ping

app.py

@@ -2999,9 +2999,6 @@ if selected_tab == 'Manage Portfolio':
 
                         
                         st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
-                        
-                        # st.session_state['export_base'] = predict_dupes(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
-                        # Load Default base from compressed storage for reassess_edge
                         default_base = load_base_frame('Default')
                         st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], default_base, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
                         team_dict = dict(zip(st.session_state['portfolio_inc_proj']['player_names'], st.session_state['portfolio_inc_proj']['team']))

global_func/optimize_lineup.py

@@ -53,31 +53,30 @@ def optimize_single_lineup(
     locked_salary = 0
     locked_player_names = set()
     
-    # IMPORTANT: Track ALL players currently in the row, not just locked ones
-    all_current_players = set()
-    
     for col in player_columns:
         player_name = row[col]
-        all_current_players.add(player_name)  # Add to set of all players in row
-        
         player_team = map_dict['team_map'].get(player_name, '')
         
         if player_team in lock_teams:
+            # Keep this player locked
             locked_players[col] = player_name
             locked_salary += get_effective_salary(player_name, col, map_dict, type_var)
             locked_player_names.add(player_name)
         else:
+            # This position is open for optimization
             open_columns.append(col)
     
+    # If no open columns, nothing to optimize
     if not open_columns:
         return optimized_row
     
+    # Calculate remaining salary budget
     remaining_salary = salary_max - locked_salary
     
-    # Filter player pool: exclude locked teams AND all players already in the row
+    # Filter player pool: exclude locked teams and already-locked players
     available_players = player_pool[
         (~player_pool['team'].isin(lock_teams)) & 
-        (~player_pool['player_names'].isin(all_current_players))  # ✅ Changed this line
+        (~player_pool['player_names'].isin(locked_player_names))
     ].copy()
     
     if available_players.empty:
@@ -107,9 +106,18 @@ def optimize_single_lineup(
     
     # Constraint 2: Each player can only be used once across all open columns
     for i in range(num_players):
-        solver.Add(sum(x[i, j] for j in range(num_open_cols)) <= 1)
+        solver.Add(sum(x[i, j] for j in range(num_player_columns)) <= 1)
+
+    # Constraint 3: Players already in ANY position in the row cannot be selected again
+    all_players_in_row = set(row[col] for col in player_columns)  # Get ALL players in row
+    for i, player in enumerate(player_list):
+        player_name = player['player_names']
+        if player_name in all_players_in_row:
+            # This player is already somewhere in the row, can't use them again
+            for j in range(num_open_cols):
+                solver.Add(x[i, j] == 0)
     
-    # Constraint 3: Position eligibility
+    # Constraint 4: Position eligibility
     for i, player in enumerate(player_list):
         player_positions = player['position'].split('/')
         for j, col in enumerate(open_columns):
@@ -120,7 +128,7 @@ def optimize_single_lineup(
                 # For Showdown, CPT and FLEX can take any player
                 pass
     
-    # Constraint 4: Total salary of selected players <= remaining_salary
+    # Constraint 5: Total salary of selected players <= remaining_salary
     salary_constraint = []
     for i, player in enumerate(player_list):
         for j, col in enumerate(open_columns):