Initial commit for structure

app.py

@@ -0,0 +1,596 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import streamlit as st
+import gspread
+import pymongo
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def init_conn():
+
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NBA_DFS"]
+
+        return db
+    
+db = init_conn()
+
+dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
+
+@st.cache_data(ttl=60)
+def load_overall_stats():
+    collection = db["DK_Player_Stats"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    dk_raw = raw_display.sort_values(by='Median', ascending=False)
+    
+    collection = db["FD_Player_Stats"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    fd_raw = raw_display.sort_values(by='Median', ascending=False)
+    
+    collection = db["Secondary_DK_Player_Stats"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
+    
+    collection = db["Secondary_FD_Player_Stats"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
+                               'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
+
+    collection = db["Player_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    roo_raw = raw_display.sort_values(by='Median', ascending=False)
+
+    timestamp = raw_display['timestamp'].values[0]
+
+    collection = db["Range_Of_Outcomes_Backlog"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'Date']]
+    roo_backlog = raw_display.sort_values(by='Date', ascending=False)
+    roo_backlog = roo_backlog[roo_backlog['slate'] == 'Main Slate']
+    
+    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog
+
+@st.cache_data(ttl = 60)
+def init_DK_lineups():  
+        
+        collection = db['DK_NBA_name_map']
+        cursor = collection.find()
+        raw_data = pd.DataFrame(list(cursor))
+        names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    
+        collection = db["DK_NBA_seed_frame"] 
+        cursor = collection.find().limit(10000)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        for col in dict_columns:
+            raw_display[col] = raw_display[col].map(names_dict)
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
+@st.cache_data(ttl = 60)
+def init_FD_lineups():  
+        
+        collection = db['FD_NBA_name_map']
+        cursor = collection.find()
+        raw_data = pd.DataFrame(list(cursor))
+        names_dict = dict(zip(raw_data['key'], raw_data['value']))
+    
+        collection = db["FD_NBA_seed_frame"] 
+        cursor = collection.find().limit(10000)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        for col in dict_columns:
+            raw_display[col] = raw_display[col].map(names_dict)
+        FD_seed = raw_display.to_numpy()
+
+        return FD_seed
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
+salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
+
+try:
+    dk_lineups = init_DK_lineups()
+    fd_lineups = init_FD_lineups()
+except:
+    dk_lineups = pd.DataFrame(columns=dk_columns)
+    fd_lineups = pd.DataFrame(columns=fd_columns)
+t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+
+tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
+
+with st.sidebar:
+    st.header("Quick Builder")
+    st.info("This is a quick hand building helper to give you some basic info about player combos and lineup feasibility")
+    sidebar_site = st.selectbox("What site are you running?", ('Draftkings', 'Fanduel'), key='sidebar_site')
+    sidebar_slate = st.selectbox("What slate are you running?", ('Main Slate', 'Secondary Slate'), key='sidebar_slate')
+
+    if sidebar_site == 'Draftkings':
+        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
+        roo_sample = roo_sample[roo_sample['site'] == 'Draftkings']
+        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
+        selected_pg = []
+        selected_sg = []
+        selected_sf = []
+        selected_pf = []
+        selected_c = []
+        selected_g = []
+        selected_f = []
+        selected_flex = []
+    elif sidebar_site == 'Fanduel':
+        roo_sample = roo_raw[roo_raw['slate'] == str(sidebar_slate)]
+        roo_sample = roo_sample[roo_sample['site'] == 'Fanduel']
+        roo_sample = roo_sample.sort_values(by='Own', ascending=False)
+        selected_pg1 = []
+        selected_pg2 = []
+        selected_sg1 = []
+        selected_sg2 = []
+        selected_sf1 = []
+        selected_sf2 = []
+        selected_pf1 = []
+        selected_pf2 = []
+        selected_c1 = []
+
+    # Get unique players by position from dk_roo_raw
+    pgs = roo_sample[roo_sample['Position'].str.contains('PG')]['Player'].unique()
+    sgs = roo_sample[roo_sample['Position'].str.contains('SG')]['Player'].unique()
+    sfs = roo_sample[roo_sample['Position'].str.contains('SF')]['Player'].unique()
+    pfs = roo_sample[roo_sample['Position'].str.contains('PF')]['Player'].unique()
+    centers = roo_sample[roo_sample['Position'].str.contains('C')]['Player'].unique()
+    guards = roo_sample[roo_sample['Position'].str.contains('G')]['Player'].unique()
+    forwards = roo_sample[roo_sample['Position'].str.contains('F')]['Player'].unique()
+    flex = roo_sample['Player'].unique()
+
+    if sidebar_site == 'Draftkings':
+        selected_pgs = st.multiselect('Select PG:', list(pgs), default=None, placeholder='Select PG', label_visibility='collapsed', key='pg1')
+        selected_sgs = st.multiselect('Select SG:', list(sgs), default=None, placeholder='Select SG', label_visibility='collapsed', key='sg1')
+        selected_sfs = st.multiselect('Select SF:', list(sfs), default=None, placeholder='Select SF', label_visibility='collapsed', key='sf1')
+        selected_pfs = st.multiselect('Select PF:', list(pfs), default=None, placeholder='Select PF', label_visibility='collapsed', key='pf1')
+        selected_cs = st.multiselect('Select C:', list(centers), default=None, placeholder='Select C', label_visibility='collapsed', key='c1')
+        selected_g = st.multiselect('Select G:', list(guards), default=None, placeholder='Select G', label_visibility='collapsed', key='g')
+        selected_f = st.multiselect('Select F:', list(forwards), default=None, placeholder='Select F', label_visibility='collapsed', key='f')
+        selected_flex = st.multiselect('Select Flex:', list(flex), default=None, placeholder='Select Flex', label_visibility='collapsed', key='flex')
+
+        # Combine all selected players
+        all_selected = selected_pgs + selected_sgs + selected_sfs + selected_pfs + selected_cs + selected_g + selected_f + selected_flex
+    
+    elif sidebar_site == 'Fanduel':
+        selected_pg1 = st.multiselect('Select PG1:', list(pgs), default=None, placeholder='Select PG1', label_visibility='collapsed', key='pg1')
+        selected_pg2 = st.multiselect('Select PG2:', list(pgs), default=None, placeholder='Select PG2', label_visibility='collapsed', key='pg2')
+        selected_sg1 = st.multiselect('Select SG1:', list(sgs), default=None, placeholder='Select SG1', label_visibility='collapsed', key='sg1')
+        selected_sg2 = st.multiselect('Select SG2:', list(sgs), default=None, placeholder='Select SG2', label_visibility='collapsed', key='sg2')
+        selected_sf1 = st.multiselect('Select SF1:', list(sfs), default=None, placeholder='Select SF1', label_visibility='collapsed', key='sf1')
+        selected_sf2 = st.multiselect('Select SF2:', list(sfs), default=None, placeholder='Select SF2', label_visibility='collapsed', key='sf2')
+        selected_pf1 = st.multiselect('Select PF1:', list(pfs), default=None, placeholder='Select PF1', label_visibility='collapsed', key='pf1')
+        selected_pf2 = st.multiselect('Select PF2:', list(pfs), default=None, placeholder='Select PF2', label_visibility='collapsed', key='pf2')
+        selected_c1 = st.multiselect('Select C1:', list(centers), default=None, placeholder='Select C1', label_visibility='collapsed', key='c1')
+
+        # Combine all selected players
+        all_selected = selected_pg1 + selected_pg2 + selected_sg1 + selected_sg2 + selected_sf1 + selected_sf2 + selected_pf1 + selected_pf2 + selected_c1
+
+    if all_selected:
+        # Get stats for selected players
+        selected_stats = roo_sample[roo_sample['Player'].isin(all_selected)]
+        
+        # Calculate sums
+        salary_sum = selected_stats['Salary'].sum()
+        median_sum = selected_stats['Median'].sum()
+        own_sum = selected_stats['Own'].sum()
+        levx_sum = selected_stats['LevX'].sum()
+
+        # Display sums
+        st.write('---')
+        if sidebar_site == 'Draftkings':
+            if salary_sum > 50000:
+                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $50,000')
+            else:
+                st.write(f'Total Salary: ${salary_sum:.2f}')
+        elif sidebar_site == 'Fanduel':
+            if salary_sum > 60000:
+                st.warning(f'Total Salary: ${salary_sum:.2f} exceeds limit of $60,000')
+            else:
+                st.write(f'Total Salary: ${salary_sum:.2f}')
+        st.write(f'Total Median: {median_sum:.2f}')
+        st.write(f'Total Ownership: {own_sum:.2f}%')
+        st.write(f'Total LevX: {levx_sum:.2f}')
+
+with tab1:
+    with st.container():
+        st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
+    with st.container():
+        # First row - timestamp and reset button
+        col1, col2 = st.columns([3, 1])
+        with col1:
+            st.info(t_stamp)
+        with col2:
+            if st.button("Load/Reset Data", key='reset1'):
+                st.cache_data.clear()
+                dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
+                id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
+                dk_lineups = init_DK_lineups()
+                fd_lineups = init_FD_lineups()
+                t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+
+        # Second row - main options
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
+        with col2:
+            site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
+
+            # Process site selection
+            if site_var2 == 'Draftkings':
+                site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
+                site_backlog = roo_backlog[roo_backlog['site'] == 'Draftkings']
+            elif site_var2 == 'Fanduel':
+                site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
+                site_backlog = roo_backlog[roo_backlog['site'] == 'Fanduel']
+        with col3:
+            slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary', 'Backlog'), key='slate_split')
+
+            if slate_split == 'Main Slate':
+                raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
+            elif slate_split == 'Secondary':
+                raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate']
+            elif slate_split == 'Backlog':
+                raw_baselines = site_backlog
+                # Third row - backlog options
+                col1, col2 = st.columns(2)
+                with col1:
+                    view_all = st.checkbox("View all dates?", key='view_all')
+                with col2:
+                    if not view_all:
+                        date_var2 = st.date_input("Select date", key='date_var2')
+                
+                if view_all:
+                    raw_baselines = raw_baselines.sort_values(by=['Median', 'Date'], ascending=[False, False])
+                else:
+                    raw_baselines = raw_baselines[raw_baselines['Date'] == date_var2.strftime('%m-%d-%Y')]
+                    raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
+        
+        with col4:
+            split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
+            if split_var2 == 'Specific Games':
+                team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
+            else:
+                team_var2 = raw_baselines.Team.values.tolist()
+
+        pos_var2 = st.selectbox('Position Filter', options=['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2')
+    
+    display_container_1 = st.empty()
+    display_dl_container_1 = st.empty()
+    display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
+    if view_var2 == 'Advanced':
+        display_proj = display_proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                                    'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
+    elif view_var2 == 'Simple':
+        display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']]
+    export_data = display_proj.copy()
+
+
+    display_proj = display_proj.set_index('Player')
+    st.session_state.display_proj = display_proj
+        
+    with display_container_1:
+        display_container = st.empty()
+        if 'display_proj' in st.session_state:
+            if pos_var2 == 'All':
+                st.session_state.display_proj = st.session_state.display_proj
+            elif pos_var2 != 'All':
+                st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
+            st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2), height=1000, use_container_width = True)
+    
+    with display_dl_container_1:
+            display_dl_container = st.empty()
+            if 'display_proj' in st.session_state:
+                st.download_button(
+                            label="Export Tables",
+                            data=convert_df_to_csv(export_data),
+                            file_name='NBA_ROO_export.csv',
+                            mime='text/csv',
+                )
+
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset2'):
+            st.cache_data.clear()
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
+            dk_lineups = init_DK_lineups()
+            fd_lineups = init_FD_lineups()
+            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+            for key in st.session_state.keys():
+                del st.session_state[key]
+              
+        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
+        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+
+        if site_var1 == 'Draftkings':
+            raw_baselines = dk_raw
+            ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
+            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
+            # Get the minimum and maximum ownership values from dk_lineups
+            min_own = np.min(dk_lineups[:,14])
+            max_own = np.max(dk_lineups[:,14])
+            column_names = dk_columns
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = dk_raw.Player.values.tolist()
+                    
+        elif site_var1 == 'Fanduel':
+            raw_baselines = fd_raw
+            ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
+            id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
+            min_own = np.min(fd_lineups[:,15])
+            max_own = np.max(fd_lineups[:,15])
+            column_names = fd_columns
+            
+            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
+            if player_var1 == 'Specific Players':
+                    player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = fd_raw.Player.values.tolist()
+
+        if st.button("Prepare data export", key='data_export'):
+            data_export = st.session_state.working_seed.copy()
+            if site_var1 == 'Draftkings':
+                for col_idx in range(8):
+                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+            elif site_var1 == 'Fanduel':
+                for col_idx in range(9):
+                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NBA_optimals_export.csv',
+                mime='text/csv',
+            )
+    with col2:
+        
+        if site_var1 == 'Draftkings':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = dk_lineups.copy()
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                st.session_state.working_seed = dk_lineups.copy()
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = dk_lineups.copy()
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            
+        elif site_var1 == 'Fanduel':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = fd_lineups.copy()
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                st.session_state.working_seed = fd_lineups.copy()
+                st.session_state.working_seed = st.session_state.working_seed
+                if player_var1 == 'Specific Players':
+                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                elif player_var1 == 'Full Slate':
+                    st.session_state.working_seed = fd_lineups.copy()
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+
+        export_file = st.session_state.data_export_display.copy()
+        if site_var1 == 'Draftkings':
+            for col_idx in range(8):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+        elif site_var1 == 'Fanduel':
+            for col_idx in range(9):
+                export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+                
+        with st.container():
+            if st.button("Reset Optimals", key='reset3'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+                if site_var1 == 'Draftkings':
+                    st.session_state.working_seed = dk_lineups.copy()
+                elif site_var1 == 'Fanduel':
+                    st.session_state.working_seed = fd_lineups.copy()
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
+            st.download_button(
+                label="Export display optimals",
+                data=convert_df(export_file),
+                file_name='NBA_display_optimals.csv',
+                mime='text/csv',
+            )
+        
+        with st.container():
+            if 'working_seed' in st.session_state:
+                # Create a new dataframe with summary statistics
+                if site_var1 == 'Draftkings':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,8]),
+                            np.mean(st.session_state.working_seed[:,8]),
+                            np.max(st.session_state.working_seed[:,8]),
+                            np.std(st.session_state.working_seed[:,8])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,14]),
+                            np.mean(st.session_state.working_seed[:,14]),
+                            np.max(st.session_state.working_seed[:,14]),
+                            np.std(st.session_state.working_seed[:,14])
+                        ]
+                    })
+                elif site_var1 == 'Fanduel':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,10]),
+                            np.mean(st.session_state.working_seed[:,10]),
+                            np.max(st.session_state.working_seed[:,10]),
+                            np.std(st.session_state.working_seed[:,10])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,15]),
+                            np.mean(st.session_state.working_seed[:,15]),
+                            np.max(st.session_state.working_seed[:,15]),
+                            np.std(st.session_state.working_seed[:,15])
+                        ]
+                    })
+
+                # Set the index of the summary dataframe as the "Metric" column
+                summary_df = summary_df.set_index('Metric')
+
+                # Display the summary dataframe
+                st.subheader("Optimal Statistics")
+                st.dataframe(summary_df.style.format({
+                    'Salary': '{:.2f}',
+                    'Proj': '{:.2f}',
+                    'Own': '{:.2f}'
+                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
+
+        with st.container():
+            tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
+            with tab1:
+                if 'data_export_display' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.data_export_display.iloc[:, :8]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    
+                    # Flatten the DataFrame and count unique values
+                    value_counts = player_columns.values.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / lineup_num_var * 100).round(2)
+                    
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values                        
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Player Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                
+                    st.download_button(
+                        label="Export player frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NBA_player_frequency.csv',
+                        mime='text/csv',
+                    )
+            with tab2:
+                if 'working_seed' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed[:, :8]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    
+                    # Flatten the DataFrame and count unique values
+                    value_counts = player_columns.flatten().tolist()
+                    value_counts = pd.Series(value_counts).value_counts()
+                    
+                    percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                    # Create a DataFrame with the results
+                    summary_df = pd.DataFrame({
+                        'Player': value_counts.index,
+                        'Salary': [salary_dict.get(player, player) for player in value_counts.index],
+                        'Frequency': value_counts.values,
+                        'Percentage': percentages.values                        
+                    })
+                    
+                    # Sort by frequency in descending order
+                    summary_df = summary_df.sort_values('Frequency', ascending=False)
+                    
+                    # Display the table
+                    st.write("Seed Frame Frequency Table:")
+                    st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True)
+                
+                    st.download_button(
+                        label="Export seed frame frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='NBA_seed_frame_frequency.csv',
+                        mime='text/csv',
+                    )

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 2500

requirements.txt

@@ -0,0 +1,9 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+pymongo
+pulp
+docker
+plotly
+scipy