updating select_cta_button

app.py

@@ -414,9 +414,13 @@ def select_cta_button(ccolor, text):
     st.write('Select which Call-To-Action button(s) you would like to analyze: \n')
     
     #st.write(st.session_state)
+    buttons_out=[]
     for x in np.arange(len(st.session_state.ccolor)):
-        st.button(st.session_state.ccolor[x], key = x)    
-    def toggle_all(change):
+        bstyle="background-color: {}; color:{}; border-radius: 0.75rem;".format(st.session_state.ccolor[x],st.session_state.ccolor[x])
+        ctab=st.button("Call_To_Action text: "+str(st.session_state.text[x]), key = x, style=bstyle)  
+        buttons_out.append(ctab)
+    return buttons_out
+    '''def toggle_all(change):
         for cb in user_input:
             cb.value = select_all.value
     
@@ -440,7 +444,7 @@ def select_cta_button(ccolor, text):
         
     select_all.observe(toggle_all)
         
-    return user_input 
+    return user_input'''
 
 def save_state():
     if uploaded_file is not None:
@@ -594,9 +598,10 @@ def get_predictions(selected_variable, selected_industry, selected_campaign,
             output_rate = predicted_rate
 
             if output_rate < 0:
-                print("Sorry, Current model couldn't provide predictions on the target variable you selected.")
+                 st.markdown("##### Sorry, Current model couldn't provide predictions on the target variable you selected.", unsafe_allow_html=True)
             else:
                 print(f'\x1b[35m\nModel Prediction on the {selected_variable} is: \x1b[1m{round(output_rate*100, 2)}%\x1b[39m\x1b[22m')
+                st.markdown("##### Model Prediction on the {} is {}".format(selected_variable, round(output_rate*100, 2)), unsafe_allow_html=True)
                 selected_industry_code = industry_code_dict.get(selected_industry)
                 selected_campaign_code = campaign_code_dict.get(selected_campaign)