syntax fix

brancheneinstufung.py

@@ -1,16 +1,15 @@
-import os
+    import os
-import time
+    import time
-import re
+    import re
-import gspread
+    import gspread
-import wikipedia
+    import wikipedia
-import requests
+    import requests
-from bs4 import BeautifulSoup
+    from bs4 import BeautifulSoup
-from oauth2client.service_account import ServiceAccountCredentials
+    from oauth2client.service_account import ServiceAccountCredentials
-from datetime import datetime
+    from datetime import datetime
-from difflib import SequenceMatcher
+    from difflib import SequenceMatcher
-import csv
+    import csv
-
+    # ==================== KONFIGURATION ====================
-# ==================== KONFIGURATION ====================
 class Config:
     VERSION = "1.1.1"
     LANG = "de"
@@ -23,9 +22,8 @@ class Config:
     DEBUG = True
     WIKIPEDIA_SEARCH_RESULTS = 8
     HTML_PARSER = "html.parser"
-
+    # ==================== HELPER FUNCTIONS ====================
-# ==================== HELPER FUNCTIONS ====================
+    def retry_on_failure(func):
-def retry_on_failure(func):
         """Decorator für Wiederholungsversuche bei Fehlern"""
     def wrapper(*args, **kwargs):
     for attempt in range(Config.MAX_RETRIES):
@@ -36,17 +34,14 @@ def retry_on_failure(func):
         time.sleep(Config.RETRY_DELAY)
         return None
         return wrapper
-
+        def debug_print(message):
-def debug_print(message):
         """Debug-Ausgabe, wenn Config.DEBUG=True"""
     if Config.DEBUG:
         print(f"[DEBUG] {message}")
-
+        def clean_text(text):
-def clean_text(text):
         """Bereinigt Text von HTML-Entitäten und überflüssigen Whitespaces"""
     if not text:
         return "k.A."
-    
             # Konvertierung und Säuberung
         text = str(text)
         text = re.sub(r'\[.*?\]', '', text)  # Entferne eckige Klammern mit Inhalt
@@ -54,16 +49,13 @@ def clean_text(text):
         text = re.sub(r'<.*?>', '', text)    # Entferne HTML-Tags
         text = re.sub(r'\s+', ' ', text).strip()
         return text if text else "k.A."
-
+            # ==================== GOOGLE SHEET HANDLER ====================
-# ==================== GOOGLE SHEET HANDLER ====================
 class GoogleSheetHandler:
     """Klasse zur Handhabung der Google Sheets Interaktion"""
-    
     def __init__(self):
         self.sheet = None
         self.sheet_values = []
         self._connect()
-        
         def _connect(self):
         """Stellt Verbindung zum Google Sheet her"""
         scope = ["https://www.googleapis.com/auth/spreadsheets"]
@@ -72,7 +64,6 @@ class GoogleSheetHandler:
         )
         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):
         """Ermittelt die erste leere Zeile in Spalte N (Index 13)"""
         filled_n = [row[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
@@ -80,22 +71,18 @@ class GoogleSheetHandler:
         (i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()),
         len(filled_n) + 1
         )
-    
         def update_row(self, row_num, values):
         """Aktualisiert eine Zeile im Sheet"""
         self.sheet.update(
         range_name=f"G{row_num}:Q{row_num}",
         values=[values]
         )
-
+            # ==================== WIKIPEDIA SCRAPER ====================
-# ==================== WIKIPEDIA SCRAPER ====================
+        class_=lambda c: c and any(
-class_=lambda c: c and any(
         kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']
-        )
+        ))
-        
         if not infobox:
         return "k.A."
-
             keywords = {
         'branche': [
         'branche', 'industrie', 'tätigkeitsfeld', 'geschäftsfeld',
@@ -106,7 +93,6 @@ class_=lambda c: c and any(
         'konzernumsatz', 'umsatzentwicklung', 'ergebnis'
         ]
         }.get(target, [])
-
             # Durchsuche alle Zeilen und Zellen
         value = "k.A."
     for row in infobox.find_all('tr'):
@@ -114,7 +100,6 @@ class_=lambda c: c and any(
         header_cells = row.find_all(['th', 'td'], attrs={'colspan': False})
     for header in header_cells:
         header_text = clean_text(header.get_text()).lower()
-                
         if any(kw in header_text for kw in keywords):
         # Hole nächste Zelle, ignoriere verschachtelte Tabellen
         value_cell = header.find_next_sibling(['td', 'th'])
@@ -125,7 +110,6 @@ class_=lambda c: c and any(
         value = ', '.join(clean_text(li.get_text()) for li in list_items)
     else:
         value = clean_text(value_cell.get_text())
-                        
             # Extrahiere numerische Umsatzwerte mit Regex
     if target == 'umsatz':
         match = re.search(
@@ -135,13 +119,10 @@ class_=lambda c: c and any(
     if match:
         value = match.group(1).replace('.', '').replace(',', '.')
         return value
-
             return "k.A."
-
+    class WikipediaScraper:
-class WikipediaScraper:
     def __init__(self):
         wikipedia.set_lang(Config.LANG)
-
         def _extract_domain_hint(self, website):
         """Extrahiert den Domain-Schlüssel aus der Website-URL"""
     if not website:
@@ -150,66 +131,52 @@ class WikipediaScraper:
         clean_url = website.lower().replace("https://", "").replace("http://", "").replace("www.", "")
         domain_parts = clean_url.split(".")
         return domain_parts[0] if domain_parts else ""
-
         def _generate_search_terms(self, company_name, website_hint=""):
         """Generiert Suchbegriffe aus Firmenname und Website"""
         search_terms = [company_name.strip()]
-            
             # Bereinigung von Rechtsformen und Sonderzeichen
         clean_name = re.sub(
         r'\s+(?:GmbH|AG|KG|OHG|e\.V\.|mbH|& Co\. KG| GmbH & Co\. KG).*$', 
         '', 
         company_name
         ).strip()
-            
             # Füge bereinigten Namen hinzu, wenn unterschiedlich
     if clean_name and clean_name != company_name:
         search_terms.append(clean_name)
-                
             # Extrahiere erste zwei relevante Wörter
         name_words = [w for w in re.split(r'\W+', clean_name) if w]
     if len(name_words) >= 2:
         search_terms.append(" ".join(name_words[:2]))
-                
             # Domain-Hint hinzufügen
         domain_hint = self._extract_domain_hint(website_hint)
     if domain_hint and domain_hint not in ["de", "com", "org", "net"]:
         search_terms.append(domain_hint)
-                
             debug_print(f"Generierte Suchbegriffe: {search_terms}")
         return list(set(search_terms))  # Duplikate entfernen
-
         def _validate_article(self, page, company_name, domain_hint=""):
         """Überprüft ob der Artikel zum Unternehmen passt"""
         # Normalisiere beide Namen
         page_title = re.sub(r'\(.*?\)', '', page.title).strip().lower()
         search_name = re.sub(r'[^a-zA-Z0-9äöüß ]', '', company_name).strip().lower()
-            
             # Ähnlichkeitsprüfung
         similarity = SequenceMatcher(None, page_title, search_name).ratio()
         debug_print(f"Ähnlichkeit '{page_title}' vs '{search_name}': {similarity:.2f}")
-            
             # Zusätzliche Domain-Prüfung
     if domain_hint:
         html_content = requests.get(page.url).text.lower()
     if domain_hint not in html_content:
         debug_print(f"Domain-Hint '{domain_hint}' nicht im Artikel gefunden")
         return False
-                    
             return similarity >= Config.SIMILARITY_THRESHOLD
-        
             @retry_on_failure
-
         def search_company_article(self, company_name, website_hint=""):
         """Hauptfunktion zur Artikelsuche"""
         search_terms = self._generate_search_terms(company_name, website_hint)
         domain_hint = self._extract_domain_hint(website_hint)
-            
         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)
@@ -221,63 +188,49 @@ class WikipediaScraper:
         debug_print(f"Fehler bei Suche nach {term}: {str(e)}")
         continue
         return None
-
         def extract_company_data(self, page_url):
         """Extrahiert Branche und Umsatz aus dem Wikipedia-Artikel"""
         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
         }
-        
-    
                 # ==================== WIKIPEDIA SCRAPER ====================
-    class WikipediaScraper:
+class WikipediaScraper:
-
     def _extract_infobox_value(self, soup, target):
         """Extrahiert spezifischen Wert aus der Infobox mit erweiterten Suchmustern"""
         # Erweiterte Infobox-Erkennung
         infobox = soup.find('table',
-
+            # ==================== DATA PROCESSOR ====================
-# ==================== DATA PROCESSOR ====================
 class DataProcessor:
     """Klasse zur Steuerung des Gesamtprozesses"""
-    
     def __init__(self):
         self.sheet_handler = GoogleSheetHandler()
         self.wiki_scraper = WikipediaScraper()
-        
         def process_rows(self, num_rows):
         """Verarbeitet die angegebene Anzahl an Zeilen"""
         start_index = self.sheet_handler.get_start_index()
         print(f"Starte bei Zeile {start_index+1}")
-        
         for i in range(start_index, min(start_index + num_rows, len(self.sheet_handler.sheet_values))):
         row = self.sheet_handler.sheet_values[i]
         self._process_single_row(i+1, row)
-            
         def _process_single_row(self, row_num, row_data):
         """Verarbeitet eine einzelne Zeile"""
         company_name = row_data[0] if len(row_data) > 0 else ""
         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}")
-        
             # Schritt 1: Wikipedia-Artikel finden
         article = self.wiki_scraper.search_company_article(company_name, website)
-        
             # Schritt 2: Daten extrahieren
     if article:
         company_data = self.wiki_scraper.extract_company_data(article.url)
     else:
         company_data = {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': ''}
-        
             # Aktualisiere Daten im Sheet
         self._update_sheet(row_num, company_data)
         time.sleep(Config.RETRY_DELAY)
-        
         def _update_sheet(self, row_num, data):
         """Aktualisiert die Zeile mit den neuen Daten"""
         current_values = self.sheet_handler.sheet.row_values(row_num)
@@ -293,9 +246,8 @@ class DataProcessor:
         ]
         self.sheet_handler.update_row(row_num, new_values)
         print(f"✅ Aktualisiert: Branche: {new_values[0]}, Umsatz: {new_values[2]}, URL: {new_values[6]}")
-
+            # ==================== MAIN EXECUTION ====================
-# ==================== MAIN EXECUTION ====================
+    if __name__ == "__main__":
-if __name__ == "__main__":
         num_rows = int(input("Wieviele Zeilen sollen überprüft werden? "))
         processor = DataProcessor()
         processor.process_rows(num_rows)