Update app.py

app.py

@@ -78,12 +78,17 @@ def analyze_stock(symbol):
         }
         performance_df = pd.DataFrame(performance_data)
         
-        # Extract values properly
-        current_price = float(data['Close'].iloc[-1])
-        start_price = float(data['Close'].iloc[0])
-        high_price = float(data['Close'].max())
-        low_price = float(data['Close'].min())
-        volatility = float(returns.std() * 100)
+        # Extract values properly - handle both Series and scalar values
+        current_price = float(data['Close'].iloc[-1]) if hasattr(data['Close'].iloc[-1], 'item') else data['Close'].iloc[-1]
+        start_price = float(data['Close'].iloc[0]) if hasattr(data['Close'].iloc[0], 'item') else data['Close'].iloc[0]
+        high_price = float(data['Close'].max()) if hasattr(data['Close'].max(), 'item') else data['Close'].max()
+        low_price = float(data['Close'].min()) if hasattr(data['Close'].min(), 'item') else data['Close'].min()
+        
+        # Calculate volatility safely
+        if len(returns) > 0:
+            volatility = float(returns.std()) * 100
+        else:
+            volatility = 0.0
         
         total_return = ((current_price / start_price) - 1) * 100
         price_change = current_price - start_price