Brancheneinstufung2/brancheneinstufung.py

import os
import time
import re
import gspread
import wikipedia
import requests
from bs4 import BeautifulSoup
from oauth2client.service_account import ServiceAccountCredentials
from datetime import datetime
from difflib import SequenceMatcher
import csv

# ==================== KONFIGURATION ====================
class Config:
    VERSION = "1.0.13"
    LANG = "de"
    CREDENTIALS_FILE = "service_account.json"
    SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
    MAX_RETRIES = 3
    RETRY_DELAY = 5
    LOG_CSV = "gpt_antworten_log.csv"
    SIMILARITY_THRESHOLD = 0.65
    DEBUG = True
    WIKIPEDIA_SEARCH_RESULTS = 10
    HTML_PARSER = "html.parser"

# ==================== HELPER FUNCTIONS ====================
def retry_on_failure(func):
    """Decorator für Wiederholungsversuche bei Fehlern"""
    def wrapper(*args, **kwargs):
        for attempt in range(Config.MAX_RETRIES):
            try:
                return func(*args, **kwargs)
            except Exception as e:
                print(f"⚠️ Fehler bei {func.__name__} (Versuch {attempt+1}): {str(e)[:100]}")
                time.sleep(Config.RETRY_DELAY)
        return None
    return wrapper

def debug_print(message):
    """Debug-Ausgabe, wenn Config.DEBUG=True"""
    if Config.DEBUG:
        print(f"[DEBUG] {message}")

def clean_text(text):
    """Bereinigt Text von unerwünschten Zeichen"""
    if not text:
        return "k.A."
    
    text = str(text)
    text = re.sub(r'\[\d+\]', '', text)  # Entferne Referenznummern
    text = re.sub(r'\s+', ' ', text).strip()
    return text if text else "k.A."

# ==================== GOOGLE SHEET HANDLER ====================
class GoogleSheetHandler:
    """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"]
        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):
        """Ermittelt die erste leere Zeile in Spalte N"""
        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
        )
    
    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 ====================
class WikipediaScraper:
    """Handhabung der Wikipedia-Suche und Datenextraktion"""
    
    def __init__(self):
        wikipedia.set_lang(Config.LANG)
        
    def _extract_domain_hint(self, website):
        """Extrahiert Domain-Schlüssel aus URL"""
        if not website:
            return ""
        clean_url = re.sub(r'https?://(www\.)?', '', website.lower()).split('.')[0]
        return clean_url if clean_url not in ["de", "com", "org"] else ""

    def _generate_search_terms(self, company_name, website):
        """Generiert Suchbegriffe mit verbesserter Namensanalyse"""
        terms = []
        
        # Basisbegriffe
        base_name = re.sub(r'\s+(GmbH|AG|KG|Co\. KG).*$', '', company_name).strip()
        terms.append(base_name)
        
        # Domain-Hint
        domain_hint = self._extract_domain_hint(website)
        if domain_hint:
            terms.append(domain_hint)
            
        # Schlüsselwörter extrahieren
        name_parts = [p for p in re.split(r'\W+', base_name) if p and len(p) > 3]
        if len(name_parts) >= 2:
            terms.append(" ".join(name_parts[:2]))
            
        return list(set(terms))

    def _validate_article(self, page, company_name, domain_hint):
        """Artikelvalidierung mit erweiterten Checks"""
        # Titelbereinigung
        clean_title = re.sub(r'\(.*?\)|\s-\s.*', '', page.title).lower()
        clean_company = re.sub(r'[^a-zäöüß ]', '', company_name.lower())
        
        similarity = SequenceMatcher(None, clean_title, clean_company).ratio()
        debug_print(f"Ähnlichkeitscheck: {clean_title} vs {clean_company} = {similarity:.2f}")
        
        # Domain-Check
        if domain_hint:
            try:
                response = requests.get(page.url)
                if domain_hint not in response.text.lower():
                    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._extract_domain_hint(website)
        
        for term in search_terms:
            try:
                results = wikipedia.search(term, results=Config.WIKIPEDIA_SEARCH_RESULTS)
                debug_print(f"Suche '{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):
        """Detaillierte Infobox-Extraktion"""
        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"""
        debug_print(f"Starte Extraktion für: {target}")
        
        # Erweiterte Infobox-Erkennung
        infobox = soup.find('table', class_=lambda c: c and any(
            kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen', 'firmendaten']
        ))
        
        if not infobox:
            debug_print("Keine Infobox gefunden")
            return "k.A."

        # Erweiterte Keywords für Deutsch
        keywords = {
            'branche': [
                'branche', 'industrie', 'tätigkeitsfeld',
                'geschäftsfeld', 'sektor', 'branchen',
                'wirtschaftszweig', 'tätigkeitsbereich',
                'produkte', 'leistungen', 'aktivität'
            ],
            'umsatz': [
                'umsatz', 'jahresumsatz', 'konzernumsatz',
                'gesamtumsatz', 'umsatzerlöse', 'erlöse',
                'umsatzentwicklung', 'ergebnis', 
                'einnahmen', 'jahresergebnis'
            ]
        }[target]

        # Durchsuche alle Tabellenzeilen
        for row in infobox.find_all('tr'):
            header = row.find('th')
            if header:
                header_text = clean_text(header.get_text()).lower()
                debug_print(f"Prüfe Header: {header_text}")
                
                if any(kw in header_text for kw in keywords):
                    value_cell = row.find('td')
                    if value_cell:
                        value = clean_text(value_cell.get_text())
                        
                        # Branchenbereinigung
                        if target == 'branche':
                            # Entferne Klammerzusätze und Formatierungen
                            value = re.sub(r'\[.*?\]|\(.*?\)', '', value)
                            return ' '.join(value.split()).strip()
                        
                        # Umsatzbereinigung
                        if target == 'umsatz':
                            # Finde numerische Werte
                            match = re.search(
                                r'(\d{1,3}(?:[.,]\d{3})*)\s*'
                                r'(?:Mio\.?|Millionen|Mrd\.?|Milliarden)?\s*'
                                r'(?:€|Euro|EUR)?',
                                value.replace('.', '').replace(',', '.'),
                                re.IGNORECASE
                            )
                            if match:
                                num_value = float(match.group(1))
                                if 'mrd' in value.lower() or 'milliarden' in value.lower():
                                    num_value *= 1000
                                return f"{num_value:.1f} Mio €"
                            return value.strip()
        
        debug_print(f"{target} nicht gefunden")
        return "k.A."

# ==================== DATA PROCESSOR ====================
class DataProcessor:
    """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):
        """Verarbeitung einer einzelnen 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}")
        
        # 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 ""
        }
        
        # 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."),
            "k.A.", "k.A.", "k.A.",
            data['url'] or (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]}")

# ==================== MAIN ====================
if __name__ == "__main__":
    num_rows = int(input("Wieviele Zeilen sollen überprüft werden? "))
    processor = DataProcessor()
    processor.process_rows(num_rows)
    print("\n✅ Wikipedia-Auswertung abgeschlossen")