properly defined mape and sape

app.py

@@ -65,20 +65,24 @@ def run_prediction():
     })
 
     # --- Calculate Metrics --- #
-    pred_df["% Error"] = ((pred_df["Predicted Close"] - pred_df["Actual Close"]) / pred_df["Actual Close"]) * 100
+    pred_df["% Error Raw"] = ((pred_df["Predicted Close"] - pred_df["Actual Close"]) / pred_df["Actual Close"]) * 100
 
-    # Round numeric columns to 2 decimal places
+    # Compute MAPE and SAPE first
+    mape = np.mean(np.abs(pred_df["% Error Raw"]))
+    sape = np.std(np.abs(pred_df["% Error Raw"]))
+
+    # Format the columns
     pred_df["Actual Close"] = pred_df["Actual Close"].round(2)
     pred_df["Predicted Close"] = pred_df["Predicted Close"].round(2)
-
-    # Format % Error as "$ ±xx.xx"
-    pred_df["% Error"] = pred_df["% Error"].apply(lambda x: f"$ {x:+.2f}")
+    pred_df["% Error"] = pred_df["% Error Raw"].apply(lambda x: f"$ {x:+.2f}")
+    pred_df.drop(columns=["% Error Raw"], inplace=True)
 
     # Add MAPE Range per row to table
     pred_df["±MAPE Range"] = pred_df["Predicted Close"].apply(
         lambda x: f"${x * (1 - mape/100):.2f} to ${x * (1 + mape/100):.2f}"
     )
 
+
     # --- Next Day Prediction --- #
     latest_close = float(data["Close"].iloc[-1])
     latest_date = data.index[-1].strftime("%Y-%m-%d")