Update app.py

app.py

@@ -59,6 +59,16 @@ for i, row in df_multi.iterrows():
         df_multi.at[i,'FinBERT_Confidence'] = 0.0
 
     # Regression (Tesla & MSFT)
+    try:
+        if company == "Tesla, Inc.":
+            val = price_pipeline_tesla(row['Summary'])[0]['score']
+            df_multi.at[i,'Predicted'] = min(val, 1.0)
+        elif company == "Microsoft":
+            val = price_pipeline_msft(row['Summary'])[0]['score']
+            df_multi.at[i,'Predicted'] = min(val, 1.0)
+    except:
+        df_multi.at[i,'Predicted'] = 0.0
+
 # --- FETCH STOCK PRICES ---
 prices = {}
 for company, ticker in TICKERS.items():