chore: refactor relative calculation

app.py

@@ -70,22 +70,26 @@ 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!")
-                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 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'], [[k for k in list(df_right.keys()) if k != 'baseline'][-2]])
-                    relative_key = f"relative_{option[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[option[0]][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!")
 
+with col2:
+    if 'df' in list(st.session_state.keys()):
+        df_right = st.session_state.df.copy(deep=True)
+        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 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'], [[k for k in list(df_right.keys()) if k != 'baseline'][-3]])
+        relative_key = f"relative_{option[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[option[0]][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.")
+
 if not df_base.empty:
     st.warning("Consider running outlier detection to clean your data!", icon="⚠️")