import os import requests import re from dotenv import load_dotenv # Load env from root env_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '.env')) load_dotenv(dotenv_path=env_path, override=True) SERP_API_KEY = os.getenv("SERP_API") if not SERP_API_KEY: print(f"DEBUG: Failed to load SERP_API from {env_path}") # Fallback: try reading directly if file exists try: with open(env_path, 'r') as f: for line in f: if line.startswith('SERP_API='): SERP_API_KEY = line.split('=')[1].strip().strip('"') print("DEBUG: Loaded key via manual parsing.") except: pass import json # --- Helper: Get Gemini Key --- def get_gemini_key(): candidates = [ "gemini_api_key.txt", # Current dir "/app/gemini_api_key.txt", # Docker default os.path.join(os.path.dirname(__file__), "gemini_api_key.txt"), # Script dir os.path.join(os.path.dirname(os.path.dirname(__file__)), 'gemini_api_key.txt') # Parent dir ] for path in candidates: if os.path.exists(path): try: with open(path, 'r') as f: return f.read().strip() except: pass return os.getenv("GEMINI_API_KEY") def extract_role_with_llm(name, company, search_results): """Uses Gemini to identify the job title from search snippets.""" api_key = get_gemini_key() if not api_key: return None context = "\n".join([f"- {r.get('title')}: {r.get('snippet')}" for r in search_results]) prompt = f""" Analyze these Google Search results to identify the professional role of "{name}" at "{company}". SEARCH RESULTS: {context} TASK: Extract the exact Job Title / Role. Look for terms like "Geschäftsführer", "CEO", "CFO", "Leiter", "Head of", "Manager", "Inhaber", "Arzt". RULES: 1. If multiple roles appear (e.g. "CFO & CEO"), pick the most senior one current role. 2. Return ONLY the role string. No full sentences. 3. If absolutely no role is mentioned in the snippets, return "Unbekannt". Example Input: "Georg Stahl ... CFO at KLEMM..." Example Output: CFO """ url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}" try: response = requests.post(url, headers={'Content-Type': 'application/json'}, json={"contents": [{"parts": [{"text": prompt}]}]}) if response.status_code == 200: role = response.json()['candidates'][0]['content']['parts'][0]['text'].strip() # Cleanup: remove punctuation at the end role = role.rstrip('.') return None if "Unbekannt" in role else role except: pass return None def lookup_person_role(name, company): """ Searches for a person's role via SerpAPI and extracts it using LLM. """ if not SERP_API_KEY: print("Error: SERP_API key not found in .env") return None # Broad query to find role/position query = f'{name} {company} Position Job' params = { "engine": "google", "q": query, "api_key": SERP_API_KEY, "num": 5, "hl": "de", # Force German UI "gl": "de" # Force German Location } try: response = requests.get("https://serpapi.com/search", params=params) response.raise_for_status() data = response.json() organic_results = data.get("organic_results", []) if not organic_results: return None # Delegate extraction to LLM return extract_role_with_llm(name, company, organic_results) except Exception as e: print(f"SerpAPI lookup failed: {e}") return None if __name__ == "__main__": # Test cases print(f"Markus Drees: {lookup_person_role('Markus Drees', 'Ärztehaus Rünthe')}") print(f"Georg Stahl: {lookup_person_role('Georg Stahl', 'Klemm Bohrtechnik GmbH')}")