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@eaDir/brancheneinstufung - Kopie.py@SynoEAStream
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@eaDir/brancheneinstufung - Kopie.py@SynoEAStream
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@eaDir/service_account.json@SynoEAStream
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@eaDir/service_account.json@SynoEAStream
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Bestandsfirmen.xlsx
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Bestandsfirmen.xlsx
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brancheneinstufung - Kopie.py
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brancheneinstufung - Kopie.py
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import os
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import time
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import pandas as pd
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import gspread
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import openai
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import wikipedia
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from bs4 import BeautifulSoup
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import requests
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from oauth2client.service_account import ServiceAccountCredentials
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from datetime import datetime
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# === CONFIG ===
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EXCEL = "Bestandsfirmen.xlsx"
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SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
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CREDENTIALS = "service_account.json"
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CHUNK = 10
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LANG = "de"
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# === AUTHENTICATION ===
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scope = ["https://www.googleapis.com/auth/spreadsheets"]
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creds = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS, scope)
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sheet = gspread.authorize(creds).open_by_url(SHEET_URL).sheet1
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# OpenAI API-Key aus externer Datei laden
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with open("api_key.txt", "r") as f:
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openai.api_key = f.read().strip()
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# === LOAD DATA ===
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df = pd.read_excel(EXCEL)
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for col in ["Wikipedia-URL", "Wikipedia-Branche", "LinkedIn-Branche", "Umsatz (Mio €)",
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"Empfohlene Neueinstufung", "Begründung Neueinstufung", "FSM-Relevanz", "Letzte Prüfung",
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"Techniker-Einschätzung (Auto)", "Techniker-Einschätzung (Begründung)", "Techniker-Einschätzung (Manuell)"]:
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if col not in df.columns:
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df[col] = ""
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# === STARTE BEI ERSTER LEERER ZEILE IN SPALTE 'Letzte Prüfung' (Spalte N) ===
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sheet_values = sheet.get_all_values()
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filled_n = [row[13] if len(row) > 13 else '' for row in sheet_values[1:]]
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start = next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip() or str(v).lower() == 'nan'), len(filled_n) + 1)
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print(f"Starte bei Zeile {start+1} (erste leere Zeile in Spalte N)")
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# === ANZAHL ABFRAGEN ERMITTELN ===
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try:
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limit = int(input("Wieviele Firmen sollen analysiert werden? (z.B. 1000): ").strip())
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except:
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print("Ungültige Eingabe, verwende alle verbleibenden Firmen.")
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limit = len(df) - (start - 1)
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wikipedia.set_lang(LANG)
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# === SYSTEMPROMPT ===
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SYSTEM_PROMPT = (
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"Du bist ein Klassifizierungs-Experte für Unternehmensbranchen. "
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"Ordne jedes Unternehmen genau einer der folgenden Kategorien zu (nur eine):\n\n"
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"1. Hersteller / Produzenten > Maschinenbau\n"
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"2. Hersteller / Produzenten > Automobil\n"
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"3. Hersteller / Produzenten > Anlagenbau\n"
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"4. Hersteller / Produzenten > Medizintechnik\n"
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"5. Hersteller / Produzenten > Chemie & Pharma\n"
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"6. Hersteller / Produzenten > Elektrotechnik\n"
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"7. Hersteller / Produzenten > Lebensmittelproduktion\n"
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"8. Hersteller / Produzenten > IT / Telekommunikation\n"
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"9. Hersteller / Produzenten > Bürotechnik\n"
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"10. Hersteller / Produzenten > Automaten (Vending, Slot)\n"
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"11. Hersteller / Produzenten > Gebäudetechnik Heizung, Lüftung, Klima\n"
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"12. Hersteller / Produzenten > Gebäudetechnik Allgemein\n"
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"13. Hersteller / Produzenten > Schädlingsbekämpfung\n"
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"14. Hersteller / Produzenten > Fertigung\n"
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"15. Hersteller / Produzenten > Braune & Weiße Ware\n"
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"16. Versorger > Stadtwerk\n"
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"17. Versorger > Verteilnetzbetreiber\n"
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"18. Versorger > Telekommunikation\n"
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"19. Dienstleister > Messdienstleister\n"
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"20. Dienstleister > Facility Management\n"
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"21. Dienstleister > Healthcare/Pflegedienste\n"
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"22. Dienstleister > Servicedienstleister / Reparatur ohne Produktion\n"
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"23. Handel & Logistik > Auslieferdienste\n"
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"24. Handel & Logistik > Energie (Brennstoffe)\n"
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"25. Handel & Logistik > Großhandel\n"
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"26. Handel & Logistik > Einzelhandel\n"
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"27. Handel & Logistik > Logistik Sonstige\n"
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"28. Sonstige > Unternehmensberatung (old)\n"
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"29. Sonstige > Sonstige\n"
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"30. Sonstige > Agrar, Pellets (old)\n"
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"31. Sonstige > Sonstiger Service (old)\n"
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"32. Sonstige > IT Beratung\n"
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"33. Sonstige > Engineering\n"
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"34. Baubranche > Baustoffhandel\n"
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"35. Baubranche > Baustoffindustrie\n"
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"36. Baubranche > Logistiker Baustoffe\n"
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"37. Baubranche > Bauunternehmen\n"
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"38. Gutachter / Versicherungen > Versicherungsgutachten\n"
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"39. Gutachter / Versicherungen > Technische Gutachter\n"
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"40. Gutachter / Versicherungen > Medizinische Gutachten\n\n"
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"Antwortformat: Wikipedia-Branche; LinkedIn-Branche; Umsatz (Mio €); Empfohlene Neueinstufung; Begründung; FSM-Relevanz; Techniker-Einschätzung (Auto); Techniker-Einschätzung (Begründung)"
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)
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system_prompt = {"role": "system", "content": SYSTEM_PROMPT}
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# === WIKIPEDIA LOOKUP ===
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def get_wikipedia_data(firmenname):
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suchbegriffe = [firmenname.strip(), " ".join(firmenname.split()[:2])]
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for suchbegriff in suchbegriffe:
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try:
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page = wikipedia.page(suchbegriff, auto_suggest=False)
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url = page.url
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html = requests.get(url).text
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soup = BeautifulSoup(html, 'html.parser')
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infobox = soup.find("table", {"class": "infobox"})
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branche = ""
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umsatz = ""
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if infobox:
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for row in infobox.find_all("tr"):
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header = row.find("th")
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data = row.find("td")
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if not header or not data:
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continue
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if "Branche" in header.text:
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branche = data.text.strip()
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if "Umsatz" in header.text:
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umsatz = data.text.strip()
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if not branche:
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cats = page.categories
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branche = cats[0] if cats else ""
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return url, branche, umsatz
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except:
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continue
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return "", "", ""
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# === KLASSIFIZIERUNG ===
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def classify_company(row):
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content = (
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f"Beschreibung: {row['Beschreibung des Unternehmens'] or ''}\n"
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f"Einstufung: {row['Aktuelle Einstufung'] or ''}\n"
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f"Website: {row['Website'] or ''}"
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)
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try:
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resp = openai.chat.completions.create(
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model="gpt-4",
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messages=[system_prompt, {"role": "user", "content": content}],
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temperature=0
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)
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result = resp.choices[0].message.content.strip()
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parts = [v.strip().strip('"') if v.strip() else "k.A." for v in result.split(";", 7)]
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while len(parts) < 8:
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parts.append("k.A.")
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return parts
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except Exception as e:
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print(f"⚠️ Fehler bei Zeile: {row['Firmenname']} → {e}")
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return ["k.A."] * 8
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# === LOOP ===
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count = 0
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for df_idx in range(start - 1, len(df)):
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if count >= limit:
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break
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row = df.iloc[df_idx]
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if str(row.get("Letzte Prüfung", "")).strip():
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continue
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print(f"[{time.strftime('%H:%M:%S')}] Verarbeite Zeile {df_idx+1}: {row['Firmenname']}")
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count += 1
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url, wiki_branche, umsatz = get_wikipedia_data(row['Firmenname'])
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df.at[df_idx, "Wikipedia-URL"] = url or "k.A."
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df.at[df_idx, "Wikipedia-Branche"] = wiki_branche.strip('"') or "k.A."
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if not df.at[df_idx, "Umsatz (Mio €)"]:
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df.at[df_idx, "Umsatz (Mio €)"] = umsatz or "k.A."
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wiki, linkedin, umsatz_chat, new_cat, reason, fsm_relevant, techniker, techniker_reason = classify_company(row)
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df.at[df_idx, "Wikipedia-Branche"] = wiki or wiki_branche or "k.A."
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df.at[df_idx, "LinkedIn-Branche"] = linkedin or "k.A."
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if not df.at[df_idx, "Umsatz (Mio €)"] or df.at[df_idx, "Umsatz (Mio €)"] == "k.A.":
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df.at[df_idx, "Umsatz (Mio €)"] = umsatz_chat or "k.A."
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df.at[df_idx, "Empfohlene Neueinstufung"] = new_cat or "k.A."
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current_cat = str(row.get("Aktuelle Einstufung") or "").strip().strip('"')
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if new_cat != current_cat:
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df.at[df_idx, "Begründung Neueinstufung"] = reason or "k.A."
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else:
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df.at[df_idx, "Begründung Neueinstufung"] = ""
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df.at[df_idx, "FSM-Relevanz"] = fsm_relevant or "k.A."
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df.at[df_idx, "Techniker-Einschätzung (Auto)"] = techniker or "k.A."
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df.at[df_idx, "Techniker-Einschätzung (Begründung)"] = techniker_reason or "k.A."
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now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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df.at[df_idx, "Letzte Prüfung"] = now
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sheet.update(
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values=[df.loc[df_idx, [
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"Wikipedia-Branche", "LinkedIn-Branche", "Umsatz (Mio €)",
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"Empfohlene Neueinstufung", "Begründung Neueinstufung",
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"FSM-Relevanz", "Wikipedia-URL", "Letzte Prüfung",
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"Techniker-Einschätzung (Auto)", "Techniker-Einschätzung (Begründung)"
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]].tolist()],
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range_name=f"G{df_idx+2}:Q{df_idx+2}"
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)
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time.sleep(5)
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print("✅ Fertig!")
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212
brancheneinstufung.py
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brancheneinstufung.py
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import os
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import time
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import pandas as pd
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import gspread
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import openai
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import wikipedia
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from bs4 import BeautifulSoup
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import requests
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from oauth2client.service_account import ServiceAccountCredentials
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from datetime import datetime
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# === CONFIG ===
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EXCEL = "Bestandsfirmen.xlsx"
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SHEET_URL = "https://docs.google.com/spreadsheets/d/1u_gHr9JUfmV1-iviRzbSe3575QEp7KLhK5jFV_gJcgo"
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CREDENTIALS = "service_account.json"
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CHUNK = 10
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LANG = "de"
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# === AUTHENTICATION ===
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scope = ["https://www.googleapis.com/auth/spreadsheets"]
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creds = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS, scope)
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sheet = gspread.authorize(creds).open_by_url(SHEET_URL).sheet1
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# OpenAI API-Key aus externer Datei laden
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with open("api_key.txt", "r") as f:
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openai.api_key = f.read().strip()
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# === LOAD DATA ===
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df = pd.read_excel(EXCEL)
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for col in ["Wikipedia-URL", "Wikipedia-Branche", "LinkedIn-Branche", "Umsatz (Mio €)",
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"Empfohlene Neueinstufung", "Begründung Neueinstufung", "FSM-Relevanz", "Letzte Prüfung",
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"Techniker-Einschätzung (Auto)", "Techniker-Einschätzung (Begründung)", "Techniker-Einschätzung (Manuell)"]:
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if col not in df.columns:
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df[col] = ""
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# === STARTE BEI ERSTER LEERER ZEILE IN SPALTE 'Letzte Prüfung' (Spalte N) ===
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sheet_values = sheet.get_all_values()
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filled_n = [row[13] if len(row) > 13 else '' for row in sheet_values[1:]]
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start = next((i + 1 for i, v in enumerate(filled_n, start=1) if not str(v).strip() or str(v).lower() == 'nan'), len(filled_n) + 1)
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print(f"Starte bei Zeile {start+1} (erste leere Zeile in Spalte N)")
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mapping_dict = {}
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wikipedia.set_lang(LANG)
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# === ÜBERSETZUNGSTABELLE VORBEREITEN ===
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sheet_trans_title = "Branchen-Mapping"
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try:
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sheet_trans = sheet.spreadsheet.worksheet(sheet_trans_title)
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except gspread.exceptions.WorksheetNotFound:
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sheet_trans = sheet.spreadsheet.add_worksheet(title=sheet_trans_title, rows="100", cols="3")
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sheet_trans.clear()
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sheet_trans.update(range_name="A1:B1", values=[["Wikipedia-Branche", "Ziel-Branchenschema"]])
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# === BRANCHENSCHEMA ===
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branches = [
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"Hersteller / Produzenten > Maschinenbau",
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"Hersteller / Produzenten > Automobil",
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"Hersteller / Produzenten > Anlagenbau",
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"Hersteller / Produzenten > Medizintechnik",
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"Hersteller / Produzenten > Chemie & Pharma",
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"Hersteller / Produzenten > Elektrotechnik",
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"Hersteller / Produzenten > Lebensmittelproduktion",
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"Hersteller / Produzenten > IT / Telekommunikation",
|
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"Hersteller / Produzenten > Bürotechnik",
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"Hersteller / Produzenten > Automaten (Vending, Slot)",
|
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"Hersteller / Produzenten > Gebäudetechnik Heizung, Lüftung, Klima",
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"Hersteller / Produzenten > Gebäudetechnik Allgemein",
|
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"Hersteller / Produzenten > Schädlingsbekämpfung",
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"Hersteller / Produzenten > Fertigung",
|
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"Hersteller / Produzenten > Braune & Weiße Ware",
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||||
"Versorger > Stadtwerk",
|
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"Versorger > Verteilnetzbetreiber",
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"Versorger > Telekommunikation",
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"Dienstleister > Messdienstleister",
|
||||
"Dienstleister > Facility Management",
|
||||
"Dienstleister > Healthcare/Pflegedienste",
|
||||
"Dienstleister > Servicedienstleister / Reparatur ohne Produktion",
|
||||
"Handel & Logistik > Auslieferdienste",
|
||||
"Handel & Logistik > Energie (Brennstoffe)",
|
||||
"Handel & Logistik > Großhandel",
|
||||
"Handel & Logistik > Einzelhandel",
|
||||
"Handel & Logistik > Logistik Sonstige",
|
||||
"Sonstige > Unternehmensberatung (old)",
|
||||
"Sonstige > Sonstige",
|
||||
"Sonstige > Agrar, Pellets (old)",
|
||||
"Sonstige > Sonstiger Service (old)",
|
||||
"Sonstige > IT Beratung",
|
||||
"Sonstige > Engineering",
|
||||
"Baubranche > Baustoffhandel",
|
||||
"Baubranche > Baustoffindustrie",
|
||||
"Baubranche > Logistiker Baustoffe",
|
||||
"Baubranche > Bauunternehmen",
|
||||
"Gutachter / Versicherungen > Versicherungsgutachten",
|
||||
"Gutachter / Versicherungen > Technische Gutachter",
|
||||
"Gutachter / Versicherungen > Medizinische Gutachten"
|
||||
]
|
||||
|
||||
system_prompt = {
|
||||
"role": "system",
|
||||
"content": (
|
||||
"Du bist ein Experte für Brancheneinstufung und FSM-Potenzialbewertung. Nutze das folgende Ziel‑Branchenschema als Referenz:\n\n"
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||||
+ "\n".join(branches)
|
||||
)
|
||||
}
|
||||
|
||||
# === WIKIPEDIA LOOKUP ===
|
||||
def get_wikipedia_data(firmenname):
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||||
suchbegriffe = [firmenname.strip(), " ".join(firmenname.split()[:2])]
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||||
for suchbegriff in suchbegriffe:
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||||
try:
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||||
page = wikipedia.page(suchbegriff, auto_suggest=False)
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||||
url = page.url
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||||
html = requests.get(url).text
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||||
soup = BeautifulSoup(html, 'html.parser')
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||||
infobox = soup.find("table", {"class": "infobox"})
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||||
branche = ""
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||||
umsatz = ""
|
||||
if infobox:
|
||||
for row in infobox.find_all("tr"):
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||||
header = row.find("th")
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||||
data = row.find("td")
|
||||
if not header or not data:
|
||||
continue
|
||||
if "Branche" in header.text:
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||||
branche = data.text.strip()
|
||||
if "Umsatz" in header.text:
|
||||
umsatz = data.text.strip()
|
||||
if not branche:
|
||||
cats = page.categories
|
||||
branche = cats[0] if cats else ""
|
||||
return url, branche, umsatz
|
||||
except:
|
||||
continue
|
||||
return "", "", ""
|
||||
|
||||
# === KLASSIFIZIERUNG ===
|
||||
def classify_company(row):
|
||||
user_prompt = {
|
||||
"role": "user",
|
||||
"content": (
|
||||
"Bitte prüfe die vorliegenden Informationen zum Unternehmen. Gib die Antwort im CSV-Format zurück:\n"
|
||||
"Wikipedia-Branche; LinkedIn-Branche; Umsatz (Mio €); Empfohlene Neueinstufung; Begründung; FSM-Relevanz (Ja/Nein/k.A. mit Begründung); Techniker-Einschätzung (<50/>50/>100/>500); Techniker-Begründung\n\n"
|
||||
f"Beschreibung: {row['Beschreibung des Unternehmens'] or ''}\n"
|
||||
f"Aktuelle Einstufung: {row['Aktuelle Einstufung'] or ''}\n"
|
||||
f"Externe Branchenbeschreibung: {row['Beschreibung der Branche Extern'] or ''}\n"
|
||||
f"Website: {row['Website'] or ''}"
|
||||
)
|
||||
}
|
||||
resp = openai.chat.completions.create(
|
||||
model="gpt-4",
|
||||
messages=[system_prompt, user_prompt],
|
||||
temperature=0
|
||||
)
|
||||
result = resp.choices[0].message.content.strip()
|
||||
parts = [v.strip().strip('"') for v in result.split(";", 7)]
|
||||
while len(parts) < 8:
|
||||
parts.append("k.A.")
|
||||
return parts
|
||||
|
||||
# === LOOP ===
|
||||
for df_idx in range(start - 1, len(df)):
|
||||
row = df.iloc[df_idx]
|
||||
if str(row.get("Letzte Prüfung", "")).strip():
|
||||
continue
|
||||
|
||||
print(f"[{time.strftime('%H:%M:%S')}] Verarbeite Zeile {df_idx+1}: {row['Firmenname']}")
|
||||
|
||||
url, wiki_branche, umsatz = get_wikipedia_data(row['Firmenname'])
|
||||
df.at[df_idx, "Wikipedia-URL"] = url
|
||||
df.at[df_idx, "Wikipedia-Branche"] = wiki_branche.strip('"')
|
||||
if not df.at[df_idx, "Umsatz (Mio €)"]:
|
||||
df.at[df_idx, "Umsatz (Mio €)"] = umsatz
|
||||
|
||||
wiki, linkedin, umsatz_chat, new_cat, reason, fsm_relevant, techniker, techniker_reason = classify_company(row)
|
||||
df.at[df_idx, "Wikipedia-Branche"] = wiki or wiki_branche
|
||||
df.at[df_idx, "LinkedIn-Branche"] = linkedin
|
||||
if not df.at[df_idx, "Umsatz (Mio €)"]:
|
||||
df.at[df_idx, "Umsatz (Mio €)"] = umsatz_chat
|
||||
df.at[df_idx, "Empfohlene Neueinstufung"] = new_cat
|
||||
|
||||
current_cat = str(row.get("Aktuelle Einstufung") or "").strip().strip('"')
|
||||
if new_cat != current_cat:
|
||||
df.at[df_idx, "Begründung Neueinstufung"] = reason
|
||||
else:
|
||||
df.at[df_idx, "Begründung Neueinstufung"] = ""
|
||||
|
||||
df.at[df_idx, "FSM-Relevanz"] = fsm_relevant
|
||||
df.at[df_idx, "Techniker-Einschätzung (Auto)"] = techniker
|
||||
df.at[df_idx, "Techniker-Einschätzung (Begründung)"] = techniker_reason
|
||||
|
||||
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
df.at[df_idx, "Letzte Prüfung"] = now
|
||||
|
||||
key = df.at[df_idx, "Wikipedia-Branche"]
|
||||
val = df.at[df_idx, "Empfohlene Neueinstufung"]
|
||||
if key and val and key not in mapping_dict:
|
||||
mapping_dict[key] = val
|
||||
sheet_trans.update(range_name=f"A{len(mapping_dict)+1}:B{len(mapping_dict)+1}", values=[[key, val]])
|
||||
|
||||
sheet.update(
|
||||
values=[df.loc[df_idx, [
|
||||
"Wikipedia-Branche", "LinkedIn-Branche", "Umsatz (Mio €)",
|
||||
"Empfohlene Neueinstufung", "Begründung Neueinstufung",
|
||||
"FSM-Relevanz", "Wikipedia-URL", "Letzte Prüfung",
|
||||
"Techniker-Einschätzung (Auto)", "Techniker-Einschätzung (Begründung)"
|
||||
]].tolist()],
|
||||
range_name=f"G{df_idx+2}:Q{df_idx+2}"
|
||||
)
|
||||
|
||||
time.sleep(5)
|
||||
|
||||
print("✅ Fertig!")
|
||||
13
service_account.json
Normal file
13
service_account.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"type": "service_account",
|
||||
"project_id": "kalender-400315",
|
||||
"private_key_id": "e18c9ed28d358b7c1dd33767ae40d76646d09ee5",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\nteiDU5B/5caV7jNk+nluzMg=\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "brancheneinstufung@kalender-400315.iam.gserviceaccount.com",
|
||||
"client_id": "114291880309376322726",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/brancheneinstufung%40kalender-400315.iam.gserviceaccount.com",
|
||||
"universe_domain": "googleapis.com"
|
||||
}
|
||||
2691
update.log
Normal file
2691
update.log
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user