update utils

utils.py

@@ -275,14 +275,18 @@ 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,
-                temperature=1.0,
-                # REMOVED: 'top_k=50' and 'top_p=0.9' to eliminate warnings
+                prediction_length=prediction_days
             )
         
-        predictions = forecast[0].numpy()
+        # Handle case where output is a tuple (common for Seq2Seq models)
+        if isinstance(forecast, tuple):
+             predictions = forecast[0].numpy()
+        else:
+             predictions = forecast[0].numpy()
         
         # Calculate prediction statistics
         last_price = prices[-1]