Add custom Streamlit tab styling with golden color scheme and hover effects

app.py

@@ -20,6 +20,37 @@ freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
 dk_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
+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_data(ttl = 600)
 def init_DK_seed_frames(sharp_split):  
     
@@ -129,122 +160,9 @@ dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
 fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
 
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
-with tab2:
-    col1, col2 = st.columns([1, 7])
-    with col1:
-        if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-              DK_seed = init_DK_seed_frames(10000)
-              FD_seed = init_FD_seed_frames(10000)
-              dk_raw, fd_raw, teams_playing_count = init_baselines()
-              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
-              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
-              
-        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
-        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
-        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
-
-        if site_var1 == 'Draftkings':
-            
-            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
-            if team_var1 == 'Specific Teams':
-                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
-            elif team_var1 == 'Full Slate':
-                    team_var2 = dk_raw.Team.values.tolist()
-            
-            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
-            if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
-            elif stack_var1 == 'Full Slate':
-                    stack_var2 = [5, 4, 3, 2, 1, 0]
-                    
-        elif site_var1 == 'Fanduel':
-            
-            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
-            if team_var1 == 'Specific Teams':
-                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
-            elif team_var1 == 'Full Slate':
-                    team_var2 = fd_raw.Team.values.tolist()
-            
-            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
-            if stack_var1 == 'Specific Stack Sizes':
-                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
-            elif stack_var1 == 'Full Slate':
-                    stack_var2 = [5, 4, 3, 2, 1, 0]
-        
-
-        if st.button("Prepare data export", key='data_export'):
-                if 'working_seed' in st.session_state:
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                elif 'working_seed' not in st.session_state:
-                    if site_var1 == 'Draftkings':
-                        if slate_var1 == 'Main Slate':
-                            st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
-                            
-                        raw_baselines = dk_raw
-                        column_names = dk_columns
-                
-                    elif site_var1 == 'Fanduel':
-                        if slate_var1 == 'Main Slate':
-                            st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
-                            
-                        raw_baselines = fd_raw
-                        column_names = fd_columns
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                data_export = st.session_state.working_seed.copy()
-                st.download_button(
-                    label="Export optimals set",
-                    data=convert_df(data_export),
-                    file_name='NHL_optimals_export.csv',
-                    mime='text/csv',
-                )
-                for key in st.session_state.keys():
-                  del st.session_state[key]
-            
-    with col2:
-        if st.button("Load Data", key='load_data'):
-            if site_var1 == 'Draftkings':
-                if 'working_seed' in st.session_state:
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                elif 'working_seed' not in st.session_state:
-                    if slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
-                        
-                    raw_baselines = dk_raw
-                    column_names = dk_columns
-                
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                
-            elif site_var1 == 'Fanduel':
-                if 'working_seed' in st.session_state:
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                elif 'working_seed' not in st.session_state:
-                    if slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
-                        
-                    raw_baselines = fd_raw
-                    column_names = fd_columns
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                
-        with st.container():
-            if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
             
 with tab1:
-    col1, col2 = st.columns([1, 7])
-    with col1:
+    with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
               for key in st.session_state.keys():
@@ -279,195 +197,194 @@ with tab1:
         elif strength_var1 == 'Very':
             sharp_split = 10000
 
-    
-    with col2:
-        if st.button("Run Contest Sim"):
-            if 'working_seed' in st.session_state:
-                st.session_state.maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
-                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
-                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
-                        }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                
-                #st.table(Sim_Winner_Frame)
-                            
-                # Initial setup
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
-                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
-                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-                
-                # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
-                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
-                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                
-            else:
-                if sim_site_var1 == 'Draftkings':
-                    if sim_slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+    if st.button("Run Contest Sim"):
+        if 'working_seed' in st.session_state:
+            st.session_state.maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                    }
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+            
+            #st.table(Sim_Winner_Frame)
                         
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+            
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+            
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+            st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            
+        else:
+            if sim_site_var1 == 'Draftkings':
+                if sim_slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_DK_seed_frames(sharp_split)
+                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
                     raw_baselines = dk_raw
                     column_names = dk_columns
-                elif sim_site_var1 == 'Fanduel':
-                    if sim_slate_var1 == 'Main Slate':
-                        st.session_state.working_seed = init_FD_seed_frames(sharp_split)
-                        
+            elif sim_site_var1 == 'Fanduel':
+                if sim_slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_FD_seed_frames(sharp_split)
+                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
                     raw_baselines = fd_raw
                     column_names = fd_columns
-                st.session_state.maps_dict = {
-                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
-                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
-                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
-                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
-                        }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                
-                #st.table(Sim_Winner_Frame)
-                            
-                # Initial setup
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
-                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
-                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
-                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-                
-                # Type Casting
-                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
-                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-                
-                # Sorting
-                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-                
-               # Data Copying
-                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-                for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
-                    st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
-                st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
-                
-                # Data Copying
-                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
-                st.session_state.freq_copy = st.session_state.Sim_Winner_Display
-                
-            if sim_site_var1 == 'Draftkings':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            freq_working['Freq'] = freq_working['Freq'].astype(int)
-            freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            freq_working['Exposure'] = freq_working['Freq']/(1000)
-            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
-            freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.player_freq = freq_working.copy()
 
-            if sim_site_var1 == 'Draftkings':
-                center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            center_working['Freq'] = center_working['Freq'].astype(int)
-            center_working['Position'] = center_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            center_working['Salary'] = center_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            center_working['Proj Own'] = center_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            center_working['Exposure'] = center_working['Freq']/(1000)
-            center_working['Edge'] = center_working['Exposure'] - center_working['Proj Own']
-            center_working['Team'] = center_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.center_freq = center_working.copy()
+            st.session_state.maps_dict = {
+                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                    'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                    'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                    'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                    'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
+                    }
+            Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size, teams_playing_count)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
             
-            if sim_site_var1 == 'Draftkings':
-                wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:5].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            wing_working['Freq'] = wing_working['Freq'].astype(int)
-            wing_working['Position'] = wing_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            wing_working['Salary'] = wing_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            wing_working['Proj Own'] = wing_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            wing_working['Exposure'] = wing_working['Freq']/(1000)
-            wing_working['Edge'] = wing_working['Exposure'] - wing_working['Proj Own']
-            wing_working['Team'] = wing_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.wing_freq = wing_working.copy()
+            #st.table(Sim_Winner_Frame)
+                        
+            # Initial setup
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+            Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+            Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+            Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
             
-            if sim_site_var1 == 'Draftkings':
-                dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            dmen_working['Freq'] = dmen_working['Freq'].astype(int)
-            dmen_working['Position'] = dmen_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            dmen_working['Salary'] = dmen_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            dmen_working['Proj Own'] = dmen_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            dmen_working['Exposure'] = dmen_working['Freq']/(1000)
-            dmen_working['Edge'] = dmen_working['Exposure'] - dmen_working['Proj Own']
-            dmen_working['Team'] = dmen_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.dmen_freq = dmen_working.copy()
+            # Type Casting
+            type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+            Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
             
-            if sim_site_var1 == 'Draftkings':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            flex_working['Freq'] = flex_working['Freq'].astype(int)
-            flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            flex_working['Exposure'] = flex_working['Freq']/(1000)
-            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
-            flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.flex_freq = flex_working.copy()
+            # Sorting
+            st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
             
-            if sim_site_var1 == 'Draftkings':
-                goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            goalie_working['Freq'] = goalie_working['Freq'].astype(int)
-            goalie_working['Position'] = goalie_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            goalie_working['Salary'] = goalie_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            goalie_working['Proj Own'] = goalie_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            goalie_working['Exposure'] = goalie_working['Freq']/(1000)
-            goalie_working['Edge'] = goalie_working['Exposure'] - goalie_working['Proj Own']
-            goalie_working['Team'] = goalie_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.goalie_freq = goalie_working.copy()
+            # Data Copying
+            st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            for col in st.session_state.Sim_Winner_Export.iloc[:, 0:9].columns:
+                st.session_state.Sim_Winner_Export[col] = st.session_state.Sim_Winner_Export[col].map(dk_id_dict)
+            st.session_state.Sim_Winner_Export = st.session_state.Sim_Winner_Export.drop_duplicates(subset=['Team', 'Secondary', 'salary', 'unique_id'])
+            
+            # Data Copying
+            st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            st.session_state.freq_copy = st.session_state.Sim_Winner_Display
+            
+        if sim_site_var1 == 'Draftkings':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        freq_working['Freq'] = freq_working['Freq'].astype(int)
+        freq_working['Position'] = freq_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        freq_working['Exposure'] = freq_working['Freq']/(1000)
+        freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+        freq_working['Team'] = freq_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.player_freq = freq_working.copy()
 
-            if sim_site_var1 == 'Draftkings':
-                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            team_working['Freq'] = team_working['Freq'].astype(int)
-            team_working['Exposure'] = team_working['Freq']/(1000)
-            st.session_state.team_freq = team_working.copy()
+        if sim_site_var1 == 'Draftkings':
+            center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            center_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        center_working['Freq'] = center_working['Freq'].astype(int)
+        center_working['Position'] = center_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        center_working['Salary'] = center_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        center_working['Proj Own'] = center_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        center_working['Exposure'] = center_working['Freq']/(1000)
+        center_working['Edge'] = center_working['Exposure'] - center_working['Proj Own']
+        center_working['Team'] = center_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.center_freq = center_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:5].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            wing_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        wing_working['Freq'] = wing_working['Freq'].astype(int)
+        wing_working['Position'] = wing_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        wing_working['Salary'] = wing_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        wing_working['Proj Own'] = wing_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        wing_working['Exposure'] = wing_working['Freq']/(1000)
+        wing_working['Edge'] = wing_working['Exposure'] - wing_working['Proj Own']
+        wing_working['Team'] = wing_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.wing_freq = wing_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            dmen_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        dmen_working['Freq'] = dmen_working['Freq'].astype(int)
+        dmen_working['Position'] = dmen_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        dmen_working['Salary'] = dmen_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        dmen_working['Proj Own'] = dmen_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        dmen_working['Exposure'] = dmen_working['Freq']/(1000)
+        dmen_working['Edge'] = dmen_working['Exposure'] - dmen_working['Proj Own']
+        dmen_working['Team'] = dmen_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.dmen_freq = dmen_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        flex_working['Freq'] = flex_working['Freq'].astype(int)
+        flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        flex_working['Exposure'] = flex_working['Freq']/(1000)
+        flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
+        flex_working['Team'] = flex_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.flex_freq = flex_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            goalie_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        goalie_working['Freq'] = goalie_working['Freq'].astype(int)
+        goalie_working['Position'] = goalie_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        goalie_working['Salary'] = goalie_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        goalie_working['Proj Own'] = goalie_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        goalie_working['Exposure'] = goalie_working['Freq']/(1000)
+        goalie_working['Edge'] = goalie_working['Exposure'] - goalie_working['Proj Own']
+        goalie_working['Team'] = goalie_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.goalie_freq = goalie_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        team_working['Freq'] = team_working['Freq'].astype(int)
+        team_working['Exposure'] = team_working['Freq']/(1000)
+        st.session_state.team_freq = team_working.copy()
             
         with st.container():
             if st.button("Reset Sim", key='reset_sim'):
@@ -697,4 +614,128 @@ with tab1:
                         file_name='team_freq.csv',
                         mime='text/csv',
                         key='team'
-                    )
+                    )
+
+with tab2:
+    with st.expander("Info and Filters"):
+        if st.button("Load/Reset Data", key='reset1'):
+                st.cache_data.clear()
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+                DK_seed = init_DK_seed_frames(10000)
+                FD_seed = init_FD_seed_frames(10000)
+                dk_raw, fd_raw, teams_playing_count = init_baselines()
+                dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+                fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+                
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Other Main Slate'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
+        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=500, value=10, step=1)
+
+        if site_var1 == 'Draftkings':
+            
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = dk_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+            
+            raw_baselines = dk_raw
+            column_names = dk_columns
+                    
+        elif site_var1 == 'Fanduel':
+            
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = fd_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+            
+            raw_baselines = fd_raw
+            column_names = fd_columns
+        
+
+        if st.button("Prepare data export", key='data_export'):
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                elif 'working_seed' not in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+
+                            dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+                            raw_baselines = dk_raw
+                            column_names = dk_columns
+                
+                    elif site_var1 == 'Fanduel':
+                        if slate_var1 == 'Main Slate':
+                            st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+
+                            fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+                            raw_baselines = fd_raw
+                            column_names = fd_columns
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='NHL_optimals_export.csv',
+                    mime='text/csv',
+                )
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            
+    if st.button("Load Data", key='load_data'):
+        if site_var1 == 'Draftkings':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                if slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+            
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            
+        elif site_var1 == 'Fanduel':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                if slate_var1 == 'Main Slate':
+                    st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+                    
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            
+    with st.container():
+        if 'data_export_display' in st.session_state:
+            st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)