chore: refactor

app.py

@@ -77,18 +77,18 @@ 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.")
+            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="⚠️")