191 lines
6.9 KiB
Python
191 lines
6.9 KiB
Python
# Neue Version (1.0.11) mit optimierter Wikipedia-Suchlogik, Original-Auswertung bleibt erhalten
|
||
|
||
import os
|
||
import time
|
||
import re
|
||
import gspread
|
||
import wikipedia
|
||
import requests
|
||
import openai
|
||
import csv
|
||
from bs4 import BeautifulSoup
|
||
from lxml import html as lh
|
||
from oauth2client.service_account import ServiceAccountCredentials
|
||
from datetime import datetime
|
||
from difflib import SequenceMatcher
|
||
|
||
# === KONFIGURATION ===
|
||
VERSION = "1.0.11"
|
||
LANG = "de"
|
||
CREDENTIALS = "service_account.json"
|
||
SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
|
||
DURCHLÄUFE = int(input("Wieviele Zeilen sollen überprüft werden? "))
|
||
MAX_RETRIES = 3
|
||
RETRY_DELAY = 5
|
||
LOG_CSV = "gpt_antworten_log.csv"
|
||
SIMILARITY_THRESHOLD = 0.6
|
||
|
||
# === OpenAI API-KEY LADEN ===
|
||
with open("api_key.txt", "r") as f:
|
||
openai.api_key = f.read().strip()
|
||
|
||
# === GOOGLE SHEET VERBINDUNG ===
|
||
scope = ["https://www.googleapis.com/auth/spreadsheets"]
|
||
creds = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS, scope)
|
||
sheet = gspread.authorize(creds).open_by_url(SHEET_URL).sheet1
|
||
sheet_values = sheet.get_all_values()
|
||
|
||
# === STARTINDEX SUCHEN (Spalte N = Index 13) ===
|
||
filled_n = [row[13] if len(row) > 13 else '' for row in sheet_values[1:]]
|
||
start = next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip()), len(filled_n) + 1)
|
||
print(f"Starte bei Zeile {start+1}")
|
||
|
||
wikipedia.set_lang(LANG)
|
||
|
||
def similar(a, b):
|
||
return SequenceMatcher(None, a.lower(), b.lower()).ratio()
|
||
|
||
def get_wikipedia_data(name, website_hint=""):
|
||
begriffe = [name.strip(), " ".join(name.split()[:2])]
|
||
domain_key = ""
|
||
if website_hint:
|
||
parts = website_hint.replace("https://", "").replace("http://", "").split(".")
|
||
if len(parts) > 1:
|
||
domain_key = parts[0]
|
||
begriffe.append(domain_key)
|
||
|
||
for suchbegriff in begriffe:
|
||
try:
|
||
results = wikipedia.search(suchbegriff, results=5)
|
||
except:
|
||
continue
|
||
|
||
for title in results:
|
||
try:
|
||
page = wikipedia.page(title, auto_suggest=False)
|
||
html_raw = requests.get(page.url).text
|
||
if domain_key and domain_key not in html_raw.lower():
|
||
continue
|
||
if similar(page.title, name) < SIMILARITY_THRESHOLD:
|
||
continue
|
||
|
||
soup = BeautifulSoup(html_raw, 'html.parser')
|
||
infobox = soup.find("table", class_="infobox")
|
||
branche = umsatz = ""
|
||
if infobox:
|
||
for row in infobox.find_all("tr"):
|
||
th, td = row.find("th"), row.find("td")
|
||
if not th or not td:
|
||
continue
|
||
if "branche" in th.text.lower():
|
||
branche = td.text.strip()
|
||
if "umsatz" in th.text.lower():
|
||
umsatz_raw = td.text.strip()
|
||
umsatz = re.sub(r"\[[^\]]*\]", "", umsatz_raw)
|
||
|
||
if not branche:
|
||
cats = page.categories
|
||
branche = cats[0] if cats else "k.A."
|
||
return page.url, branche or "k.A.", umsatz or "k.A."
|
||
except:
|
||
continue
|
||
|
||
return "", "k.A.", "k.A."
|
||
|
||
# === VERARBEITUNG ===
|
||
for i in range(start, min(start + DURCHLÄUFE, len(sheet_values))):
|
||
row = sheet_values[i]
|
||
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] Verarbeite Zeile {i+1}: {row[0]}")
|
||
url, branche, umsatz = get_wikipedia_data(row[0], row[1])
|
||
branche_final = branche if url else "k.A."
|
||
umsatz_final = umsatz if url else "k.A."
|
||
values = [
|
||
branche_final,
|
||
"k.A.",
|
||
umsatz_final,
|
||
"k.A.", "k.A.", "k.A.",
|
||
url,
|
||
datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||
"k.A.", "k.A.",
|
||
VERSION
|
||
]
|
||
sheet.update(range_name=f"G{i+1}:Q{i+1}", values=[values])
|
||
print(f"✅ Aktualisiert: {values[:3]}...")
|
||
time.sleep(RETRY_DELAY)
|
||
|
||
print("\n✅ Wikipedia-Auswertung abgeschlossen")
|
||
|
||
|
||
|
||
|
||
|
||
|
||
# === SCHRITT 2: GPT-BEWERTUNG ===
|
||
def classify_company(row, wikipedia_url=""):
|
||
user_prompt = {
|
||
"role": "user",
|
||
"content": f"{row[0]};{row[1]};{row[2]};{row[4]};{row[5]}\nWikipedia-Link: {wikipedia_url}"
|
||
}
|
||
for attempt in range(MAX_RETRIES):
|
||
try:
|
||
response = openai.chat.completions.create(
|
||
model="gpt-3.5-turbo",
|
||
messages=[
|
||
{
|
||
"role": "system",
|
||
"content": (
|
||
"Du bist ein Experte für Brancheneinstufung und FSM-Potenzialbewertung.\n"
|
||
"Bitte beziehe dich ausschließlich auf das konkret genannte Unternehmen.\n"
|
||
"FSM steht für Field Service Management. Ziel ist es, Unternehmen mit >50 Technikern im Außendienst zu identifizieren.\n\n"
|
||
"Struktur: Firmenname; Website; Ort; Aktuelle Einstufung; Beschreibung der Branche Extern\n\n"
|
||
"Gib deine Antwort im CSV-Format zurück (1 Zeile, 8 Spalten):\n"
|
||
"Wikipedia-Branche;LinkedIn-Branche;Umsatz (Mio €);Empfohlene Neueinstufung;Begründung;FSM-Relevanz;Techniker-Einschätzung;Techniker-Begründung"
|
||
)
|
||
},
|
||
user_prompt
|
||
],
|
||
temperature=0,
|
||
timeout=15
|
||
)
|
||
full_text = response.choices[0].message.content.strip()
|
||
break
|
||
except Exception as e:
|
||
print(f"⚠️ GPT-Fehler (Versuch {attempt+1}): {str(e)[:100]}")
|
||
time.sleep(RETRY_DELAY)
|
||
else:
|
||
print("❌ GPT 3x fehlgeschlagen – Standardwerte")
|
||
full_text = "k.A.;k.A.;k.A.;k.A.;k.A.;k.A.;k.A.;k.A."
|
||
|
||
lines = full_text.splitlines()
|
||
csv_line = next((l for l in lines if ";" in l), "")
|
||
parts = [v.strip() for v in csv_line.split(";")] if csv_line else ["k.A."] * 8
|
||
|
||
with open(LOG_CSV, "a", newline="", encoding="utf-8") as log:
|
||
writer = csv.writer(log, delimiter=";")
|
||
writer.writerow([datetime.now().strftime("%Y-%m-%d %H:%M:%S"), row[0], *parts, full_text])
|
||
|
||
return parts
|
||
|
||
# === SCHRITT 2 DURCHFÜHREN ===
|
||
for i in range(start, min(start + DURCHLÄUFE, len(sheet_values))):
|
||
row = sheet_values[i]
|
||
print(f"\n[{datetime.now().strftime('%H:%M:%S')}] GPT-Bewertung für Zeile {i+1}: {row[0]}")
|
||
wiki_url = row[12] if len(row) > 12 else ""
|
||
wiki, linkedin, umsatz_chat, new_cat, reason, fsm, techniker, techniker_reason = classify_company(row, wikipedia_url=wiki_url)
|
||
values = [
|
||
wiki,
|
||
linkedin,
|
||
umsatz_chat,
|
||
new_cat,
|
||
reason,
|
||
fsm,
|
||
wiki_url,
|
||
datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||
techniker,
|
||
techniker_reason
|
||
]
|
||
sheet.update(range_name=f"G{i+1}:P{i+1}", values=[values])
|
||
time.sleep(RETRY_DELAY)
|
||
|
||
print("\n✅ GPT-Bewertung abgeschlossen")
|