Update Gradio app with multiple files

utils.py

@@ -160,7 +160,8 @@ def predict_prices(data, model=None, tokenizer=None, prediction_days=30):
         from chronos import BaseChronosPipeline
         pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-base", device_map="auto")
         with torch.no_grad():
-            forecast = pipeline.predict(context=torch.tensor(prices), prediction_length=prediction_days)
+            # Fix: Use context_tensor instead of context
+            forecast = pipeline.predict(context_tensor=torch.tensor(prices), prediction_length=prediction_days)
         forecast_np = forecast.squeeze().cpu().numpy() if isinstance(forecast, torch.Tensor) else np.array(forecast)
         if forecast_np.ndim > 1:
             mean_forecast = forecast_np.mean(axis=tuple(range(forecast_np.ndim - 1)))