Update app.py

app.py

@@ -78,7 +78,7 @@ def visualize_input_data():
 
     return fig
 
-title = " Lasso model selection via information criteria"
+title = "Lasso model selection via information criteria"
 
 import gradio as gr
 import matplotlib.pyplot as plt
@@ -166,7 +166,7 @@ with gr.Blocks(title=title) as demo:
   gr.Markdown(f"# {title}")
   gr.Markdown(
         """
-        # Probabilistic model selection using Information Criterion. 
+        Probabilistic model selection using Information Criterion. 
         This method in statistics is useful because they dont require a hold out set test set(cross validation set).
         
         AIC and BIC are two ways of scoring a model based on its log-likelihood and complexity.
@@ -184,7 +184,7 @@ with gr.Blocks(title=title) as demo:
     
 
     
-  gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py**")
+  gr.Markdown("See original example [here](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-ic-py).")
     
   ##process
   X,y = load_dataset()