upload latest app.py

app.py

@@ -39,8 +39,11 @@ async def predict(data: MarketData):
         
         # prediction[0] is the result for the first (and only) batch
         # We take the median (50th percentile) as our forecast
+        # We also take 10th and 90th percentiles for confidence bands
         # Shape is (samples, horizon)
         forecast = prediction[0].median(dim=0).values.tolist()
+        low_forecast = prediction[0].quantile(0.1, dim=0).tolist()
+        high_forecast = prediction[0].quantile(0.9, dim=0).tolist()
         
         # Calculate Confidence Score (Monte Carlo Agreement)
         # 1. Determine if the median predicts market going UP or DOWN
@@ -62,6 +65,8 @@ async def predict(data: MarketData):
         
         return {
             "forecast": forecast,
+            "low": low_forecast,
+            "high": high_forecast,
             "confidence": confidence_score,
             "horizon": data.horizon,
             "input_size": len(data.prices)