Add custom tab styling to improve UI aesthetics and user experience

Implement custom CSS styling for Streamlit tabs to:
- Enhance visual appeal with gold color scheme
- Improve tab readability and interaction
- Add smooth transitions and hover effects
- Create a more polished and professional look for the application interface

app.py

@@ -38,6 +38,37 @@ expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
 
 all_dk_player_projections = st.secrets["NFL_data"]
 
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+        padding: 4px;
+    }
+
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        background-color: #FFD700;
+        color: white;
+        border-radius: 10px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+
+    .stTabs [aria-selected="true"] {
+        background-color: #DAA520;
+        color: white;
+    }
+
+    .stTabs [data-baseweb="tab"]:hover {
+        background-color: #DAA520;
+        cursor: pointer;
+    }
+</style>""", unsafe_allow_html=True)
+
 @st.cache_resource(ttl=60)
 def init_baselines():
     collection = nba_db["Player_SD_Range_Of_Outcomes"] 
@@ -115,55 +146,56 @@ def convert_df_to_csv(df):
 tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimizer'])
 
 with tab1:
-    if st.button("Load/Reset Data", key='reset2'):
-          st.cache_data.clear()
-          nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
-    info_container = st.container()
-    with info_container:
-        st.info("Simple view is better for mobile and shows just the most valuable stats, Advanced view is better for desktop and shows all stats and thresholds")
-    options_container = st.container()
-    with options_container:
-        col1, col2, col3, col4 = st.columns(4)
-        
-        with col1:
-            view_var2 = st.radio("View Type", ("Simple", "Advanced"), key='view_var2')
-        
-        with col2:
-            sport_var2 = st.radio("Sport", ('NBA', 'NFL'), key='sport_var2')
-            if sport_var2 == 'NBA':
-                dk_roo_raw = nba_dk_sd_raw
-                fd_roo_raw = nba_fd_sd_raw
-            elif sport_var2 == 'NFL':
-                dk_roo_raw = nfl_dk_sd_raw
-                fd_roo_raw = nfl_fd_sd_raw
-        
-        with col3:
-            slate_var2 = st.radio("Slate", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var2')
-        
-        with col4:
-            site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
-        
-        if site_var2 == 'Draftkings':
-            if slate_var2 == 'Paydirt (Main)':
-                raw_baselines = dk_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-            elif slate_var2 == 'Paydirt (Secondary)':
-                raw_baselines = dk_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
-            elif slate_var2 == 'Paydirt (Auxiliary)':
-                raw_baselines = dk_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
-        
-        elif site_var2 == 'Fanduel':
-            if slate_var2 == 'Paydirt (Main)':
-                raw_baselines = fd_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
-            elif slate_var2 == 'Paydirt (Secondary)':
-                raw_baselines = fd_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
-            elif slate_var2 == 'Paydirt (Auxiliary)':
-                raw_baselines = fd_roo_raw
-                raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
+    with st.expander('Info and Filters'):
+        if st.button("Load/Reset Data", key='reset2'):
+            st.cache_data.clear()
+            nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
+        info_container = st.container()
+        with info_container:
+            st.info("Simple view is better for mobile and shows just the most valuable stats, Advanced view is better for desktop and shows all stats and thresholds")
+        options_container = st.container()
+        with options_container:
+            col1, col2, col3, col4 = st.columns(4)
+            
+            with col1:
+                view_var2 = st.radio("View Type", ("Simple", "Advanced"), key='view_var2')
+            
+            with col2:
+                sport_var2 = st.radio("Sport", ('NBA', 'NFL'), key='sport_var2')
+                if sport_var2 == 'NBA':
+                    dk_roo_raw = nba_dk_sd_raw
+                    fd_roo_raw = nba_fd_sd_raw
+                elif sport_var2 == 'NFL':
+                    dk_roo_raw = nfl_dk_sd_raw
+                    fd_roo_raw = nfl_fd_sd_raw
+            
+            with col3:
+                slate_var2 = st.radio("Slate", ('Paydirt (Main)', 'Paydirt (Secondary)', 'Paydirt (Auxiliary)'), key='slate_var2')
+            
+            with col4:
+                site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
+            
+            if site_var2 == 'Draftkings':
+                if slate_var2 == 'Paydirt (Main)':
+                    raw_baselines = dk_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+                elif slate_var2 == 'Paydirt (Secondary)':
+                    raw_baselines = dk_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+                elif slate_var2 == 'Paydirt (Auxiliary)':
+                    raw_baselines = dk_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
+            
+            elif site_var2 == 'Fanduel':
+                if slate_var2 == 'Paydirt (Main)':
+                    raw_baselines = fd_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #1']
+                elif slate_var2 == 'Paydirt (Secondary)':
+                    raw_baselines = fd_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #2']
+                elif slate_var2 == 'Paydirt (Auxiliary)':
+                    raw_baselines = fd_roo_raw
+                    raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
     
     hold_container = st.empty()
     
@@ -204,14 +236,13 @@ with tab1:
     )
 
 with tab2:
-    col1, col2 = st.columns([1, 5])
-    with col1:
+    with st.expander('Info and Filters'):
         if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-        sport_var1 = st.radio("What sport are you optimizing?", ('NFL', 'NBA'), key='sport_var1')
+                st.cache_data.clear()
+                nba_dk_sd_raw, nba_fd_sd_raw, nfl_dk_sd_raw, nfl_fd_sd_raw, nba_timestamp, nfl_dk_timestamp, nba_dk_id_dict, nfl_dk_id_dict, nba_fd_id_dict, nfl_fd_id_dict = init_baselines()
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+        sport_var1 = st.radio("What sport are you optimizing?", ('NBA', 'NFL'), key='sport_var1')
         if sport_var1 == 'NBA':
             dk_roo_raw = nba_dk_sd_raw
             fd_roo_raw = nba_fd_sd_raw
@@ -243,7 +274,7 @@ with tab2:
                 st.info("Showdown on Fanduel sucks, you should not do that, but I understand degen's gotta degen")
                 raw_baselines = fd_roo_raw
                 raw_baselines = raw_baselines[raw_baselines['slate'] == 'Showdown #3']
-                  
+                    
         contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
         lock_var1 = st.multiselect("Are there any players you want to use in all lineups in the CAPTAIN (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
         lock_var2 = st.multiselect("Are there any players you want to use in all lineups in the FLEX (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var2')
@@ -350,329 +381,328 @@ with tab2:
             flex_proj['Own'] = display_baselines['Own']
             flex_proj['lock'] = display_baselines['lock']
             flex_proj['roster'] = 'FLEX'
-        
-        combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True)
     
-    with col2:
+    combo_file = pd.concat([cpt_proj, flex_proj], ignore_index=True)
+    
+    display_container = st.empty()
+    display_dl_container = st.empty()
+    optimize_container = st.empty()
+    download_container = st.empty()
+    freq_container = st.empty()
+    if st.button('Optimize'):
+        for key in st.session_state.keys():
+            del st.session_state[key]
+        max_proj = 1000
+        max_own = 1000
+        total_proj = 0
+        total_own = 0
         display_container = st.empty()
         display_dl_container = st.empty()
         optimize_container = st.empty()
         download_container = st.empty()
         freq_container = st.empty()
-        if st.button('Optimize'):
-            for key in st.session_state.keys():
-                del st.session_state[key]
-            max_proj = 1000
-            max_own = 1000
-            total_proj = 0
-            total_own = 0
-            display_container = st.empty()
-            display_dl_container = st.empty()
-            optimize_container = st.empty()
-            download_container = st.empty()
-            freq_container = st.empty()
-            lineup_display = []
-            check_list = []
-            lineups = []
-            portfolio = pd.DataFrame()
-            x = 1
-    
-            with st.spinner('Wait for it...'):
-                with optimize_container:
-    
-                        while x <= linenum_var1:
-                            sorted_lineup = []
-                            p_used = []
+        lineup_display = []
+        check_list = []
+        lineups = []
+        portfolio = pd.DataFrame()
+        x = 1
+
+        with st.spinner('Wait for it...'):
+            with optimize_container:
+
+                    while x <= linenum_var1:
+                        sorted_lineup = []
+                        p_used = []
+                        
+                        raw_proj_file = combo_file
+                        raw_flex_file = raw_proj_file.dropna(how='all')
+                        raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
+                        flex_file = raw_flex_file
+                        flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
+                        flex_file['name_var'] = flex_file['Player']
+                        flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var2), 1, 0)
+                        flex_file = flex_file[~flex_file['Player'].isin(avoid_var1)]
+                        flex_file['Player'] = np.where(flex_file['roster'] == 'CPT', flex_file['Player'] + ' - CPT', flex_file['Player'] + ' - FLEX')
+                        player_ids = flex_file.index
+
+                        overall_players = flex_file[['Player']]
+                        overall_players['player_var_add'] = flex_file.index
+                        overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
+
+                        player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
+                        total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
+                        player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
+                        player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
+
+                        player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
+                        player_team = dict(zip(flex_file['Player'], flex_file['Team']))
+                        player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
+                        player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
+                        player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
+
+                        obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                        total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+
+                        obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                        obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+
+                        obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
+                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
+                        total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
+
+                        if site_var1 == 'Draftkings':
                             
-                            raw_proj_file = combo_file
-                            raw_flex_file = raw_proj_file.dropna(how='all')
-                            raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
-                            flex_file = raw_flex_file
-                            flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
-                            flex_file['name_var'] = flex_file['Player']
-                            flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var2), 1, 0)
-                            flex_file = flex_file[~flex_file['Player'].isin(avoid_var1)]
-                            flex_file['Player'] = np.where(flex_file['roster'] == 'CPT', flex_file['Player'] + ' - CPT', flex_file['Player'] + ' - FLEX')
-                            player_ids = flex_file.index
-    
-                            overall_players = flex_file[['Player']]
-                            overall_players['player_var_add'] = flex_file.index
-                            overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
-    
-                            player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
-                            total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
-                            player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
-                            player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
-    
-                            player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
-                            player_team = dict(zip(flex_file['Player'], flex_file['Team']))
-                            player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
-                            player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
-                            player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
-    
-                            obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-    
-                            obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                            obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
-    
-                            obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
-                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
-                            total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
-    
-                            if site_var1 == 'Draftkings':
-                                
-                                for flex in flex_file['lock'].unique():
-                                    sub_idx = flex_file[flex_file['lock'] == 1].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "CPT"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "FLEX"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
-                                    
-                                for playerid in player_ids:
-                                    total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
-                                                       (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
-    
-                            elif site_var1 == 'Fanduel':
-                                
-                                for flex in flex_file['lock'].unique():
-                                    sub_idx = flex_file[flex_file['lock'] == 1].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
-                                
-                                for flex in flex_file['Position'].unique():
-                                    sub_idx = flex_file[flex_file['Position'] != "Var"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
-                                
-                                for flex in flex_file['roster'].unique():
-                                    sub_idx = flex_file[flex_file['roster'] == "CPT"].index
-                                    total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                            for flex in flex_file['lock'].unique():
+                                sub_idx = flex_file[flex_file['lock'] == 1].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
+                            
+                            for flex in flex_file['roster'].unique():
+                                sub_idx = flex_file[flex_file['roster'] == "CPT"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                            
+                            for flex in flex_file['roster'].unique():
+                                sub_idx = flex_file[flex_file['roster'] == "FLEX"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
                                 
-                                for playerid in player_ids:
-                                    total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
-                                                       (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
-    
-                            player_count = []
-                            player_trim = []
-                            lineup_list = []
+                            for playerid in player_ids:
+                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                    (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
+
+                        elif site_var1 == 'Fanduel':
                             
-                            if contest_var1 == 'Cash':
-                                obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
-                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
-                            elif contest_var1 != 'Cash':
-                                obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
-                                total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
-                                total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
-                            if trim_var1 == 1:
-                                total_score += pulp.lpSum([player_vars[idx]*obj_own_max[idx] for idx in flex_file.index]) <= max_own - .001
+                            for flex in flex_file['lock'].unique():
+                                sub_idx = flex_file[flex_file['lock'] == 1].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var2)
                             
-                            total_score.solve()
-                            for v in total_score.variables():
-                                if v.varValue > 0:
-                                    lineup_list.append(v.name)
-                            df = pd.DataFrame(lineup_list)
-                            df['Names'] = df[0].map(player_match)
-                            df['Cost'] = df['Names'].map(player_sal)
-                            df['Proj'] = df['Names'].map(player_proj)
-                            df['Own'] = df['Names'].map(player_own)
-                            total_cost = sum(df['Cost'])
-                            total_own = sum(df['Own'])
-                            total_proj = sum(df['Proj'])
-                            lineup_raw = pd.DataFrame(lineup_list)
-                            lineup_raw['Names'] = lineup_raw[0].map(player_match)
-                            lineup_raw['value'] = lineup_raw[0].map(player_index_match)
-                            lineup_final = lineup_raw.sort_values(by=['value'])
-                            del lineup_final[lineup_final.columns[0]]
-                            del lineup_final[lineup_final.columns[1]]
-                            lineup_final['Team'] = lineup_final['Names'].map(player_team)
-                            lineup_final['Position'] = lineup_final['Names'].map(player_pos)
-                            lineup_final['Salary'] = lineup_final['Names'].map(player_sal)
-                            lineup_final['Proj'] = lineup_final['Names'].map(player_proj)
-                            lineup_final['Own'] = lineup_final['Names'].map(player_own)
-                            lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0)
-                            lineup_final = lineup_final.reset_index(drop=True)
-    
-                            max_proj = total_proj
-                            max_own = total_own
+                            for flex in flex_file['Position'].unique():
+                                sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5
                             
-                            if site_var1 == 'Draftkings':
-                                if len(lineup_final) == 7:
-                                    port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
-                                    
-                                    port_display['Cost'] = total_cost
-                                    port_display['Proj'] = total_proj
-                                    port_display['Own'] = total_own
-                                    st.table(port_display)
-        
-                                    portfolio = pd.concat([portfolio, port_display], ignore_index = True)
-                            elif site_var1 == 'Fanduel':
-                                if len(lineup_final) == 6:
-                                    port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
-                                    
-                                    port_display['Cost'] = total_cost
-                                    port_display['Proj'] = total_proj
-                                    port_display['Own'] = total_own
-                                    st.table(port_display)
-        
-                                    portfolio = pd.concat([portfolio, port_display], ignore_index = True)
-    
-                            x += 1
-    
+                            for flex in flex_file['roster'].unique():
+                                sub_idx = flex_file[flex_file['roster'] == "CPT"].index
+                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                            
+                            for playerid in player_ids:
+                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                    (flex_file['name_var'][i] == flex_file['name_var'][playerid])]) <= 1
+
+                        player_count = []
+                        player_trim = []
+                        lineup_list = []
+                        
+                        if contest_var1 == 'Cash':
+                            obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                            total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
+                        elif contest_var1 != 'Cash':
+                            obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                            total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                            total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
+                        if trim_var1 == 1:
+                            total_score += pulp.lpSum([player_vars[idx]*obj_own_max[idx] for idx in flex_file.index]) <= max_own - .001
+                        
+                        total_score.solve()
+                        for v in total_score.variables():
+                            if v.varValue > 0:
+                                lineup_list.append(v.name)
+                        df = pd.DataFrame(lineup_list)
+                        df['Names'] = df[0].map(player_match)
+                        df['Cost'] = df['Names'].map(player_sal)
+                        df['Proj'] = df['Names'].map(player_proj)
+                        df['Own'] = df['Names'].map(player_own)
+                        total_cost = sum(df['Cost'])
+                        total_own = sum(df['Own'])
+                        total_proj = sum(df['Proj'])
+                        lineup_raw = pd.DataFrame(lineup_list)
+                        lineup_raw['Names'] = lineup_raw[0].map(player_match)
+                        lineup_raw['value'] = lineup_raw[0].map(player_index_match)
+                        lineup_final = lineup_raw.sort_values(by=['value'])
+                        del lineup_final[lineup_final.columns[0]]
+                        del lineup_final[lineup_final.columns[1]]
+                        lineup_final['Team'] = lineup_final['Names'].map(player_team)
+                        lineup_final['Position'] = lineup_final['Names'].map(player_pos)
+                        lineup_final['Salary'] = lineup_final['Names'].map(player_sal)
+                        lineup_final['Proj'] = lineup_final['Names'].map(player_proj)
+                        lineup_final['Own'] = lineup_final['Names'].map(player_own)
+                        lineup_final.loc['Column_Total'] = lineup_final.sum(numeric_only=True, axis=0)
+                        lineup_final = lineup_final.reset_index(drop=True)
+
+                        max_proj = total_proj
+                        max_own = total_own
+                        
                         if site_var1 == 'Draftkings':
-                            portfolio.rename(columns={0: "CPT", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4", 5: "FLEX5"}, inplace = True)
+                            if len(lineup_final) == 7:
+                                port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
+                                
+                                port_display['Cost'] = total_cost
+                                port_display['Proj'] = total_proj
+                                port_display['Own'] = total_own
+                                st.table(port_display)
+    
+                                portfolio = pd.concat([portfolio, port_display], ignore_index = True)
                         elif site_var1 == 'Fanduel':
-                            portfolio.rename(columns={0: "MVP", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4"}, inplace = True)
-                        portfolio = portfolio.dropna()
-                        portfolio = portfolio.reset_index()
-                        portfolio['Lineup_num'] = portfolio['index'] + 1
-                        portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
-                        portfolio = portfolio.set_index('Lineup')
-                        portfolio = portfolio.drop(columns=['index'])
-                        st.session_state.portfolio = portfolio.drop_duplicates()
+                            if len(lineup_final) == 6:
+                                port_display = pd.DataFrame(lineup_final['Names'][:-1].values.reshape(1, -1))
+                                
+                                port_display['Cost'] = total_cost
+                                port_display['Proj'] = total_proj
+                                port_display['Own'] = total_own
+                                st.table(port_display)
     
-                        final_outcomes = portfolio
-                        st.session_state.final_outcomes = portfolio
+                                portfolio = pd.concat([portfolio, port_display], ignore_index = True)
+
+                        x += 1
+
+                    if site_var1 == 'Draftkings':
+                        portfolio.rename(columns={0: "CPT", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4", 5: "FLEX5"}, inplace = True)
+                    elif site_var1 == 'Fanduel':
+                        portfolio.rename(columns={0: "MVP", 1: "FLEX1", 2: "FLEX2", 3: "FLEX3", 4: "FLEX4"}, inplace = True)
+                    portfolio = portfolio.dropna()
+                    portfolio = portfolio.reset_index()
+                    portfolio['Lineup_num'] = portfolio['index'] + 1
+                    portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
+                    portfolio = portfolio.set_index('Lineup')
+                    portfolio = portfolio.drop(columns=['index'])
+                    st.session_state.portfolio = portfolio.drop_duplicates()
+
+                    final_outcomes = portfolio
+                    st.session_state.final_outcomes = portfolio
+                    
+                    player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:6].values, return_counts=True)),
+                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                    player_freq['Freq'] = player_freq['Freq'].astype(int)
+                    player_freq['Position'] = player_freq['Player'].map(player_pos)
+                    player_freq['Salary'] = player_freq['Player'].map(player_sal)
+                    player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
+                    player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
+                    player_freq['Team'] = player_freq['Player'].map(player_team)
+                    
+                    final_outcomes_export = pd.DataFrame()
+                    split_portfolio = pd.DataFrame()
+                    
+                    if site_var1 == 'Draftkings':
                         
-                        player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.portfolio.iloc[:,0:6].values, return_counts=True)),
-                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-                        player_freq['Freq'] = player_freq['Freq'].astype(int)
-                        player_freq['Position'] = player_freq['Player'].map(player_pos)
-                        player_freq['Salary'] = player_freq['Player'].map(player_sal)
-                        player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
-                        player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
-                        player_freq['Team'] = player_freq['Player'].map(player_team)
+                        split_portfolio[['CPT', 'CPT_ID']] = final_outcomes.CPT.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX5', 'FLEX5_ID']] = final_outcomes.FLEX5.str.split("-", n=1, expand = True)
+
+                        split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
+                        split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
+                        split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
+                        split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
+                        split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
+                        split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
                         
-                        final_outcomes_export = pd.DataFrame()
-                        split_portfolio = pd.DataFrame()
+                        final_outcomes_export['CPT'] = split_portfolio['CPT']
+                        final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
+                        final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
+                        final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
+                        final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
+                        final_outcomes_export['FLEX5'] = split_portfolio['FLEX5']
                         
-                        if site_var1 == 'Draftkings':
-                            
-                            split_portfolio[['CPT', 'CPT_ID']] = final_outcomes.CPT.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX5', 'FLEX5_ID']] = final_outcomes.FLEX5.str.split("-", n=1, expand = True)
-  
-                            split_portfolio['CPT'] = split_portfolio['CPT'].str.strip()
-                            split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                            split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                            split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                            split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                            split_portfolio['FLEX5'] = split_portfolio['FLEX5'].str.strip()
-                            
-                            final_outcomes_export['CPT'] = split_portfolio['CPT']
-                            final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
-                            final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
-                            final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
-                            final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
-                            final_outcomes_export['FLEX5'] = split_portfolio['FLEX5']
-                            
-                            if sport_var1 == 'NFL':
-                                final_outcomes_export['CPT'].replace(nfl_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX1'].replace(nfl_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX2'].replace(nfl_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX3'].replace(nfl_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX4'].replace(nfl_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX5'].replace(nfl_dk_id_dict, inplace=True)
-                            elif sport_var1 == 'NBA':
-                                final_outcomes_export['CPT'].replace(nba_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX1'].replace(nba_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX2'].replace(nba_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX3'].replace(nba_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX4'].replace(nba_dk_id_dict, inplace=True)
-                                final_outcomes_export['FLEX5'].replace(nba_dk_id_dict, inplace=True)
-                            final_outcomes_export['Salary'] = final_outcomes['Cost']
-                            final_outcomes_export['Own'] = final_outcomes['Own']
-                            final_outcomes_export['Proj'] = final_outcomes['Proj']
-                            
-                            st.session_state.final_outcomes_export = final_outcomes_export.copy()
-                            
-                        elif site_var1 == 'Fanduel':
-                            
-                            split_portfolio[['MVP', 'CPT_ID']] = final_outcomes.MVP.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
-                            split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
-  
-                            split_portfolio['MVP'] = split_portfolio['MVP'].str.strip()
-                            split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
-                            split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
-                            split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
-                            split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
-                            
-                            final_outcomes_export['MVP'] = split_portfolio['MVP']
-                            final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
-                            final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
-                            final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
-                            final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
-                            
-                            if sport_var1 == 'NFL':
-                                final_outcomes_export['MVP'].replace(nfl_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX1'].replace(nfl_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX2'].replace(nfl_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX3'].replace(nfl_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX4'].replace(nfl_fd_id_dict, inplace=True)
-                            elif sport_var1 == 'NBA':
-                                final_outcomes_export['MVP'].replace(nba_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX1'].replace(nba_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX2'].replace(nba_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX3'].replace(nba_fd_id_dict, inplace=True)
-                                final_outcomes_export['FLEX4'].replace(nba_fd_id_dict, inplace=True)
-                            final_outcomes_export['Salary'] = final_outcomes['Cost']
-                            final_outcomes_export['Own'] = final_outcomes['Own']
-                            final_outcomes_export['Proj'] = final_outcomes['Proj']
-                            
-                            st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
-          
-                        st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
-        with display_container:
-                    display_container = st.empty()
-                    if 'display_baselines' in st.session_state:
-                        st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        
-        with display_dl_container:
-                    display_dl_container = st.empty()
-                    if 'export_baselines' in st.session_state:
-                        st.download_button(
-                            label="Export Projections",
-                            data=convert_df_to_csv(st.session_state.export_baselines),
-                            file_name='showdown_proj_export.csv',
-                            mime='text/csv',
-                        )        
-                
-        with optimize_container:
-                    optimize_container = st.empty()
-                    if 'final_outcomes' in st.session_state:
-                        st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                        if sport_var1 == 'NFL':
+                            final_outcomes_export['CPT'].replace(nfl_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(nfl_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(nfl_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(nfl_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(nfl_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX5'].replace(nfl_dk_id_dict, inplace=True)
+                        elif sport_var1 == 'NBA':
+                            final_outcomes_export['CPT'].replace(nba_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(nba_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(nba_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(nba_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(nba_dk_id_dict, inplace=True)
+                            final_outcomes_export['FLEX5'].replace(nba_dk_id_dict, inplace=True)
+                        final_outcomes_export['Salary'] = final_outcomes['Cost']
+                        final_outcomes_export['Own'] = final_outcomes['Own']
+                        final_outcomes_export['Proj'] = final_outcomes['Proj']
+                        
+                        st.session_state.final_outcomes_export = final_outcomes_export.copy()
+                        
+                    elif site_var1 == 'Fanduel':
+                        
+                        split_portfolio[['MVP', 'CPT_ID']] = final_outcomes.MVP.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX1', 'FLEX1_ID']] = final_outcomes.FLEX1.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX2', 'FLEX2_ID']] = final_outcomes.FLEX2.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX3', 'FLEX3_ID']] = final_outcomes.FLEX3.str.split("-", n=1, expand = True)
+                        split_portfolio[['FLEX4', 'FLEX4_ID']] = final_outcomes.FLEX4.str.split("-", n=1, expand = True)
+
+                        split_portfolio['MVP'] = split_portfolio['MVP'].str.strip()
+                        split_portfolio['FLEX1'] = split_portfolio['FLEX1'].str.strip()
+                        split_portfolio['FLEX2'] = split_portfolio['FLEX2'].str.strip()
+                        split_portfolio['FLEX3'] = split_portfolio['FLEX3'].str.strip()
+                        split_portfolio['FLEX4'] = split_portfolio['FLEX4'].str.strip()
+                        
+                        final_outcomes_export['MVP'] = split_portfolio['MVP']
+                        final_outcomes_export['FLEX1'] = split_portfolio['FLEX1']
+                        final_outcomes_export['FLEX2'] = split_portfolio['FLEX2']
+                        final_outcomes_export['FLEX3'] = split_portfolio['FLEX3']
+                        final_outcomes_export['FLEX4'] = split_portfolio['FLEX4']
+                        
+                        if sport_var1 == 'NFL':
+                            final_outcomes_export['MVP'].replace(nfl_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(nfl_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(nfl_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(nfl_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(nfl_fd_id_dict, inplace=True)
+                        elif sport_var1 == 'NBA':
+                            final_outcomes_export['MVP'].replace(nba_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX1'].replace(nba_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX2'].replace(nba_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX3'].replace(nba_fd_id_dict, inplace=True)
+                            final_outcomes_export['FLEX4'].replace(nba_fd_id_dict, inplace=True)
+                        final_outcomes_export['Salary'] = final_outcomes['Cost']
+                        final_outcomes_export['Own'] = final_outcomes['Own']
+                        final_outcomes_export['Proj'] = final_outcomes['Proj']
+                        
+                        st.session_state.FD_final_outcomes_export = final_outcomes_export.copy()
         
-        with download_container:
-            download_container = st.empty()
-            if site_var1 == 'Draftkings':
-                if 'final_outcomes_export' in st.session_state:
-                    st.download_button(
-                        label="Export Optimals",
-                        data=convert_df_to_csv(st.session_state.final_outcomes_export),
-                        file_name='NFL_optimals_export.csv',
-                        mime='text/csv',
-                    )
-            elif site_var1 == 'Fanduel':
-                if 'FD_final_outcomes_export' in st.session_state:
+                    st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
+    with display_container:
+                display_container = st.empty()
+                if 'display_baselines' in st.session_state:
+                    st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    
+    with display_dl_container:
+                display_dl_container = st.empty()
+                if 'export_baselines' in st.session_state:
                     st.download_button(
-                        label="Export Optimals",
-                        data=convert_df_to_csv(st.session_state.FD_final_outcomes_export),
-                        file_name='FD_NFL_optimals_export.csv',
+                        label="Export Projections",
+                        data=convert_df_to_csv(st.session_state.export_baselines),
+                        file_name='showdown_proj_export.csv',
                         mime='text/csv',
-                    )
-        
-        with freq_container:
-            freq_container = st.empty()
-            if 'player_freq' in st.session_state:
-                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
-                
+                    )        
+            
+    with optimize_container:
+                optimize_container = st.empty()
+                if 'final_outcomes' in st.session_state:
+                    st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+    
+    with download_container:
+        download_container = st.empty()
+        if site_var1 == 'Draftkings':
+            if 'final_outcomes_export' in st.session_state:
+                st.download_button(
+                    label="Export Optimals",
+                    data=convert_df_to_csv(st.session_state.final_outcomes_export),
+                    file_name='NFL_optimals_export.csv',
+                    mime='text/csv',
+                )
+        elif site_var1 == 'Fanduel':
+            if 'FD_final_outcomes_export' in st.session_state:
+                st.download_button(
+                    label="Export Optimals",
+                    data=convert_df_to_csv(st.session_state.FD_final_outcomes_export),
+                    file_name='FD_NFL_optimals_export.csv',
+                    mime='text/csv',
+                )
+    
+    with freq_container:
+        freq_container = st.empty()
+        if 'player_freq' in st.session_state:
+            st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)
+