Update app.py

app.py

@@ -59,20 +59,6 @@ def display_prediction(prediction, prediction_probabilities):
     prob_df = pd.DataFrame(prediction_probabilities, index=class_labels, columns=["Probability"])
     st.bar_chart(prob_df)
 
-    # Example chart: Feature Importance
-    st.subheader("Feature Importance:")
-    feature_importance = np.random.rand(len(class_labels))  # Random feature importance values for demonstration
-    features_names = ['use_of_ip_address', 'abnormal_url', 'google_index', 'count_www', 'count@', 
-                      'count_dir', 'count_embed_domian', 'short_url', 'count_https', 
-                      'count_http', 'count%', 'count?', 'count-', 'count=', 'url_length', 
-                      'hostname_length', 'sus_url', 'fd_length', 'tld_length', 'count-digits', 
-                      'count-letters']
-    
-    fig, ax = plt.subplots()
-    ax.barh(features_names, feature_importance, color='skyblue')
-    ax.set_xlabel('Importance')
-    ax.set_title('Feature Importance')
-    st.pyplot(fig)
     
     # Another example chart: Prediction distribution (simulated)
     st.subheader("Prediction Distribution (Simulated):")