removed hardcoded value

app.py

@@ -238,10 +238,29 @@ def plot(ts_index, test_dataset, forecasts, prediction_length):
         mode='lines',
     )
 
+    forecast_median = np.median(forecasts[ts_index][0][:prediction_length])
+    forecast_median_data = go.Scatter(
+        x=index[target_length:],
+        y=[forecast_median] * prediction_length,
+        name="Prediction Median",
+        mode='lines',
+    )
+
+    forecast_std = np.std(forecasts[ts_index][0][:prediction_length])
+    forecast_std_data = go.Scatter(
+        x=index[target_length:],
+        y=[forecast_median + forecast_std] * prediction_length,
+        name="Prediction Std",
+        mode='lines',
+    )
+
+
     # Create the figure
     fig = make_subplots(rows=1, cols=1)
     fig.add_trace(actual_data, row=1, col=1)
     fig.add_trace(forecast_data, row=1, col=1)
+    fig.add_trace(forecast_median_data, row=1, col=1)
+    fig.add_trace(forecast_std_data, row=1, col=1)
 
     # Set layout and title
     fig.update_layout(
@@ -256,7 +275,7 @@ def plot(ts_index, test_dataset, forecasts, prediction_length):
 def do_prediction(days_to_predict: int):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     # Define the desired prediction length
-    prediction_length = 7  # Number of time steps to predict into the future
+    prediction_length = days_to_predict  # Number of time steps to predict into the future
     freq = "1D"  # Daily frequency
 
     dataset = load_dataset("thesven/BTC-Daily-Avg-Market-Value")