Floke
74b1395c57
Erweiterte String-Bereinigung: Zusätzliche Ersetzungen in extract_numeric_value (Entfernung von "ca.", "circa", "etwa", "über", "rund") zur robusteren Extraktion numerischer Werte aus Rohtexten. Fallback-Handling: Bei Fehlern in der Umwandlung wird der Originaltext oder "k.A." als Fallback ausgegeben. Zusätzliche Debug-Ausgabe: Vor dem Vergleich der CRM- und Wikipedia-Umsätze werden die bereinigten Werte im Debug-Log ausgegeben. Kurze Pause (1 Sekunde): Eine 1-Sekunden-Pause wird nach dem Update in Google Sheets eingefügt, um sicherzustellen, dass die Werte vor dem Vergleich vollständig gespeichert sind.
486 lines
22 KiB
Python
486 lines
22 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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import openai
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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 unicodedata
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import csv
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# ==================== KONFIGURATION ====================
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class Config:
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VERSION = "v1.2.6" # v1.2.6: Erweiterte String-Bereinigung und robustere Umwandlung von Umsatz/Mitarbeiterwerten
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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 = unicodedata.normalize("NFKC", 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 extract_numeric_value(raw_value, is_umsatz=False):
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# Erweiterte Vorverarbeitung: Entferne gängige Zusätze und unerwünschte Zeichen.
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raw_value = raw_value.strip()
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if not raw_value:
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return "k.A."
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# Entferne häufige Zusätze
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raw_value = re.sub(r'\b(ca\.?|circa|etwa|über|rund)\b', '', raw_value, flags=re.IGNORECASE)
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# Entferne unnötige Leerzeichen und Sonderzeichen
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raw_value = raw_value.replace("\xa0", " ").strip()
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raw = raw_value.lower()
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match = re.search(r'([\d.,]+)', raw, flags=re.UNICODE)
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if not match or not match.group(1).strip():
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debug_print(f"Keine numerischen Zeichen gefunden im Rohtext: '{raw_value}'")
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return "k.A."
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num_str = match.group(1)
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# Prüfe, ob Komma als Dezimaltrennzeichen genutzt wird (deutsches Format)
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if ',' in num_str and num_str.count(',') == 1 and len(num_str.split(',')[1]) <= 2:
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num_str = num_str.replace('.', '').replace(',', '.')
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else:
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num_str = num_str.replace(' ', '').replace('.', '')
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try:
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num = float(num_str)
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except Exception as e:
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debug_print(f"Fehler bei der Umwandlung von '{num_str}' (Rohtext: '{raw_value}'): {e}")
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return raw_value
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if is_umsatz:
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if "mrd" in raw or "milliarden" in raw:
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num *= 1000
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elif "mio" in raw or "millionen" in raw:
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pass
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else:
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num /= 1e6
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return str(int(round(num)))
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else:
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return str(int(round(num)))
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def compare_umsatz_values(crm, wiki):
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debug_print(f"Vergleich CRM Umsatz: '{crm}' mit Wikipedia Umsatz: '{wiki}'")
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try:
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crm_val = float(crm)
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wiki_val = float(wiki)
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except Exception as e:
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debug_print(f"Fehler beim Umwandeln der Werte: CRM='{crm}', Wiki='{wiki}': {e}")
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return "Daten unvollständig"
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if crm_val == 0:
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return "CRM Umsatz 0"
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diff = abs(crm_val - wiki_val) / crm_val
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if diff < 0.1:
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return "OK"
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else:
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diff_mio = abs(crm_val - wiki_val)
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return f"Abweichung: {int(round(diff_mio))} Mio €"
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def evaluate_umsatz_chatgpt(company_name, wiki_umsatz):
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try:
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with open("api_key.txt", "r") as f:
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api_key = f.read().strip()
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except Exception as e:
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debug_print(f"Fehler beim Lesen des API-Tokens: {e}")
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return "k.A."
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openai.api_key = api_key
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prompt = (f"Bitte schätze den Umsatz in Mio. Euro für das Unternehmen '{company_name}'. "
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f"Die Wikipedia-Daten zeigen: '{wiki_umsatz}'. "
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"Antworte nur mit der Zahl.")
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try:
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.0
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)
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result = response.choices[0].message.content.strip()
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debug_print(f"ChatGPT Antwort: '{result}'")
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try:
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value = float(result.replace(',', '.'))
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return str(int(round(value)))
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except Exception as conv_e:
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debug_print(f"Fehler bei der Verarbeitung der ChatGPT-Antwort '{result}': {conv_e}")
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return result
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except Exception as e:
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debug_print(f"Fehler beim Aufruf der ChatGPT API: {e}")
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return "k.A."
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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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# ==================== ALIGNMENT DEMO (Modus 3) ====================
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def alignment_demo(sheet):
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new_headers = [
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"Spalte A (ReEval Flag)",
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"Spalte B (Firmenname)",
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"Spalte C (Website)",
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"Spalte D (Ort)",
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"Spalte E (Beschreibung)",
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"Spalte F (Aktuelle Branche)",
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"Spalte G (Beschreibung Branche extern)",
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"Spalte H (Anzahl Techniker CRM)",
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"Spalte I (Umsatz CRM)",
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"Spalte J (Anzahl Mitarbeiter CRM)",
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"Spalte K (Vorschlag Wiki URL)",
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"Spalte L (Wikipedia URL)",
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"Spalte M (Wikipedia Absatz)",
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"Spalte N (Wikipedia Branche)",
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"Spalte O (Wikipedia Umsatz)",
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"Spalte P (Wikipedia Mitarbeiter)",
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"Spalte Q (Wikipedia Kategorien)",
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"Spalte R (Konsistenzprüfung)",
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"Spalte S (Begründung bei Inkonsistenz)",
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"Spalte T (Vorschlag Wiki Artikel ChatGPT)",
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"Spalte U (Begründung bei Abweichung)",
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"Spalte V (Vorschlag neue Branche)",
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"Spalte W (Konsistenzprüfung Branche)",
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"Spalte X (Begründung Abweichung Branche)",
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"Spalte Y (FSM Relevanz Ja / Nein)",
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"Spalte Z (Begründung für FSM Relevanz)",
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"Spalte AA (Schätzung Anzahl Mitarbeiter)",
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"Spalte AB (Konsistenzprüfung Mitarbeiterzahl)",
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"Spalte AC (Begründung für Abweichung Mitarbeiterzahl)",
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"Spalte AD (Einschätzung Anzahl Servicetechniker)",
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"Spalte AE (Begründung bei Abweichung Anzahl Servicetechniker)",
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"Spalte AF (Schätzung Umsatz ChatGPT)",
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"Spalte AG (Begründung für Abweichung Umsatz)",
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"Spalte AH (Timestamp letzte Prüfung)",
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"Spalte AI (Version)"
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]
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header_range = "A11200:AI11200"
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sheet.update(values=[new_headers], range_name=header_range)
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print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.")
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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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return website.split('/')[0]
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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_categories(self, soup):
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cat_div = soup.find('div', id="mw-normal-catlinks")
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if cat_div:
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ul = cat_div.find('ul')
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if ul:
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cats = [clean_text(li.get_text()) for li in ul.find_all('li')]
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return ", ".join(cats)
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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', 'angestellte', 'belegschaft', 'personalstärke']
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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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return extract_numeric_value(raw_value, is_umsatz=True)
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if target == 'mitarbeiter':
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return extract_numeric_value(raw_value, is_umsatz=False)
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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 field.lower() in token.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.',
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'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', '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', 'Mitarbeiter'])
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raw_branche = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche'))
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raw_umsatz = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz'))
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raw_mitarbeiter = extracted_fields.get('Mitarbeiter', self._extract_infobox_value(soup, 'mitarbeiter'))
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umsatz_val = extract_numeric_value(raw_umsatz, is_umsatz=True)
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mitarbeiter_val = extract_numeric_value(raw_mitarbeiter, is_umsatz=False)
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categories_val = self.extract_categories(soup)
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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': raw_branche,
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'umsatz': umsatz_val,
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'mitarbeiter': mitarbeiter_val,
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'categories': categories_val,
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'full_infobox': full_infobox
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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.',
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'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.', 'full_infobox': '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)}")
|
||
continue
|
||
return None
|
||
|
||
# ==================== DATA PROCESSOR ====================
|
||
class DataProcessor:
|
||
def __init__(self):
|
||
self.sheet_handler = GoogleSheetHandler()
|
||
self.wiki_scraper = WikipediaScraper()
|
||
def process_rows(self, num_rows=None):
|
||
if MODE == "2":
|
||
print("Re-Evaluierungsmodus: Verarbeitung aller Zeilen mit 'x' in Spalte A.")
|
||
elif MODE == "3":
|
||
print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Zeile 11200.")
|
||
alignment_demo(self.sheet_handler.sheet)
|
||
return
|
||
else:
|
||
start_index = self.sheet_handler.get_start_index()
|
||
print(f"Starte bei Zeile {start_index+1}")
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if MODE == "2":
|
||
if row[0].strip().lower() == "x":
|
||
self._process_single_row(i, row)
|
||
else:
|
||
if i >= self.sheet_handler.get_start_index():
|
||
self._process_single_row(i, row)
|
||
def _process_single_row(self, row_num, row_data):
|
||
# Neues Schema:
|
||
# B: Firmenname, C: Website, CRM-Umsatz in Spalte I (Index 8)
|
||
# Wikipedia-Daten: Spalten K bis Q
|
||
# ChatGPT Umsatz in Spalte AF, Vergleich in Spalte AG,
|
||
# Timestamp in Spalte AH, Version in Spalte AI.
|
||
company_name = row_data[1] if len(row_data) > 1 else ""
|
||
website = row_data[2] if len(row_data) > 2 else ""
|
||
wiki_update_range = f"K{row_num}:Q{row_num}"
|
||
chatgpt_range = f"AF{row_num}"
|
||
abgleich_range = f"AG{row_num}"
|
||
dt_range = f"AH{row_num}"
|
||
ver_range = f"AI{row_num}"
|
||
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
|
||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||
if article:
|
||
company_data = self.wiki_scraper.extract_company_data(article.url)
|
||
else:
|
||
company_data = {
|
||
'url': 'k.A.',
|
||
'first_paragraph': 'k.A.',
|
||
'branche': 'k.A.',
|
||
'umsatz': 'k.A.',
|
||
'mitarbeiter': 'k.A.',
|
||
'categories': 'k.A.',
|
||
'full_infobox': 'k.A.'
|
||
}
|
||
wiki_values = [
|
||
"k.A.", # Vorschlag Wiki URL
|
||
company_data.get('url', 'k.A.'),
|
||
company_data.get('first_paragraph', 'k.A.'),
|
||
company_data.get('branche', 'k.A.'),
|
||
company_data.get('umsatz', 'k.A.'),
|
||
company_data.get('mitarbeiter', 'k.A.'),
|
||
company_data.get('categories', 'k.A.')
|
||
]
|
||
self.sheet_handler.sheet.update(values=[wiki_values], range_name=wiki_update_range)
|
||
time.sleep(1) # 1 Sekunde Pause, um sicherzustellen, dass die Daten gespeichert wurden
|
||
# ChatGPT API: Umsatzbewertung
|
||
wiki_umsatz = company_data.get('umsatz', 'k.A.')
|
||
if wiki_umsatz != "k.A.":
|
||
chatgpt_umsatz = evaluate_umsatz_chatgpt(company_name, wiki_umsatz)
|
||
else:
|
||
chatgpt_umsatz = "k.A."
|
||
self.sheet_handler.sheet.update(values=[[chatgpt_umsatz]], range_name=chatgpt_range)
|
||
# Umsatz-Abgleich: CRM-Umsatz (Spalte I) vs. Wikipedia-Umsatz
|
||
crm_umsatz = row_data[8] if len(row_data) > 8 else "k.A."
|
||
debug_print(f"Bereinigte Vergleichswerte vor Umwandlung: CRM Umsatz: '{crm_umsatz}', Wiki Umsatz: '{wiki_umsatz}'")
|
||
abgleich_result = compare_umsatz_values(crm_umsatz, wiki_umsatz)
|
||
self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=abgleich_range)
|
||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_range)
|
||
self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=ver_range)
|
||
print(f"✅ Aktualisiert: URL: {company_data.get('url', 'k.A.')}, Absatz: {company_data.get('first_paragraph', 'k.A.')[:30]}..., "
|
||
f"Branche: {company_data.get('branche', 'k.A.')}, Wikipedia Umsatz: {company_data.get('umsatz', 'k.A.')}, "
|
||
f"Mitarbeiter: {company_data.get('mitarbeiter', 'k.A.')}, Kategorien: {company_data.get('categories', 'k.A.')}, "
|
||
f"ChatGPT Umsatz: {chatgpt_umsatz}, Umsatz-Abgleich: {abgleich_result}")
|
||
if MODE == "2":
|
||
print("----- Vollständiger Infobox-Inhalt -----")
|
||
print(company_data.get("full_infobox", "k.A."))
|
||
print("----------------------------------------")
|
||
time.sleep(Config.RETRY_DELAY)
|
||
|
||
if __name__ == "__main__":
|
||
mode_input = input("Wählen Sie den Modus: 1 für normalen Modus, 2 für Re-Evaluierungsmodus, 3 für Alignment-Demo: ").strip()
|
||
if mode_input == "2":
|
||
MODE = "2"
|
||
elif mode_input == "3":
|
||
MODE = "3"
|
||
else:
|
||
MODE = "1"
|
||
if MODE == "1":
|
||
try:
|
||
num_rows = int(input("Wieviele Zeilen sollen überprüft werden? "))
|
||
except Exception as e:
|
||
print("Ungültige Eingabe. Bitte eine Zahl eingeben.")
|
||
exit(1)
|
||
else:
|
||
num_rows = None
|
||
processor = DataProcessor()
|
||
processor.process_rows(num_rows)
|
||
print(f"\n✅ Wikipedia-Auswertung abgeschlossen ({Config.VERSION})")
|