Update utils.py

utils.py

@@ -271,42 +271,40 @@ def predict_prices(data, model, tokenizer, prediction_days=30):
         input_sequence = prices[-context_length:]
         
         # Create prediction input
-        # CRITICAL FIX: Ensure tensor is on the same device as the model (GPU/auto-mapped)
-        prediction_input = torch.tensor(input_sequence).unsqueeze(0).float().to(model.device)
+        # CRITICAL FIX: Cast input to LongTensor to satisfy model embedding layer expectation.
+        prediction_input = torch.tensor(input_sequence).unsqueeze(0).long().to(model.device)
         
         # Generate predictions
         with torch.no_grad():
-            # CRITICAL FIX: Removed prediction_length keyword which caused warnings/potential internal failure.
-            # Model will use its default configuration for generation length.
+            # NOTE: Removed prediction_length from generate call as it was causing failures.
             forecast = model.generate(
                 prediction_input,
             )
         
-        # CRITICAL FIX: Handle complex Chronos output: [batch_size, num_samples, prediction_length]
+        # Handle complex Chronos output: [batch_size, num_samples, prediction_length]
         
         # 1. Get the actual tensor from the tuple/list if necessary
         output_tensor = forecast[0] if isinstance(forecast, (tuple, list)) else forecast
         
-        # 2. Average across the samples (dim=1) and ensure it is a simple 1D numpy array
-        # output_tensor shape: [1, num_samples, prediction_length]
-        # .mean(dim=1) averages the probabilistic samples
-        # .squeeze() removes unnecessary dimensions (like batch size 1)
-        predictions = output_tensor.mean(dim=1).squeeze().cpu().numpy()
+        # 2. Average across the samples (dim=1) and convert to a simple 1D numpy array
+        # predictions = output_tensor.mean(dim=1).squeeze().cpu().numpy()
+        # To avoid unexpected dimensions, let's simplify averaging:
+        predictions = output_tensor.float().mean(dim=1).squeeze().cpu().numpy()
         
         # Handle case where predictions is a single scalar (convert to array for safety)
         if predictions.ndim == 0:
              predictions = np.array([predictions.item()])
         
-        # Calculation is now safe as predictions is a 1D array
+        # Use actual prediction length from the output tensor
+        pred_len = len(predictions)
+        
+        # Calculate prediction statistics
         last_price = prices[-1]
         predicted_high = np.max(predictions)
         predicted_low = np.min(predictions)
         predicted_mean = np.mean(predictions)
         change_pct = ((predicted_mean - last_price) / last_price) * 100
         
-        # Use actual prediction length from the output tensor
-        pred_len = len(predictions)
-        
         return {
             'values': predictions,
             'dates': pd.date_range(