Update get_market_cap_tier to use ScreenerScraper

forecaster_cli.py

@@ -96,20 +96,17 @@ def fetch_live_features(ticker):
         return None
 
 def get_market_cap_tier(ticker):
-    print(f"[{ticker}] Resolving market cap classification from NSE servers...")
+    print(f"[{ticker}] Resolving market cap classification via Screener...")
     try:
-        ua = {'User-Agent': 'Mozilla/5.0'}
-        large = pd.read_csv(TIERS["Large"], storage_options=ua)['Symbol'].tolist()
-        if ticker in large: return "Large"
-        
-        mid = pd.read_csv(TIERS["Mid"], storage_options=ua)['Symbol'].tolist()
-        if ticker in mid: return "Mid"
+        scraper = ScreenerScraper()
+        cap_class = scraper.get_cap_info(ticker)['market_cap_class']
         
-        small = pd.read_csv(TIERS["Small"], storage_options=ua)['Symbol'].tolist()
-        if ticker in small: return "Small"
+        if "Large" in cap_class: return "Large"
+        if "Mid" in cap_class: return "Mid"
+        if "Small" in cap_class: return "Small"
         
     except Exception as e:
-        print(f"Warning: Could not fetch NSE lists ({e}). Defaulting to Small Cap.")
+        print(f"Warning: Could not fetch market cap ({e}). Defaulting to Small.")
         
     return "Small" # Default to small cap model for everything else