diff --git "a/app.py" "b/app.py"
new file mode 100644--- /dev/null
+++ "b/app.py"
@@ -0,0 +1,1765 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import pymongo
+from itertools import combinations
+
+@st.cache_resource
+def init_conn():
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NFL_Database"]
+
+        return db
+    
+db = init_conn()
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
+                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
+
+dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+@st.cache_resource(ttl=60)
+def player_stat_table():
+
+    collection = db["Player_Baselines"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
+                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
+    player_stats = raw_display[raw_display['Position'] != 'K']
+
+    collection = db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    dk_roo_raw = load_display.dropna(subset=['Median'])
+
+    collection = db["FD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    fd_roo_raw = load_display.dropna(subset=['Median'])
+
+    collection = db["DK_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    dk_stacks_raw = raw_display.copy()
+
+    collection = db["FD_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    fd_stacks_raw = raw_display.copy()
+
+    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw
+
+@st.cache_resource(ttl = 60)
+def init_DK_lineups():  
+    
+        collection = db["DK_NFL_seed_frame"] 
+        cursor = collection.find().limit(10000)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
+@st.cache_resource(ttl = 60)
+def init_FD_lineups():  
+    
+        collection = db["FD_NFL_seed_frame"] 
+        cursor = collection.find().limit(10000)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        FD_seed = raw_display.to_numpy()
+
+        return FD_seed
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+try:
+    dk_lineups = init_DK_lineups()
+    fd_lineups = init_FD_lineups()
+except:
+    dk_lineups = pd.DataFrame(columns=dk_columns)
+    fd_lineups = pd.DataFrame(columns=fd_columns)
+
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+
+tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Team Stacks Range of Outcomes", "Overall Range of Outcomes", "QB Range of Outcomes", "RB Range of Outcomes", "WR Range of Outcomes", "TE Range of Outcomes", "Optimals"])
+
+# ... existing code ...
+
+@st.cache_data
+def optimize_lineup(player_data, sidebar_site):
+    """
+    Creates optimal lineup based on median projections while respecting position and salary constraints
+    """
+    # Create optimization problem
+    prob = pulp.LpProblem("NFL_Lineup_Optimization", pulp.LpMaximize)
+    
+    # Create player dictionary with binary variables
+    players = {}
+    for idx, row in player_data.iterrows():
+        players[row['Player']] = pulp.LpVariable(f"player_{idx}", 0, 1, pulp.LpBinary)
+    
+    # Objective: Maximize total median points
+    prob += pulp.lpSum([players[row['Player']] * row['Median'] for idx, row in player_data.iterrows()])
+    
+    # Constraint: Salary cap
+    if sidebar_site == 'Draftkings':
+        prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 50000
+    elif sidebar_site == 'Fanduel':
+        prob += pulp.lpSum([players[row['Player']] * row['Salary'] for idx, row in player_data.iterrows()]) <= 60000
+
+    # Constraint: 9 players
+    prob += pulp.lpSum([players[row['Player']] for idx, row in player_data.iterrows()]) == 9
+    
+    # Position constraints
+    qbs = player_data[player_data['Position'] == 'QB']['Player'].tolist()
+    rbs = player_data[player_data['Position'] == 'RB']['Player'].tolist()
+    wrs = player_data[player_data['Position'] == 'WR']['Player'].tolist()
+    tes = player_data[player_data['Position'] == 'TE']['Player'].tolist()
+    dsts = player_data[player_data['Position'] == 'DST']['Player'].tolist()
+    
+    # QB: exactly 1
+    prob += pulp.lpSum([players[p] for p in qbs]) == 1
+    
+    # RB: 2-3
+    prob += pulp.lpSum([players[p] for p in rbs]) >= 2
+    prob += pulp.lpSum([players[p] for p in rbs]) <= 3
+    
+    # WR: 3-4
+    prob += pulp.lpSum([players[p] for p in wrs]) >= 3
+    prob += pulp.lpSum([players[p] for p in wrs]) <= 4
+    
+    # TE: 1-2
+    prob += pulp.lpSum([players[p] for p in tes]) >= 1
+    prob += pulp.lpSum([players[p] for p in tes]) <= 2
+    
+    # DST: exactly 1
+    prob += pulp.lpSum([players[p] for p in dsts]) == 1
+    
+    # Solve the problem
+    prob.solve()
+    
+    # Get selected players
+    selected_players = []
+    total_salary = 0
+    total_median = 0
+    
+    for idx, row in player_data.iterrows():
+        if players[row['Player']].value() == 1:
+            selected_players.append({
+                'Player': row['Player'],
+                'Position': row['Position'],
+                'Salary': row['Salary'],
+                'Median': row['Median']
+            })
+            total_salary += row['Salary']
+            total_median += row['Median']
+    
+    return selected_players, total_salary, total_median
+
+with st.sidebar:
+    st.header("Quick Builder")
+
+    sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site')
+    sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='sidebar_slate')
+
+    if sidebar_site == 'Draftkings':
+        roo_sample = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)]
+        roo_sample = roo_sample[roo_sample['version'] == 'overall']
+        #roo_sample = roo_sample.sort_values(by='Own', ascending=False)
+    elif sidebar_site == 'Fanduel':
+        roo_sample = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)]
+        roo_sample = roo_sample[roo_sample['version'] == 'overall']
+        #roo_sample = roo_sample.sort_values(by='Own', ascending=False)
+    
+    st.write("---")
+    if st.button("Generate Optimal Lineup"):
+        if sidebar_site == 'Draftkings':
+            roo_data = dk_roo_raw[dk_roo_raw['slate'] == str(sidebar_slate)]
+            roo_data = roo_data[roo_data['version'] == 'overall']
+        else:
+            roo_data = fd_roo_raw[fd_roo_raw['slate'] == str(sidebar_slate)]
+            roo_data = roo_data[roo_data['version'] == 'overall']
+            
+        optimal_players, total_salary, total_median = optimize_lineup(roo_data, sidebar_site)
+       
+        st.write("Optimal Lineup:")
+        
+        # Sort players into position groups
+        qb = [p for p in optimal_players if p['Position'] == 'QB'][0]
+        rbs = [p for p in optimal_players if p['Position'] == 'RB']
+        wrs = [p for p in optimal_players if p['Position'] == 'WR']
+        tes = [p for p in optimal_players if p['Position'] == 'TE']
+        dst = [p for p in optimal_players if p['Position'] == 'DST'][0]
+        
+        # Display QB
+        st.write(f"QB: {qb['Player']} (${qb['Salary']:,})")
+        
+        # Display RB1 and RB2
+        st.write(f"RB: {rbs[0]['Player']} (${rbs[0]['Salary']:,})")
+        st.write(f"RB: {rbs[1]['Player']} (${rbs[1]['Salary']:,})")
+        
+        # Display WR1, WR2, WR3
+        st.write(f"WR: {wrs[0]['Player']} (${wrs[0]['Salary']:,})")
+        st.write(f"WR: {wrs[1]['Player']} (${wrs[1]['Salary']:,})")
+        st.write(f"WR: {wrs[2]['Player']} (${wrs[2]['Salary']:,})")
+        
+        # Display TE1
+        st.write(f"TE: {tes[0]['Player']} (${tes[0]['Salary']:,})")
+        
+        # Display FLEX (either RB3, WR4, or TE2)
+        if len(rbs) > 2:
+            st.write(f"FLEX (RB): {rbs[2]['Player']} (${rbs[2]['Salary']:,})")
+        elif len(wrs) > 3:
+            st.write(f"FLEX (WR): {wrs[3]['Player']} (${wrs[3]['Salary']:,})")
+        elif len(tes) > 1:
+            st.write(f"FLEX (TE): {tes[1]['Player']} (${tes[1]['Salary']:,})")
+            
+        # Display DST
+        st.write(f"DST: {dst['Player']} (${dst['Salary']:,})")
+        
+        st.write(f"Total Salary: ${total_salary:,}")
+        st.write(f"Projected Points: {total_median:.2f}")
+
+    # Create empty lists to store selected players
+    selected_qbs = []
+    selected_rb1 = []
+    selected_rb2 = []
+    selected_wr1 = []
+    selected_wr2 = []
+    selected_wr3 = []
+    selected_te = []
+    selected_flex = []
+    selected_dst = []
+    
+    # Get unique players by position from dk_roo_raw
+    qbs = roo_sample[roo_sample['Position'] == 'QB']['Player'].unique()
+    rbs = roo_sample[roo_sample['Position'] == 'RB']['Player'].unique()
+    wrs = roo_sample[roo_sample['Position'] == 'WR']['Player'].unique()
+    tes = roo_sample[roo_sample['Position'] == 'TE']['Player'].unique()
+    flex = roo_sample['Player'].unique()
+    dst = roo_sample[roo_sample['Position'] == 'DST']['Player'].unique()
+    # Create multiselect dropdowns for each position
+    selected_qbs = st.multiselect('Select QB:', list(qbs), default=None, key='qb1')
+    if selected_qbs:
+        qb_team = roo_sample[roo_sample['Player'] == selected_qbs[0]]['Team'].values[0]
+        qb_sample = roo_sample[roo_sample['Team'] == qb_team]
+        bb_sample = roo_sample[roo_sample['Opp'] == qb_team]
+        wr_suggest = qb_sample[qb_sample['Position'] == 'WR']['Player'].values[0]
+        wr2_suggest = bb_sample[bb_sample['Position'] == 'WR']['Player'].values[0]
+        te_suggest = qb_sample[qb_sample['Position'] == 'TE']['Player'].values[0]
+    selected_rb1 = st.multiselect('Select RBs:', list(rbs), default=None, key='rb1')
+    selected_rb2 = st.multiselect('Select RB2:', list(rbs), default=None, label_visibility='collapsed', key='rb2')
+    if selected_qbs:
+        selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, placeholder=f'Suggestion: {wr_suggest}', key='wr1')
+    else:
+        selected_wr1 = st.multiselect('Select WRs:', list(wrs), default=None, key='wr1')
+    if selected_qbs:
+        selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, placeholder=f'Suggestion: {wr2_suggest}', label_visibility='collapsed', key='wr2')
+    else:
+        selected_wr2 = st.multiselect('Select WR2:', list(wrs), default=None, label_visibility='collapsed', key='wr2')
+    selected_wr3 = st.multiselect('Select WR3:', list(wrs), default=None, label_visibility='collapsed', key='wr3')
+    if selected_qbs:
+        selected_te = st.multiselect('Select TE:', list(tes), default=None, placeholder=f'Suggestion: {te_suggest}', key='te')
+    else:
+        selected_te = st.multiselect('Select TE:', list(tes), default=None, key='te')
+    selected_flex = st.multiselect('Select Flex:', list(flex), default=None, key='flex')
+    selected_dst = st.multiselect('Select DST:', list(dst), default=None, key='dst')
+
+    # Combine all selected players
+    all_selected = selected_qbs + selected_rb1 + selected_rb2 + selected_wr1 + selected_wr2 + selected_wr3 + selected_te + selected_flex + selected_dst
+
+    if all_selected:
+        # Get stats for selected players
+        selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)]
+        
+        # Calculate sums
+        salary_sum = selected_stats['Salary'].sum()
+        median_sum = selected_stats['Median'].sum()
+        own_sum = selected_stats['Own'].sum()
+        levx_sum = selected_stats['LevX'].sum()
+
+        # Display sums
+        st.write('---')
+        if sidebar_site == 'Draftkings':
+            if salary_sum > 50000:
+                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000')
+            else:
+                st.write(f'Total Salary: ${salary_sum:.2f}')
+        elif sidebar_site == 'Fanduel':
+            if salary_sum > 60000:
+                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000')
+            else:
+                st.write(f'Total Salary: ${salary_sum:.2f}')
+        st.write(f'Total Median: {median_sum:.2f}')
+        st.write(f'Total Ownership: {own_sum:.2f}%')
+        st.write(f'Total LevX: {levx_sum:.2f}')
+
+with tab1:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset1'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var1')
+        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
+        view_var1 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var1')
+        custom_var1 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var1')
+        if custom_var1 == 'No':
+            if site_var1 == 'Draftkings':
+                raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            elif site_var1 == 'Fanduel':
+                raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+            if split_var1 == 'Specific Games':
+                team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+            elif split_var1 == 'Full Slate Run':
+                team_var1 = raw_baselines.Team.values.tolist()
+        if custom_var1 == 'Yes':
+            contest_var1 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var1')
+            if site_var1 == 'Draftkings':
+                raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+            elif site_var1 == 'Fanduel':
+                raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+            split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+            if split_var1 == 'Specific Games':
+                team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+            elif split_var1 == 'Full Slate Run':
+                team_var1 = raw_baselines.Team.values.tolist()
+    
+    
+    with col2:
+        if custom_var1 == 'No':
+            final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)]
+            if view_var1 == 'Simple':
+                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
+            elif view_var1 == 'Advanced':
+                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                             'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+            st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(final_stacks),
+                    file_name='NFL_stacks_export.csv',
+                    mime='text/csv',
+            )
+        elif custom_var1 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var1 == 'Draftkings':
+                            working_roo = player_stats
+                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                            working_roo.replace('', 0, inplace=True)
+                    if site_var1 == 'Fanduel':
+                            working_roo = player_stats
+                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                            working_roo.replace('', 0, inplace=True)
+                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                    
+                    total_sims = 1000
+                    
+                    salary_dict = dict(zip(working_roo.name, working_roo.Salary))
+                    own_dict = dict(zip(working_roo.name, working_roo.Own))
+                    fantasy_dict = dict(zip(working_roo.name, working_roo.Fantasy))
+
+                    QB_group = working_roo.loc[working_roo['Position'] == 'QB']
+                    stacks_df = pd.DataFrame(columns=['Team','QB', 'WR1', 'WR2_TE'])
+
+                    for stack in range(0,len(QB_group)):
+                        team_var = QB_group.iat[stack,1]
+                        WR_group_1 = working_roo.loc[working_roo['Position'] == 'WR']
+                        WR_group_2 = WR_group_1.loc[working_roo['Team'] == team_var]
+                        TE_group_1 = working_roo.loc[working_roo['Position'] == 'TE']
+                        TE_group_2 = TE_group_1.loc[working_roo['Team'] == team_var]
+                        cur_list = []
+                        qb_piece = QB_group.iat[stack,0]
+                        wr_piece = WR_group_2.iat[0,0]
+                        te_piece = TE_group_2.iat[0,0]
+                        cur_list.append(team_var)
+                        cur_list.append(qb_piece)
+                        cur_list.append(wr_piece)
+                        cur_list.append(te_piece)
+                        stacks_df.loc[len(stacks_df)] = cur_list
+                        cur_list = []
+                        qb_piece = QB_group.iat[stack,0]
+                        wr_piece = WR_group_2.iat[1,0]
+                        te_piece = TE_group_2.iat[0,0]
+                        cur_list.append(team_var)
+                        cur_list.append(qb_piece)
+                        cur_list.append(wr_piece)
+                        cur_list.append(te_piece)
+                        stacks_df.loc[len(stacks_df)] = cur_list
+                        cur_list = []
+                        qb_piece = QB_group.iat[stack,0]
+                        wr_piece = WR_group_2.iat[0,0]
+                        te_piece = WR_group_2.iat[1,0]
+                        cur_list.append(team_var)
+                        cur_list.append(qb_piece)
+                        cur_list.append(wr_piece)
+                        cur_list.append(te_piece)
+                        stacks_df.loc[len(stacks_df)] = cur_list
+                    
+                    stacks_df['Salary'] = sum([stacks_df['QB'].map(salary_dict),
+                            stacks_df['WR1'].map(salary_dict),
+                            stacks_df['WR2_TE'].map(salary_dict)])
+
+                    stacks_df['Fantasy'] = sum([stacks_df['QB'].map(fantasy_dict),
+                            stacks_df['WR1'].map(fantasy_dict),
+                            stacks_df['WR2_TE'].map(fantasy_dict)])
+
+                    stacks_df['Own'] = sum([stacks_df['QB'].map(own_dict),
+                            stacks_df['WR1'].map(own_dict),
+                            stacks_df['WR2_TE'].map(own_dict)])
+
+                    stacks_df['team_combo'] = stacks_df['Team'] + " " + stacks_df['QB'] + " " + stacks_df['WR1'] + " " + stacks_df['WR2_TE']
+
+                    own_dict = dict(zip(stacks_df.team_combo, stacks_df.Own))
+                    qb_dict = dict(zip(stacks_df.team_combo, stacks_df.QB))
+                    wr1_dict = dict(zip(stacks_df.team_combo, stacks_df.WR1))
+                    wr2_dict = dict(zip(stacks_df.team_combo, stacks_df.WR2_TE))
+                    team_dict = dict(zip(stacks_df.team_combo, stacks_df.Team))
+
+                    flex_file = stacks_df[['team_combo', 'Salary', 'Fantasy']]
+                    flex_file.rename(columns={"Fantasy": "Median"}, inplace = True)
+                    flex_file['Floor'] = flex_file['Median']*.25
+                    flex_file['Ceiling'] = flex_file['Median'] + flex_file['Floor']
+                    flex_file['STD'] = flex_file['Median']/4
+                    flex_file = flex_file[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['team_combo']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['team_combo']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.team_combo,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['team_combo'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['team_combo'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['60+%'] = overall_file[overall_file >= 60].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['team_combo'] = hold_file[['team_combo']]
+
+                    final_outcomes = players_only[['team_combo', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
+
+                    final_stacks = pd.merge(hold_file, final_outcomes, on="team_combo")
+                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
+                    final_stacks['Own'] = final_stacks['team_combo'].map(own_dict)
+                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_stacks['Projection Rank'] = final_stacks.Median.rank(pct = True)
+                    final_stacks['Own Rank'] = final_stacks.Own.rank(pct = True)
+                    final_stacks['LevX'] = final_stacks['Projection Rank'] - final_stacks['Own Rank']
+                    final_stacks['Team'] = final_stacks['team_combo'].map(team_dict)
+                    final_stacks['QB'] = final_stacks['team_combo'].map(qb_dict)
+                    final_stacks['WR1_TE'] = final_stacks['team_combo'].map(wr1_dict)
+                    final_stacks['WR2_TE'] = final_stacks['team_combo'].map(wr2_dict)
+
+                    final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                        'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+
+                    final_stacks = final_stacks.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_stacks = final_stacks
+                    if view_var1 == 'Simple':
+                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
+                    elif view_var1 == 'Advanced':
+                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                                    'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                    st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(final_stacks),
+                    file_name='Custom_NFL_stacks_export.csv',
+                    mime='text/csv',
+                )
+
+with tab2:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset2'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                    del st.session_state[key]   
+        slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2')
+        site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
+        view_var2 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var2')
+        custom_var2 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var2')
+        if custom_var2 == 'No':
+            if site_var2 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            elif site_var2 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            split_var2 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
+            if split_var2 == 'Specific Games':
+                team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
+            elif split_var2 == 'Full Slate Run':
+                team_var2 = raw_baselines.Team.values.tolist()
+            pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
+            if pos_split2 == 'Specific Positions':
+                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE'])
+            elif pos_split2 == 'All Positions':
+                pos_var2 = 'All'
+            sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var2')
+        if custom_var2 == 'Yes':
+            contest_var2 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var2')
+            if site_var2 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+            elif site_var2 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+            split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
+            if split_var2 == 'Specific Games':
+                team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
+            elif split_var2 == 'Full Slate Run':
+                team_var2 = raw_baselines.Team.values.tolist()
+            pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
+            if pos_split2 == 'Specific Positions':
+                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE'])
+            elif pos_split2 == 'All Positions':
+                pos_var2 = 'All'
+            sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var2')
+    
+    
+    with col2:
+        if custom_var2 == 'No':
+            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
+            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
+            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
+            if pos_var2 != 'All':
+                    final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+            
+            if view_var2 == 'Simple':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                disp_proj = final_Proj.set_index('Player')
+            elif view_var2 == 'Advanced':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                disp_proj = final_Proj.set_index('Player')
+            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='NFL_overall_export.csv',
+                mime='text/csv',
+            )
+        elif custom_var2 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var2 == 'Draftkings':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                    if site_var2 == 'Fanduel':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                    working_roo = working_roo[working_roo['Team'].isin(team_var2)]
+                    working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]]
+                    working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]]
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                    total_sims = 1000
+
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
+                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
+                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['Player'] = hold_file[['Player']]
+
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_Proj = final_Proj
+                    if view_var2 == 'Simple':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                        disp_proj = final_Proj.set_index('Player')
+                    elif view_var2 == 'Advanced':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                        disp_proj = final_Proj.set_index('Player')
+                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(final_Proj),
+                        file_name='Custom_NFL_overall_export.csv',
+                        mime='text/csv',
+                )
+
+with tab3:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset3'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]   
+        slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var3')
+        site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3')
+        view_var3 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var3')
+        custom_var3 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var3')
+        if custom_var3 == 'No':
+            if site_var3 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            elif site_var3 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            split_var3 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var3')
+            if split_var3 == 'Specific Games':
+                team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3')
+            elif split_var3 == 'Full Slate Run':
+                team_var3 = raw_baselines.Team.values.tolist()
+            pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3')
+            if pos_split3 == 'Specific Positions':
+                pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB'], key='pos_var3')
+            elif pos_split3 == 'All Positions':
+                pos_var3 = 'All'
+            sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var3')
+        if custom_var3 == 'Yes':
+            contest_var3 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var3')
+            if site_var3 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var3)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            elif site_var3 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var3)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            split_var3 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var3')
+            if split_var3 == 'Specific Games':
+                team_var3 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var3')
+            elif split_var3 == 'Full Slate Run':
+                team_var3 = raw_baselines.Team.values.tolist()
+            pos_split3 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split3')
+            if pos_split3 == 'Specific Positions':
+                pos_var3 = st.multiselect('What Positions would you like to view?', options = ['QB'])
+            elif pos_split3 == 'All Positions':
+                pos_var3 = 'All'
+            sal_var3 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var3')
+    
+    
+    with col2:
+        if custom_var3 == 'No':
+            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var3)]
+            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var3[0]]
+            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var3[1]]
+            if pos_var3 != 'All':
+                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var3))]
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+            
+            if view_var3 == 'Simple':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                disp_proj = final_Proj.set_index('Player')
+            elif view_var3 == 'Advanced':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                disp_proj = final_Proj.set_index('Player')
+            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='NFL_qb_export.csv',
+                mime='text/csv',
+            )
+        elif custom_var3 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var3 == 'Draftkings':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'QB']
+                    if site_var3 == 'Fanduel':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'QB']
+                    working_roo = working_roo[working_roo['Team'].isin(team_var3)]
+                    working_roo = working_roo[working_roo['Salary'] >= sal_var2[0]]
+                    working_roo = working_roo[working_roo['Salary'] <= sal_var2[1]]
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                    total_sims = 1000
+
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
+                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
+                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['Player'] = hold_file[['Player']]
+
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_Proj = final_Proj
+                    if view_var3 == 'Simple':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                        disp_proj = final_Proj.set_index('Player')
+                    elif view_var3 == 'Advanced':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                        disp_proj = final_Proj.set_index('Player')
+                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(final_Proj),
+                        file_name='Custom_NFL_qb_export.csv',
+                        mime='text/csv',
+                )
+                            
+with tab4:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset4'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var4')
+        site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var4')
+        view_var4 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var4')
+        custom_var4 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var4')
+        if custom_var4 == 'No':
+            if site_var4 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            elif site_var4 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            split_var4 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var4')
+            if split_var4 == 'Specific Games':
+                team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4')
+            elif split_var4 == 'Full Slate Run':
+                team_var4 = raw_baselines.Team.values.tolist()
+            pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4')
+            if pos_split4 == 'Specific Positions':
+                pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB'], key='pos_var4')
+            elif pos_split4 == 'All Positions':
+                pos_var4 = 'All'
+            sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var4')
+        if custom_var4 == 'Yes':
+            contest_var4 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var4')
+            if site_var4 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var4)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs']
+            elif site_var4 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var4)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs']
+            split_var4 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var4')
+            if split_var4 == 'Specific Games':
+                team_var4 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var4')
+            elif split_var4 == 'Full Slate Run':
+                team_var4 = raw_baselines.Team.values.tolist()
+            pos_split4 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split4')
+            if pos_split4 == 'Specific Positions':
+                pos_var4 = st.multiselect('What Positions would you like to view?', options = ['RB'])
+            elif pos_split4 == 'All Positions':
+                pos_var4 = 'All'
+            sal_var4 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var4')
+    
+    
+    with col2:
+        if custom_var4 == 'No':
+            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var4)]
+            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var4[0]]
+            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var4[1]]
+            if pos_var4 != 'All':
+                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var4))]
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                final_Proj = final_Proj.set_index('Player')
+                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+                     
+            if view_var4 == 'Simple':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                disp_proj = final_Proj.set_index('Player')
+            elif view_var4 == 'Advanced':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                disp_proj = final_Proj.set_index('Player')
+            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='NFL_rb_export.csv',
+                mime='text/csv',
+            )
+        elif custom_var4 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var4 == 'Draftkings':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'RB']
+                    if site_var4 == 'Fanduel':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'RB']
+                    working_roo = working_roo[working_roo['Team'].isin(team_var4)]
+                    working_roo = working_roo[working_roo['Salary'] >= sal_var4[0]]
+                    working_roo = working_roo[working_roo['Salary'] <= sal_var4[1]]
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                    total_sims = 1000
+
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
+                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
+                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['Player'] = hold_file[['Player']]
+
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_Proj = final_Proj
+                    if view_var4 == 'Simple':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                        disp_proj = final_Proj.set_index('Player')
+                    elif view_var4 == 'Advanced':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                        disp_proj = final_Proj.set_index('Player')
+                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(final_Proj),
+                    file_name='Custom_NFL_rb_export.csv',
+                    mime='text/csv',
+                )
+                            
+with tab5:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset5'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var5')
+        site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var5')
+        view_var5 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var5')
+        custom_var5 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var5')
+        if custom_var5 == 'No':
+            if site_var5 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            elif site_var5 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            split_var5 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var5')
+            if split_var5 == 'Specific Games':
+                team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5')
+            elif split_var5 == 'Full Slate Run':
+                team_var5 = raw_baselines.Team.values.tolist()
+            pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5')
+            if pos_split5 == 'Specific Positions':
+                pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR'], key='pos_var5')
+            elif pos_split5 == 'All Positions':
+                pos_var5 = 'All'
+            sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var5')
+        if custom_var5 == 'Yes':
+            contest_var5 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5')
+            if site_var5 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var5)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs']
+            elif site_var5 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var5)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs']
+            split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5')
+            if split_var5 == 'Specific Games':
+                team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5')
+            elif split_var5 == 'Full Slate Run':
+                team_var5 = raw_baselines.Team.values.tolist()
+            pos_split5 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split5')
+            if pos_split5 == 'Specific Positions':
+                pos_var5 = st.multiselect('What Positions would you like to view?', options = ['WR'])
+            elif pos_split5 == 'All Positions':
+                pos_var5 = 'All'
+            sal_var5 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var5')
+    
+    
+    with col2:
+        if custom_var5 == 'No':
+            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var5)]
+            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var5[0]]
+            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var5[1]]
+            if pos_var5 != 'All':
+                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var5))]
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                final_Proj = final_Proj.set_index('Player')
+                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+                     
+            if view_var5 == 'Simple':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                disp_proj = final_Proj.set_index('Player')
+            elif view_var5 == 'Advanced':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                disp_proj = final_Proj.set_index('Player')
+            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='NFL_wr_export.csv',
+                mime='text/csv',
+            )
+        elif custom_var5 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var5 == 'Draftkings':
+                            working_roo = player_stats
+                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                            working_roo.replace('', 0, inplace=True)
+                            working_roo = working_roo[working_roo['Position'] == 'WR']
+                    if site_var5 == 'Fanduel':
+                            working_roo = player_stats
+                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                            working_roo.replace('', 0, inplace=True)
+                            working_roo = working_roo[working_roo['Position'] == 'WR']
+                    working_roo = working_roo[working_roo['Team'].isin(team_var5)]
+                    working_roo = working_roo[working_roo['Salary'] >= sal_var5[0]]
+                    working_roo = working_roo[working_roo['Salary'] <= sal_var5[1]]
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                    total_sims = 1000
+
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
+                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
+                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['Player'] = hold_file[['Player']]
+
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_Proj = final_Proj
+                    if view_var5 == 'Simple':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                        disp_proj = final_Proj.set_index('Player')
+                    elif view_var5 == 'Advanced':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                        disp_proj = final_Proj.set_index('Player')
+                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(final_Proj),
+                        file_name='Custom_NFL_wr_export.csv',
+                        mime='text/csv',
+                )
+                            
+with tab6:
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        st.info(t_stamp)
+        if st.button("Load/Reset Data", key='reset6'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+        slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var6')
+        site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var6')
+        view_var6 = st.radio("What view would you like to display?", ('Advanced', 'Simple'), key='view_var6')
+        custom_var6 = st.radio("Are you creating a custom table?", ('No', 'Yes'), key='custom_var6')
+        if custom_var6 == 'No':
+            if site_var6 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            elif site_var6 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes']
+                raw_baselines = raw_baselines.iloc[:,:-3]
+            split_var6 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var6')
+            if split_var6 == 'Specific Games':
+                team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6')
+            elif split_var6 == 'Full Slate Run':
+                team_var6 = raw_baselines.Team.values.tolist()
+            pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6')
+            if pos_split6 == 'Specific Positions':
+                pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE'], key='pos_var6')
+            elif pos_split5 == 'All Positions':
+                pos_var6 = 'All'
+            sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var6')
+        if custom_var6 == 'Yes':
+            contest_var6 = st.selectbox("What contest type are you running for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var6')
+            if site_var6 == 'Draftkings':
+                raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var6)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes']
+            elif site_var6 == 'Fanduel':
+                raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var6)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes']
+            split_var6 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var6')
+            if split_var6 == 'Specific Games':
+                team_var6 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var6')
+            elif split_var6 == 'Full Slate Run':
+                team_var6 = raw_baselines.Team.values.tolist()
+            pos_split6 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split6')
+            if pos_split6 == 'Specific Positions':
+                pos_var6 = st.multiselect('What Positions would you like to view?', options = ['TE'])
+            elif pos_split6 == 'All Positions':
+                pos_var6 = 'All'
+            sal_var6 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var6')
+    
+    
+    with col2:
+        if custom_var6 == 'No':
+            final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var6)]
+            final_Proj = final_Proj[final_Proj['Salary'] >= sal_var6[0]]
+            final_Proj = final_Proj[final_Proj['Salary'] <= sal_var6[1]]
+            if pos_var6 != 'All':
+                final_Proj = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var6))]
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                final_Proj = final_Proj.set_index('Player')
+                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+                     
+            if view_var6 == 'Simple':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                disp_proj = final_Proj.set_index('Player')
+            elif view_var6 == 'Advanced':
+                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+                disp_proj = final_Proj.set_index('Player')
+            st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_Proj),
+                file_name='NFL_te_export.csv',
+                mime='text/csv',
+            )
+        elif custom_var6 == 'Yes':
+            hold_container = st.empty()
+            if st.button('Create Range of Outcomes for Slate'):
+                with hold_container:
+                    if site_var6 == 'Draftkings':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'TE']
+                    if site_var6 == 'Fanduel':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                        working_roo = working_roo[working_roo['Position'] == 'TE']
+                    working_roo = working_roo[working_roo['Team'].isin(team_var6)]
+                    working_roo = working_roo[working_roo['Salary'] >= sal_var6[0]]
+                    working_roo = working_roo[working_roo['Salary'] <= sal_var6[1]]
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
+                    total_sims = 1000
+
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Fantasy', 'Rush Yards', 'Receptions']]
+                    flex_file.rename(columns={"Fantasy": "Median", "Pos": "Position"}, inplace = True)
+                    flex_file['Floor'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median']*.25) + (flex_file['Rush Yards']*.01),flex_file['Median']*.25)
+                    flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB',(flex_file['Median'] + flex_file['Floor']) + (flex_file['Rush Yards']*.01), flex_file['Median'] + flex_file['Floor'] + flex_file['Receptions'])
+                    flex_file['STD'] = (flex_file['Median']/4) + flex_file['Receptions']
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+
+                    overall_players = overall_file[['Player']]
+
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+
+                    salary_file = salary_file.div(1000)
+
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+
+                    salary_2x_check = (overall_file - (salary_file*2))
+                    salary_3x_check = (overall_file - (salary_file*3))
+                    salary_4x_check = (overall_file - (salary_file*4))
+
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                    players_only['Player'] = hold_file[['Player']]
+
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
+                    final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = 0
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
+                    final_Proj['CPT_Own'] = final_Proj['Own'] / 4
+
+                    final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                    final_Proj = final_Proj.set_index('Player')
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+
+                with hold_container:
+                    hold_container = st.empty()
+                    final_Proj = final_Proj
+                    if view_var6 == 'Simple':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+                        disp_proj = final_Proj.set_index('Player')
+                    elif view_var6 == 'Advanced':
+                        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'CPT_Own', 'LevX']]
+                        disp_proj = final_Proj.set_index('Player')
+                    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(final_Proj),
+                    file_name='Custom_NFL_te_export.csv',
+                    mime='text/csv',
+                )
+with tab7:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset7'):
+            st.cache_data.clear()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+
+        if site_var1 == 'Draftkings':
+            ROO_slice = dk_roo_raw[dk_roo_raw['site'] == 'Draftkings']
+            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id))
+            column_names = dk_columns
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = dk_roo_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = dk_roo_raw.Player.values.tolist()
+                    
+        elif site_var1 == 'Fanduel':
+            ROO_slice = fd_roo_raw[fd_roo_raw['site'] == 'Fanduel']
+            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_id))
+            column_names = fd_columns
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = fd_roo_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = fd_roo_raw.Player.values.tolist()
+
+        if st.button("Prepare data export", key='data_export'):
+            data_export = st.session_state.working_seed.copy()
+            if site_var1 == 'Draftkings':
+                for col_idx in range(9):
+                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+            elif site_var1 == 'Fanduel':
+                for col_idx in range(9):
+                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NFL_optimals_export.csv',
+                mime='text/csv',
+            )
+    with col2:
+        
+        if site_var1 == 'Draftkings':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = dk_lineups.copy()
+                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:
+                st.session_state.working_seed = dk_lineups.copy()
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = dk_lineups.copy()
+                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
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = fd_lineups.copy()
+                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:
+                st.session_state.working_seed = fd_lineups.copy()
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = fd_lineups.copy()
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+
+        export_file = st.session_state.data_export_display.copy()
+        if site_var1 == 'Draftkings':
+            for col_idx in range(9):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+        elif site_var1 == 'Fanduel':
+            for col_idx in range(9):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+                
+        with st.container():
+            if st.button("Reset Optimals", key='reset9'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+                if site_var1 == 'Draftkings':
+                    st.session_state.working_seed = dk_lineups.copy()
+                elif site_var1 == 'Fanduel':
+                    st.session_state.working_seed = fd_lineups.copy()
+            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)
+            st.download_button(
+                label="Export display optimals",
+                data=convert_df(export_file),
+                file_name='NFL_display_optimals.csv',
+                mime='text/csv',
+            )
+        
+        with st.container():
+            if 'working_seed' in st.session_state:
+                # Create a new dataframe with summary statistics
+                if site_var1 == 'Draftkings':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,10]),
+                            np.mean(st.session_state.working_seed[:,10]),
+                            np.max(st.session_state.working_seed[:,10]),
+                            np.std(st.session_state.working_seed[:,10])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,15]),
+                            np.mean(st.session_state.working_seed[:,15]),
+                            np.max(st.session_state.working_seed[:,15]),
+                            np.std(st.session_state.working_seed[:,15])
+                        ]
+                    })
+                elif site_var1 == 'Fanduel':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,10]),
+                            np.mean(st.session_state.working_seed[:,10]),
+                            np.max(st.session_state.working_seed[:,10]),
+                            np.std(st.session_state.working_seed[:,10])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,15]),
+                            np.mean(st.session_state.working_seed[:,15]),
+                            np.max(st.session_state.working_seed[:,15]),
+                            np.std(st.session_state.working_seed[:,15])
+                        ]
+                    })
+
+                # Set the index of the summary dataframe as the "Metric" column
+                summary_df = summary_df.set_index('Metric')
+
+                # Display the summary dataframe
+                st.subheader("Optimal Statistics")
+                st.dataframe(summary_df.style.format({
+                    'Salary': '{:.2f}',
+                    'Proj': '{:.2f}',
+                    'Own': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
+
+        with st.container():
+            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
+            with tab1:
+                if 'data_export_display' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    
+                    # Flatten the DataFrame and count unique values
+                    value_counts = player_columns.values.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / lineup_num_var * 100).round(2)
+                    
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Player Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
+                
+                    st.download_button(
+                        label="Export player frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NFL_player_frequency.csv',
+                        mime='text/csv',
+                    )
+            with tab2:
+                if 'working_seed' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    
+                    # Flatten the DataFrame and count unique values
+                    value_counts = player_columns.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Seed Frame Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
+                
+                    st.download_button(
+                        label="Export seed frame frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NFL_seed_frame_frequency.csv',
+                        mime='text/csv',
+                    )
\ No newline at end of file