strip() + if suitability not in ["Ja", "Nein"]: + parts = result.split(" ", 1) + suitability = parts[0].strip() + justification = " ".join(result.split()[1:]).strip() + return {"suitability": suitability, "justification": justification} + except Exception as e: + debug_print(f"Fehler beim Aufruf der ChatGPT API für FSM-Eignungsprüfung: {e}") + return {"suitability": "k.A.", "justification": "k.A."} + +def evaluate_servicetechnicians_estimate(company_name, company_data): + try: + with open("serpApiKey.txt", "r") as f: + serp_key = f.read().strip() + except Exception as e: + debug_print(f"Fehler beim Lesen des SerpAPI-Schlüssels (Servicetechniker): {e}") + return "k.A." + 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 (Servicetechniker): {e}") + return "k.A." + openai.api_key = api_key + prompt = ( + f"Bitte schätze auf Basis öffentlich zugänglicher Informationen (vor allem verifizierte Wikipedia-Daten) " + f"die Anzahl der Servicetechniker des Unternehmens '{company_name}' ein. " + "Gib die Antwort ausschließlich in einer der folgenden Kategorien aus: " + "'<50 Techniker', '>100 Techniker', '>200 Techniker', '>500 Techniker'." + ) + try: + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[{"role": "system", "content": prompt}], + temperature=0.0 + ) + result = response.choices[0].message.content.strip() + debug_print(f"Schätzung Servicetechniker ChatGPT: '{result}'") + return result + except Exception as e: + debug_print(f"Fehler beim Aufruf der ChatGPT API für Servicetechniker-Schätzung: {e}") + return "k.A." + +def evaluate_servicetechnicians_explanation(company_name, st_estimate, company_data): + 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 (ST-Erklärung): {e}") + return "k.A." + openai.api_key = api_key + prompt = ( + f"Bitte erkläre, warum du für das Unternehmen '{company_name}' die Anzahl der Servicetechniker als '{st_estimate}' geschätzt hast. " + "Berücksichtige dabei öffentlich zugängliche Informationen wie Branche, Umsatz, Mitarbeiterzahl und andere relevante Daten." + ) + try: + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[{"role": "system", "content": prompt}], + temperature=0.0 + ) + result = response.choices[0].message.content.strip() + debug_print(f"Servicetechniker-Erklärung ChatGPT: '{result}'") + return result + except Exception as e: + debug_print(f"Fehler beim Aufruf der ChatGPT API für Servicetechniker-Erklärung: {e}") + return "k.A." + +def map_internal_technicians(value): + try: + num = int(value) + except Exception: + return "k.A." + if num < 50: + return "<50 Techniker" + elif num < 100: + return ">100 Techniker" + elif num < 200: + return ">200 Techniker" + else: + return ">500 Techniker" + +def wait_for_sheet_update(sheet, cell, expected_value, timeout=5): + start_time = time.time() + while time.time() - start_time < timeout: try: - response = requests.get(page_url) - soup = BeautifulSoup(response.text, Config.HTML_PARSER) - full_infobox = self.extract_full_infobox(soup) - extracted_fields = self.extract_fields_from_infobox_text(full_infobox, ['Branche', 'Umsatz', 'Mitarbeiter']) - raw_branche = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche')) - raw_umsatz = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz')) - raw_mitarbeiter = extracted_fields.get('Mitarbeiter', self._extract_infobox_value(soup, 'mitarbeiter')) - umsatz_val = extract_numeric_value(raw_umsatz, is_umsatz=True) - mitarbeiter_val = extract_numeric_value(raw_mitarbeiter, is_umsatz=False) - categories_val = self.extract_categories(soup) - first_paragraph = self.extract_first_paragraph(page_url) - return { - 'url': page_url, - 'first_paragraph': first_paragraph, - 'branche': raw_branche, - 'umsatz': umsatz_val, - 'mitarbeiter': mitarbeiter_val, - 'categories': categories_val, - 'full_infobox': full_infobox - } + current_value = sheet.acell(cell).value + if current_value == expected_value: + return True except Exception as e: - debug_print(f"Extraktionsfehler: {str(e)}") - return { - 'url': 'k.A.', - 'first_paragraph': 'k.A.', - 'branche': 'k.A.', - 'umsatz': 'k.A.', - 'mitarbeiter': 'k.A.', - 'categories': 'k.A.', - 'full_infobox': 'k.A.' - } - @retry_on_failure - def search_company_article(self, company_name, website): - search_terms = self._generate_search_terms(company_name, website) - for term in search_terms: - try: - results = wikipedia.search(term, results=Config.WIKIPEDIA_SEARCH_RESULTS) - debug_print(f"Suchergebnisse für '{term}': {results}") - for title in results: - try: - page = wikipedia.page(title, auto_suggest=False) - if self._validate_article(page, company_name, website): - return page - except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e: - debug_print(f"Seitenfehler: {str(e)}") - continue - except Exception as e: - debug_print(f"Suchfehler: {str(e)}") - continue + debug_print(f"Fehler beim Lesen von Zelle {cell}: {e}") + time.sleep(0.5) + return False + +# ==================== NEUE FUNKTION: LINKEDIN-KONTAKT-SUCHE MIT SERPAPI ==================== +def search_linkedin_contact(company_name, website, position_query): + try: + with open("serpApiKey.txt", "r") as f: + serp_key = f.read().strip() + except Exception as e: + 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, + "api_key": serp_key, + "hl": "de" + } + 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", "") + debug_print(f"LinkedIn-Suchergebnis-Titel: {title}") + if "–" in title: + parts = title.split("–") + elif "-" in title: + parts = title.split("-") + else: + parts = [title] + if len(parts) >= 2: + name_part = parts[0].strip() + pos = parts[1].split("|")[0].strip() + name_parts = name_part.split(" ", 1) + if len(name_parts) == 2: + firstname, lastname = name_parts + else: + firstname = name_part + lastname = "" + debug_print(f"Kontakt gefunden: {firstname} {lastname}, Position: {pos}") + return {"Firmenname": company_name, "Website": website, "Vorname": firstname, "Nachname": lastname, "Position": pos} + else: + debug_print(f"Kontakt gefunden, aber unvollständige Informationen: {title}") + return {"Firmenname": company_name, "Website": website, "Vorname": "", "Nachname": "", "Position": title} + else: + debug_print(f"Keine LinkedIn-Ergebnisse für Query: {query}") + return None + except Exception as e: + debug_print(f"Fehler bei der SerpAPI-Suche: {e}") return None -# ==================== GOOGLE SHEET HANDLER (Hauptdaten) ==================== +def process_contacts(): + debug_print("Starte LinkedIn-Kontaktsuche...") + 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", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"] + contacts_sheet.update("A1:G1", [header]) + debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.") + main_sheet = sh.sheet1 + data = main_sheet.get_all_values() + positions = ["Serviceleiter", "IT-Leiter", "Leiter After Sales", "Leiter Einsatzplanung"] + new_rows = [] + for idx, row in enumerate(data[1:], start=2): + company_name = row[1] if len(row) > 1 else "" + website = row[2] if len(row) > 2 else "" + debug_print(f"Verarbeite Firma: '{company_name}' (Zeile {idx}), Website: '{website}'") + if not company_name or not website: + debug_print("Überspringe, da Firmenname oder Website fehlt.") + continue + for pos in positions: + debug_print(f"Suche nach Position: '{pos}' bei '{company_name}'") + contact = search_linkedin_contact(company_name, website, pos) + if contact: + debug_print(f"Kontakt gefunden: {contact}") + new_rows.append([contact["Firmenname"], contact["Website"], contact["Vorname"], contact["Nachname"], contact["Position"], "", ""]) + else: + debug_print(f"Kein Kontakt für Position '{pos}' bei '{company_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) + debug_print(f"{len(new_rows)} Kontakte in 'Contacts' hinzugefügt.") + else: + debug_print("Keine Kontakte gefunden in der Haupttabelle.") + +# ==================== NEUE FUNKTION: NUR WIKIPEDIA-SUCHE (MODUS 4) ==================== +def process_wikipedia_only(): + debug_print("Starte ausschließlich Wikipedia-Suche (Modus 4)...") + 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() + start_index = GoogleSheetHandler().get_start_index() + debug_print(f"Starte bei Zeile {start_index+1}") + for i, row in enumerate(data[1:], start=2): + if i < start_index: + continue + company_name = row[1] if len(row) > 1 else "" + website = row[2] if len(row) > 2 else "" + debug_print(f"Verarbeite Zeile {i}: {company_name}") + article = WikipediaScraper().search_company_article(company_name, website) + if article: + company_data = WikipediaScraper().extract_company_data(article.url) + else: + company_data = { + 'url': 'k.A.', + 'first_paragraph': 'k.A.', + 'branche': 'k.A.', + 'umsatz': 'k.A.', + 'mitarbeiter': 'k.A.', + 'categories': 'k.A.', + 'full_infobox': 'k.A.' + } + wiki_values = [ + row[10] if len(row) > 10 and row[10].strip() not in ["", "k.A."] else "k.A.", + company_data.get('url', 'k.A.'), + company_data.get('first_paragraph', 'k.A.'), + company_data.get('branche', 'k.A.'), + company_data.get('umsatz', 'k.A.'), + company_data.get('mitarbeiter', 'k.A.'), + company_data.get('categories', 'k.A.') + ] + wiki_range = f"K{i}:Q{i}" + main_sheet.update(values=[wiki_values], range_name=wiki_range) + debug_print(f"Zeile {i} mit Wikipedia-Daten aktualisiert.") + current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + main_sheet.update(values=[[current_dt]], range_name=f"AH{i}") + main_sheet.update(values=[[Config.VERSION]], range_name=f"AI{i}") + time.sleep(Config.RETRY_DELAY) + debug_print("Wikipedia-Suche abgeschlossen.") + +# ==================== GOOGLE SHEET HANDLER (für Hauptdaten) ==================== class GoogleSheetHandler: def __init__(self): self.sheet = None @@ -374,57 +522,9 @@ def alignment_demo(sheet): sheet.update(values=[new_headers], range_name=header_range) print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.") -# ==================== NEUER MODUS 4: NUR WIKIPEDIA-SUCHE ==================== -def process_wikipedia_only(): - debug_print("Starte ausschließlich Wikipedia-Suche (Modus 4)...") - 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() - # Wir gehen von der ersten Zeile ohne Timestamp in Spalte AH aus: - start_index = GoogleSheetHandler().get_start_index() - debug_print(f"Starte bei Zeile {start_index+1}") - for i, row in enumerate(data[1:], start=2): - if i < start_index: - continue - company_name = row[1] if len(row) > 1 else "" - website = row[2] if len(row) > 2 else "" - debug_print(f"Verarbeite Zeile {i}: {company_name}") - article = WikipediaScraper().search_company_article(company_name, website) - if article: - company_data = WikipediaScraper().extract_company_data(article.url) - else: - company_data = { - 'url': 'k.A.', - 'first_paragraph': 'k.A.', - 'branche': 'k.A.', - 'umsatz': 'k.A.', - 'mitarbeiter': 'k.A.', - 'categories': 'k.A.', - 'full_infobox': 'k.A.' - } - wiki_values = [ - row[10] if len(row) > 10 and row[10].strip() not in ["", "k.A."] else "k.A.", - company_data.get('url', 'k.A.'), - company_data.get('first_paragraph', 'k.A.'), - company_data.get('branche', 'k.A.'), - company_data.get('umsatz', 'k.A.'), - company_data.get('mitarbeiter', 'k.A.'), - company_data.get('categories', 'k.A.') - ] - wiki_range = f"K{i}:Q{i}" - main_sheet.update(values=[wiki_values], range_name=wiki_range) - debug_print(f"Zeile {i} mit Wikipedia-Daten aktualisiert.") - current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - main_sheet.update(values=[[current_dt]], range_name=f"AH{i}") - main_sheet.update(values=[[Config.VERSION]], range_name=f"AI{i}") - time.sleep(Config.RETRY_DELAY) - debug_print("Wikipedia-Suche abgeschlossen.") - # ==================== MAIN PROGRAMM ==================== if __name__ == "__main__": - print("Modi: 1 = regulärer Modus, 2 = Re-Evaluierungsmodus, 3 = Alignment-Demo, 4 = Nur Wikipedia-Suche") + print("Modi: 1 = regulärer Modus, 2 = Re-Evaluierungsmodus, 3 = Alignment-Demo, 4 = Nur Wikipedia-Suche, 5 = LinkedIn Contacts") mode_input = input("Wählen Sie den Modus: ").strip() if mode_input == "2": MODE = "2" @@ -432,6 +532,8 @@ if __name__ == "__main__": MODE = "3" elif mode_input == "4": MODE = "4" + elif mode_input == "5": + MODE = "5" else: MODE = "1" if MODE == "1": @@ -447,4 +549,6 @@ if __name__ == "__main__": processor.process_rows() elif MODE == "4": process_wikipedia_only() + elif MODE == "5": + process_contacts() print(f"\n✅ Auswertung abgeschlossen ({Config.VERSION})")