chore: refactor code and comments

app.py

@@ -13,7 +13,8 @@ col1, col2 = st.columns(2)
 def convert_df(df):
    return df.to_csv(index=False).encode('utf-8')
 
-
+    
+# Load a prepare data
 file_names, file_bytes = [], []
 with st.sidebar:
     files = st.file_uploader("Load files", accept_multiple_files = True)
@@ -45,6 +46,7 @@ with col1:
     else:
         st.caption("Upload your data using the sidebar to start :sunglasses:")
 
+# Cleaning starts
 with col2:
     if not df_base.empty:
         st.markdown("**Cleaning actions**")
@@ -80,6 +82,7 @@ with col2:
         elif detect_blinking and not number_of_blinks:
             st.caption("No blinking values were found in your data! ")
 
+# Add calculated fields
 with col2:
     if 'df' in list(st.session_state.keys()):
         df_right = st.session_state.df.copy(deep=True)
@@ -94,20 +97,11 @@ with col2:
                 st.markdown("After adding calculated fields")
                 st.dataframe(df_right)
                 csv = convert_df(df_right)
-                downl = st.download_button(
-                   "Download CSV 💾",
-                   csv,
-                   "file.csv",
-                   "text/csv",
-                   key='download-csv'
-                )
+
+                # Save transformations to disk
+                downl = st.download_button("Download CSV 💾", csv, "file.csv", "text/csv", key='download-csv')
                 if downl:
                     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="⚠️")
-
-# for key, value in st.session_state.items():
-#    st.success(f"{key}: {value}")
-
-# reloading new samples would damage the st-session_state loading, vars are only written once
+    st.warning("Consider running outlier detection to clean your data!", icon="⚠️")