749 lines
38 KiB
Python
749 lines
38 KiB
Python
import os
|
||
import time
|
||
import re
|
||
import gspread
|
||
import wikipedia
|
||
import requests
|
||
import openai
|
||
from bs4 import BeautifulSoup
|
||
from oauth2client.service_account import ServiceAccountCredentials
|
||
from datetime import datetime
|
||
from difflib import SequenceMatcher
|
||
import unicodedata
|
||
import csv
|
||
|
||
try:
|
||
import tiktoken
|
||
except ImportError:
|
||
tiktoken = None
|
||
|
||
# ==================== KONFIGURATION ====================
|
||
class Config:
|
||
VERSION = "v1.4.0" # Version 1.4.0
|
||
LANG = "de"
|
||
CREDENTIALS_FILE = "service_account.json"
|
||
SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
|
||
MAX_RETRIES = 3
|
||
RETRY_DELAY = 5
|
||
LOG_CSV = "gpt_antworten_log.csv"
|
||
SIMILARITY_THRESHOLD = 0.65
|
||
DEBUG = True
|
||
WIKIPEDIA_SEARCH_RESULTS = 5
|
||
HTML_PARSER = "html.parser"
|
||
BATCH_SIZE = 10
|
||
TOKEN_MODEL = "gpt-3.5-turbo"
|
||
|
||
# ==================== RETRY-DECORATOR ====================
|
||
def retry_on_failure(func):
|
||
def wrapper(*args, **kwargs):
|
||
for attempt in range(Config.MAX_RETRIES):
|
||
try:
|
||
return func(*args, **kwargs)
|
||
except Exception as e:
|
||
print(f"⚠️ Fehler bei {func.__name__} (Versuch {attempt+1}): {str(e)[:100]}")
|
||
time.sleep(Config.RETRY_DELAY)
|
||
return None
|
||
return wrapper
|
||
|
||
# ==================== LOGGING & HELPER FUNCTIONS ====================
|
||
if not os.path.exists("Log"):
|
||
os.makedirs("Log")
|
||
timestamp = datetime.now().strftime('%d-%m-%Y_%H-%M')
|
||
version = Config.VERSION
|
||
modi_mapping = {"2": "ReEvaluierung", "3": "AlignmentDemo", "4": "WikiOnly", "5": "ChatGPTOnly", "51": "Verifizierung", "8": "BatchTokenCount"}
|
||
|
||
def debug_print(message):
|
||
if Config.DEBUG:
|
||
print(f"[DEBUG] {message}")
|
||
try:
|
||
with open(os.path.join("Log", f"{timestamp}_{version}.txt"), "a", encoding="utf-8") as f:
|
||
f.write(f"[DEBUG] {message}\n")
|
||
except Exception as e:
|
||
print(f"[DEBUG] Log-Schreibfehler: {e}")
|
||
|
||
def clean_text(text):
|
||
if not text:
|
||
return "k.A."
|
||
text = unicodedata.normalize("NFKC", str(text))
|
||
text = re.sub(r'\[\d+\]', '', text)
|
||
text = re.sub(r'\s+', ' ', text).strip()
|
||
return text if text else "k.A."
|
||
|
||
def normalize_company_name(name):
|
||
if not name:
|
||
return ""
|
||
forms = [
|
||
r'gmbh', r'g\.m\.b\.h\.', r'ug', r'u\.g\.', r'ug \(haftungsbeschränkt\)',
|
||
r'u\.g\. \(haftungsbeschränkt\)', r'ag', r'a\.g\.', r'ohg', r'o\.h\.g\.',
|
||
r'kg', r'k\.g\.', r'gmbh & co\.?\s*kg', r'g\.m\.b\.h\. & co\.?\s*k\.g\.',
|
||
r'ag & co\.?\s*kg', r'a\.g\. & co\.?\s*k\.g\.', r'e\.k\.', r'e\.kfm\.',
|
||
r'e\.kfr\.', r'ltd\.', r'ltd & co\.?\s*kg', r's\.a r\.l\.', r'stiftung',
|
||
r'genossenschaft', r'ggmbh', r'gug', r'partg', r'partgmbb', r'kgaa', r'se',
|
||
r'og', r'o\.g\.', r'e\.u\.', r'ges\.n\.b\.r\.', r'genmbh', r'verein',
|
||
r'kollektivgesellschaft', r'kommanditgesellschaft', r'einzelfirma', r'sàrl',
|
||
r'sa', r'sagl', r'gmbh & co\.?\s*ohg', r'ag & co\.?\s*ohg', r'gmbh & co\.?\s*kgaa',
|
||
r'ag & co\.?\s*kgaa', r's\.a\.', r's\.p\.a\.', r'b\.v\.', r'n\.v\.'
|
||
]
|
||
pattern = r'\b(' + '|'.join(forms) + r')\b'
|
||
normalized = re.sub(pattern, '', name, flags=re.IGNORECASE)
|
||
normalized = re.sub(r'[\-–]', ' ', normalized)
|
||
normalized = re.sub(r'\s+', ' ', normalized).strip()
|
||
return normalized.lower()
|
||
|
||
def extract_numeric_value(raw_value, is_umsatz=False):
|
||
raw_value = raw_value.strip()
|
||
if not raw_value:
|
||
return "k.A."
|
||
raw_value = re.sub(r'\b(ca\.?|circa|über)\b', '', raw_value, flags=re.IGNORECASE)
|
||
raw = raw_value.lower().replace("\xa0", " ")
|
||
match = re.search(r'([\d.,]+)', raw, flags=re.UNICODE)
|
||
if not match or not match.group(1).strip():
|
||
debug_print(f"Keine numerischen Zeichen gefunden im Rohtext: '{raw_value}'")
|
||
return "k.A."
|
||
num_str = match.group(1)
|
||
if ',' in num_str:
|
||
num_str = num_str.replace('.', '').replace(',', '.')
|
||
try:
|
||
num = float(num_str)
|
||
except Exception as e:
|
||
debug_print(f"Fehler bei der Umwandlung von '{num_str}' (Rohtext: '{raw_value}'): {e}")
|
||
return raw_value
|
||
else:
|
||
num_str = num_str.replace(' ', '').replace('.', '')
|
||
try:
|
||
num = float(num_str)
|
||
except Exception as e:
|
||
debug_print(f"Fehler bei der Umwandlung von '{num_str}' (Rohtext: '{raw_value}'): {e}")
|
||
return raw_value
|
||
if is_umsatz:
|
||
if "mrd" in raw or "milliarden" in raw:
|
||
num *= 1000
|
||
elif "mio" in raw or "millionen" in raw:
|
||
pass
|
||
else:
|
||
num /= 1e6
|
||
return str(int(round(num)))
|
||
else:
|
||
return str(int(round(num)))
|
||
|
||
def compare_umsatz_values(crm, wiki):
|
||
debug_print(f"Vergleich CRM Umsatz: '{crm}' mit Wikipedia Umsatz: '{wiki}'")
|
||
try:
|
||
crm_val = float(crm)
|
||
wiki_val = float(wiki)
|
||
except Exception as e:
|
||
debug_print(f"Fehler beim Umwandeln der Werte: CRM='{crm}', Wiki='{wiki}': {e}")
|
||
return "Daten unvollständig"
|
||
if crm_val == 0:
|
||
return "CRM Umsatz 0"
|
||
diff = abs(crm_val - wiki_val) / crm_val
|
||
if diff < 0.1:
|
||
return "OK"
|
||
else:
|
||
diff_mio = abs(crm_val - wiki_val)
|
||
return f"Abweichung: {int(round(diff_mio))} Mio €"
|
||
|
||
def map_internal_technicians(value):
|
||
try:
|
||
num = int(value)
|
||
except Exception:
|
||
return "k.A."
|
||
if num < 50:
|
||
return "<50 Techniker"
|
||
elif num < 100:
|
||
return ">100 Techniker"
|
||
elif num < 200:
|
||
return ">200 Techniker"
|
||
else:
|
||
return ">500 Techniker"
|
||
|
||
# ==================== CHATGPT CALL WRAPPERS ====================
|
||
def chatgpt_call(prompt, input_text):
|
||
try:
|
||
response = openai.ChatCompletion.create(
|
||
model=Config.TOKEN_MODEL,
|
||
messages=[{"role": "user", "content": prompt}],
|
||
temperature=0.0
|
||
)
|
||
result = response.choices[0].message.content.strip()
|
||
tokens = 0
|
||
if tiktoken:
|
||
try:
|
||
enc = tiktoken.encoding_for_model(Config.TOKEN_MODEL)
|
||
tokens = len(enc.encode(prompt))
|
||
except Exception as e:
|
||
debug_print(f"Tokenzählung fehlgeschlagen: {e}")
|
||
return result, tokens
|
||
except Exception as e:
|
||
debug_print(f"Fehler im chatgpt_call: {e}")
|
||
return "k.A.", 0
|
||
|
||
def safe_chatgpt_call(prompt, input_text, wiki_value):
|
||
if wiki_value == "k.A.":
|
||
return "Skipped (k.A.)", 0
|
||
return chatgpt_call(prompt, input_text)
|
||
|
||
# ==================== PROMPT ÜBERSICHT ====================
|
||
def prompt_overview():
|
||
data = [
|
||
["Funktion", "Prompt"],
|
||
["Wiki Artikel Vorschlag", "Bitte schlage einen passenden Wikipedia-Artikel für das folgende Unternehmen vor: {company_name}"],
|
||
["Begründung Wiki Inkonsistenz", "Bitte begründe, warum die Angaben aus Wikipedia von den folgenden CRM-Daten abweichen: {crm_data}"],
|
||
["Mitarbeiter Schätzung", "Wie viele Mitarbeiter hat das folgende Unternehmen? Kontext: {wiki_text}"],
|
||
["Mitarbeiter Konsistenz", "Vergleiche die Mitarbeiterzahlen aus CRM ({crm_value}) und Wikipedia ({wiki_value}). Gibt es Abweichungen? Welche Zahl erscheint plausibler?"],
|
||
["Umsatz Schätzung", "Wie hoch ist der geschätzte Jahresumsatz des Unternehmens basierend auf den folgenden Angaben? Kontext: {crm_data}, Wikipedia: {wiki_text}"],
|
||
["Branchenvorschlag", "Welcher Branche gehört das folgende Unternehmen an? Kontext: {wiki_text}, {crm_data}"]
|
||
]
|
||
return data
|
||
|
||
def print_prompt_overview():
|
||
print("----- Prompt Übersicht -----")
|
||
prompts = prompt_overview()
|
||
for row in prompts:
|
||
print(f"{row[0]}: {row[1]}")
|
||
print("-----------------------------\n")
|
||
|
||
# ==================== INITIALISIERUNG DES LOGS ====================
|
||
def initialize_log(modus):
|
||
filename = f"{timestamp}_{version}_{modi_mapping.get(modus, f'Modus{modus}')}.txt"
|
||
logfile = open(filename, "w", encoding="utf-8")
|
||
logfile.write(f"Modus: {modus} - {modi_mapping.get(modus, f'Modus{modus}')}\n")
|
||
logfile.write(f"Version: {version}\n")
|
||
logfile.write(f"Timestamp: {timestamp}\n\n")
|
||
print_prompt_overview() # Ausgabe der Prompt Übersicht
|
||
return logfile
|
||
|
||
# ==================== 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 ====================
|
||
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[13] if len(row) > 13 else '' for row in self.sheet_values[1:]]
|
||
return next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
|
||
|
||
# ==================== ALIGNMENT DEMO ====================
|
||
def alignment_demo(sheet):
|
||
new_headers = [
|
||
[ # Spaltenname (Zeile 1)
|
||
"ReEval Flag", # A
|
||
"CRM Name", # B
|
||
"CRM Kurzform", # C
|
||
"CRM Website", # D
|
||
"CRM Ort", # E
|
||
"CRM Beschreibung", # F
|
||
"CRM Branche", # G
|
||
"CRM Beschreibung Branche extern", # H
|
||
"CRM Anzahl Techniker", # I
|
||
"CRM Umsatz", # J
|
||
"CRM Anzahl Mitarbeiter", # K
|
||
"CRM Vorschlag Wiki URL", # L
|
||
"Wiki URL", # M
|
||
"Wiki Absatz", # N
|
||
"Wiki Branche", # O
|
||
"Wiki Umsatz", # P
|
||
"Wiki Mitarbeiter", # Q
|
||
"Wiki Kategorien", # R
|
||
"Chat Wiki Konsistenzprüfung", # S
|
||
"Chat Begründung Wiki Inkonsistenz", # T
|
||
"Chat Vorschlag Wiki Artikel", # U
|
||
"Begründung bei Abweichung", # V
|
||
"Chat Vorschlag Branche", # W
|
||
"Chat Konsistenz Branche", # X
|
||
"Chat Begründung Abweichung Branche", # Y
|
||
"Chat Prüfung FSM Relevanz", # Z
|
||
"Chat Begründung für FSM Relevanz", # AA
|
||
"Chat Schätzung Anzahl Mitarbeiter", # AB
|
||
"Chat Konsistenzprüfung Mitarbeiterzahl", # AC
|
||
"Chat Begründung Abweichung Mitarbeiterzahl", # AD
|
||
"Chat Einschätzung Anzahl Servicetechniker", # AE
|
||
"Chat Begründung Abweichung Servicetechniker", # AF
|
||
"Chat Schätzung Umsatz", # AG
|
||
"Chat Begründung Abweichung Umsatz", # AH
|
||
"Linked Serviceleiter gefunden", # AI
|
||
"Linked It-Leiter gefunden", # AJ
|
||
"Linked Management gefunden", # AK
|
||
"Linked Disponent gefunden", # AL
|
||
"Contact Search Timestamp", # AM
|
||
"Wikipedia Timestamp", # AN
|
||
"Timestamp letzte Prüfung", # AO
|
||
"Version", # AP
|
||
"Tokens" # AQ
|
||
],
|
||
[ # Quelle der Daten
|
||
"CRM", "CRM", "CRM", "CRM", "CRM", "CRM", "CRM", "CRM", "CRM", "CRM",
|
||
"CRM", "CRM", "Wikipediascraper", "Wikipediascraper", "Wikipediascraper",
|
||
"Wikipediascraper", "Wikipediascraper", "Wikipediascraper", "Chat GPT API",
|
||
"Chat GPT API", "Chat GPT API", "Chat GPT API", "Chat GPT API", "Chat GPT API",
|
||
"Chat GPT API", "Chat GPT API", "Chat GPT API", "Chat GPT API", "Chat GPT API",
|
||
"Chat GPT API", "Chat GPT API", "Chat GPT API", "Chat GPT API", "LinkedIn (via SerpApi)",
|
||
"LinkedIn (via SerpApi)", "LinkedIn (via SerpApi)", "LinkedIn (via SerpApi)", "System",
|
||
"System", "System", "System", "System"
|
||
],
|
||
[ # Feldkategorie
|
||
"Prozess", "Firmenname", "Firmenname", "Website", "Ort", "Beschreibung (Text)",
|
||
"Branche", "Branche", "Anzahl Servicetechniker", "Umsatz", "Anzahl Mitarbeiter",
|
||
"Wikipedia Artikel URL", "Wikipedia Artikel", "Beschreibung (Text)", "Branche",
|
||
"Umsatz", "Anzahl Mitarbeiter", "Kategorien (Text)", "Verifizierung",
|
||
"Begründung bei Abweichung", "Wikipedia Artikel", "Wikipedia Artikel", "Branche",
|
||
"Branche", "Branche", "FSM Relevanz", "FSM Relevanz", "Anzahl Mitarbeiter",
|
||
"Anzahl Mitarbeiter", "Anzahl Mitarbeiter", "Anzahl Servicetechniker",
|
||
"Anzahl Servicetechniker", "Umsatz", "Umsatz", "Kontakte zur Firma",
|
||
"Kontakte zur Firma", "Kontakte zur Firma", "Kontakte zur Firma", "Timestamp",
|
||
"Timestamp", "Timestamp", "Version des Skripts die verwendet wurde", "ChatGPT Tokens"
|
||
],
|
||
[ # Kurzbeschreibung
|
||
"Systemspalte, irrelevant für den Prompt. Wird zur manuellen Neuprüfung genutzt.",
|
||
"Enthält den Firmennamen (CRM).", "Enthält die manuell gepflegte Kurzform.",
|
||
"Ermittelte Website des Unternehmens.", "Ermittelter Ort des Unternehmens.",
|
||
"Kurze Unternehmensbeschreibung.", "Aktuelle Branchenzuweisung (CRM).",
|
||
"Externe Branchenbeschreibung (z.B. von Dealfront).", "Recherchierte Anzahl Servicetechniker.",
|
||
"Recherchierter Umsatz in Mio. €.", "Recherchierte Anzahl Mitarbeiter.",
|
||
"Vorschlag für Wikipedia URL.", "Wikipedia URL aus laufender Suche.",
|
||
"Erster Absatz des Wikipedia-Artikels.", "Branche aus Wikipedia.",
|
||
"Umsatz laut Wikipedia.", "Mitarbeiterzahl laut Wikipedia.",
|
||
"Wikipedia Kategorien.", "\"OK\" oder \"X\" bei Wiki-Validierung.",
|
||
"Begründung bei Wiki Inkonsistenz.", "Neu recherchierter Wikipedia Artikel.",
|
||
"Begründung bei Abweichung.", "ChatGPT basierte Branchenzuordnung.",
|
||
"Vergleich CRM vs. ChatGPT Branche.", "Begründung bei Branchenabweichung.",
|
||
"Prüfung FSM-Relevanz (ChatGPT).", "Begründung zur FSM-Eignung.",
|
||
"Schätzung Mitarbeiterzahl (ChatGPT).", "Konsistenzprüfung Mitarbeiterzahl (ChatGPT).",
|
||
"Begründung bei Mitarbeiterabweichung.", "Schätzung Servicetechniker (ChatGPT).",
|
||
"Begründung bei Technikerabweichung.", "Schätzung Umsatz (ChatGPT).",
|
||
"Begründung bei Umsatzabweichung.", "LinkedIn Serviceleiter Kontakte.",
|
||
"LinkedIn IT-Leiter Kontakte.", "LinkedIn Management Kontakte.",
|
||
"LinkedIn Disponent Kontakte.", "Timestamp Kontaktsuche.",
|
||
"Timestamp Wikipedia-Abruf.", "Timestamp ChatGPT-Prüfung.",
|
||
"Skriptversion.", "Gesamte GPT-Tokens."
|
||
],
|
||
[ # Aufgabe / Funktion
|
||
"Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle",
|
||
"Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle", "Datenquelle",
|
||
"Wird durch Wikipedia Scraper bereitgestellt.", "Wird als Vergleich genutzt (Validierung).",
|
||
"Für finale Branchenermittlung.", "Zur Umsatzvalidierung.", "Zur Validierung der Mitarbeiterzahl.",
|
||
"Zur Kategorisierung des Wikipedia-Artikels.", "Prüfung, ob Wikipedia-Artikel passt.",
|
||
"Begründung bei Wiki Inkonsistenz.", "Neues Recherchieren, falls unpassend.", "Begründung bei Abweichung.",
|
||
"ChatGPT Branchenzuordnung.", "Vergleich CRM vs. ChatGPT Branche.", "Begründung bei Branchenabweichung.",
|
||
"Prüfung FSM-Relevanz (ChatGPT).", "Begründung zur FSM-Eignung.", "Schätzung Mitarbeiterzahl (ChatGPT).",
|
||
"Konsistenzprüfung Mitarbeiterzahl.", "Begründung bei Mitarbeiterabweichung.",
|
||
"Schätzung Servicetechniker (ChatGPT).", "Begründung bei Technikerabweichung.",
|
||
"Schätzung Umsatz (ChatGPT).", "Begründung bei Umsatzabweichung.",
|
||
"LinkedIn Suche Serviceleiter.", "LinkedIn Suche IT-Leiter.", "LinkedIn Suche Management.",
|
||
"LinkedIn Suche Disponent.", "Timestamp Kontaktsuche.", "Timestamp Wikipedia-Abruf.",
|
||
"Timestamp ChatGPT-Prüfung.", "Skriptversion.", "Gesamte GPT-Tokens."
|
||
]
|
||
]
|
||
header_range = "A1:AQ5"
|
||
sheet.update(values=new_headers, range_name=header_range)
|
||
print("Alignment-Demo abgeschlossen: Neues Spaltenschema in Zeilen A1 bis AQ5 geschrieben.")
|
||
|
||
def alignment_demo_full():
|
||
alignment_demo(GoogleSheetHandler().sheet)
|
||
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", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
|
||
contacts_sheet.update(values=[header], range_name="A1:H1")
|
||
debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
|
||
alignment_demo(contacts_sheet)
|
||
debug_print("Alignment-Demo für Hauptblatt und Contacts abgeschlossen.")
|
||
|
||
# ==================== DATA PROCESSOR ====================
|
||
class DataProcessor:
|
||
def __init__(self):
|
||
self.sheet_handler = GoogleSheetHandler()
|
||
self.wiki_scraper = WikipediaScraper()
|
||
def process_rows(self, num_rows=None):
|
||
MODE = "default" # Ersetze mit tatsächlicher Moduswahl
|
||
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)
|
||
elif MODE == "3":
|
||
print("Alignment-Demo-Modus: Schreibe neue Spaltenüberschriften in Hauptblatt und Contacts.")
|
||
alignment_demo_full()
|
||
elif MODE == "4":
|
||
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=True, process_chatgpt=False)
|
||
elif MODE == "5":
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if len(row) <= 40 or row[40].strip() == "":
|
||
self._process_single_row(i, row, process_wiki=False, process_chatgpt=True)
|
||
elif MODE == "51":
|
||
for i, row in enumerate(self.sheet_handler.sheet_values[1:], start=2):
|
||
if len(row) <= 25 or row[24].strip() == "":
|
||
self._process_verification_row(i, row)
|
||
elif MODE == "8":
|
||
process_batch_token_count()
|
||
else:
|
||
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, process_wiki=True, process_chatgpt=True):
|
||
total_tokens = 0
|
||
company_name = row_data[1] if len(row_data) > 1 else ""
|
||
website = row_data[3] if len(row_data) > 3 else ""
|
||
# Default-Initialisierung für company_data, falls Wiki-Auswertung übersprungen wird
|
||
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-Daten werden in Spalten L bis R abgelegt
|
||
wiki_update_range = f"L{row_num}:R{row_num}"
|
||
dt_wiki_range = f"AN{row_num}"
|
||
dt_chat_range = f"AO{row_num}"
|
||
ver_range = f"AP{row_num}"
|
||
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")
|
||
# Wiki-Verarbeitung
|
||
if process_wiki:
|
||
if len(row_data) <= 39 or row_data[39].strip() == "":
|
||
if len(row_data) > 10 and row_data[10].strip() not in ["", "k.A."]:
|
||
wiki_url = row_data[10].strip()
|
||
try:
|
||
company_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)
|
||
company_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:
|
||
wiki_url = "k.A."
|
||
article = self.wiki_scraper.search_company_article(company_name, website)
|
||
company_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.",
|
||
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)
|
||
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-Verarbeitung
|
||
if process_chatgpt:
|
||
crm_umsatz = row_data[9] if len(row_data) > 9 else "k.A."
|
||
consistency_result = compare_umsatz_values(crm_umsatz, company_data.get('umsatz', 'k.A.'))
|
||
self.sheet_handler.sheet.update(values=[[consistency_result]], range_name=f"S{row_num}")
|
||
crm_data = ";".join(row_data[1:10])
|
||
wiki_data_str = ";".join(row_data[11:18])
|
||
prompt = ("Bitte überprüfe, ob die folgenden beiden Datensätze grundsätzlich zum gleichen Unternehmen gehören. "
|
||
f"CRM-Daten: {crm_data} | Wikipedia-Daten: {wiki_data_str}")
|
||
valid_result, tokens = safe_chatgpt_call(prompt, crm_data + " " + wiki_data_str, row_data[10] if len(row_data) > 10 else "k.A.")
|
||
total_tokens += tokens
|
||
if valid_result.strip().upper() == "OK":
|
||
wiki_consistency = "OK"
|
||
wiki_article_suggestion = ""
|
||
else:
|
||
wiki_consistency = "X"
|
||
wiki_article_suggestion = valid_result
|
||
self.sheet_handler.sheet.update(values=[[wiki_consistency]], range_name=f"T{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[wiki_article_suggestion]], range_name=f"U{row_num}")
|
||
prompt_fsm = f"Bitte bewerte, ob das Unternehmen '{company_name}' für den Einsatz einer Field Service Management Lösung geeignet ist. Antworte mit 'Ja' oder 'Nein' und begründe kurz."
|
||
fsm_result, tokens = safe_chatgpt_call(prompt_fsm, company_name, row_data[10] if len(row_data) > 10 else "k.A.")
|
||
total_tokens += tokens
|
||
parts = fsm_result.split("-", 1)
|
||
fsm_suitability = parts[0].strip() if parts else fsm_result
|
||
fsm_justification = parts[1].strip() if len(parts) > 1 else ""
|
||
self.sheet_handler.sheet.update(values=[[fsm_suitability]], range_name=f"Z{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[fsm_justification]], range_name=f"AA{row_num}")
|
||
prompt_st = f"Bitte schätze die Anzahl der Servicetechniker für das Unternehmen '{company_name}' ein. Antwortoptionen: '<50 Techniker', '>100 Techniker', '>200 Techniker', '>500 Techniker'."
|
||
st_estimate, tokens = safe_chatgpt_call(prompt_st, company_name, row_data[10] if len(row_data) > 10 else "k.A.")
|
||
total_tokens += tokens
|
||
self.sheet_handler.sheet.update(values=[[st_estimate]], range_name=f"AE{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:
|
||
prompt_st_expl = f"Bitte erkläre, warum du für das Unternehmen '{company_name}' die Anzahl der Servicetechniker als '{st_estimate}' geschätzt hast."
|
||
st_explanation, tokens = safe_chatgpt_call(prompt_st_expl, company_name, row_data[10] if len(row_data) > 10 else "k.A.")
|
||
total_tokens += tokens
|
||
technician_explanation = st_explanation
|
||
else:
|
||
technician_explanation = "ok"
|
||
self.sheet_handler.sheet.update(values=[[technician_explanation]], range_name=f"AF{row_num}")
|
||
crm_mitarbeiter = row_data[10] if len(row_data) > 10 else "k.A."
|
||
wiki_mitarbeiter = company_data.get('mitarbeiter', "k.A.")
|
||
try:
|
||
crm_emp = float(crm_mitarbeiter)
|
||
wiki_emp = float(wiki_mitarbeiter)
|
||
diff = abs(crm_emp - wiki_emp) / ((crm_emp + wiki_emp) / 2) * 100
|
||
reason = "Beide Werte ähnlich" if diff < 30 else "Signifikante Abweichung"
|
||
mitarbeiter_result = f"CRM: {crm_mitarbeiter}, Wikipedia: {wiki_mitarbeiter}, Differenz: {diff:.2f}%, Einschätzung: {reason}"
|
||
except Exception as e:
|
||
mitarbeiter_result = "k.A."
|
||
self.sheet_handler.sheet.update(values=[[mitarbeiter_result]], range_name=f"AB{row_num}")
|
||
prompt_umsatz = f"Bitte schätze den Jahresumsatz (in Mio. €) für das Unternehmen '{company_name}' ein basierend auf den Daten: CRM: {crm_umsatz}, Wikipedia: {company_data.get('umsatz', 'k.A.')}. Antworte nur mit der Zahl."
|
||
umsatz_estimate, tokens = safe_chatgpt_call(prompt_umsatz, company_name, row_data[10] if len(row_data) > 10 else "k.A.")
|
||
total_tokens += tokens
|
||
self.sheet_handler.sheet.update(values=[[umsatz_estimate]], range_name=f"AG{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[str(total_tokens)]], range_name=f"AQ{row_num}")
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=dt_chat_range)
|
||
self.sheet_handler.sheet.update(values=[[current_dt]], range_name=ver_range)
|
||
self.sheet_handler.sheet.update(values=[[Config.VERSION]], range_name=ver_range)
|
||
debug_print(f"Zeile {row_num} verifiziert: URL: {company_data.get('url', 'k.A.')}, Branche: {company_data.get('branche', 'k.A.')}")
|
||
time.sleep(Config.RETRY_DELAY)
|
||
|
||
# ==================== NEUER MODUS: 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()
|
||
for i, row in enumerate(data[1:], start=2):
|
||
company_name = row[1] if len(row) > 1 else ""
|
||
search_name = row[2].strip() if len(row) > 2 and row[2].strip() not in ["", "k.A."] else company_name
|
||
website = row[3] if len(row) > 3 else ""
|
||
if not company_name or not website:
|
||
continue
|
||
count_service = count_linkedin_contacts(search_name, website, "Serviceleiter")
|
||
count_it = count_linkedin_contacts(search_name, website, "IT-Leiter")
|
||
count_management = count_linkedin_contacts(search_name, website, "Geschäftsführer")
|
||
count_disponent = count_linkedin_contacts(search_name, website, "Disponent")
|
||
current_dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
main_sheet.update(values=[[str(count_service)]], range_name=f"AI{i}")
|
||
main_sheet.update(values=[[str(count_it)]], range_name=f"AJ{i)")
|
||
main_sheet.update(values=[[str(count_management)]], range_name=f"AK{i}")
|
||
main_sheet.update(values=[[str(count_disponent)]], range_name=f"AL{i}")
|
||
main_sheet.update(values=[[current_dt]], range_name=f"AM{i}")
|
||
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: CONTACTS (LinkedIn) ====================
|
||
def process_contacts():
|
||
debug_print("Starte LinkedIn-Kontaktsuche...")
|
||
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", "Kurzform", "Vorname", "Nachname", "Position", "Anrede", "E-Mail"]
|
||
contacts_sheet.update(values=[header], range_name="A1:H1")
|
||
debug_print("Neues Blatt 'Contacts' erstellt und Header eingetragen.")
|
||
main_sheet = sh.sheet1
|
||
data = main_sheet.get_all_values()
|
||
positions = ["Serviceleiter", "IT-Leiter", "Leiter After Sales", "Leiter Einsatzplanung"]
|
||
# Fortsetzung der Kontaktsuche...
|
||
debug_print("LinkedIn-Kontaktsuche abgeschlossen.")
|