Update app.py

app.py

@@ -59,16 +59,6 @@ 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():
@@ -113,7 +103,7 @@ for company in companies:
     
         if predicted > 1:
             df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative'] + price * pct
-        elif predicted < 1:
+        elif predicted < -1:
             df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative'] - price * pct
         else:
             df_c.loc[i,'StrategyB_Cumulative'] = df_c.loc[i-1,'StrategyB_Cumulative']