Update app.py

app.py

@@ -253,7 +253,7 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown(" **[Demo is based on sklearn docs found here](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py)** <br>")
 
         
-    with gr.Tab("# Plot true and estimated coefficients"):
+    with gr.Tab("Plot true and estimated coefficients"):
         
         with gr.Row():
             n_iter = gr.Slider(value=5, minimum=5, maximum=50, step=1, label="n_iterations")
@@ -266,7 +266,7 @@ with gr.Blocks(title=title) as demo:
          One can observe that with the added noise, none of the models can perfectly recover the coefficients of the original model. All models have more thab 10 non-zero coefficients,
         where only 10 are useful. The Bayesian Ridge Regression manages to recover most of the coefficients, while the ARD is more conservative.
         """)
-    with gr.Tab("# Plot marginal log likelihoods"):        
+    with gr.Tab("Plot marginal log likelihoods"):        
         with gr.Row():
             n_iter = gr.Slider(value=5, minimum=5, maximum=50, step=1, label="n_iterations")
         btn = gr.Button(value="Plot marginal log likelihoods")
@@ -277,7 +277,7 @@ with gr.Blocks(title=title) as demo:
         Both ARD and Bayesian Ridge minimized the log-likelihood upto an arbitrary cuttoff defined the the n_iter parameter.
         """
         )
-    with gr.Tab("# Plot bayesian regression with polynomial features"):
+    with gr.Tab("Plot bayesian regression with polynomial features"):
         with gr.Row():
             degree = gr.Slider(value=5, minimum=5, maximum=50, step=1, label="n_degrees")
         btn = gr.Button(value="Plot bayesian regression with polynomial features")