Update app.py

app.py

@@ -60,27 +60,27 @@ description3 = "We will use the toy dataset as given in the scikit-learn page fo
 
 with gr.Blocks(title=title) as demo:
     
-    gr.Markdown(f"# {title}")
+    gr.Markdown(f" # {title}")
     gr.Markdown(
     """
-    The isolation forest is an Ensemble of Isolation trees and it isolates the data points using recursive random partitioning.
+    The isolation forest is an ensemble of isolation trees and it isolates the data points using recursive random partitioning.
     In case of outliers the number of splits required is greater than those required for inliers.
     We will use the toy dataset for our educational demo as given in the scikit-learn page for Isolation Forest.
     
     """)
 
-    gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
+    gr.Markdown("You can see the associated scikit-learn example [here](https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py).")
     
     loaded_model = load_hf_model_hub()
 
-    with gr.Tab("# Visualize Input dataset"):
+    with gr.Tab("Visualize Input dataset"):
         btn = gr.Button(value="Visualize input dataset")
         btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )
 
-    with gr.Tab("# Plot Decision Boundary"):
+    with gr.Tab("Plot Decision Boundary"):
         image_decision = gr.Image('./downloaded-model/decision_boundary.png')
     
-    with gr.Tab("# Plot Path"):
+    with gr.Tab("Plot Path"):
         image_path = gr.Image('./downloaded-model/plot_path.png')