Update app.py

app.py

@@ -22,7 +22,7 @@ from statsforecast.models import (
 )
 
 from utilsforecast.evaluation import evaluate
-from utilsforecast.losses import *
+from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
 
 # Import for MLForecast
 from mlforecast import MLForecast
@@ -321,11 +321,14 @@ def run_forecast(
                     y_true_clean = y_true[mask]
                     y_pred_clean = y_pred[mask]
 
+                    # Calculate RMSE manually
+                    rmse_value = np.sqrt(mean_squared_error(y_true_clean, y_pred_clean))
+                    
                     metrics_list.append({
                         'Model': col,
-                        'MAE': mae(y_true_clean, y_pred_clean),
-                        'RMSE': rmse(y_true_clean, y_pred_clean),
-                        'MAPE': mape(y_true_clean, y_pred_clean)
+                        'MAE': mean_absolute_error(y_true_clean, y_pred_clean),
+                        'RMSE': rmse_value,
+                        'MAPE': mean_absolute_percentage_error(y_true_clean, y_pred_clean) * 100
                     })
 
         eval_metrics = pd.DataFrame(metrics_list)