Floke
ed0cea7cc7
Vollständige Domain-Extraktion: Implementiert über die neue Methode _get_full_domain, die nun den kompletten Domainnamen (inklusive TLD) liefert (z. B. "heimbach.com"). Normalisierung der Firmennamen: Einführung der Funktion normalize_company_name, welche gängige Firmierungsformen (z. B. GmbH, AG, Aktiengesellschaft, Co. KG, mbH, & Co. KG, e.V., Limited, Ltd, Inc, Corp, Corporation, Gruppe) entfernt. Dies führt zu einem konsistenten Vergleich zwischen den Unternehmensdaten und Wikipedia-Titeln. Verbesserte Artikelvalidierung: In _validate_article werden nun: Infobox-Links sowie externe Links geprüft, ob sie den vollständigen Domainnamen enthalten (ohne Dateilinks). Der Vergleich der Wikipedia-Titel und des Firmennamens erfolgt auf Basis der normalisierten Namen. Ein dynamischer Schwellenwert wird verwendet (0.60 statt 0.65), wenn ein definitiver Link-Match gefunden wurde.
313 lines
14 KiB
Python
313 lines
14 KiB
Python
import os
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import time
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import re
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import gspread
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import wikipedia
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import requests
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from bs4 import BeautifulSoup
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from oauth2client.service_account import ServiceAccountCredentials
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from datetime import datetime
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from difflib import SequenceMatcher
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import csv
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# ==================== KONFIGURATION ====================
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class Config:
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VERSION = "1.1.3"
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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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MAX_RETRIES = 3
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RETRY_DELAY = 5
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LOG_CSV = "gpt_antworten_log.csv"
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SIMILARITY_THRESHOLD = 0.65
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DEBUG = True
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WIKIPEDIA_SEARCH_RESULTS = 5
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HTML_PARSER = "html.parser"
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# ==================== HELPER FUNCTIONS ====================
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def retry_on_failure(func):
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"""Decorator für Wiederholungsversuche bei Fehlern"""
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def wrapper(*args, **kwargs):
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for attempt in range(Config.MAX_RETRIES):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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print(f"⚠️ Fehler bei {func.__name__} (Versuch {attempt+1}): {str(e)[:100]}")
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time.sleep(Config.RETRY_DELAY)
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return None
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return wrapper
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def debug_print(message):
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"""Debug-Ausgabe, wenn Config.DEBUG=True"""
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if Config.DEBUG:
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print(f"[DEBUG] {message}")
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def clean_text(text):
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"""Bereinigt Text von unerwünschten Zeichen"""
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if not text:
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return "k.A."
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text = str(text)
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text = re.sub(r'\[\d+\]', '', text) # Entferne Referenznummern
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text = re.sub(r'\s+', ' ', text).strip()
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return text if text else "k.A."
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def normalize_company_name(name):
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"""Entfernt gängige Firmierungsformen und normalisiert den Namen."""
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if not name:
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return ""
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# Liste gängiger Firmierungsformen, inklusive "Gruppe"
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pattern = r'\b(gmbh|ag|aktiengesellschaft|co\.?\s*kg|mbh|&\s*co\.?\s*kg|e\.v\.|limited|ltd|inc|corp|corporation|gruppe)\b'
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normalized = re.sub(pattern, '', name, flags=re.IGNORECASE)
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normalized = re.sub(r'[\-–]', ' ', normalized) # Ersetze Bindestriche durch Leerzeichen
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normalized = re.sub(r'\s+', ' ', normalized).strip()
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return normalized.lower()
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# ==================== GOOGLE SHEET HANDLER ====================
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class GoogleSheetHandler:
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"""Handhabung der Google Sheets Interaktion"""
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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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"""Stellt Verbindung zum Google Sheet her"""
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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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"""Ermittelt die erste leere Zeile in Spalte N"""
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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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def update_row(self, row_num, values):
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"""Aktualisiert eine Zeile im Sheet (Spalten G bis R, also 12 Spalten)"""
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self.sheet.update(range_name=f"G{row_num}:R{row_num}", values=[values])
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# ==================== WIKIPEDIA SCRAPER ====================
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class WikipediaScraper:
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"""Klasse zur Handhabung der Wikipedia-Suche und Datenextraktion"""
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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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"""Extrahiert den vollständigen Domainnamen (inklusive Topleveldomain) aus der URL."""
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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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website = website.split('/')[0]
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return website
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def _generate_search_terms(self, company_name, website):
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"""
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Generiert Suchbegriffe in folgender Reihenfolge:
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1. Vollständiger Domainname (z. B. "heimbach.com")
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2. Die ersten zwei Wörter des normalisierten Firmennamens
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3. Der vollständige, normalisierte Firmenname
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"""
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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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"""
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Validiert den Artikel:
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- Sucht in der Infobox und in den externen Links nach Links, die den vollständigen Domainnamen enthalten.
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Wird ein solcher Link gefunden, wird ein niedrigerer Schwellenwert (0.60) angewendet.
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- Andernfalls werden der normalisierte Wikipedia-Titel und der normalisierte Firmenname verglichen.
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"""
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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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# Suche in der Infobox
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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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# Überspringe Dateilinks
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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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# Suche in den externen Links, falls vorhanden
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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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ext_link = ext_link.lower()
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if full_domain in ext_link:
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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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# Normalisierte Namen für Vergleich
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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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@retry_on_failure
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def search_company_article(self, company_name, website):
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"""Sucht mit optimierten Suchbegriffen (vollständiger Domainname, Candidate, normalisierter Name) nach dem Wikipedia-Artikel."""
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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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def _extract_infobox_value(self, soup, target):
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"""Extrahiert Werte aus der Infobox (Fallback-Methode)"""
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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 = {
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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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}[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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match = re.search(r'(\d{1,3}(?:[.,]\d{3})*)\s*(?:Mio\.?|Millionen|Mrd\.?|Milliarden)?\s*€?', raw_value.replace('.', '').replace(',', '.'), re.IGNORECASE)
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if match:
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num = float(match.group(1))
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if 'mrd' in raw_value.lower():
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num *= 1000
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return f"{num:.1f} Mio €"
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return raw_value.strip()
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return "k.A."
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def extract_full_infobox(self, soup):
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"""Extrahiert die komplette Infobox als Text"""
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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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"""Extrahiert die gewünschten Felder aus dem Infobox-Text (getrennt durch ' | ')"""
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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 token.lower() == field.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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"""Extrahiert Daten aus dem Wikipedia-Artikel (Infobox, Branche, Umsatz)"""
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if not page_url:
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return {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': '', 'full_infobox': 'k.A.'}
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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'])
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branche_val = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche'))
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umsatz_val = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz'))
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return {'full_infobox': full_infobox, 'branche': branche_val, 'umsatz': umsatz_val, 'url': page_url}
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except Exception as e:
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debug_print(f"Extraktionsfehler: {str(e)}")
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return {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': page_url, 'full_infobox': 'k.A.'}
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# ==================== DATA PROCESSOR ====================
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class DataProcessor:
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"""Steuerung des Gesamtprozesses"""
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def __init__(self):
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self.sheet_handler = GoogleSheetHandler()
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self.wiki_scraper = WikipediaScraper()
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def process_rows(self, num_rows):
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"""Verarbeitet die angegebene Anzahl an Zeilen"""
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start_index = self.sheet_handler.get_start_index()
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print(f"Starte bei Zeile {start_index+1}")
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for i in range(start_index, min(start_index + num_rows, len(self.sheet_handler.sheet_values))):
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row = self.sheet_handler.sheet_values[i]
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self._process_single_row(i+1, row)
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def _process_single_row(self, row_num, row_data):
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"""Verarbeitung einer einzelnen Zeile"""
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company_name = row_data[0] if len(row_data) > 0 else ""
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website = row_data[1] if len(row_data) > 1 else ""
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print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
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article = self.wiki_scraper.search_company_article(company_name, website)
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if article:
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company_data = self.wiki_scraper.extract_company_data(article.url)
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else:
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company_data = {'branche': 'k.A.', 'umsatz': 'k.A.', 'url': '', 'full_infobox': 'k.A.'}
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current_values = self.sheet_handler.sheet.row_values(row_num)
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new_values = [
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company_data.get('full_infobox', 'k.A.'), # Spalte G: kompletter Infobox-Text
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company_data['branche'] if company_data['branche'] != "k.A." else current_values[6] if len(current_values) > 6 else "k.A.",
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"k.A.",
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company_data['umsatz'] if company_data['umsatz'] != "k.A." else current_values[8] if len(current_values) > 8 else "k.A.",
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"k.A.", "k.A.", "k.A.",
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company_data['url'] if company_data['url'] else current_values[12] if len(current_values) > 12 else "",
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datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"k.A.", "k.A.",
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Config.VERSION
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]
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self.sheet_handler.update_row(row_num, new_values)
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print(f"✅ Aktualisiert: Branche: {new_values[1]}, Umsatz: {new_values[3]}, URL: {new_values[7]}")
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time.sleep(Config.RETRY_DELAY)
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# ==================== MAIN ====================
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if __name__ == "__main__":
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try:
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num_rows = int(input("Wieviele Zeilen sollen überprüft werden? "))
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except Exception as e:
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print("Ungültige Eingabe. Bitte eine Zahl eingeben.")
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exit(1)
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processor = DataProcessor()
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processor.process_rows(num_rows)
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print("\n✅ Wikipedia-Auswertung abgeschlossen")
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