url check ergänzt

duplicate_checker.py

@@ -3,18 +3,19 @@ import sys
 import logging
 import pandas as pd
 from thefuzz import fuzz
-from helpers import normalize_company_name, simple_normalize_url
+from helpers import normalize_company_name, simple_normalize_url, serp_website_lookup
+from config import Config
 from google_sheet_handler import GoogleSheetHandler
 
-# duplicate_checker.py v2.9 (Bulletproof Name-Partial/SORT/SET + Bonus)
+# duplicate_checker.py v2.10 (Mit SerpAPI-Fallback für fehlende Domains)
-# Version: 2025-08-06_18-10
+# Version: 2025-08-06_18-45
 
 # --- Konfiguration ---
 CRM_SHEET_NAME      = "CRM_Accounts"
 MATCHING_SHEET_NAME = "Matching_Accounts"
 SCORE_THRESHOLD     = 80   # Score-Schwelle
 LOG_DIR             = "Log"
-LOG_FILE            = "duplicate_check_v2.9.txt"
+LOG_FILE            = "duplicate_check_v2.10.log"
 
 # --- Logging Setup ---
 if not os.path.exists(LOG_DIR):
@@ -22,58 +23,51 @@ if not os.path.exists(LOG_DIR):
 log_path = os.path.join(LOG_DIR, LOG_FILE)
 root = logging.getLogger()
 root.setLevel(logging.DEBUG)
-# Remove existing handlers
+for h in list(root.handlers): root.removeHandler(h)
-for h in list(root.handlers):
-    root.removeHandler(h)
 formatter = logging.Formatter("%(asctime)s - %(levelname)-8s - %(message)s")
-# Console handler (INFO+)
 ch = logging.StreamHandler(sys.stdout)
 ch.setLevel(logging.INFO)
 ch.setFormatter(formatter)
 root.addHandler(ch)
-# File handler (DEBUG+)
 fh = logging.FileHandler(log_path, mode='a', encoding='utf-8')
 fh.setLevel(logging.DEBUG)
 fh.setFormatter(formatter)
 root.addHandler(fh)
 logger = logging.getLogger(__name__)
 logger.info(f"Logging to console and file: {log_path}")
-logger.info("Starting duplicate_checker.py v2.9 | Version: 2025-08-06_18-10")
+logger.info("Starting duplicate_checker.py v2.10 | Version: 2025-08-06_18-45")
+
+# --- SerpAPI Key laden ---
+try:
+    Config.load_api_keys()
+    serp_key = Config.API_KEYS.get('serpapi')
+    if not serp_key:
+        logger.warning("SerpAPI Key nicht gefunden; Serp-Fallback deaktiviert.")
+except Exception as e:
+    logger.warning(f"Fehler beim Laden API-Keys: {e}")
+    serp_key = None
 
 # --- Ähnlichkeitsberechnung ---
 def calculate_similarity(record1, record2):
-    """Berechnet Score-Komponenten: Domain, Name (SET,PARTIAL,SORT), Ort und Bonus."""
+    dom1 = record1.get('normalized_domain','')
-    # Domain exact match
+    dom2 = record2.get('normalized_domain','')
-    dom1 = record1.get('normalized_domain', '')
-    dom2 = record2.get('normalized_domain', '')
     domain_flag = 1 if dom1 and dom1 == dom2 else 0
-
+    loc_flag = 1 if (record1.get('CRM Ort')==record2.get('CRM Ort') and record1.get('CRM Land')==record2.get('CRM Land')) else 0
-    # Location exact match
+    n1, n2 = record1.get('normalized_name',''), record2.get('normalized_name','')
-    loc_flag = 1 if (record1.get('CRM Ort') == record2.get('CRM Ort') and
-                     record1.get('CRM Land') == record2.get('CRM Land')) else 0
-
-    # Name scores
-    n1 = record1.get('normalized_name', '')
-    n2 = record2.get('normalized_name', '')
     if n1 and n2:
-        ts = fuzz.token_set_ratio(n1, n2)
+        ts = fuzz.token_set_ratio(n1,n2)
-        pr = fuzz.partial_ratio(n1, n2)
+        pr = fuzz.partial_ratio(n1,n2)
-        ss = fuzz.token_sort_ratio(n1, n2)
+        ss = fuzz.token_sort_ratio(n1,n2)
-        name_score = max(ts, pr, ss)
+        name_score = max(ts,pr,ss)
     else:
         name_score = 0
-
+    bonus_flag = 1 if domain_flag==0 and loc_flag==0 and name_score>=85 else 0
-    # Bonus für reine Name-Matches
+    total = domain_flag*100 + name_score*1.0 + loc_flag*20 + bonus_flag*20
-    bonus_flag = 1 if domain_flag == 0 and loc_flag == 0 and name_score >= 85 else 0
-
-    # Gesamtscore
-    total = domain_flag * 100 + name_score * 1.0 + loc_flag * 20 + bonus_flag * 20
     return round(total), domain_flag, name_score, loc_flag, bonus_flag
 
 # --- Hauptfunktion ---
 def main():
-    logger.info("Starte Duplikats-Check v2.9 (Bulletproof)")
+    logger.info("Starte Duplikats-Check v2.10 mit SerpAPI-Fallback")
-    # GoogleSheetHandler init
     try:
         sheet = GoogleSheetHandler()
         logger.info("GoogleSheetHandler initialisiert")
@@ -81,20 +75,31 @@ def main():
         logger.critical(f"Init GoogleSheetHandler fehlgeschlagen: {e}")
         sys.exit(1)
 
-    # Daten laden
     logger.info(f"Lade CRM-Daten aus '{CRM_SHEET_NAME}'...")
     crm_df = sheet.get_sheet_as_dataframe(CRM_SHEET_NAME)
     logger.info(f"{0 if crm_df is None else len(crm_df)} CRM-Datensätze geladen")
     logger.info(f"Lade Matching-Daten aus '{MATCHING_SHEET_NAME}'...")
     match_df = sheet.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
     logger.info(f"{0 if match_df is None else len(match_df)} Matching-Datensätze geladen")
-
     if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
         logger.critical("Leere Daten in einem der Sheets. Abbruch.")
         return
 
+    # --- SerpAPI-Fallback für leere Domains ---
+    if serp_key:
+        for df, label in [(crm_df,'CRM'), (match_df,'Matching')]:
+            for idx, row in df[df['CRM Website'].fillna('').astype(str).str.strip()==''].iterrows():
+                company = row['CRM Name']
+                try:
+                    url = serp_website_lookup(company)
+                    if url and 'http' in url:
+                        df.at[idx,'CRM Website'] = url
+                        logger.info(f"Serp-Fallback ({label}): '{company}' -> {url}")
+                except Exception as e:
+                    logger.warning(f"Serp lookup fehlgeschlagen für '{company}': {e}")
+
     # Normalisierung & Blocking-Key
-    for df, label in [(crm_df, 'CRM'), (match_df, 'Matching')]:
+    for df, label in [(crm_df,'CRM'), (match_df,'Matching')]:
         df['normalized_name']   = df['CRM Name'].astype(str).apply(normalize_company_name)
         df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
         df['CRM Ort']           = df['CRM Ort'].astype(str).str.lower().str.strip()
@@ -102,52 +107,47 @@ def main():
         df['block_key']         = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
         logger.debug(f"{label}-Sample: {df.iloc[0][['normalized_name','normalized_domain','block_key']].to_dict()}")
 
-    # Blocking-Index erzeugen
+    # Blocking-Index erstellen
     crm_index = {}
     for _, row in crm_df.iterrows():
         key = row['block_key']
         if key:
-            crm_index.setdefault(key, []).append(row)
+            crm_index.setdefault(key,[]).append(row)
     logger.info(f"Blocking-Index mit {len(crm_index)} Keys erstellt")
 
     # Matching
-    results = []
+    results=[]
-    total = len(match_df)
+    total=len(match_df)
     logger.info("Starte Matching-Prozess...")
-    for i, mrow in match_df.iterrows():
+    for i,mrow in match_df.iterrows():
-        key = mrow['block_key']
+        key = mrow['block_key']; cands=crm_index.get(key,[])
-        cands = crm_index.get(key, [])
         logger.info(f"Prüfe {i+1}/{total}: '{mrow['CRM Name']}' -> {len(cands)} Kandidaten")
         if not cands:
-            results.append({'Match':'', 'Score':0})
+            results.append({'Match':'','Score':0}); continue
-            continue
+        scored=[]
-
-        scored = []
         for crow in cands:
-            sc, dm, ns, lm, bf = calculate_similarity(mrow, crow)
+            sc,dm,ns,lm,bf=calculate_similarity(mrow,crow)
-            scored.append((crow['CRM Name'], sc, dm, ns, lm, bf))
+            scored.append((crow['CRM Name'],sc,dm,ns,lm,bf))
-        # Top 3 loggen
+        for name,sc,dm,ns,lm,bf in sorted(scored,key=lambda x:x[1],reverse=True)[:3]:
-        for name, sc, dm, ns, lm, bf in sorted(scored, key=lambda x: x[1], reverse=True)[:3]:
             logger.debug(f"  Kandidat: {name}, Score={sc}, Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}")
-
+        best_name,best_score,dm,ns,lm,bf=max(scored,key=lambda x:x[1])
-        best_name, best_score, dm, ns, lm, bf = max(scored, key=lambda x: x[1])
+        if best_score>=SCORE_THRESHOLD:
-        if best_score >= SCORE_THRESHOLD:
+            results.append({'Match':best_name,'Score':best_score})
-            results.append({'Match':best_name, 'Score':best_score})
+            logger.info(f" --> Match: '{best_name}' ({best_score}) [Dom={dm},Name={ns},Ort={lm},Bonus={bf}]")
-            logger.info(f" --> Match: '{best_name}' ({best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
         else:
-            results.append({'Match':'', 'Score':best_score})
+            results.append({'Match':'','Score':best_score})
-            logger.info(f" --> Kein Match (Score={best_score}) [Dom={dm}, Name={ns}, Ort={lm}, Bonus={bf}]")
+            logger.info(f" --> Kein Match (Score={best_score}) [Dom={dm},Name={ns},Ort={lm},Bonus={bf}]")
 
     # Ergebnisse zurückschreiben
     logger.info("Schreibe Ergebnisse ins Sheet...")
-    out = pd.DataFrame(results)
+    out=pd.DataFrame(results)
-    output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
+    output=match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
-    output = pd.concat([output.reset_index(drop=True), out], axis=1)
+    output=pd.concat([output.reset_index(drop=True),out],axis=1)
-    data = [output.columns.tolist()] + output.values.tolist()
+    data=[output.columns.tolist()]+output.values.tolist()
-    if sheet.clear_and_write_data(MATCHING_SHEET_NAME, data):
+    if sheet.clear_and_write_data(MATCHING_SHEET_NAME,data):
         logger.info("Ergebnisse erfolgreich geschrieben")
     else:
         logger.error("Fehler beim Schreiben ins Google Sheet")
 
-if __name__ == '__main__':
+if __name__=='__main__':
     main()