Floke
3cbb74047e
• Neue Funktionen parse_currency_value und parse_employee_value extrahieren und wandeln Umsatz- bzw. Mitarbeiterzahlen korrekt um. • Tausenderpunkte werden entfernt und Kommas als Dezimaltrenner ersetzt. • Skalierung: Bei "mrd." wird der Wert mit 1000 multipliziert, bei "mio." bleibt er unverändert, andernfalls wird angenommen, dass der Wert in Euro vorliegt und durch 1e6 geteilt wird. • Im Vergleichsabschnitt werden die zu vergleichenden Werte vor dem Versuch der Umwandlung geloggt. • Es wird eine 1-Sekunden-Pause nach dem Schreiben in Google Sheets eingefügt, um sicherzustellen, dass die Daten übernommen wurden.
359 lines
17 KiB
Python
359 lines
17 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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import openai # falls für ChatGPT-API benötigt
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# ==================== KONFIGURATION ====================
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class Config:
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VERSION = "1.2.8"
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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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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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if Config.DEBUG:
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print(f"[DEBUG] {message}")
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def clean_text(text):
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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)
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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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if not name:
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return ""
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forms = [
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r'gmbh', r'g\.m\.b\.h\.', r'ug', r'u\.g\.', r'ug \(haftungsbeschränkt\)',
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r'u\.g\. \(haftungsbeschränkt\)', r'ag', r'a\.g\.', r'ohg', r'o\.h\.g\.',
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r'kg', r'k\.g\.', r'gmbh & co\.?\s*kg', r'g\.m\.b\.h\. & co\.?\s*k\.g\.',
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r'ag & co\.?\s*kg', r'a\.g\. & co\.?\s*k\.g\.', r'e\.k\.', r'e\.kfm\.',
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r'e\.kfr\.', r'ltd\.', r'ltd & co\.?\s*kg', r's\.a r\.l\.', r'stiftung',
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r'genossenschaft', r'ggmbh', r'gug', r'partg', r'partgmbb', r'kgaa', r'se',
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r'og', r'o\.g\.', r'e\.u\.', r'ges\.n\.b\.r\.', r'genmbh', r'verein',
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r'kollektivgesellschaft', r'kommanditgesellschaft', r'einzelfirma', r'sàrl',
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r'sa', r'sagl', r'gmbh & co\.?\s*ohg', r'ag & co\.?\s*ohg', r'gmbh & co\.?\s*kgaa',
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r'ag & co\.?\s*kgaa', r's\.a\.', r's\.p\.a\.', r'b\.v\.', r'n\.v\.'
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]
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pattern = r'\b(' + '|'.join(forms) + r')\b'
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normalized = re.sub(pattern, '', name, flags=re.IGNORECASE)
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normalized = re.sub(r'[\-–]', ' ', normalized)
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normalized = re.sub(r'\s+', ' ', normalized).strip()
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return normalized.lower()
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def parse_currency_value(text):
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"""
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Parst einen Umsatz-Text. Entfernt Tausenderpunkte, ersetzt Komma als Dezimaltrenner.
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Ermittelt den Skalierungsfaktor: 'mrd' -> 1000, 'mio' -> 1, sonst Annahme: Euro -> /1e6.
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Liefert den Wert in Mio. Euro als float.
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"""
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raw = text.lower()
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# Entferne unnötige Zeichen und Einheiten
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raw = raw.replace("€", "").replace("eur", "")
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# Prüfe auf Skalierung
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scale = None
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if "mrd" in raw or "milliarden" in raw:
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scale = 1000
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elif "mio" in raw or "millionen" in raw:
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scale = 1
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# Extrahiere den numerischen Teil (erlaubt Punkte und Kommas)
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match = re.search(r'([\d.,]+)', raw)
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if not match:
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return None
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num_str = match.group(1).strip()
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# Wenn ein Komma vorkommt, nehme an, dass es der Dezimaltrenner ist und entferne Punkte
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if ',' in num_str:
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num_str = num_str.replace('.', '')
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num_str = num_str.replace(',', '.')
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else:
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# sonst entferne alle Punkte (als Tausendertrenner)
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num_str = num_str.replace('.', '')
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try:
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value = float(num_str)
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except Exception as e:
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debug_print(f"Fehler beim Parsen von Umsatz: {num_str} - {e}")
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return None
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if scale is None:
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# Wenn keine Skalierung angegeben, gehe davon aus, dass der Wert in Euro vorliegt
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value /= 1e6
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else:
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value *= scale
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return value
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def parse_employee_value(text):
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"""
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Parst Mitarbeiterzahlen. Entfernt "ca.", Leerzeichen und Tausenderpunkte.
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Liefert den Wert als int.
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"""
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raw = text.lower()
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raw = raw.replace("ca.", "").strip()
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# Entferne Klammerinhalt (z. B. (2021/22))
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raw = re.sub(r'\(.*?\)', '', raw)
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# Entferne Leerzeichen
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raw = raw.replace(" ", "")
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# Entferne Tausendertrenner (Punkt) und ersetze Komma durch Punkt
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raw = raw.replace(".", "").replace(",", ".")
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try:
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return int(float(raw))
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except Exception as e:
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debug_print(f"Fehler beim Parsen von Mitarbeiterzahl: {raw} - {e}")
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return None
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# ==================== GOOGLE SHEET HANDLER ====================
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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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# ==================== 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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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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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_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']
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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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parsed = parse_currency_value(raw_value)
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return str(parsed) if parsed is not None else raw_value.strip()
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if target == 'mitarbeiter':
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parsed = parse_employee_value(raw_value)
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return str(parsed) if parsed is not None else raw_value.strip()
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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 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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if not page_url:
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return {'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', 'umsatz': 'k.A.', 'mitarbeiter': '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', 'Mitarbeiterzahl'])
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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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mitarbeiter_val = extracted_fields.get('Mitarbeiterzahl', self._extract_infobox_value(soup, 'mitarbeiter'))
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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': branche_val,
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'umsatz': umsatz_val,
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'mitarbeiter': mitarbeiter_val
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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 {'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.'}
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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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# ==================== DATA PROCESSOR ====================
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class DataProcessor:
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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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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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company_name = row_data[1] if len(row_data) > 1 else ""
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website = row_data[2] if len(row_data) > 2 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 = {'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.', 'umsatz': 'k.A.', 'mitarbeiter': 'k.A.'}
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# Beispiel: Schreibe die Wikipedia-Daten in Spalten J bis N (anpassen an das neue Schema)
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self.sheet_handler.sheet.update(values=[[
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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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]], range_name=f"J{row_num}:N{row_num}")
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# Füge 1 Sekunde Pause ein, damit die Sheets-Daten sicher gespeichert werden
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time.sleep(1)
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# Hier folgt der Umsatzvergleich (CRM vs. Wikipedia) – Debug-Ausgabe
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crm_umsatz = row_data[?] # Hier anpassen: Spalte mit CRM-Umsatz
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wiki_umsatz = company_data.get('umsatz', 'k.A.')
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debug_print(f"Vergleich CRM Umsatz: '{crm_umsatz}' mit Wikipedia Umsatz: '{wiki_umsatz}'")
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try:
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crm_value = float(crm_umsatz.replace(',', '.'))
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wiki_value = float(wiki_umsatz)
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debug_print(f"Vergleich CRM Umsatz: '{crm_value}' mit Wikipedia Umsatz: '{wiki_value}'")
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except Exception as e:
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debug_print(f"Fehler beim Umwandeln der Werte: CRM='{crm_umsatz}', Wiki='{wiki_umsatz}': {e}")
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# Aktualisiere weitere Spalten (Timestamp, Version etc.)
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current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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self.sheet_handler.sheet.update(values=[[current_dt]], range_name=f"AH{row_num}")
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self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=f"AI{row_num}")
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print(f"✅ Aktualisiert: URL: {company_data.get('url', 'k.A.')}, Absatz: {company_data.get('first_paragraph', 'k.A.')[:30]}..., Branche: {company_data.get('branche', 'k.A.')}, Umsatz: {company_data.get('umsatz', 'k.A.')}, Mitarbeiter: {company_data.get('mitarbeiter', 'k.A.')}")
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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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