Upload streamlit_app (1).py

streamlit_app (1).py

@@ -0,0 +1,1229 @@
+import pulp
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import time
+from itertools import combinations
+
+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)
+
+st.set_page_config(layout="wide")
+
+wrong_acro = ['WSH', 'AZ']
+right_acro = ['WAS', 'ARI']
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
+                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}
+
+expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'}
+
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
+
+@st.cache_resource(ttl = 600)
+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
+    raw_display = raw_display.sort_values(by='Own', ascending=False)
+
+    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
+    raw_display = raw_display.sort_values(by='Own', ascending=False)
+
+    return raw_display
+
+@st.cache_resource(ttl = 3600)
+def set_export_ids():
+    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'])
+    dk_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+    
+    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'])
+    fd_ids = dict(zip(raw_display['Player'], raw_display['player_id']))
+
+    return dk_ids, fd_ids
+
+@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_fd_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()
+col1, col2 = st.columns([1, 5])
+dk_Max_Rank = dk_stacks_raw['Team'][0]
+fd_Max_Rank = dk_stacks_raw['Team'][0]
+dk_stacks_raw = dk_stacks_raw.sort_values(by='Median', ascending=False)
+fd_stacks_raw = fd_stacks_raw.sort_values(by='Median', ascending=False)
+dk_Max_Upside = dk_stacks_raw['Team'][0]
+fd_Max_Upside = dk_stacks_raw['Team'][0]
+opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
+dkid_dict, fdid_dict = set_export_ids()
+
+tab1, tab2 = st.tabs(['Uploads and Info', 'Optimizer'])
+
+with tab1:
+    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")
+    col1, col2 = st.columns([1, 5])
+
+    with col1:
+        proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+    
+        if proj_file is not None:
+                  try:
+                            proj_dataframe = pd.read_csv(proj_file)
+                  except:
+                            proj_dataframe = pd.read_excel(proj_file)
+    with col2:
+        if proj_file is not None:  
+                  st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        
+with tab2:
+    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_fd_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', 'All Games', 'User'), key='slate_var1')
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+        if site_var1 == 'Draftkings':
+              if slate_var1 == 'User':
+                  raw_baselines = proj_dataframe
+                  Max_Rank = dk_Max_Rank
+                  Max_Upside = dk_Max_Upside
+              elif slate_var1 != 'User':
+                  raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var1)]
+                  raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                  Max_Rank = dk_Max_Rank
+                  Max_Upside = dk_Max_Upside
+        elif site_var1 == 'Fanduel':
+              if slate_var1 == 'User':
+                  raw_baselines = proj_dataframe
+                  Max_Rank = fd_Max_Rank
+                  Max_Upside = fd_Max_Upside
+              elif slate_var1 != 'User':
+                  raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var1)]
+                  raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                  Max_Rank = fd_Max_Rank
+                  Max_Upside = fd_Max_Upside
+        contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP', 'Round Robin'), key='contest_var1')
+        if contest_var1 == 'Small Field GPP':
+            opto_var1 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var1')
+            if opto_var1 == "Manual":
+                stack_var1 = st.selectbox('Which teams are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1')
+                opp_var1 = opp_dict[stack_var1]
+                qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1')
+                ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1')
+            elif opto_var1 == "Pivot Optimal":
+                stack_var1 = Max_Rank
+                opp_var1 = opp_dict[stack_var1]
+                qbstack_var1 = 2
+                ministack_var1 = 0
+        elif contest_var1 == 'Large Field GPP':
+            opto_var1 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var1')
+            if opto_var1 == "Manual":
+                stack_var1 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var1')
+                opp_var1 = opp_dict[stack_var1]
+                qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1')
+                ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1')
+            elif opto_var1 == "Pivot Optimal":
+                stack_var1 = Max_Upside
+                opp_var1 = opp_dict[stack_var1]
+                qbstack_var1 = 2
+                ministack_var1 = 1
+        elif contest_var1 == 'Round Robin':
+            st.info('A Round Robin optimization will run a single optimal for all the teams on the slate based on your stacking inputs')
+            qbstack_var1 = st.selectbox('How many forced WR/TE stacked with QB?', options = [1, 2], key='qbstack_var1')
+            ministack_var1 = st.selectbox('How many forced bring backs?', options = [0, 1, 2], key='ministack_var1')
+        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 optimization?', options = raw_baselines['Team'].unique(), key='team_var1')
+        elif split_var1 == 'Full Slate Run':
+            team_var1 = raw_baselines.Team.values.tolist()
+        lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var1')
+        avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var1')
+        if contest_var1 == 'Round Robin':
+            linenum_var1 = len(raw_baselines['Team'].unique())
+        elif contest_var1 != 'Round Robin':
+            linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var1')
+        if site_var1 == 'Draftkings':
+            min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1')
+            max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1')
+        elif site_var1 == 'Fanduel':
+            min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 59900, value = 59000, step = 100, key='min_sal1')
+            max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 60000, value = 60000, step = 100, key='max_sal1')
+    with col2:
+        raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var1)]
+        raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var1)]
+        ownframe = raw_baselines.copy()
+        if contest_var1 == 'Cash':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
+        if contest_var1 == 'Small Field GPP':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
+        if contest_var1 == 'Large Field GPP':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'QB', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'QB') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'QB', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (900 / ownframe['Own%'].sum())
+        raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']]
+        raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
+        raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0)
+        st.dataframe(raw_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+                label="Export Projections",
+                data=convert_df_to_csv(raw_baselines),
+                file_name='NFL_proj_export.csv',
+                mime='text/csv',
+            )
+        if st.button('Optimize'):
+            max_proj = 1000
+            max_own = 1000
+            total_proj = 0
+            total_own = 0
+            optimize_container = st.empty()
+            download_container = st.empty()
+            freq_container = st.empty()
+            lineup_display = []
+            check_list = []
+            lineups = []
+            portfolio = pd.DataFrame()
+            x = 1
+    
+            with st.spinner('Wait for it...'):
+                with optimize_container:
+                        if contest_var1 == 'Round Robin':
+                            
+                            while x <= len(raw_baselines['Team'].unique()):
+                                stack_var1 = raw_baselines['Team'].unique()[x-1]
+                                opp_var1 = opp_dict[stack_var1]
+                                st.write(stack_var1)
+                                sorted_lineup = []
+                                p_used = []
+                                cvar = 0
+                                firvar = 0
+                                secvar = 0
+                                thirvar = 0
+        
+                                raw_proj_file = raw_baselines
+                                raw_flex_file = raw_proj_file.dropna(how='all')
+                                raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
+                                flex_file = raw_flex_file
+                                flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
+                                flex_file['name_var'] = flex_file['Player']
+                                flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0)
+                                player_ids = flex_file.index
+        
+                                overall_players = flex_file[['Player']]
+                                overall_players['player_var_add'] = flex_file.index
+                                overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
+        
+                                player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
+                                total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
+                                player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
+                                player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
+        
+                                player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
+                                player_team = dict(zip(flex_file['Player'], flex_file['Team']))
+                                player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
+                                player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
+                                player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
+        
+                                obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
+                                total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
+                                total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
+        
+                                if site_var1 == 'Draftkings':
+                                    
+                                    if contest_var1 == 'Cash':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                    elif contest_var1 == 'Small Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Round Robin':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Large Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] +
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['lock'].unique():
+                                        sub_idx = flex_file[flex_file['lock'] == 1].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "QB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+                                        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "DST"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+        
+                                elif site_var1 == 'Fanduel':
+        
+                                    if contest_var1 == 'Cash':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                    elif contest_var1 == 'Small Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Round Robin':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Large Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] +
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['lock'].unique():
+                                        sub_idx = flex_file[flex_file['lock'] == 1].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "QB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+                                        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "D"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+        
+                                player_count = []
+                                player_trim = []
+                                lineup_list = []
+                                
+                                if contest_var1 == 'Cash':
+                                    obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+                                    total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                elif contest_var1 != 'Cash':
+                                    obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                                    total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                
+        
+                                total_score.solve()
+                                for v in total_score.variables():
+                                    if v.varValue > 0:
+                                        lineup_list.append(v.name)
+                                df = pd.DataFrame(lineup_list)
+                                df['Names'] = df[0].map(player_match)
+                                df['Cost'] = df['Names'].map(player_sal)
+                                df['Proj'] = df['Names'].map(player_proj)
+                                df['Own'] = df['Names'].map(player_own)
+                                total_cost = sum(df['Cost'])
+                                total_own = sum(df['Own'])
+                                total_proj = sum(df['Proj'])
+                                lineup_raw = pd.DataFrame(lineup_list)
+                                lineup_raw['Names'] = lineup_raw[0].map(player_match)
+                                lineup_raw['value'] = lineup_raw[0].map(player_index_match)
+                                lineup_final = lineup_raw.sort_values(by=['value'])
+                                del lineup_final[lineup_final.columns[0]]
+                                del lineup_final[lineup_final.columns[1]]
+                                lineup_final = lineup_final.reset_index(drop=True)
+                                
+                                if site_var1 == 'Draftkings':
+                                    line_hold = lineup_final[['Names']]
+                                    line_hold['pos'] = line_hold['Names'].map(player_pos)
+        
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'QB':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)        
+                                    rbvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if rbvar == 2:
+                                            pname = len(line_hold)
+                                        elif rbvar < 2:
+                                            if line_hold.iat[pname,1] == 'RB':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    rbvar = rbvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    wrvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if wrvar == 3:
+                                            pname = len(line_hold)
+                                        elif wrvar < 3:
+                                            if line_hold.iat[pname,1] == 'WR':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    wrvar = wrvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    tevar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if tevar == 1:
+                                            pname = len(line_hold)
+                                        elif tevar < 1:
+                                            if line_hold.iat[pname,1] == 'TE':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    tevar = tevar + 1
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] != 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    lineup_final['sorted'] = sorted_lineup
+                                    lineup_final = lineup_final.drop(columns=['Names'])
+                                    lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                                      
+                                elif site_var1 == 'Fanduel':
+                                    line_hold = lineup_final[['Names']]
+                                    line_hold['pos'] = line_hold['Names'].map(player_pos)
+        
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'QB':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)        
+                                    rbvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if rbvar == 2:
+                                            pname = len(line_hold)
+                                        elif rbvar < 2:
+                                            if line_hold.iat[pname,1] == 'RB':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    rbvar = rbvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    wrvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if wrvar == 3:
+                                            pname = len(line_hold)
+                                        elif wrvar < 3:
+                                            if line_hold.iat[pname,1] == 'WR':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    wrvar = wrvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    tevar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if tevar == 1:
+                                            pname = len(line_hold)
+                                        elif tevar < 1:
+                                            if line_hold.iat[pname,1] == 'TE':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    tevar = tevar + 1
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] != 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    lineup_final['sorted'] = sorted_lineup
+                                    lineup_final = lineup_final.drop(columns=['Names'])
+                                    lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                                      
+                                lineup_test = lineup_final
+                                lineup_final = lineup_final.T
+                                lineup_final['Cost'] = total_cost
+                                lineup_final['Proj'] = total_proj
+                                lineup_final['Own'] = total_own
+                            
+                                lineup_test['Team'] = lineup_test['Names'].map(player_team)
+                                lineup_test['Position'] = lineup_test['Names'].map(player_pos)
+                                lineup_test['Salary'] = lineup_test['Names'].map(player_sal)
+                                lineup_test['Proj'] = lineup_test['Names'].map(player_proj)
+                                lineup_test['Own'] = lineup_test['Names'].map(player_own)
+                                lineup_test = lineup_test.set_index('Names')
+                                lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0)
+        
+                                lineup_display.append(lineup_test)
+        
+                                with col2:
+                                    with st.container():
+                                        st.table(lineup_test)
+        
+                                max_proj = total_proj
+                                max_own = total_own
+        
+                                check_list.append(total_proj)
+        
+                                portfolio = pd.concat([portfolio, lineup_final], ignore_index = True)
+        
+                                x += 1
+                            
+                        elif contest_var1 != 'Round Robin':
+                            while x <= linenum_var1:
+                                sorted_lineup = []
+                                p_used = []
+                                cvar = 0
+                                firvar = 0
+                                secvar = 0
+                                thirvar = 0
+        
+                                raw_proj_file = raw_baselines
+                                raw_flex_file = raw_proj_file.dropna(how='all')
+                                raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0]
+                                flex_file = raw_flex_file
+                                flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True)
+                                flex_file['name_var'] = flex_file['Player']
+                                flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var1), 1, 0)
+                                player_ids = flex_file.index
+        
+                                overall_players = flex_file[['Player']]
+                                overall_players['player_var_add'] = flex_file.index
+                                overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str)
+        
+                                player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger)
+                                total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize)
+                                player_match = dict(zip(overall_players['player_var'], overall_players['Player']))
+                                player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add']))
+        
+                                player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%']))
+                                player_team = dict(zip(flex_file['Player'], flex_file['Team']))
+                                player_pos = dict(zip(flex_file['Player'], flex_file['Position']))
+                                player_sal = dict(zip(flex_file['Player'], flex_file['Salary']))
+                                player_proj = dict(zip(flex_file['Player'], flex_file['Median']))
+        
+                                obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index}
+                                total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal1
+                                total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1
+        
+                                if site_var1 == 'Draftkings':
+                                    
+                                    if contest_var1 == 'Cash':
+                                        qbfile = flex_file
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                    elif contest_var1 == 'Small Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Large Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] +
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['lock'].unique():
+                                        sub_idx = flex_file[flex_file['lock'] == 1].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "QB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+                                        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "DST"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+        
+                                elif site_var1 == 'Fanduel':
+        
+                                    if contest_var1 == 'Cash':
+                                        qbfile = flex_file
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                    elif contest_var1 == 'Small Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    elif contest_var1 == 'Large Field GPP':
+                                        qbfile = flex_file[flex_file['Team'] == stack_var1]
+                                        qbfile = qbfile[qbfile['Position'] == 'QB']
+                                        qbfile = qbfile.reset_index()
+                                        qb_var = qbfile['Player'][0]
+                                        st.table(qbfile)
+                                        #st.write(stack_var1 + ' ' + qb_var)
+                                        for qbid in player_ids:
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] +
+                                                                   [-qbstack_var1*player_vars[qbid]]) >= 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == stack_var1 and 
+                                                                    flex_file['Position'][i] in ('RB'))] + 
+                                                                   [0*player_vars[qbid]]) == 0
+                                            if flex_file['Position'][qbid] == 'QB':
+                                                total_score += pulp.lpSum([player_vars[i] for i in player_ids if 
+                                                                   (flex_file['Team'][i] == opp_var1 and 
+                                                                    flex_file['Position'][i] in ('WR', 'TE'))] + 
+                                                                   [-ministack_var1*player_vars[qbid]]) >= 0
+                                        for flex in flex_file['Player'].unique():
+                                                sub_idx = flex_file[flex_file['Player'] == qb_var].index
+                                                total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['lock'].unique():
+                                        sub_idx = flex_file[flex_file['lock'] == 1].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var1)
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] != "Var"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "QB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3
+                                        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "RB"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "WR"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3
+        
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "TE"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1
+                                    
+                                    for flex in flex_file['Position'].unique():
+                                        sub_idx = flex_file[flex_file['Position'] == "D"].index
+                                        total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1
+        
+                                player_count = []
+                                player_trim = []
+                                lineup_list = []
+                                
+                                if contest_var1 == 'Cash':
+                                    obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index}
+                                    total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                    total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001
+                                elif contest_var1 != 'Cash':
+                                    obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index}
+                                    total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index])
+                                    total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01
+                                
+        
+                                total_score.solve()
+                                for v in total_score.variables():
+                                    if v.varValue > 0:
+                                        lineup_list.append(v.name)
+                                df = pd.DataFrame(lineup_list)
+                                df['Names'] = df[0].map(player_match)
+                                df['Cost'] = df['Names'].map(player_sal)
+                                df['Proj'] = df['Names'].map(player_proj)
+                                df['Own'] = df['Names'].map(player_own)
+                                total_cost = sum(df['Cost'])
+                                total_own = sum(df['Own'])
+                                total_proj = sum(df['Proj'])
+                                lineup_raw = pd.DataFrame(lineup_list)
+                                lineup_raw['Names'] = lineup_raw[0].map(player_match)
+                                lineup_raw['value'] = lineup_raw[0].map(player_index_match)
+                                lineup_final = lineup_raw.sort_values(by=['value'])
+                                del lineup_final[lineup_final.columns[0]]
+                                del lineup_final[lineup_final.columns[1]]
+                                lineup_final = lineup_final.reset_index(drop=True)
+                                
+                                if site_var1 == 'Draftkings':
+                                    line_hold = lineup_final[['Names']]
+                                    line_hold['pos'] = line_hold['Names'].map(player_pos)
+        
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'QB':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)        
+                                    rbvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if rbvar == 2:
+                                            pname = len(line_hold)
+                                        elif rbvar < 2:
+                                            if line_hold.iat[pname,1] == 'RB':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    rbvar = rbvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    wrvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if wrvar == 3:
+                                            pname = len(line_hold)
+                                        elif wrvar < 3:
+                                            if line_hold.iat[pname,1] == 'WR':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    wrvar = wrvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    tevar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if tevar == 1:
+                                            pname = len(line_hold)
+                                        elif tevar < 1:
+                                            if line_hold.iat[pname,1] == 'TE':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    tevar = tevar + 1
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] != 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    lineup_final['sorted'] = sorted_lineup
+                                    lineup_final = lineup_final.drop(columns=['Names'])
+                                    lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                                      
+                                elif site_var1 == 'Fanduel':
+                                    line_hold = lineup_final[['Names']]
+                                    line_hold['pos'] = line_hold['Names'].map(player_pos)
+        
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'QB':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)        
+                                    rbvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if rbvar == 2:
+                                            pname = len(line_hold)
+                                        elif rbvar < 2:
+                                            if line_hold.iat[pname,1] == 'RB':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    rbvar = rbvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    wrvar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if wrvar == 3:
+                                            pname = len(line_hold)
+                                        elif wrvar < 3:
+                                            if line_hold.iat[pname,1] == 'WR':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    wrvar = wrvar + 1
+                                    p_used.extend(sorted_lineup)
+                                    tevar = 0
+                                    for pname in range(0,len(line_hold)):
+                                        if tevar == 1:
+                                            pname = len(line_hold)
+                                        elif tevar < 1:
+                                            if line_hold.iat[pname,1] == 'TE':
+                                                if line_hold.iat[pname,0] not in p_used:
+                                                    sorted_lineup.append(line_hold.iat[pname,0])
+                                                    tevar = tevar + 1
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] != 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    for pname in range(0,len(line_hold)):
+                                        if line_hold.iat[pname,1] == 'DST':
+                                            if line_hold.iat[pname,0] not in p_used:
+                                                sorted_lineup.append(line_hold.iat[pname,0])
+                                    p_used.extend(sorted_lineup)
+                                    
+                                    lineup_final['sorted'] = sorted_lineup
+                                    lineup_final = lineup_final.drop(columns=['Names'])
+                                    lineup_final.rename(columns={"sorted": "Names"}, inplace = True)
+                                      
+                                lineup_test = lineup_final
+                                lineup_final = lineup_final.T
+                                lineup_final['Cost'] = total_cost
+                                lineup_final['Proj'] = total_proj
+                                lineup_final['Own'] = total_own
+                            
+                                lineup_test['Team'] = lineup_test['Names'].map(player_team)
+                                lineup_test['Position'] = lineup_test['Names'].map(player_pos)
+                                lineup_test['Salary'] = lineup_test['Names'].map(player_sal)
+                                lineup_test['Proj'] = lineup_test['Names'].map(player_proj)
+                                lineup_test['Own'] = lineup_test['Names'].map(player_own)
+                                lineup_test = lineup_test.set_index('Names')
+                                lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0)
+        
+                                lineup_display.append(lineup_test)
+        
+                                with col2:
+                                    with st.container():
+                                        st.table(lineup_test)
+        
+                                max_proj = total_proj
+                                max_own = total_own
+        
+                                check_list.append(total_proj)
+        
+                                portfolio = pd.concat([portfolio, lineup_final], ignore_index = True)
+        
+                                x += 1
+    
+                        if site_var1 == 'Draftkings':
+                            portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True)
+                        elif site_var1 == 'Fanduel':
+                            portfolio.rename(columns={0: "QB", 1: "RB1", 2: "RB2", 3: "WR1", 4: "WR2", 5: "WR3", 6: "TE", 7: "UTIL", 8: "DST"}, inplace = True)
+                        portfolio = portfolio.dropna()
+                        portfolio = portfolio.reset_index()
+                        portfolio['Lineup_num'] = portfolio['index'] + 1
+                        portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True)
+                        portfolio = portfolio.set_index('Lineup')
+                        portfolio = portfolio.drop(columns=['index'])
+    
+                        final_outcomes = portfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own']]
+                        final_outcomes = final_outcomes.set_axis(['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'UTIL', 'DST', 'Cost', 'Proj', 'Own'], axis=1)
+                        final_outcomes_export = pd.DataFrame()
+                        final_outcomes_export['QB'] = final_outcomes['QB']
+                        final_outcomes_export['RB1'] = final_outcomes['RB1']
+                        final_outcomes_export['RB2'] = final_outcomes['RB2']
+                        final_outcomes_export['WR1'] = final_outcomes['WR1']
+                        final_outcomes_export['WR2'] = final_outcomes['WR2']
+                        final_outcomes_export['WR3'] = final_outcomes['WR3']
+                        final_outcomes_export['TE'] = final_outcomes['TE']
+                        final_outcomes_export['UTIL'] = final_outcomes['UTIL']
+                        final_outcomes_export['DST'] = final_outcomes['DST']
+                        final_outcomes_export['Salary'] = final_outcomes['Cost']
+                        final_outcomes_export['Own'] = final_outcomes['Own']
+                        final_outcomes_export['Proj'] = final_outcomes['Proj']
+                        if site_var1 == 'Draftkings':
+                            final_outcomes_export['QB'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['RB1'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['RB2'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['WR1'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['WR2'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['WR3'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['TE'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True)
+                            final_outcomes_export['DST'].replace(dkid_dict, inplace=True)
+                        elif site_var1 == 'Fanduel':
+                            final_outcomes_export['QB'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['RB1'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['RB2'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['WR1'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['WR2'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['WR3'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['TE'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['UTIL'].replace(fdid_dict, inplace=True)
+                            final_outcomes_export['DST'].replace(fdid_dict, inplace=True)
+
+                        player_freq = pd.DataFrame(np.column_stack(np.unique(portfolio.iloc[:,0:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                        player_freq['Freq'] = player_freq['Freq'].astype(int)
+                        player_freq['Position'] = player_freq['Player'].map(player_pos)
+                        player_freq['Salary'] = player_freq['Player'].map(player_sal)
+                        player_freq['Proj Own'] = player_freq['Player'].map(player_own) / 100
+                        player_freq['Exposure'] = player_freq['Freq']/(linenum_var1)
+                        player_freq['Team'] = player_freq['Player'].map(player_team)
+          
+                        player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']]
+                        player_freq = player_freq.set_index('Player')
+    
+                with optimize_container:
+                    optimize_container = st.empty()
+                    st.dataframe(final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+                with download_container:
+                    download_container = st.empty()
+                    st.download_button(
+                        label="Export Optimals",
+                        data=convert_df_to_csv(final_outcomes_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                with freq_container:
+                    freq_container = st.empty()
+                    st.dataframe(player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True)