Update app.py

app.py

@@ -244,14 +244,14 @@ with st.container():
 k_threshold = 5
 
 # Function to perform bootstrap on the predicted target values
-def bootstrap_stats(predicted_target, num_samples=1000):
+def bootstrap_stats(bound_data, num_samples=1000):
     # Reshape the predicted_target array
-    predicted_target = np.array(predicted_target).reshape(-1, 1)
+    bound_data = np.array(predicted_target).reshape(-1, 1)
 
     # Bootstrap resampling
     bootstrapped_means = []
     for _ in range(num_samples):
-        bootstrap_sample = np.random.choice(predicted_target.flatten(), len(predicted_target), replace=True)
+        bootstrap_sample = np.random.choice(bound_data.flatten(), len(bound_data), replace=True)
         bootstrapped_means.append(np.mean(bootstrap_sample))
 
     # Calculate lower and higher bounds