improve description formating

app.py

@@ -40,16 +40,19 @@ def select_features(method,num_features):
         toc_bwd = time()
         selected_features = feature_names[sfs_backward.get_support()]
         execution_time = toc_bwd - tic_bwd
-    return f"Selected the following features: {','.join(selected_features)} in {execution_time:.3f} seconds"
+    return f"Selected the following features: {', '.join(selected_features)} in {execution_time:.3f} seconds"
 
 title = "Selecting features with Sequential Feature Selection"
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown("""
         This app demonstrates feature selection techniques using model based selection and sequential feature selection.\n\n 
-        Model based selection is based on feature importance. Each feature is assigned a score on how much influence they have on the model output. The feature with highest score is considered the most important feature.\n\n
-        Sequential feature selection is based on greedy approach. In greedy approach, the feature is added or removed to the selected features at each iteration based on the model performance score.\n\n
-        This app uses Ridge estimator and the diabetes dataset from sklearn. Diabetes dataset consist of quantitative measure of diabetes progression and 10 following variables obtained from 442 diabetes patients:
+        Model based selection is based on feature importance. Each feature is assigned a score on how much influence they have on the model output. 
+        The feature with highest score is considered the most important feature.\n\n
+        Sequential feature selection is based on greedy approach. In greedy approach, the feature is added or removed to the selected features at each iteration 
+        based on the model performance score.\n\n
+        This app uses Ridge estimator and the diabetes dataset from sklearn. Diabetes dataset consist of quantitative measure of diabetes progression and 
+        10 following variables obtained from 442 diabetes patients:
         1. Age (age)
         2. Sex (sex)
         3. Body mass index (bmi)