chore: refinement

app.py

@@ -70,20 +70,21 @@ with col2:
                 df_right.pupil_dilation = df_right.pupil_dilation.map(lambda ser: [f for f in ser if f != 0.0])
                 df_right.baseline = df_right.baseline.map(lambda ser: [f for f in ser if f != 0.0])
                 st.success("Blinking values have been removed!")
-                st.markdown("After transformation")
-                # st.dataframe(df_right)
                 if "baseline" in list(df_right.keys()):
-                    st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with **{[k for k in list(df_right.keys()) if k != 'baseline']}** with in a new calculated field?")
-                    relative_key = f"relative_{[k for k in list(df_right.keys()) if k != 'baseline'][0]}"
-                    add_relative = st.button(f"Add {relative_key}")
-                    if add_relative:
-                        baseline_mean = [sum(s)/len(s) for s in df['baseline']]
-                        df_right['relative_pupil_dilation'] = [df['pupil_dilation'][i] - baseline_mean[i] for i in range(len(df))]
-                        st.markdown("After adding calculated fields")
-                        st.dataframe(df_right)
-                        with open('myfile.csv') as f:
-                            st.download_button('Download CSV', f)
-                        st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
+                    st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with any of the other features in a new calculated field?")
+                    option = st.multiselect('Select a feature to merge', [k for k in list(df_right.keys()) if k != 'baseline'], [])
+                    if option:
+                        st.write('You selected:', option)
+                        relative_key = f"relative_{option}"
+                        add_relative = st.button(f"Add {relative_key}")
+                        if add_relative:
+                            baseline_mean = [sum(s)/len(s) for s in df['baseline']]
+                            df_right['relative_pupil_dilation'] = [df['pupil_dilation'][i] - baseline_mean[i] for i in range(len(df))]
+                            st.markdown("After adding calculated fields")
+                            st.dataframe(df_right)
+                            with open('myfile.csv') as f:
+                                st.download_button('Download CSV', f)
+                            st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
         elif detect_blinking and not number_of_blinks:
             st.caption("No blinking values were found in your data!")