900 lines
43 KiB
Python
900 lines
43 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 = "v1.3.9" # v1.3.9: Alle bisherigen Funktionen inkl. Reg. Modus, Re-Eval, Alignment, Wiki, ChatGPT, Contact Research.
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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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# ==================== RETRY-DECORATOR ====================
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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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# ==================== LOGGING & HELPER FUNCTIONS ====================
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if not os.path.exists("Log"):
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os.makedirs("Log")
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LOG_FILE = os.path.join("Log", f"{datetime.now().strftime('%d-%m-%Y_%H-%M')}_{Config.VERSION.replace('.', '')}.txt")
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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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try:
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with open(LOG_FILE, "a", encoding="utf-8") as f:
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f.write(f"[DEBUG] {message}\n")
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except Exception as e:
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print(f"[DEBUG] Log-Schreibfehler: {e}")
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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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raw_value = raw_value.strip()
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if not raw_value:
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return "k.A."
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raw_value = re.sub(r'\b(ca\.?|circa|über)\b', '', raw_value, flags=re.IGNORECASE)
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raw = raw_value.lower().replace("\xa0", " ")
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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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if ',' in num_str:
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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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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 = (
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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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)
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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 Umsatzschätzung: '{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 Umsatzschätzung '{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 für Umsatzschätzung: {e}")
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return "k.A."
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def validate_article_with_chatgpt(crm_data, wiki_data):
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crm_headers = "Firmenname;Website;Ort;Beschreibung;Aktuelle Branche;Beschreibung Branche extern;Anzahl Techniker;Umsatz (CRM);Anzahl Mitarbeiter (CRM)"
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wiki_headers = "Wikipedia URL;Wikipedia Absatz;Wikipedia Branche;Wikipedia Umsatz;Wikipedia Mitarbeiter;Wikipedia Kategorien"
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prompt_text = (
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"Bitte überprüfe, ob die folgenden beiden Datensätze grundsätzlich zum gleichen Unternehmen gehören. "
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"Berücksichtige dabei, dass leichte Abweichungen in Firmennamen (z. B. unterschiedliche Schreibweisen, Mutter-Tochter-Beziehungen) "
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"oder im Ort (z. B. 'Oberndorf' vs. 'Oberndorf/Neckar') tolerierbar sind. "
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"Vergleiche insbesondere den Firmennamen, den Ort und die Branche. Unterschiede im Umsatz können bis zu 10% abweichen. "
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"Wenn die Daten im Wesentlichen übereinstimmen, antworte ausschließlich mit 'OK'. "
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"Falls nicht, nenne bitte den wichtigsten Grund und eine kurze Begründung, warum die Abweichung plausibel sein könnte.\n\n"
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f"CRM-Daten:\n{crm_headers}\n{crm_data}\n\n"
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f"Wikipedia-Daten:\n{wiki_headers}\n{wiki_data}\n\n"
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"Antwort: "
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)
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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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try:
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[{"role": "system", "content": prompt_text}],
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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"Validierungsantwort ChatGPT: '{result}'")
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return result
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except Exception as e:
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debug_print(f"Fehler beim Validierungs-API-Aufruf: {e}")
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return "k.A."
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def evaluate_fsm_suitability(company_name, company_data):
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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 (FSM): {e}")
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return {"suitability": "k.A.", "justification": "k.A."}
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openai.api_key = api_key
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prompt = (
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f"Bitte bewerte, ob das Unternehmen '{company_name}' für den Einsatz einer Field Service Management Lösung geeignet ist. "
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"Berücksichtige, dass ein Unternehmen mit einem technischen Außendienst, idealerweise mit über 50 Technikern und "
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"Disponenten, die mit der Planung mobiler Ressourcen beschäftigt sind, als geeignet gilt. Nutze dabei verifizierte "
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"Wikipedia-Daten und deine eigene Einschätzung. Antworte ausschließlich mit 'Ja' oder 'Nein' und gib eine kurze Begründung."
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)
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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": "system", "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"FSM-Eignungsantwort ChatGPT: '{result}'")
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suitability = "k.A."
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justification = ""
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lines = result.split("\n")
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if len(lines) == 1:
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parts = result.split(" ", 1)
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suitability = parts[0].strip()
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justification = parts[1].strip() if len(parts) > 1 else ""
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else:
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for line in lines:
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if line.lower().startswith("eignung:"):
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suitability = line.split(":", 1)[1].strip()
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elif line.lower().startswith("begründung:"):
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justification = line.split(":", 1)[1].strip()
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if suitability not in ["Ja", "Nein"]:
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parts = result.split(" ", 1)
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suitability = parts[0].strip()
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justification = " ".join(result.split()[1:]).strip()
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return {"suitability": suitability, "justification": justification}
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except Exception as e:
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debug_print(f"Fehler beim Aufruf der ChatGPT API für FSM-Eignungsprüfung: {e}")
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return {"suitability": "k.A.", "justification": "k.A."}
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def evaluate_servicetechnicians_estimate(company_name, company_data):
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try:
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with open("serpApiKey.txt", "r") as f:
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serp_key = f.read().strip()
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except Exception as e:
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debug_print(f"Fehler beim Lesen des SerpAPI-Schlüssels (Servicetechniker): {e}")
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return "k.A."
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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 (Servicetechniker): {e}")
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return "k.A."
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openai.api_key = api_key
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prompt = (
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f"Bitte schätze auf Basis öffentlich zugänglicher Informationen (vor allem verifizierte Wikipedia-Daten) "
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f"die Anzahl der Servicetechniker des Unternehmens '{company_name}' ein. "
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"Gib die Antwort ausschließlich in einer der folgenden Kategorien aus: "
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"'<50 Techniker', '>100 Techniker', '>200 Techniker', '>500 Techniker'."
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)
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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": "system", "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"Schätzung Servicetechniker ChatGPT: '{result}'")
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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 für Servicetechniker-Schätzung: {e}")
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return "k.A."
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def evaluate_servicetechnicians_explanation(company_name, st_estimate, company_data):
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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 (ST-Erklärung): {e}")
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return "k.A."
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openai.api_key = api_key
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prompt = (
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f"Bitte erkläre, warum du für das Unternehmen '{company_name}' die Anzahl der Servicetechniker als '{st_estimate}' geschätzt hast. "
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"Berücksichtige dabei öffentlich zugängliche Informationen wie Branche, Umsatz, Mitarbeiterzahl und andere relevante Daten."
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)
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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": "system", "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"Servicetechniker-Erklärung ChatGPT: '{result}'")
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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 für Servicetechniker-Erklärung: {e}")
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return "k.A."
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def map_internal_technicians(value):
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try:
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num = int(value)
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except Exception:
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return "k.A."
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if num < 50:
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return "<50 Techniker"
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elif num < 100:
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return ">100 Techniker"
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elif num < 200:
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return ">200 Techniker"
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else:
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return ">500 Techniker"
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def wait_for_sheet_update(sheet, cell, expected_value, timeout=5):
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start_time = time.time()
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while time.time() - start_time < timeout:
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try:
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current_value = sheet.acell(cell).value
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if current_value == expected_value:
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return True
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except Exception as e:
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debug_print(f"Fehler beim Lesen von Zelle {cell}: {e}")
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time.sleep(0.5)
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return False
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# ==================== NEUE FUNKTION: LINKEDIN-KONTAKT-SUCHE (Einzelkontakt) ====================
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def search_linkedin_contact(company_name, website, position_query):
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try:
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with open("serpApiKey.txt", "r") as f:
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serp_key = f.read().strip()
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except Exception as e:
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debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e))
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return None
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query = f'site:linkedin.com/in "{position_query}" "{company_name}"'
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debug_print(f"Erstelle LinkedIn-Query: {query}")
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params = {
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"engine": "google",
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"q": query,
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"api_key": serp_key,
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"hl": "de"
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}
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try:
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response = requests.get("https://serpapi.com/search", params=params)
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data = response.json()
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debug_print(f"SerpAPI-Response für Query '{query}': {data.get('organic_results', [])[:1]}")
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if "organic_results" in data and len(data["organic_results"]) > 0:
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result = data["organic_results"][0]
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title = result.get("title", "")
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debug_print(f"LinkedIn-Suchergebnis-Titel: {title}")
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if "–" in title:
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parts = title.split("–")
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elif "-" in title:
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parts = title.split("-")
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else:
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parts = [title]
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if len(parts) >= 2:
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name_part = parts[0].strip()
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pos = parts[1].split("|")[0].strip()
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name_parts = name_part.split(" ", 1)
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if len(name_parts) == 2:
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firstname, lastname = name_parts
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else:
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firstname = name_part
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lastname = ""
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debug_print(f"Kontakt gefunden: {firstname} {lastname}, Position: {pos}")
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return {"Firmenname": company_name, "Website": website, "Vorname": firstname, "Nachname": lastname, "Position": pos}
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else:
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debug_print(f"Kontakt gefunden, aber unvollständige Informationen: {title}")
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return {"Firmenname": company_name, "Website": website, "Vorname": "", "Nachname": "", "Position": title}
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else:
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debug_print(f"Keine LinkedIn-Ergebnisse für Query: {query}")
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return None
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except Exception as e:
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debug_print(f"Fehler bei der SerpAPI-Suche: {e}")
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return None
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# ==================== NEUE FUNKTION: ZÄHLEN DER LINKEDIN-KONTAKTE (für Contact Research) ====================
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def count_linkedin_contacts(company_name, website, position_query):
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try:
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with open("serpApiKey.txt", "r") as f:
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serp_key = f.read().strip()
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except Exception as e:
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debug_print("Fehler beim Lesen des SerpAPI-Schlüssels: " + str(e))
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return 0
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query = f'site:linkedin.com/in "{position_query}" "{company_name}"'
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debug_print(f"Erstelle LinkedIn-Query (Count): {query}")
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params = {
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"engine": "google",
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"q": query,
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"api_key": serp_key,
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"hl": "de"
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}
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try:
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response = requests.get("https://serpapi.com/search", params=params)
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data = response.json()
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if "organic_results" in data:
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count = len(data["organic_results"])
|
||
debug_print(f"Anzahl Kontakte für Query '{query}': {count}")
|
||
return count
|
||
else:
|
||
debug_print(f"Keine Ergebnisse für Query: {query}")
|
||
return 0
|
||
except Exception as e:
|
||
debug_print(f"Fehler bei der SerpAPI-Suche (Count): {e}")
|
||
return 0
|
||
|
||
# ==================== NEUER MODUS 6: CONTACT RESEARCH (via SerpAPI) ====================
|
||
def process_contact_research():
|
||
debug_print("Starte Contact Research (Modus 6)...")
|
||
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
|
||
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
|
||
sh = gc.open_by_url(Config.SHEET_URL)
|
||
main_sheet = sh.sheet1
|
||
data = main_sheet.get_all_values()
|
||
# Für jeden Datensatz werden für vier Kategorien die Trefferanzahl ermittelt:
|
||
for i, row in enumerate(data[1:], start=2):
|
||
company_name = row[1] if len(row) > 1 else ""
|
||
website = row[2] if len(row) > 2 else ""
|
||
if not company_name or not website:
|
||
continue
|
||
count_service = count_linkedin_contacts(company_name, website, "Serviceleiter")
|
||
count_it = count_linkedin_contacts(company_name, website, "IT-Leiter")
|
||
count_management = count_linkedin_contacts(company_name, website, "Geschäftsführer")
|
||
count_disponent = count_linkedin_contacts(company_name, website, "Disponent")
|
||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
# Verwende die korrekte Parameterreihenfolge: Werte zuerst, dann Range-Name
|
||
main_sheet.update([[str(count_service)]], f"AH{i}")
|
||
main_sheet.update([[str(count_it)]], f"AI{i}")
|
||
main_sheet.update([[str(count_management)]], f"AJ{i}")
|
||
main_sheet.update([[str(count_disponent)]], f"AK{i}")
|
||
main_sheet.update([[current_dt]], f"AL{i}")
|
||
debug_print(f"Zeile {i}: Serviceleiter {count_service}, IT-Leiter {count_it}, Management {count_management}, Disponent {count_disponent} – Timestamp gesetzt.")
|
||
time.sleep(Config.RETRY_DELAY * 1.5)
|
||
debug_print("Contact Research abgeschlossen.")
|
||
|
||
# ==================== ALIGNMENT DEMO (Modus 3) ====================
|
||
def alignment_demo(sheet):
|
||
new_headers = [
|
||
"Spalte A (ReEval Flag)",
|
||
"Spalte B (Firmenname)",
|
||
"Spalte C (Website)",
|
||
"Spalte D (Ort)",
|
||
"Spalte E (Beschreibung)",
|
||
"Spalte F (Aktuelle Branche)",
|
||
"Spalte G (Beschreibung Branche extern)",
|
||
"Spalte H (Anzahl Techniker CRM)",
|
||
"Spalte I (Umsatz CRM)",
|
||
"Spalte J (Anzahl Mitarbeiter CRM)",
|
||
"Spalte K (Vorschlag Wiki URL)",
|
||
"Spalte L (Wikipedia URL)",
|
||
"Spalte M (Wikipedia Absatz)",
|
||
"Spalte N (Wikipedia Branche)",
|
||
"Spalte O (Wikipedia Umsatz)",
|
||
"Spalte P (Wikipedia Mitarbeiter)",
|
||
"Spalte Q (Wikipedia Kategorien)",
|
||
"Spalte R (Konsistenzprüfung)",
|
||
"Spalte S (Begründung bei Inkonsistenz)",
|
||
"Spalte T (Vorschlag Wiki Artikel ChatGPT)",
|
||
"Spalte U (Begründung bei Abweichung)",
|
||
"Spalte V (Vorschlag neue Branche)",
|
||
"Spalte W (Konsistenzprüfung Branche)",
|
||
"Spalte X (Begründung Abweichung Branche)",
|
||
"Spalte Y (FSM Relevanz Ja / Nein)",
|
||
"Spalte Z (Begründung für FSM Relevanz)",
|
||
"Spalte AA (Schätzung Anzahl Mitarbeiter)",
|
||
"Spalte AB (Konsistenzprüfung Mitarbeiterzahl)",
|
||
"Spalte AC (Begründung für Abweichung Mitarbeiterzahl)",
|
||
"Spalte AD (Einschätzung Anzahl Servicetechniker)",
|
||
"Spalte AE (Begründung bei Abweichung Anzahl Servicetechniker)",
|
||
"Spalte AF (Schätzung Umsatz ChatGPT)",
|
||
"Spalte AG (Begründung für Abweichung Umsatz)",
|
||
"Spalte AH (Serviceleiter gefunden)",
|
||
"Spalte AI (IT-Leiter gefunden)",
|
||
"Spalte AJ (Management gefunden)",
|
||
"Spalte AK (Disponent gefunden)",
|
||
"Spalte AL (Contact Search Timestamp)",
|
||
"Spalte AM (Wikipedia Timestamp)",
|
||
"Spalte AN (ChatGPT Timestamp)",
|
||
"Spalte AO (Version)"
|
||
]
|
||
header_range = "A11200:AO11200"
|
||
sheet.update(values=[new_headers], range_name=header_range)
|
||
print("Alignment-Demo abgeschlossen: Neue Spaltenüberschriften in Zeile 11200 geschrieben.")
|
||
|
||
# ==================== WIKIPEDIA SCRAPER ====================
|
||
class WikipediaScraper:
|
||
def __init__(self):
|
||
wikipedia.set_lang(Config.LANG)
|
||
def _get_full_domain(self, website):
|
||
if not website:
|
||
return ""
|
||
website = website.lower().strip()
|
||
website = re.sub(r'^https?:\/\/', '', website)
|
||
website = re.sub(r'^www\.', '', website)
|
||
return website.split('/')[0]
|
||
def _generate_search_terms(self, company_name, website):
|
||
terms = []
|
||
full_domain = self._get_full_domain(website)
|
||
if full_domain:
|
||
terms.append(full_domain)
|
||
normalized_name = normalize_company_name(company_name)
|
||
candidate = " ".join(normalized_name.split()[:2]).strip()
|
||
if candidate and candidate not in terms:
|
||
terms.append(candidate)
|
||
if normalized_name and normalized_name not in terms:
|
||
terms.append(normalized_name)
|
||
debug_print(f"Generierte Suchbegriffe: {terms}")
|
||
return terms
|
||
def _validate_article(self, page, company_name, website):
|
||
full_domain = self._get_full_domain(website)
|
||
domain_found = False
|
||
if full_domain:
|
||
try:
|
||
html_raw = requests.get(page.url).text
|
||
soup = BeautifulSoup(html_raw, Config.HTML_PARSER)
|
||
infobox = soup.find('table', class_=lambda c: c and 'infobox' in c.lower())
|
||
if infobox:
|
||
links = infobox.find_all('a', href=True)
|
||
for link in links:
|
||
href = link.get('href').lower()
|
||
if href.startswith('/wiki/datei:'):
|
||
continue
|
||
if full_domain in href:
|
||
debug_print(f"Definitiver Link-Match in Infobox gefunden: {href}")
|
||
domain_found = True
|
||
break
|
||
if not domain_found and hasattr(page, 'externallinks'):
|
||
for ext_link in page.externallinks:
|
||
if full_domain in ext_link.lower():
|
||
debug_print(f"Definitiver Link-Match in externen Links gefunden: {ext_link}")
|
||
domain_found = True
|
||
break
|
||
except Exception as e:
|
||
debug_print(f"Fehler beim Extrahieren von Links: {str(e)}")
|
||
normalized_title = normalize_company_name(page.title)
|
||
normalized_company = normalize_company_name(company_name)
|
||
similarity = SequenceMatcher(None, normalized_title, normalized_company).ratio()
|
||
debug_print(f"Ähnlichkeit (normalisiert): {similarity:.2f} ({normalized_title} vs {normalized_company})")
|
||
threshold = 0.60 if domain_found else Config.SIMILARITY_THRESHOLD
|
||
return similarity >= threshold
|
||
def extract_first_paragraph(self, page_url):
|
||
try:
|
||
response = requests.get(page_url)
|
||
soup = BeautifulSoup(response.text, Config.HTML_PARSER)
|
||
paragraphs = soup.find_all('p')
|
||
for p in paragraphs:
|
||
text = clean_text(p.get_text())
|
||
if len(text) > 50:
|
||
return text
|
||
return "k.A."
|
||
except Exception as e:
|
||
debug_print(f"Fehler beim Extrahieren des ersten Absatzes: {e}")
|
||
return "k.A."
|
||
def extract_categories(self, soup):
|
||
cat_div = soup.find('div', id="mw-normal-catlinks")
|
||
if cat_div:
|
||
ul = cat_div.find('ul')
|
||
if ul:
|
||
cats = [clean_text(li.get_text()) for li in ul.find_all('li')]
|
||
return ", ".join(cats)
|
||
return "k.A."
|
||
def _extract_infobox_value(self, soup, target):
|
||
infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']))
|
||
if not infobox:
|
||
return "k.A."
|
||
keywords_map = {
|
||
'branche': ['branche', 'industrie', 'tätigkeit', 'geschäftsfeld', 'sektor', 'produkte', 'leistungen', 'aktivitäten', 'wirtschaftszweig'],
|
||
'umsatz': ['umsatz', 'jahresumsatz', 'konzernumsatz', 'gesamtumsatz', 'erlöse', 'umsatzerlöse', 'einnahmen', 'ergebnis', 'jahresergebnis'],
|
||
'mitarbeiter': ['mitarbeiter', 'beschäftigte', 'personal', 'mitarbeiterzahl', 'angestellte', 'belegschaft', 'personalstärke']
|
||
}
|
||
keywords = keywords_map.get(target, [])
|
||
for row in infobox.find_all('tr'):
|
||
header = row.find('th')
|
||
if header:
|
||
header_text = clean_text(header.get_text()).lower()
|
||
if any(kw in header_text for kw in keywords):
|
||
value = row.find('td')
|
||
if value:
|
||
raw_value = clean_text(value.get_text())
|
||
if target == 'branche':
|
||
clean_val = re.sub(r'\[.*?\]|\(.*?\)', '', raw_value)
|
||
return ' '.join(clean_val.split()).strip()
|
||
if target == 'umsatz':
|
||
return extract_numeric_value(raw_value, is_umsatz=True)
|
||
if target == 'mitarbeiter':
|
||
return extract_numeric_value(raw_value, is_umsatz=False)
|
||
return "k.A."
|
||
def extract_full_infobox(self, soup):
|
||
infobox = soup.find('table', class_=lambda c: c and any(kw in c.lower() for kw in ['infobox', 'vcard', 'unternehmen']))
|
||
if not infobox:
|
||
return "k.A."
|
||
return clean_text(infobox.get_text(separator=' | '))
|
||
def extract_fields_from_infobox_text(self, infobox_text, field_names):
|
||
result = {}
|
||
tokens = [token.strip() for token in infobox_text.split("|") if token.strip()]
|
||
for i, token in enumerate(tokens):
|
||
for field in field_names:
|
||
if field.lower() in token.lower():
|
||
j = i + 1
|
||
while j < len(tokens) and not tokens[j]:
|
||
j += 1
|
||
result[field] = tokens[j] if j < len(tokens) else "k.A."
|
||
return result
|
||
def extract_company_data(self, page_url):
|
||
if not page_url:
|
||
return {
|
||
'url': 'k.A.',
|
||
'first_paragraph': 'k.A.',
|
||
'branche': 'k.A.',
|
||
'umsatz': 'k.A.',
|
||
'mitarbeiter': 'k.A.',
|
||
'categories': 'k.A.',
|
||
'full_infobox': 'k.A.'
|
||
}
|
||
try:
|
||
response = requests.get(page_url)
|
||
soup = BeautifulSoup(response.text, Config.HTML_PARSER)
|
||
full_infobox = self.extract_full_infobox(soup)
|
||
extracted_fields = self.extract_fields_from_infobox_text(full_infobox, ['Branche', 'Umsatz', 'Mitarbeiter'])
|
||
raw_branche = extracted_fields.get('Branche', self._extract_infobox_value(soup, 'branche'))
|
||
raw_umsatz = extracted_fields.get('Umsatz', self._extract_infobox_value(soup, 'umsatz'))
|
||
raw_mitarbeiter = extracted_fields.get('Mitarbeiter', self._extract_infobox_value(soup, 'mitarbeiter'))
|
||
umsatz_val = extract_numeric_value(raw_umsatz, is_umsatz=True)
|
||
mitarbeiter_val = extract_numeric_value(raw_mitarbeiter, is_umsatz=False)
|
||
categories_val = self.extract_categories(soup)
|
||
first_paragraph = self.extract_first_paragraph(page_url)
|
||
return {
|
||
'url': page_url,
|
||
'first_paragraph': first_paragraph,
|
||
'branche': raw_branche,
|
||
'umsatz': umsatz_val,
|
||
'mitarbeiter': mitarbeiter_val,
|
||
'categories': categories_val,
|
||
'full_infobox': full_infobox
|
||
}
|
||
except Exception as e:
|
||
debug_print(f"Extraktionsfehler: {str(e)}")
|
||
return {
|
||
'url': 'k.A.',
|
||
'first_paragraph': 'k.A.',
|
||
'branche': 'k.A.',
|
||
'umsatz': 'k.A.',
|
||
'mitarbeiter': 'k.A.',
|
||
'categories': 'k.A.',
|
||
'full_infobox': 'k.A.'
|
||
}
|
||
@retry_on_failure
|
||
def search_company_article(self, company_name, website):
|
||
search_terms = self._generate_search_terms(company_name, website)
|
||
for term in search_terms:
|
||
try:
|
||
results = wikipedia.search(term, results=Config.WIKIPEDIA_SEARCH_RESULTS)
|
||
debug_print(f"Suchergebnisse für '{term}': {results}")
|
||
for title in results:
|
||
try:
|
||
page = wikipedia.page(title, auto_suggest=False)
|
||
if self._validate_article(page, company_name, website):
|
||
return page
|
||
except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e:
|
||
debug_print(f"Seitenfehler: {str(e)}")
|
||
continue
|
||
except Exception as e:
|
||
debug_print(f"Suchfehler: {str(e)}")
|
||
continue
|
||
return None
|
||
|
||
# ==================== GOOGLE SHEET HANDLER (für Hauptdaten) ====================
|
||
class GoogleSheetHandler:
|
||
def __init__(self):
|
||
self.sheet = None
|
||
self.sheet_values = []
|
||
self._connect()
|
||
def _connect(self):
|
||
scope = ["https://www.googleapis.com/auth/spreadsheets"]
|
||
creds = ServiceAccountCredentials.from_json_keyfile_name(Config.CREDENTIALS_FILE, scope)
|
||
self.sheet = gspread.authorize(creds).open_by_url(Config.SHEET_URL).sheet1
|
||
self.sheet_values = self.sheet.get_all_values()
|
||
def get_start_index(self):
|
||
filled_n = [row[38] if len(row) > 38 else '' for row in self.sheet_values[1:]] # Spalte AM = Wikipedia Timestamp
|
||
return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
|
||
|
||
# ==================== DATA PROCESSOR ====================
|
||
class DataProcessor:
|
||
def __init__(self):
|
||
self.sheet_handler = GoogleSheetHandler()
|
||
self.wiki_scraper = WikipediaScraper()
|
||
def process_rows(self, num_rows=None):
|
||
# MODE 1: Regulärer Modus – nur Zeilen ohne entsprechende Timestamps werden bearbeitet
|
||
if MODE == "2":
|
||
print("Re-Evaluierungsmodus: Verarbeitung aller Zeilen mit 'x' in Spalte A.")
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if row[0].strip().lower() == "x":
|
||
self._process_single_row(i, row, force_all=True)
|
||
elif MODE == "3":
|
||
print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Zeile 11200.")
|
||
alignment_demo(self.sheet_handler.sheet)
|
||
elif MODE == "4":
|
||
# Nur Wikipedia-Suche: nur Zeilen ohne Wikipedia-Timestamp (Spalte AM, Index 38)
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if len(row) <= 38 or row[38].strip() == "":
|
||
self._process_single_row(i, row, process_wiki=True, process_chatgpt=False)
|
||
elif MODE == "5":
|
||
# Nur ChatGPT Bewertung: nur Zeilen ohne ChatGPT-Timestamp (Spalte AN, Index 39)
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if len(row) <= 39 or row[39].strip() == "":
|
||
self._process_single_row(i, row, process_wiki=False, process_chatgpt=True)
|
||
else:
|
||
# Regulärer Modus: Bearbeite nur Zeilen, die noch nicht vollständig bewertet wurden
|
||
start_index = self.sheet_handler.get_start_index()
|
||
print(f"Starte bei Zeile {start_index+1}")
|
||
rows_processed = 0
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if i < start_index:
|
||
continue
|
||
if num_rows is not None and rows_processed >= num_rows:
|
||
break
|
||
self._process_single_row(i, row)
|
||
rows_processed += 1
|
||
def _process_single_row(self, row_num, row_data, force_all=False, process_wiki=True, process_chatgpt=True):
|
||
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}"
|
||
dt_wiki_range = f"AM{row_num}" # Wikipedia Timestamp
|
||
dt_chat_range = f"AN{row_num}" # ChatGPT Timestamp
|
||
ver_range = f"AO{row_num}" # Version
|
||
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {row_num}: {company_name}")
|
||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
# Wikipedia-Teil: Wird ausgeführt, wenn process_wiki True ist oder force_all aktiv ist.
|
||
if force_all or process_wiki:
|
||
# Hier zuerst prüfen, ob wir bereits einen Wiki-Timestamp haben (Spalte AM)
|
||
if len(row_data) <= 38 or row_data[38].strip() == "":
|
||
# Führe die Wikipedia-Auswertung durch
|
||
if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."]:
|
||
wiki_url = row_data[10].strip()
|
||
try:
|
||
wiki_data = self.wiki_scraper.extract_company_data(wiki_url)
|
||
except Exception as e:
|
||
debug_print(f"Fehler beim Laden des vorgeschlagenen Wikipedia-Artikels: {e}")
|
||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||
wiki_data = self.wiki_scraper.extract_company_data(article.url) if article else {
|
||
'url': 'k.A.', 'first_paragraph': 'k.A.', 'branche': 'k.A.',
|
||
'umsatz': 'k.A.', 'mitarbeiter': 'k.A.', 'categories': 'k.A.',
|
||
'full_infobox': 'k.A.'
|
||
}
|
||
else:
|
||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||
wiki_data = self.wiki_scraper.extract_company_data(article.url) if article else {
|
||
'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 = [
|
||
row_data[10] if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."] else "k.A.",
|
||
wiki_data.get('url', 'k.A.'),
|
||
wiki_data.get('first_paragraph', 'k.A.'),
|
||
wiki_data.get('branche', 'k.A.'),
|
||
wiki_data.get('umsatz', 'k.A.'),
|
||
wiki_data.get('mitarbeiter', 'k.A.'),
|
||
wiki_data.get('categories', 'k.A.')
|
||
]
|
||
self.sheet_handler.sheet.update(values=[wiki_values], range_name=wiki_update_range)
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_wiki_range)
|
||
else:
|
||
debug_print(f"Zeile {row_num}: Wikipedia-Timestamp bereits gesetzt – überspringe Wiki-Auswertung.")
|
||
|
||
# ChatGPT-Teil: Wird nur ausgeführt, wenn process_chatgpt True ist oder force_all aktiv ist.
|
||
if force_all or process_chatgpt:
|
||
# Hier prüfen, ob bereits ein ChatGPT-Timestamp in Spalte AN vorliegt
|
||
if len(row_data) <= 39 or row_data[39].strip() == "":
|
||
crm_umsatz = row_data[8] if len(row_data) > 8 else "k.A."
|
||
abgleich_result = compare_umsatz_values(crm_umsatz, wiki_data.get('umsatz', 'k.A.') if 'wiki_data' in locals() else "k.A.")
|
||
self.sheet_handler.sheet.update(values=[[abgleich_result]], range_name=f"AG{row_num}")
|
||
crm_data = ";".join(row_data[1:10])
|
||
wiki_data_str = ";".join(row_data[11:17])
|
||
valid_result = validate_article_with_chatgpt(crm_data, wiki_data_str)
|
||
self.sheet_handler.sheet.update(values=[[valid_result]], range_name=f"R{row_num}")
|
||
fsm_result = evaluate_fsm_suitability(company_name, wiki_data if 'wiki_data' in locals() else {})
|
||
self.sheet_handler.sheet.update(values=[[fsm_result["suitability"]]], range_name=f"Y{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[fsm_result["justification"]]], range_name=f"Z{row_num}")
|
||
st_estimate = evaluate_servicetechnicians_estimate(company_name, wiki_data if 'wiki_data' in locals() else {})
|
||
self.sheet_handler.sheet.update(values=[[st_estimate]], range_name=f"AD{row_num}")
|
||
internal_value = row_data[7] if len(row_data) > 7 else "k.A."
|
||
internal_category = map_internal_technicians(internal_value) if internal_value != "k.A." else "k.A."
|
||
if internal_category != "k.A." and st_estimate != internal_category:
|
||
explanation = evaluate_servicetechnicians_explanation(company_name, st_estimate, wiki_data if 'wiki_data' in locals() else {})
|
||
discrepancy = explanation
|
||
else:
|
||
discrepancy = "ok"
|
||
self.sheet_handler.sheet.update(values=[[discrepancy]], range_name=f"AE{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=f"AN{row_num}")
|
||
else:
|
||
debug_print(f"Zeile {row_num}: ChatGPT-Timestamp bereits gesetzt – überspringe ChatGPT-Auswertung.")
|
||
|
||
# Aktualisiere letzten Timestamp und Version (Spalte AO)
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=f"AO{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=f"AO{row_num}")
|
||
debug_print(f"✅ Aktualisiert: URL: {(wiki_data.get('url', 'k.A.') if 'wiki_data' in locals() else 'k.A.')}, "
|
||
f"Branche: {(wiki_data.get('branche', 'k.A.') if 'wiki_data' in locals() else 'k.A.')}, "
|
||
f"Umsatz-Abgleich: {abgleich_result if 'abgleich_result' in locals() else 'k.A.'}, "
|
||
f"Validierung: {valid_result if 'valid_result' in locals() else 'k.A.'}, "
|
||
f"FSM: {fsm_result['suitability'] if 'fsm_result' in locals() else 'k.A.'}, "
|
||
f"Servicetechniker-Schätzung: {st_estimate if 'st_estimate' in locals() else 'k.A.'}")
|
||
time.sleep(Config.RETRY_DELAY)
|
||
|
||
# ==================== NEUER MODUS 6: CONTACT RESEARCH (via SerpAPI) ====================
|
||
def process_contact_research():
|
||
debug_print("Starte Contact Research (Modus 6)...")
|
||
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
|
||
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
|
||
sh = gc.open_by_url(Config.SHEET_URL)
|
||
main_sheet = sh.sheet1
|
||
data = main_sheet.get_all_values()
|
||
# Für jeden Datensatz werden für vier Kategorien die Trefferanzahl ermittelt:
|
||
for i, row in enumerate(data[1:], start=2):
|
||
company_name = row[1] if len(row) > 1 else ""
|
||
website = row[2] if len(row) > 2 else ""
|
||
if not company_name or not website:
|
||
continue
|
||
count_service = count_linkedin_contacts(company_name, website, "Serviceleiter")
|
||
count_it = count_linkedin_contacts(company_name, website, "IT-Leiter")
|
||
count_management = count_linkedin_contacts(company_name, website, "Geschäftsführer")
|
||
count_disponent = count_linkedin_contacts(company_name, website, "Disponent")
|
||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
main_sheet.update(values=[[str(count_service)]], range_name=f"AH{i}")
|
||
main_sheet.update(f"AI{i}", [[str(count_it)]])
|
||
main_sheet.update(f"AJ{i}", [[str(count_management)]])
|
||
main_sheet.update(f"AK{i}", [[str(count_disponent)]])
|
||
main_sheet.update(f"AL{i}", [[current_dt]])
|
||
debug_print(f"Zeile {i}: Serviceleiter {count_service}, IT-Leiter {count_it}, Management {count_management}, Disponent {count_disponent} – Contact Search Timestamp gesetzt.")
|
||
time.sleep(Config.RETRY_DELAY * 1.5)
|
||
debug_print("Contact Research abgeschlossen.")
|
||
|
||
# ==================== NEUER MODUS: ALIGNMENT DEMO (für Hauptblatt und Contacts) ====================
|
||
def alignment_demo_full():
|
||
# Aktualisiere Hauptblatt
|
||
alignment_demo(GoogleSheetHandler().sheet)
|
||
# Aktualisiere auch das Contacts-Blatt
|
||
gc = gspread.authorize(ServiceAccountCredentials.from_json_keyfile_name(
|
||
Config.CREDENTIALS_FILE, ["https://www.googleapis.com/auth/spreadsheets"]))
|
||
sh = gc.open_by_url(Config.SHEET_URL)
|
||
try:
|
||
contacts_sheet = sh.worksheet("Contacts")
|
||
except gspread.exceptions.WorksheetNotFound:
|
||
contacts_sheet = sh.add_worksheet(title="Contacts", rows="1000", cols="10")
|
||
header = ["Firmenname", "Website", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
|
||
contacts_sheet.update("A1:G1", [header])
|
||
alignment_demo(contacts_sheet)
|
||
debug_print("Alignment-Demo für Hauptblatt und Contacts abgeschlossen.")
|
||
|
||
# ==================== MAIN PROGRAMM ====================
|
||
if __name__ == "__main__":
|
||
print("Modi:")
|
||
print("1 = Regulärer Modus")
|
||
print("2 = Re-Evaluierungsmodus (nur Zeilen mit 'x' in Spalte A)")
|
||
print("3 = Alignment-Demo (Header in Hauptblatt und Contacts)")
|
||
print("4 = Nur Wikipedia-Suche (Zeilen ohne Wikipedia-Timestamp)")
|
||
print("5 = Nur ChatGPT-Bewertung (Zeilen ohne ChatGPT-Timestamp)")
|
||
print("6 = Contact Research (via SerpAPI)")
|
||
mode_input = input("Wählen Sie den Modus: ").strip()
|
||
if mode_input == "2":
|
||
MODE = "2"
|
||
elif mode_input == "3":
|
||
MODE = "3"
|
||
elif mode_input == "4":
|
||
MODE = "4"
|
||
elif mode_input == "5":
|
||
MODE = "5"
|
||
elif mode_input == "6":
|
||
MODE = "6"
|
||
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)
|
||
processor = DataProcessor()
|
||
processor.process_rows(num_rows)
|
||
elif MODE in ["2", "3"]:
|
||
processor = DataProcessor()
|
||
processor.process_rows()
|
||
elif MODE == "4":
|
||
processor = DataProcessor()
|
||
# Für Mode 4: Nur Wikipedia-Suche
|
||
processor.process_rows(num_rows=0) # Unser _process_single_row prüft dann die Wiki-Timestamp-Bedingung
|
||
process_wikipedia_only()
|
||
elif MODE == "5":
|
||
processor = DataProcessor()
|
||
# Für Mode 5: Nur ChatGPT-Bewertung
|
||
processor.process_rows(num_rows=0)
|
||
elif MODE == "6":
|
||
process_contact_research()
|
||
print(f"\n✅ Auswertung abgeschlossen ({Config.VERSION})")
|