v1.3.7: Vollständige Version mit allen Funktionen, Modus 4: Nur Wikipedia-Suche
- Beibehaltung aller Funktionen aus früheren Versionen (Modi 1-3, LinkedIn Contacts) - Neuer Modus 4 implementiert, der ausschließlich Wikipedia-Suchen ausführt und keine ChatGPT-Anfragen stellt - Debug-Ausgaben wurden verbessert, um den Ablauf und die Ergebnisse besser nachvollziehen zu können - Startindex wird anhand des letzten Timestamps in Spalte AH korrekt ermittelt, sodass bestehende Datensätze nicht überschrieben werden
This commit is contained in:
@@ -14,7 +14,7 @@ import csv
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# ==================== KONFIGURATION ====================
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class Config:
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VERSION = "v1.3.7" # v1.3.7: Alle bisherigen Funktionen beibehalten, neuer Modus 4: Nur Wikipedia-Suche.
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VERSION = "v1.3.7" # v1.3.7: Alle bisherigen Funktionen bleiben erhalten; neuer Modus 4: Nur Wikipedia-Suche.
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LANG = "de"
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CREDENTIALS_FILE = "service_account.json"
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SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
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@@ -417,7 +417,7 @@ def process_contacts():
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else:
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debug_print("Keine Kontakte gefunden in der Haupttabelle.")
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# ==================== NEUE FUNKTION: NUR WIKIPEDIA-SUCHE (MODUS 4) ====================
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# ==================== NEUER MODUS 4: NUR WIKIPEDIA-SUCHE ====================
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def process_wikipedia_only():
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debug_print("Starte ausschließlich Wikipedia-Suche (Modus 4)...")
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gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
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@@ -522,6 +522,508 @@ def alignment_demo(sheet):
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sheet.update(values=[new_headers], range_name=header_range)
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print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.")
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# ==================== WIKIPEDIA SCRAPER ====================
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class WikipediaScraper:
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def __init__(self):
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wikipedia.set_lang(Config.LANG)
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def _get_full_domain(self, website):
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if not website:
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return ""
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website = website.lower().strip()
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website = re.sub(r'^https?:\/\/', '', website)
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website = re.sub(r'^www\.', '', website)
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return website.split('/')[0]
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def _generate_search_terms(self, company_name, website):
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terms = []
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full_domain = self._get_full_domain(website)
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if full_domain:
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terms.append(full_domain)
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normalized_name = normalize_company_name(company_name)
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candidate = " ".join(normalized_name.split()[:2]).strip()
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if candidate and candidate not in terms:
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terms.append(candidate)
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if normalized_name and normalized_name not in terms:
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terms.append(normalized_name)
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debug_print(f"Generierte Suchbegriffe: {terms}")
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return terms
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def _validate_article(self, page, company_name, website):
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full_domain = self._get_full_domain(website)
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domain_found = False
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if full_domain:
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try:
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html_raw = requests.get(page.url).text
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soup = BeautifulSoup(html_raw, Config.HTML_PARSER)
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infobox = soup.find('table', class_=lambda c: c and 'infobox' in c.lower())
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if infobox:
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links = infobox.find_all('a', href=True)
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for link in links:
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href = link.get('href').lower()
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if href.startswith('/wiki/datei:'):
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continue
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if full_domain in href:
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debug_print(f"Definitiver Link-Match in Infobox gefunden: {href}")
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domain_found = True
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break
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if not domain_found and hasattr(page, 'externallinks'):
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for ext_link in page.externallinks:
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if full_domain in ext_link.lower():
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debug_print(f"Definitiver Link-Match in externen Links gefunden: {ext_link}")
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domain_found = True
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break
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except Exception as e:
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debug_print(f"Fehler beim Extrahieren von Links: {str(e)}")
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normalized_title = normalize_company_name(page.title)
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normalized_company = normalize_company_name(company_name)
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similarity = SequenceMatcher(None, normalized_title, normalized_company).ratio()
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debug_print(f"Ähnlichkeit (normalisiert): {similarity:.2f} ({normalized_title} vs {normalized_company})")
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threshold = 0.60 if domain_found else Config.SIMILARITY_THRESHOLD
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return similarity >= threshold
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def extract_first_paragraph(self, page_url):
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try:
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response = requests.get(page_url)
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soup = BeautifulSoup(response.text, Config.HTML_PARSER)
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paragraphs = soup.find_all('p')
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for p in paragraphs:
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text = clean_text(p.get_text())
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if len(text) > 50:
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return text
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return "k.A."
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except Exception as e:
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debug_print(f"Fehler beim Extrahieren des ersten Absatzes: {e}")
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return "k.A."
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def extract_categories(self, soup):
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cat_div = soup.find('div', id="mw-normal-catlinks")
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if cat_div:
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ul = cat_div.find('ul')
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if ul:
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cats = [clean_text(li.get_text()) for li in ul.find_all('li')]
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return ", ".join(cats)
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return "k.A."
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def _extract_infobox_value(self, soup, target):
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infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']))
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if not infobox:
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return "k.A."
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keywords_map = {
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'branche': ['branche', 'industrie', 'tätigkeit', 'geschäftsfeld', 'sektor', 'produkte', 'leistungen', 'aktivitäten', 'wirtschaftszweig'],
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'umsatz': ['umsatz', 'jahresumsatz', 'konzernumsatz', 'gesamtumsatz', 'erlöse', 'umsatzerlöse', 'einnahmen', 'ergebnis', 'jahresergebnis'],
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'mitarbeiter': ['mitarbeiter', 'beschäftigte', 'personal', 'mitarbeiterzahl', 'angestellte', 'belegschaft', 'personalstärke']
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}
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keywords = keywords_map.get(target, [])
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for row in infobox.find_all('tr'):
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header = row.find('th')
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if header:
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header_text = clean_text(header.get_text()).lower()
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if any(kw in header_text for kw in keywords):
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value = row.find('td')
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if value:
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raw_value = clean_text(value.get_text())
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if target == 'branche':
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clean_val = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value)
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return ' '.join(clean_val.split()).strip()
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if target == 'umsatz':
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return extract_numeric_value(raw_value, is_umsatz=True)
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if target == 'mitarbeiter':
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return extract_numeric_value(raw_value, is_umsatz=False)
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return "k.A."
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def extract_full_infobox(self, soup):
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infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']))
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if not infobox:
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return "k.A."
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return clean_text(infobox.get_text(separator=' | '))
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def extract_fields_from_infobox_text(self, infobox_text, field_names):
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result = {}
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tokens = [token.strip() for token in infobox_text.split("|") if token.strip()]
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for i, token in enumerate(tokens):
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for field in field_names:
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if field.lower() in token.lower():
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j = i + 1
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while j < len(tokens) and not tokens[j]:
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j += 1
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result[field] = tokens[j] if j < len(tokens) else "k.A."
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return result
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def extract_company_data(self, page_url):
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if not page_url:
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return {
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'url': 'k.A.',
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'first_paragraph': 'k.A.',
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'branche': 'k.A.',
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'umsatz': 'k.A.',
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'mitarbeiter': 'k.A.',
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'categories': 'k.A.',
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'full_infobox': 'k.A.'
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}
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try:
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response = requests.get(page_url)
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soup = BeautifulSoup(response.text, Config.HTML_PARSER)
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full_infobox = self.extract_full_infobox(soup)
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extracted_fields = self.extract_fields_from_infobox_text(full_infobox, ['Branche', 'Umsatz', 'Mitarbeiter'])
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raw_branche = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche'))
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raw_umsatz = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz'))
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raw_mitarbeiter = extracted_fields.get('Mitarbeiter', self._extract_infobox_value(soup, 'mitarbeiter'))
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umsatz_val = extract_numeric_value(raw_umsatz, is_umsatz=True)
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mitarbeiter_val = extract_numeric_value(raw_mitarbeiter, is_umsatz=False)
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categories_val = self.extract_categories(soup)
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first_paragraph = self.extract_first_paragraph(page_url)
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return {
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'url': page_url,
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'first_paragraph': first_paragraph,
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'branche': raw_branche,
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'umsatz': umsatz_val,
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'mitarbeiter': mitarbeiter_val,
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'categories': categories_val,
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'full_infobox': full_infobox
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}
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except Exception as e:
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debug_print(f"Extraktionsfehler: {str(e)}")
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return {
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'url': 'k.A.',
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'first_paragraph': 'k.A.',
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'branche': 'k.A.',
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'umsatz': 'k.A.',
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'mitarbeiter': 'k.A.',
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'categories': 'k.A.',
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'full_infobox': 'k.A.'
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}
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@retry_on_failure
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def search_company_article(self, company_name, website):
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search_terms = self._generate_search_terms(company_name, website)
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for term in search_terms:
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try:
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results = wikipedia.search(term, results=Config.WIKIPEDIA_SEARCH_RESULTS)
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debug_print(f"Suchergebnisse für '{term}': {results}")
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for title in results:
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try:
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page = wikipedia.page(title, auto_suggest=False)
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if self._validate_article(page, company_name, website):
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return page
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except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e:
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debug_print(f"Seitenfehler: {str(e)}")
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continue
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except Exception as e:
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debug_print(f"Suchfehler: {str(e)}")
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continue
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return None
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# ==================== GOOGLE SHEET HANDLER (für Hauptdaten) ====================
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class GoogleSheetHandler:
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def __init__(self):
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self.sheet = None
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self.sheet_values = []
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self._connect()
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def _connect(self):
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scope = ["https://www.googleapis.com/auth/spreadsheets"]
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creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
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self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
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self.sheet_values = self.sheet.get_all_values()
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def get_start_index(self):
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filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
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return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
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# ==================== ALIGNMENT DEMO (Modus 3) ====================
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def alignment_demo(sheet):
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new_headers = [
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"Spalte A (ReEval Flag)",
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"Spalte B (Firmenname)",
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"Spalte C (Website)",
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"Spalte D (Ort)",
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"Spalte E (Beschreibung)",
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"Spalte F (Aktuelle Branche)",
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"Spalte G (Beschreibung Branche extern)",
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"Spalte H (Anzahl Techniker CRM)",
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"Spalte I (Umsatz CRM)",
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"Spalte J (Anzahl Mitarbeiter CRM)",
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"Spalte K (Vorschlag Wiki URL)",
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"Spalte L (Wikipedia URL)",
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"Spalte M (Wikipedia Absatz)",
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"Spalte N (Wikipedia Branche)",
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"Spalte O (Wikipedia Umsatz)",
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"Spalte P (Wikipedia Mitarbeiter)",
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"Spalte Q (Wikipedia Kategorien)",
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"Spalte R (Konsistenzprüfung)",
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"Spalte S (Begründung bei Inkonsistenz)",
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"Spalte T (Vorschlag Wiki Artikel ChatGPT)",
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"Spalte U (Begründung bei Abweichung)",
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"Spalte V (Vorschlag neue Branche)",
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"Spalte W (Konsistenzprüfung Branche)",
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"Spalte X (Begründung Abweichung Branche)",
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"Spalte Y (FSM Relevanz Ja / Nein)",
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"Spalte Z (Begründung für FSM Relevanz)",
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"Spalte AA (Schätzung Anzahl Mitarbeiter)",
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"Spalte AB (Konsistenzprüfung Mitarbeiterzahl)",
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"Spalte AC (Begründung für Abweichung Mitarbeiterzahl)",
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"Spalte AD (Einschätzung Anzahl Servicetechniker)",
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"Spalte AE (Begründung bei Abweichung Anzahl Servicetechniker)",
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"Spalte AF (Schätzung Umsatz ChatGPT)",
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"Spalte AG (Begründung für Abweichung Umsatz)",
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"Spalte AH (Timestamp letzte Prüfung)",
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"Spalte AI (Version)"
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]
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header_range = "A11200:AI11200"
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sheet.update(values=[new_headers], range_name=header_range)
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print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.")
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# ==================== NEUER MODUS 4: NUR WIKIPEDIA-SUCHE ====================
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def process_wikipedia_only():
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debug_print("Starte ausschließlich Wikipedia-Suche (Modus 4)...")
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gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
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Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
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sh = gc.open_by_url(Config.SHEET_URL)
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main_sheet = sh.sheet1
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data = main_sheet.get_all_values()
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start_index = GoogleSheetHandler().get_start_index()
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debug_print(f"Starte bei Zeile {start_index+1}")
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for i, row in enumerate(data[1:], start=2):
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if i < start_index:
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continue
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company_name = row[1] if len(row) > 1 else ""
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website = row[2] if len(row) > 2 else ""
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debug_print(f"Verarbeite Zeile {i}: {company_name}")
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article = WikipediaScraper().search_company_article(company_name, website)
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if article:
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company_data = WikipediaScraper().extract_company_data(article.url)
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else:
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company_data = {
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'url': 'k.A.',
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'first_paragraph': 'k.A.',
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'branche': 'k.A.',
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'umsatz': 'k.A.',
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'mitarbeiter': 'k.A.',
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'categories': 'k.A.',
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'full_infobox': 'k.A.'
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}
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wiki_values = [
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row[10] if len(row) > 10 and row[10].strip() not in ["", "k.A."] else "k.A.",
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company_data.get('url', 'k.A.'),
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company_data.get('first_paragraph', 'k.A.'),
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company_data.get('branche', 'k.A.'),
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company_data.get('umsatz', 'k.A.'),
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company_data.get('mitarbeiter', 'k.A.'),
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company_data.get('categories', 'k.A.')
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]
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wiki_range = f"K{i}:Q{i}"
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main_sheet.update(values=[wiki_values], range_name=wiki_range)
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debug_print(f"Zeile {i} mit Wikipedia-Daten aktualisiert.")
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current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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main_sheet.update(values=[[current_dt]], range_name=f"AH{i}")
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main_sheet.update(values=[[Config.VERSION]], range_name=f"AI{i}")
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time.sleep(Config.RETRY_DELAY)
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debug_print("Wikipedia-Suche abgeschlossen.")
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# ==================== GOOGLE SHEET HANDLER (für Hauptdaten) ====================
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class GoogleSheetHandler:
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def __init__(self):
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self.sheet = None
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self.sheet_values = []
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self._connect()
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def _connect(self):
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scope = ["https://www.googleapis.com/auth/spreadsheets"]
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creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
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self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
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self.sheet_values = self.sheet.get_all_values()
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def get_start_index(self):
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filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
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return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
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# ==================== NEUER MODUS: CONTACTS (LinkedIn) ====================
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def process_contacts():
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debug_print("Starte LinkedIn-Kontaktsuche...")
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gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
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Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
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sh = gc.open_by_url(Config.SHEET_URL)
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try:
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contacts_sheet = sh.worksheet("Contacts")
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except gspread.exceptions.WorksheetNotFound:
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contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10")
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header = ["Firmenname", "Website", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
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contacts_sheet.update("A1:G1", [header])
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debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
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main_sheet = sh.sheet1
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data = main_sheet.get_all_values()
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positions = ["Serviceleiter", "IT-Leiter", "Leiter After Sales", "Leiter Einsatzplanung"]
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new_rows = []
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for idx, row in enumerate(data[1:], start=2):
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company_name = row[1] if len(row) > 1 else ""
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website = row[2] if len(row) > 2 else ""
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debug_print(f"Verarbeite Firma: '{company_name}' (Zeile {idx}), Website: '{website}'")
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if not company_name or not website:
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debug_print("Überspringe, da Firmenname oder Website fehlt.")
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continue
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for pos in positions:
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debug_print(f"Suche nach Position: '{pos}' bei '{company_name}'")
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contact = search_linkedin_contact(company_name, website, pos)
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if contact:
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debug_print(f"Kontakt gefunden: {contact}")
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new_rows.append([contact["Firmenname"], contact["Website"], contact["Vorname"], contact["Nachname"], contact["Position"], "", ""])
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else:
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debug_print(f"Kein Kontakt für Position '{pos}' bei '{company_name}' gefunden.")
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if new_rows:
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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.")
|
||||
|
||||
# ==================== DATA PROCESSOR ====================
|
||||
class DataProcessor:
|
||||
def __init__(self):
|
||||
self.sheet_handler = GoogleSheetHandler()
|
||||
self.wiki_scraper = WikipediaScraper()
|
||||
def process_rows(self, num_rows=None):
|
||||
if MODE == "2":
|
||||
print("Re-Evaluierungsmodus: Verarbeitung aller Zeilen mit 'x' in Spalte A.")
|
||||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||||
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)
|
||||
else:
|
||||
start_index = self.sheet_handler.get_start_index()
|
||||
print(f"Starte bei Zeile {start_index+1}")
|
||||
rows_processed = 0
|
||||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||||
if i < start_index:
|
||||
continue
|
||||
if num_rows is not None and rows_processed >= num_rows:
|
||||
break
|
||||
self._process_single_row(i, row)
|
||||
rows_processed += 1
|
||||
def _process_single_row(self, row_num, row_data):
|
||||
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}"
|
||||
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
|
||||
|
||||
if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."]:
|
||||
wiki_url = row_data[10].strip()
|
||||
try:
|
||||
company_data = self.wiki_scraper.extract_company_data(wiki_url)
|
||||
except Exception as e:
|
||||
debug_print(f"Fehler beim Laden des vorgeschlagenen Wikipedia-Artikels: {e}")
|
||||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||||
company_data = self.wiki_scraper.extract_company_data(article.url) if article else {
|
||||
'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.',
|
||||
'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.',
|
||||
'full_infobox': 'k.A.'
|
||||
}
|
||||
else:
|
||||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||||
company_data = self.wiki_scraper.extract_company_data(article.url) if article else {
|
||||
'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_data[10] if len(row_data) > 10 and row_data[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.')
|
||||
]
|
||||
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])
|
||||
time.sleep(3)
|
||||
|
||||
self.sheet_handler.sheet.update(values=[["XX"]], range_name=chatgpt_range)
|
||||
|
||||
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.'))
|
||||
self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=abgleich_range)
|
||||
|
||||
crm_data = ";".join(row_data[1:10])
|
||||
wiki_data = ";".join(row_data[11:17])
|
||||
valid_result = validate_article_with_chatgpt(crm_data, wiki_data)
|
||||
self.sheet_handler.sheet.update(values=[[valid_result]], range_name=valid_range)
|
||||
|
||||
crm_branche = row_data[5] if len(row_data) > 5 else "k.A."
|
||||
beschreibung_branche = row_data[6] if len(row_data) > 6 else "k.A."
|
||||
wiki_branche = company_data.get('branche', 'k.A.')
|
||||
wiki_kategorien = company_data.get('categories', 'k.A.')
|
||||
branche_result = evaluate_branche_chatgpt(crm_branche, beschreibung_branche, wiki_branche, wiki_kategorien)
|
||||
branche_v_range = f"V{row_num}"
|
||||
branche_w_range = f"W{row_num}"
|
||||
branche_x_range = f"X{row_num}"
|
||||
self.sheet_handler.sheet.update(values=[[branche_result["branch"]]], range_name=branche_v_range)
|
||||
self.sheet_handler.sheet.update(values=[[branche_result["consistency"]]], range_name=branche_w_range)
|
||||
self.sheet_handler.sheet.update(values=[[branche_result["justification"]]], range_name=branche_x_range)
|
||||
|
||||
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)
|
||||
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)
|
||||
discrepancy = explanation
|
||||
else:
|
||||
discrepancy = "ok"
|
||||
self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AE{row_num}")
|
||||
|
||||
self.sheet_handler.sheet.update(values=[["XX"]], range_name="AF" + str(row_num))
|
||||
self.sheet_handler.sheet.update(values=[["XX"]], range_name="AG" + str(row_num))
|
||||
|
||||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_range)
|
||||
self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=ver_range)
|
||||
|
||||
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}, Branchenvorschlag: {branche_result['branch']}, "
|
||||
f"FSM: {fsm_result['suitability']}, Servicetechniker-Schätzung: {st_estimate}")
|
||||
time.sleep(Config.RETRY_DELAY)
|
||||
|
||||
# ==================== NEUER MODUS: CONTACTS (LinkedIn) ====================
|
||||
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.")
|
||||
|
||||
# ==================== MAIN PROGRAMM ====================
|
||||
if __name__ == "__main__":
|
||||
print("Modi: 1 = regulärer Modus, 2 = Re-Evaluierungsmodus, 3 = Alignment-Demo, 4 = Nur Wikipedia-Suche, 5 = LinkedIn Contacts")
|
||||
|
||||
Reference in New Issue
Block a user