Refactor export matching logic in app.py: move the name-to-ID mapping and best match application to a dedicated button, improving user interaction and streamlining the export process while removing redundant debug messages.

app.py

@@ -144,35 +144,9 @@ with tab1:
                 st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
             
             # Update projections_df with any new matches
-            st.write('starting name matching')
             st.session_state['projections_df'] = find_name_mismatches(st.session_state['portfolio'], st.session_state['projections_df'])
-            if csv_file is not None and 'export_dict' not in st.session_state:
-                    if st.button('Prepare export matching'):
-                        # Create a dictionary of Name to Name+ID from csv_file
-                        try:
-                            name_id_map = dict(zip(
-                                st.session_state['csv_file']['Name'], 
-                                st.session_state['csv_file']['Name + ID']
-                            ))
-                        except:
-                            name_id_map = dict(zip(
-                                st.session_state['csv_file']['Nickname'], 
-                                st.session_state['csv_file']['Id']
-                            ))
-                        
-                        # Function to find best match
-                        def find_best_match(name):
-                            best_match = process.extractOne(name, name_id_map.keys())
-                            if best_match and best_match[1] >= 85:  # 85% match threshold
-                                return name_id_map[best_match[0]]
-                            return name  # Return original name if no good match found
-                        
-                        # Apply the matching
-                        projections['upload_match'] = projections['player_names'].apply(find_best_match)
-                        st.session_state['export_dict'] = dict(zip(projections['player_names'], projections['upload_match']))
             
             st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
-            st.write('name matching complete')
 
 # with tab2:
 #     if st.button('Clear data', key='reset2'):
@@ -881,6 +855,30 @@ with tab2:
                 )
                 st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
             st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
+            if st.button('Prepare exports'):
+                # Create a dictionary of Name to Name+ID from csv_file
+                try:
+                    name_id_map = dict(zip(
+                        st.session_state['csv_file']['Name'], 
+                        st.session_state['csv_file']['Name + ID']
+                    ))
+                except:
+                    name_id_map = dict(zip(
+                        st.session_state['csv_file']['Nickname'], 
+                        st.session_state['csv_file']['Id']
+                    ))
+                
+                # Function to find best match
+                def find_best_match(name):
+                    best_match = process.extractOne(name, name_id_map.keys())
+                    if best_match and best_match[1] >= 85:  # 85% match threshold
+                        return name_id_map[best_match[0]]
+                    return name  # Return original name if no good match found
+                
+                # Apply the matching
+                projections['upload_match'] = projections['player_names'].apply(find_best_match)
+                st.session_state['export_dict'] = dict(zip(projections['player_names'], projections['upload_match']))
+            st.info('Click the above option after you have set up your portfolio if you want to export it as a set ready to upload to DFS sites')
             with st.expander("Download options"):
                 if stack_dict is not None:
                     download_type = st.selectbox("Simple or Advanced Download?", options=['Simple', 'Advanced'], key='download_choice')