Updated description and use slider as input

app.py

@@ -40,15 +40,34 @@ 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: {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. The app uses the diabetes dataset from sklearn. 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)")
+    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 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)
+    """)
 
     method = gr.Radio(["model", "sfs-forward", "sfs-backward"], label="Method")
-    num_features = gr.Textbox(label="Number of features")
+    num_features = gr.Slider(minimum=2, maximum=10, step=1, label = "Number of features")
     output = gr.Textbox(label="Output Box")
     select_btn = gr.Button("Select")
     select_btn.click(fn=select_features, inputs=[method,num_features], outputs=output)