update utils

utils.py

@@ -275,17 +275,16 @@ def predict_prices(data, model, tokenizer, prediction_days=30):
         
         # Generate predictions
         with torch.no_grad():
-            # FIXED: Removed unnecessary generation flags (top_p, top_k, temperature) 
-            # to eliminate warnings and potential conflicts with model's custom implementation.
             forecast = model.generate(
                 prediction_input,
                 prediction_length=prediction_days
             )
         
-        # Handle case where output is a tuple (common for Seq2Seq models)
+        # FIXED: Handle the case where the output is a tuple (common for Seq2Seq models)
         if isinstance(forecast, tuple):
              predictions = forecast[0].numpy()
         else:
+             # Ensure the output is a numpy array (even if it's already a tensor)
              predictions = forecast[0].numpy()
         
         # Calculate prediction statistics