Upload app (4).py

app (4).py

@@ -0,0 +1,1232 @@
+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 gspread
+from itertools import combinations
+
+@st.cache_resource
+def init_conn():
+          scope = ['https://www.googleapis.com/auth/spreadsheets',
+                    "https://www.googleapis.com/auth/drive"]
+          
+          credentials = {
+            "type": "service_account",
+            "project_id": "sheets-api-connect-378620",
+            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
+            "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
+            "client_id": "106625872877651920064",
+            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+            "token_uri": "https://oauth2.googleapis.com/token",
+            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+            "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+          }
+
+          gc = gspread.service_account_from_dict(credentials)
+          return gc
+
+gc = 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%}'}
+
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
+
+@st.cache_resource(ttl=3600)
+def set_slate_teams():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Site_Info')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    return raw_display
+
+@st.cache_resource(ttl=600)
+def player_stat_table():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('Player_Projections')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+
+    return raw_display
+
+@st.cache_resource(ttl=600)
+def load_dk_player_projections():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('DK_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_resource(ttl=600)
+def load_fd_player_projections():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('FD_ROO')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    load_display.replace('', np.nan, inplace=True)
+    raw_display = load_display.dropna(subset=['Median'])
+
+    return raw_display
+
+@st.cache_resource(ttl=600)
+def load_dk_stacks():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('DK_Stacks')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_resource(ttl=600)
+def load_fd_stacks():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('FD_Stacks')
+    load_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = load_display
+
+    return raw_display
+
+@st.cache_data
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+player_stats = player_stat_table()
+dk_stacks_raw = load_dk_stacks()
+fd_stacks_raw = load_fd_stacks()
+dk_roo_raw = load_dk_player_projections()
+fd_roo_raw = load_fd_player_projections()
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+site_slates = set_slate_teams()
+
+tab1, tab2, tab3, tab4, tab5, tab6 = 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"])
+
+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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var1')
+        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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)]
+              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
+                          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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var2')
+        site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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, 10000, (2000, 10000), 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, 10000, (2000, 10000), 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', 'CPT_Own', 'LevX']]
+                     final_Proj = final_Proj.set_index('Player')
+                     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+              st.dataframe(final_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
+                          st.dataframe(final_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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var3')
+        site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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, 10000, (2000, 10000), 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, 10000, (2000, 10000), 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', 'CPT_Own', 'LevX']]
+                     final_Proj = final_Proj.set_index('Player')
+                     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+              st.dataframe(final_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
+                          st.dataframe(final_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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var4')
+        site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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, 10000, (2000, 10000), 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, 10000, (2000, 10000), 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', 'CPT_Own', 'LevX']]
+                     final_Proj = final_Proj.set_index('Player')
+                     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+              st.dataframe(final_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
+                          st.dataframe(final_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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var5')
+        site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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, 10000, (2000, 10000), 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, 10000, (2000, 10000), 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', 'CPT_Own', 'LevX']]
+                     final_Proj = final_Proj.set_index('Player')
+                     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+              st.dataframe(final_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
+                          st.dataframe(final_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 = player_stat_table()
+              dk_stacks_raw = load_dk_stacks()
+              fd_stacks_raw = load_fd_stacks()
+              dk_roo_raw = load_dk_player_projections()
+              fd_roo_raw = load_fd_player_projections()
+              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              site_slates = set_slate_teams()
+        slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Thurs-Mon Slate'), key='slate_var6')
+        site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_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, 10000, (2000, 10000), 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, 10000, (2000, 10000), 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', 'CPT_Own', 'LevX']]
+                     final_Proj = final_Proj.set_index('Player')
+                     final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+              st.dataframe(final_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
+                          st.dataframe(final_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',
+                      )