Update app.py

app.py

@@ -47,7 +47,8 @@ parameters = [
     gr.inputs.Slider(10, 100, step=10, default=50, label="Number of data points (n)"),
     gr.inputs.Slider(-50, 50, step=1, default=-50, label="Random Value Range (Min)"),
     gr.inputs.Slider(-50, 50, step=1, default=50, label="Random Value Range (Max)"),
-    gr.inputs.Dropdown(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"),
+    gr.inputs.Radio(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"),
+    # gr.inputs.Dropdown(["clip", "nan", "raise"], default="clip", label="Out of Bounds Strategy"),
 ]
 
 description = "This app presents an illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise). The isotonic regression algorithm finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data. The benefit of such a non-parametric model is that it does not assume any shape for the target function besides monotonicity. For comparison a linear regression is also presented. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html"
@@ -58,5 +59,5 @@ examples = [
     [70, -10, 20, "raise"],
 ]
 
-iface = gr.Interface(fn=visualize_isotonic_regression, inputs=parameters, outputs="plot", title="Isotonic Regression Visualization", description=description, examples=examples)
+iface = gr.Interface(fn=visualize_isotonic_regression, inputs=parameters, outputs="plot", title="Isotonic Regression Visualization", description=description, examples=examples, live=True)
 iface.launch()