Add custom tab styling and layout improvements to Streamlit app

app.py

@@ -21,6 +21,37 @@ freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
 dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', '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 = 60)
 def init_DK_seed_frames(load_size):  
         
@@ -195,6 +226,386 @@ 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 tab1:
+    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():
+                    del st.session_state[key]
+                DK_seed = init_DK_seed_frames(10000)
+                FD_seed = init_FD_seed_frames(10000)
+                DK_secondary = init_DK_secondary_seed_frames(10000)
+                FD_secondary = init_FD_secondary_seed_frames(10000)
+                dk_raw, fd_raw, dk_secondary, fd_secondary = 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))
+
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
+            
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
+        if contest_var1 == 'Small':
+            Contest_Size = 1000
+        elif contest_var1 == 'Medium':
+            Contest_Size = 5000
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
+        elif contest_var1 == 'Custom':
+            Contest_Size = st.number_input("Insert contest size", value=100, min_value=100, max_value=100000, step=50)
+        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
+        if strength_var1 == 'Not Very':
+            sharp_split = 5000000
+        elif strength_var1 == 'Below Average':
+            sharp_split = 2500000
+        elif strength_var1 == 'Average':
+            sharp_split = 100000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 50000
+        elif strength_var1 == 'Very':
+            sharp_split = 10000
+
+    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)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                        
+            # 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()
+            
+            # 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_slate_var1 == 'Secondary Slate':
+                    st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split)
+                    dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
+                    raw_baselines = dk_secondary
+                    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)
+                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+                elif sim_slate_var1 == 'Secondary Slate':
+                    st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split)
+                    fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
+                    raw_baselines = fd_secondary
+                    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)
+            Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                        
+            # 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()
+            
+            # 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[:,0:8].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':
+            pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            pg_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)
+        pg_working['Freq'] = pg_working['Freq'].astype(int)
+        pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        pg_working['Exposure'] = pg_working['Freq']/(1000)
+        pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own']
+        pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.pg_freq = pg_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            sg_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)
+        sg_working['Freq'] = sg_working['Freq'].astype(int)
+        sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        sg_working['Exposure'] = sg_working['Freq']/(1000)
+        sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own']
+        sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.sg_freq = sg_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            sf_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)
+        sf_working['Freq'] = sf_working['Freq'].astype(int)
+        sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        sf_working['Exposure'] = sf_working['Freq']/(1000)
+        sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own']
+        sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.sf_freq = sf_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            pf_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)
+        pf_working['Freq'] = pf_working['Freq'].astype(int)
+        pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        pf_working['Exposure'] = pf_working['Freq']/(1000)
+        pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own']
+        pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.pf_freq = pf_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            c_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)
+        c_working['Freq'] = c_working['Freq'].astype(int)
+        c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        c_working['Exposure'] = c_working['Freq']/(1000)
+        c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own']
+        c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.c_freq = c_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        g_working['Freq'] = g_working['Freq'].astype(int)
+        g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        g_working['Exposure'] = g_working['Freq']/(1000)
+        g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own']
+        g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.g_freq = g_working.copy()
+        
+        if sim_site_var1 == 'Draftkings':
+            f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var1 == 'Fanduel':
+            f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        f_working['Freq'] = f_working['Freq'].astype(int)
+        f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map'])
+        f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map'])
+        f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
+        f_working['Exposure'] = f_working['Freq']/(1000)
+        f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own']
+        f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map'])
+        st.session_state.f_freq = f_working.copy()
+
+        if sim_site_var1 == 'Draftkings':
+            flex_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':
+            flex_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)
+        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':
+            team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,10:11].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'):
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        if 'player_freq' in st.session_state: 
+            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+            if player_split_var2 == 'Specific Players':
+                        find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+            elif player_split_var2 == 'Full Players':
+                        find_var2 = st.session_state.player_freq.Player.values.tolist()
+
+            if player_split_var2 == 'Specific Players':
+                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+            if player_split_var2 == 'Full Players':
+                        st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+        if 'Sim_Winner_Display' in st.session_state:
+            st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        if 'Sim_Winner_Export' in st.session_state:
+            st.download_button(
+                label="Export Full Frame",
+                data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                file_name='MLB_consim_export.csv',
+                mime='text/csv',
+            )  
+    tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
+    
+    with tab1:
+        if 'Sim_Winner_Display' in st.session_state:
+            # Create a new dataframe with summary statistics
+            summary_df = pd.DataFrame({
+                'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                'Salary': [
+                    st.session_state.Sim_Winner_Display['salary'].min(),
+                    st.session_state.Sim_Winner_Display['salary'].mean(),
+                    st.session_state.Sim_Winner_Display['salary'].max(),
+                    st.session_state.Sim_Winner_Display['salary'].std()
+                ],
+                'Proj': [
+                    st.session_state.Sim_Winner_Display['proj'].min(),
+                    st.session_state.Sim_Winner_Display['proj'].mean(),
+                    st.session_state.Sim_Winner_Display['proj'].max(),
+                    st.session_state.Sim_Winner_Display['proj'].std()
+                ],
+                'Own': [
+                    st.session_state.Sim_Winner_Display['Own'].min(),
+                    st.session_state.Sim_Winner_Display['Own'].mean(),
+                    st.session_state.Sim_Winner_Display['Own'].max(),
+                    st.session_state.Sim_Winner_Display['Own'].std()
+                ],
+                'Fantasy': [
+                    st.session_state.Sim_Winner_Display['Fantasy'].min(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].mean(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].max(),
+                    st.session_state.Sim_Winner_Display['Fantasy'].std()
+                ],
+                'GPP_Proj': [
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
+                    st.session_state.Sim_Winner_Display['GPP_Proj'].std()
+                ]
+            })
+
+            # Set the index of the summary dataframe as the "Metric" column
+            summary_df = summary_df.set_index('Metric')
+
+            # Display the summary dataframe
+            st.subheader("Winning Frame Statistics")
+            st.dataframe(summary_df.style.format({
+                'Salary': '{:.2f}',
+                'Proj': '{:.2f}',
+                'Fantasy': '{:.2f}',
+                'GPP_Proj': '{:.2f}'
+            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
+
+    with tab2:
+        if 'Sim_Winner_Display' in st.session_state:
+            st.write("Yeah man that's crazy")
+            
+        else:
+            st.write("Simulation data or position mapping not available.")
+    with st.container():
+        tab1, tab2 = st.tabs(['Overall Exposures', 'Team Exposures'])
+        with tab1:
+            if 'player_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                    file_name='player_freq_export.csv',
+                    mime='text/csv',
+                    key='overall'
+                )
+        
+        with tab2:
+            if 'team_freq' in st.session_state:
+                
+                st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                st.download_button(
+                    label="Export Exposures",
+                    data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                    file_name='team_freq.csv',
+                    mime='text/csv',
+                    key='team'
+                )
 
 with tab2:
     col1, col2 = st.columns([1, 7])
@@ -354,387 +765,4 @@ with tab2:
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
                 
             if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
-            
-with tab1:
-    col1, col2 = st.columns([1, 7])
-    with col1:
-        if st.button("Load/Reset Data", key='reset2'):
-              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_secondary = init_DK_secondary_seed_frames(10000)
-              FD_secondary = init_FD_secondary_seed_frames(10000)
-              dk_raw, fd_raw, dk_secondary, fd_secondary = 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))
-
-        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
-        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
-            
-        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
-        if contest_var1 == 'Small':
-            Contest_Size = 1000
-        elif contest_var1 == 'Medium':
-            Contest_Size = 5000
-        elif contest_var1 == 'Large':
-            Contest_Size = 10000
-        elif contest_var1 == 'Custom':
-            Contest_Size = st.number_input("Insert contest size", value=100, min_value=100, max_value=100000, step=50)
-        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
-        if strength_var1 == 'Not Very':
-            sharp_split = 5000000
-        elif strength_var1 == 'Below Average':
-            sharp_split = 2500000
-        elif strength_var1 == 'Average':
-            sharp_split = 100000
-        elif strength_var1 == 'Above Average':
-            sharp_split = 50000
-        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)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                            
-                # 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()
-                
-                # 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_slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split)
-                        dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
-                        raw_baselines = dk_secondary
-                        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)
-                        fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
-                        raw_baselines = fd_raw
-                        column_names = fd_columns
-                    elif sim_slate_var1 == 'Secondary Slate':
-                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split)
-                        fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
-                        raw_baselines = fd_secondary
-                        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)
-                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
-                            
-                # 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()
-                
-                # 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[:,0:8].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':
-                pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                pg_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)
-            pg_working['Freq'] = pg_working['Freq'].astype(int)
-            pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            pg_working['Exposure'] = pg_working['Freq']/(1000)
-            pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own']
-            pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.pg_freq = pg_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                sg_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)
-            sg_working['Freq'] = sg_working['Freq'].astype(int)
-            sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            sg_working['Exposure'] = sg_working['Freq']/(1000)
-            sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own']
-            sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.sg_freq = sg_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                sf_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)
-            sf_working['Freq'] = sf_working['Freq'].astype(int)
-            sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            sf_working['Exposure'] = sf_working['Freq']/(1000)
-            sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own']
-            sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.sf_freq = sf_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                pf_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)
-            pf_working['Freq'] = pf_working['Freq'].astype(int)
-            pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            pf_working['Exposure'] = pf_working['Freq']/(1000)
-            pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own']
-            pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.pf_freq = pf_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                c_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)
-            c_working['Freq'] = c_working['Freq'].astype(int)
-            c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            c_working['Exposure'] = c_working['Freq']/(1000)
-            c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own']
-            c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.c_freq = c_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            g_working['Freq'] = g_working['Freq'].astype(int)
-            g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            g_working['Exposure'] = g_working['Freq']/(1000)
-            g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own']
-            g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.g_freq = g_working.copy()
-            
-            if sim_site_var1 == 'Draftkings':
-                f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var1 == 'Fanduel':
-                f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            f_working['Freq'] = f_working['Freq'].astype(int)
-            f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map'])
-            f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map'])
-            f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
-            f_working['Exposure'] = f_working['Freq']/(1000)
-            f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own']
-            f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map'])
-            st.session_state.f_freq = f_working.copy()
-
-            if sim_site_var1 == 'Draftkings':
-                flex_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':
-                flex_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)
-            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':
-                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,10:11].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'):
-                for key in st.session_state.keys():
-                    del st.session_state[key]
-            if 'player_freq' in st.session_state: 
-                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
-                if player_split_var2 == 'Specific Players':
-                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
-                elif player_split_var2 == 'Full Players':
-                          find_var2 = st.session_state.player_freq.Player.values.tolist()
-    
-                if player_split_var2 == 'Specific Players':
-                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
-                if player_split_var2 == 'Full Players':
-                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
-            if 'Sim_Winner_Display' in st.session_state:
-                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-            if 'Sim_Winner_Export' in st.session_state:
-                st.download_button(
-                    label="Export Full Frame",
-                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
-                    file_name='MLB_consim_export.csv',
-                    mime='text/csv',
-                )  
-        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
-        
-        with tab1:
-            if 'Sim_Winner_Display' in st.session_state:
-                # Create a new dataframe with summary statistics
-                summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        st.session_state.Sim_Winner_Display['salary'].min(),
-                        st.session_state.Sim_Winner_Display['salary'].mean(),
-                        st.session_state.Sim_Winner_Display['salary'].max(),
-                        st.session_state.Sim_Winner_Display['salary'].std()
-                    ],
-                    'Proj': [
-                        st.session_state.Sim_Winner_Display['proj'].min(),
-                        st.session_state.Sim_Winner_Display['proj'].mean(),
-                        st.session_state.Sim_Winner_Display['proj'].max(),
-                        st.session_state.Sim_Winner_Display['proj'].std()
-                    ],
-                    'Own': [
-                        st.session_state.Sim_Winner_Display['Own'].min(),
-                        st.session_state.Sim_Winner_Display['Own'].mean(),
-                        st.session_state.Sim_Winner_Display['Own'].max(),
-                        st.session_state.Sim_Winner_Display['Own'].std()
-                    ],
-                    'Fantasy': [
-                        st.session_state.Sim_Winner_Display['Fantasy'].min(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].mean(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].max(),
-                        st.session_state.Sim_Winner_Display['Fantasy'].std()
-                    ],
-                    'GPP_Proj': [
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
-                        st.session_state.Sim_Winner_Display['GPP_Proj'].std()
-                    ]
-                })
-
-                # Set the index of the summary dataframe as the "Metric" column
-                summary_df = summary_df.set_index('Metric')
-
-                # Display the summary dataframe
-                st.subheader("Winning Frame Statistics")
-                st.dataframe(summary_df.style.format({
-                    'Salary': '{:.2f}',
-                    'Proj': '{:.2f}',
-                    'Fantasy': '{:.2f}',
-                    'GPP_Proj': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)
-
-        with tab2:
-            if 'Sim_Winner_Display' in st.session_state:
-                st.write("Yeah man that's crazy")
-                
-            else:
-                st.write("Simulation data or position mapping not available.")
-        with st.container():
-            tab1, tab2 = st.tabs(['Overall Exposures', 'Team Exposures'])
-            with tab1:
-                if 'player_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
-                        file_name='player_freq_export.csv',
-                        mime='text/csv',
-                        key='overall'
-                    )
-            
-            with tab2:
-                if 'team_freq' in st.session_state:
-                    
-                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                    st.download_button(
-                        label="Export Exposures",
-                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
-                        file_name='team_freq.csv',
-                        mime='text/csv',
-                        key='team'
-                    )
+                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)