Deepseek bugfix v10

Wichtigste Änderungen:

Hinzugefügte search_company_article-Methode

extract_company_data-Methode ergänzt

Konsistente Fehlerbehandlung

Domain-Validierung in der Artikelsuche
This commit is contained in:
2025-03-31 17:12:33 +00:00
parent 44193fc6c1
commit 0875a7c7df

View File

@@ -12,7 +12,7 @@ import csv
# ==================== KONFIGURATION ====================
class Config:
VERSION = "1.0.13"
VERSION = "1.0.14"
LANG = "de"
CREDENTIALS_FILE = "service_account.json"
SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
@@ -97,11 +97,9 @@ class WikipediaScraper:
if not website:
return ""
# Entferne Protokoll, Pfad und Query-Parameter
domain = re.sub(r'^https?:\/\/(www\.)?', '', website.lower())
domain = re.sub(r'\/.*$', '', domain)
domain = domain.split('.')[0] # Nur den Subdomain-Teil
domain = domain.split('.')[0]
debug_print(f"Normalisierte Domain: {domain}")
return domain
@@ -109,7 +107,6 @@ class WikipediaScraper:
"""Generiert Suchbegriffe mit optimierter URL-Verarbeitung"""
terms = []
# 1. Originalname mit und ohne Rechtsform
clean_name = re.sub(
r'\s+(GmbH|AG|KG|Co\. KG|e\.V\.|mbH|& Co).*$',
'',
@@ -117,12 +114,10 @@ class WikipediaScraper:
).strip()
terms.extend([company_name.strip(), clean_name])
# 2. Domain-Name aus URL
domain = self._normalize_domain(website)
if domain and domain not in ["de", "com", "org"]:
terms.append(domain)
# 3. Erste zwei relevanten Wörter
name_parts = [p for p in re.split(r'\W+', clean_name) if p and len(p) > 3]
if len(name_parts) >= 2:
terms.append(" ".join(name_parts[:2]))
@@ -130,8 +125,68 @@ class WikipediaScraper:
debug_print(f"Generierte Suchbegriffe: {list(set(terms))}")
return list(set(terms))
def _validate_article(self, page, company_name, domain_hint):
"""Überprüft Artikelrelevanz"""
clean_title = re.sub(r'\(.*?\)', '', page.title).lower()
clean_company = re.sub(r'[^a-zäöüß ]', '', company_name.lower())
similarity = SequenceMatcher(None, clean_title, clean_company).ratio()
debug_print(f"Ähnlichkeit: {similarity:.2f} ({clean_title} vs {clean_company})")
if domain_hint:
try:
html_content = requests.get(page.url).text.lower()
if domain_hint not in html_content:
debug_print(f"Domain-Hint '{domain_hint}' nicht gefunden")
return False
except Exception as e:
debug_print(f"Domain-Check fehlgeschlagen: {str(e)}")
return similarity >= Config.SIMILARITY_THRESHOLD
@retry_on_failure
def search_company_article(self, company_name, website):
"""Hauptfunktion zur Artikelsuche"""
search_terms = self._generate_search_terms(company_name, website)
domain_hint = self._normalize_domain(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, domain_hint):
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
return None
def extract_company_data(self, page_url):
"""Extrahiert Daten aus dem Wikipedia-Artikel"""
if not page_url:
return {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': ''}
try:
response = requests.get(page_url)
soup = BeautifulSoup(response.text, Config.HTML_PARSER)
return {
'branche': self._extract_infobox_value(soup, 'branche'),
'umsatz': self._extract_infobox_value(soup, 'umsatz'),
'url': page_url
}
except Exception as e:
debug_print(f"Extraktionsfehler: {str(e)}")
return {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': page_url}
def _extract_infobox_value(self, soup, target):
"""Robuste Infobox-Extraktion mit erweiterten Mustern"""
"""Extrahiert Werte aus der Infobox"""
infobox = soup.find('table', class_=lambda c: c and any(
kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']
))
@@ -139,23 +194,19 @@ class WikipediaScraper:
if not infobox:
return "k.A."
# Erweiterte Keywords für Deutsch
keywords = {
'branche': [
'branche', 'industrie', 'tätigkeit',
'geschäftsfeld', 'sektor', 'produkte',
'leistungen', 'aktivitäten', 'wirtschaftszweig',
'geschäftsbereich', 'tätigkeitsbereich'
'leistungen', 'aktivitäten', 'wirtschaftszweig'
],
'umsatz': [
'umsatz', 'jahresumsatz', 'konzernumsatz',
'gesamtumsatz', 'erlöse', 'umsatzerlöse',
'einnahmen', 'ergebnis', 'betriebsergebnis',
'jahresergebnis', 'gewinn'
'einnahmen', 'ergebnis', 'jahresergebnis'
]
}[target]
# Durchsuche alle Tabellenzellen
for row in infobox.find_all('tr'):
header = row.find('th')
if header:
@@ -163,33 +214,27 @@ class WikipediaScraper:
if any(kw in header_text for kw in keywords):
value = row.find('td')
if not value:
continue
if value:
raw_value = clean_text(value.get_text())
raw_value = clean_text(value.get_text())
# Branchenbereinigung
if target == 'branche':
# Entferne Klammern und Sonderzeichen
clean = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value)
return ' '.join(clean.split()).strip()
# Umsatzbereinigung
if target == 'umsatz':
# Finde numerische Werte mit optionaler Einheit
match = re.search(
r'(\d{1,3}(?:[.,]\d{3})*)\s*'
r'(?:Mio\.?|Millionen|Mrd\.?|Milliarden)?\s*'
r'€?',
raw_value.replace('.', '').replace(',', '.'),
re.IGNORECASE
)
if match:
num = float(match.group(1))
if 'mrd' in raw_value.lower() or 'milliarden' in raw_value.lower():
num *= 1000
return f"{num:.1f} Mio €"
return raw_value.strip()
if target == 'branche':
clean = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value)
return ' '.join(clean.split()).strip()
if target == 'umsatz':
match = re.search(
r'(\d{1,3}(?:[.,]\d{3})*)\s*'
r'(?:Mio\.?|Millionen|Mrd\.?|Milliarden)?\s*'
r'€?',
raw_value.replace('.', '').replace(',', '.'),
re.IGNORECASE
)
if match:
num = float(match.group(1))
if 'mrd' in raw_value.lower():
num *= 1000
return f"{num:.1f} Mio €"
return raw_value.strip()
return "k.A."
@@ -216,35 +261,27 @@ class DataProcessor:
website = row_data[1] if len(row_data) > 1 else ""
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
# Wikipedia-Suche
article = self.wiki_scraper.search_company_article(company_name, website)
# Datenextraktion
company_data = self.wiki_scraper.extract_company_data(article.url) if article else {
'branche': 'k.A.',
'umsatz': 'k.A.',
'url': row_data[12] if len(row_data) > 12 else ""
}
if article:
company_data = self.wiki_scraper.extract_company_data(article.url)
else:
company_data = {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': ''}
# Sheet-Update
self._update_sheet(row_num, company_data)
time.sleep(Config.RETRY_DELAY)
def _update_sheet(self, row_num, data):
"""Aktualisiert die Zeilendaten"""
current_values = self.sheet_handler.sheet.row_values(row_num)
new_values = [
data['branche'] or (current_values[6] if len(current_values) > 6 else "k.A."),
"k.A.", # LinkedIn-Branche
data['umsatz'] or (current_values[8] if len(current_values) > 8 else "k.A."),
company_data['branche'] if company_data['branche'] != "k.A." else current_values[6] if len(current_values) > 6 else "k.A.",
"k.A.",
company_data['umsatz'] if company_data['umsatz'] != "k.A." else current_values[8] if len(current_values) > 8 else "k.A.",
"k.A.", "k.A.", "k.A.",
data['url'] or (current_values[12] if len(current_values) > 12 else ""),
company_data['url'] if company_data['url'] else current_values[12] if len(current_values) > 12 else "",
datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"k.A.", "k.A.",
Config.VERSION
]
self.sheet_handler.update_row(row_num, new_values)
print(f"✅ Aktualisiert: Branche: {new_values[0]}, Umsatz: {new_values[2]}, URL: {new_values[6]}")
time.sleep(Config.RETRY_DELAY)
# ==================== MAIN ====================
if __name__ == "__main__":