fix description formating

app.py

@@ -40,29 +40,26 @@ 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 
-        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
-        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 
-        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:\n
-        1. Age\n
-        2. Sex\n
-        3. Body mass index\n
-        4. Average blood pressure\n
-        5. Total serum cholesterol\n
-        6. Low-density lipoproteins\n
-        7. High-density lipoproteins\n
-        8. Total cholesterol / HDL\n
-        9. Possibly log of serum triglycerides level\n
-        10. Blood sugar level\n 
+        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:
+        1. Age (age)
+        2. Sex (sex)
+        3. Body mass index (bmi)
+        4. Average blood pressure (bp)
+        5. Total serum cholesterol (s1)
+        6. Low-density lipoproteins (s2)
+        7. High-density lipoproteins (s3)
+        8. Total cholesterol / HDL (s4)
+        9. Possibly log of serum triglycerides level (s5)
+        10. Blood sugar level (s6)\n\n 
         This app is developed based on [scikit-learn example](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-diabetes-py)
     """)