Initial commit from old project

Dockerfile

@@ -14,6 +14,11 @@ COPY src/ ./src/
 
 RUN pip3 install -r requirements.txt
 
+# Expose the secret SECRET_EXAMPLE at buildtime and use its value as git remote URL
+RUN --mount=type=secret,id=mongo_uri,mode=0444,required=true \
+    git init && \
+    git remote add origin $(cat /run/secrets/mongo_uri)
+
 EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+streamlit
+openpyxl
+matplotlib
+pulp
+docker
+plotly
+scipy
+pymongo

src/streamlit_app.py

@@ -1,40 +1,1314 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+import pymongo
+import re
+
+print(f"Streamlit version: {st.__version__}")
+
+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["NFL_Database"]
+
+        return db
+    
+db = init_conn()
+
+game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
+              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
+
+player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
+                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
+
+dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_hb_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+fd_hb_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+        padding: 4px;
+    }
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        background-color: #DAA520;
+        color: white;
+        border-radius: 10px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+    .stTabs [aria-selected="true"] {
+        background-color: #DAA520;
+        border: 3px solid #FFD700;
+        color: white;
+    }
+    .stTabs [data-baseweb="tab"]:hover {
+        background-color: #FFD700;
+        cursor: pointer;
+    }
+
+    div[data-baseweb="select"] > div {
+        background-color: #DAA520;
+        color: white;
+    }
+
+    div{
+        box-sizing: content-box !important;
+    }
+
+</style>""", unsafe_allow_html=True)
+
+@st.cache_resource(ttl=60)
+def init_baselines():
+
+    collection = db["Player_Baselines"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
+                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
+    player_stats = raw_display[raw_display['Position'] != 'K']
+
+    collection = db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    dk_roo_raw = load_display.dropna(subset=['Median'])
+
+    dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
+
+    collection = db["FD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    fd_roo_raw = load_display.dropna(subset=['Median'])
+
+    fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
+
+    collection = db["DK_SD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    dk_sd_roo_raw = load_display.dropna(subset=['Median'])
+
+    dk_sd_id_map = dict(zip(dk_sd_roo_raw['Player'], dk_sd_roo_raw['player_id']))
+
+    collection = db["FD_SD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+    load_display = raw_display[raw_display['Position'] != 'K']
+    fd_sd_roo_raw = load_display.dropna(subset=['Median'])
+
+    fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_id']))
+
+    collection = db["DK_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    dk_stacks_raw = raw_display.copy()
+
+    collection = db["FD_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    fd_stacks_raw = raw_display.copy()
+
+    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map
+
+@st.cache_resource(ttl = 60)
+def init_DK_lineups(type_var, slate_var):  
+    
+    if type_var == 'Regular':
+        if slate_var == 'Main':
+            collection = db['DK_NFL_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
+            collection = db['DK_NFL_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+        elif slate_var == 'Secondary':
+            collection = db['DK_NFL_Secondary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+            
+            collection = db['DK_NFL_Secondary_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+        elif slate_var == 'Auxiliary':
+            collection = db['DK_NFL_Turbo_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+            
+            collection = db['DK_NFL_Turbo_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+    elif type_var == 'Showdown':
+        if slate_var == 'Main':
+            collection = db['DK_NFL_SD1_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        elif slate_var == 'Secondary':
+            collection = db['DK_NFL_SD2_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        elif slate_var == 'Auxiliary':
+            collection = db['DK_NFL_SD3_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+
+    DK_seed = raw_display.to_numpy()
+
+    return DK_seed
+
+@st.cache_resource(ttl = 60)
+def init_FD_lineups(type_var, slate_var):  
+        
+        if type_var == 'Regular':
+            if slate_var == 'Main':
+                collection = db['FD_NFL_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
+                collection = db['FD_NFL_seed_frame']
+                cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+            elif slate_var == 'Secondary':
+                collection = db['FD_NFL_Secondary_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
+                collection = db['FD_NFL_Secondary_seed_frame']
+                cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+            elif slate_var == 'Auxiliary':
+                collection = db['FD_NFL_Turbo_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
+                collection = db['FD_NFL_Turbo_seed_frame']
+                cursor = collection.find().limit(10000)
+            
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+
+        elif type_var == 'Showdown':
+            if slate_var == 'Main':
+                collection = db['FD_NFL_SD1_seed_frame']
+                cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            elif slate_var == 'Secondary':
+                collection = db['FD_NFL_SD2_seed_frame']
+                cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            elif slate_var == 'Auxiliary':
+                collection = db['FD_NFL_SD3_seed_frame']
+                cursor = collection.find().limit(10000)
+            
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    
+        FD_seed = raw_display.to_numpy()
+
+        return FD_seed
+
+@st.cache_data
+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')
+
+@st.cache_data
+def convert_pm_df(array):
+    array = pd.DataFrame(array)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def convert_hb_df(array, column_names):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+col1, col2 = st.columns([1, 9])
+with col1:
+    if st.button("Load/Reset Data", key='reset'):
+        st.cache_data.clear()
+        player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
+        dk_lineups = init_DK_lineups('Regular', 'Main')
+        fd_lineups = init_FD_lineups('Regular', 'Main')
+        for key in st.session_state.keys():
+            del st.session_state[key]
+with col2:
+    with st.container():
+        col1, col2 = st.columns([3, 3])
+        with col1:
+            view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_var')
+        with col2:
+            site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_var')
+
+player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
+
+t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+
+tab1, tab2, tab3, tab4 = st.tabs(["Stacks ROO", "Player ROO", "Optimals", "Handbuilder"])
+
+with tab1:
+    with st.expander("Info and Filters"):
+        st.info(t_stamp)
+        with st.container():
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var1')
+            split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
+            if site_var == 'Draftkings':
+                raw_baselines = dk_stacks_raw[dk_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            elif site_var == 'Fanduel':
+                raw_baselines = fd_stacks_raw[fd_stacks_raw['slate'] == str(slate_var1)]
+                raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                raw_baselines = raw_baselines.iloc[:,:-2]
+            if split_var1 == 'Specific Games':
+                team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
+            elif split_var1 == 'Full Slate Run':
+                team_var1 = raw_baselines.Team.values.tolist()
+    
+    final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)]
+    if view_var == 'Simple':
+        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
+    elif view_var == 'Advanced':
+        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                        'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+    st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True)
+    st.download_button(
+            label="Export Tables",
+            data=convert_df_to_csv(final_stacks),
+            file_name='NFL_stacks_export.csv',
+            mime='text/csv',
+    )
+
+with tab2:
+    with st.expander("Info and Filters"):
+        st.info(t_stamp)
+        slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2')
+        if site_var == 'Draftkings':
+            raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
+            
+            raw_baselines = raw_baselines.iloc[:,:-2]
+        elif site_var == 'Fanduel':
+            raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
+            raw_baselines = raw_baselines.iloc[:,:-2]
+        split_var2 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
+        if split_var2 == 'Specific Games':
+            team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
+        elif split_var2 == 'Full Slate Run':
+            team_var2 = raw_baselines.Team.values.tolist()
+        pos_split2 = st.selectbox('What Position table would you like to view?', options = ['Overall', 'QB', 'RB', 'WR', 'TE'], key='pos_split2')
+        pos_combos2 = st.multiselect('If Overall, specific positions?', options = ['QB', 'RB', 'WR', 'TE', 'DST'], default = ['QB', 'RB', 'WR', 'TE', 'DST'], key='pos_combos2')
+        sal_var2 = st.slider("Is there a certain price range you want to view?", 2000, 15000, (2000, 15000), key='sal_var2')
+
+    if pos_split2 == 'Overall':
+        raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+    elif pos_split2 == 'QB':
+        if site_var == 'Draftkings':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_qbs']
+        elif site_var == 'Fanduel':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_qbs']
+    elif pos_split2 == 'RB':
+        if site_var == 'Draftkings':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_rbs']
+        elif site_var == 'Fanduel':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_rbs']
+    elif pos_split2 == 'WR':
+        if site_var == 'Draftkings':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_wrs']
+        elif site_var == 'Fanduel':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_wrs']
+    elif pos_split2 == 'TE':
+        if site_var == 'Draftkings':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'dk_tes']
+        elif site_var == 'Fanduel':
+            raw_baselines = raw_baselines[raw_baselines['version'] == 'fd_tes']
+    raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_combos2))]
+    final_Proj = raw_baselines[raw_baselines['Team'].isin(team_var2)]
+    final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
+    final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
+    
+    if view_var == 'Simple':
+        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Top_5_finish', '4x%']]
+        disp_proj = final_Proj.set_index('Player')
+    elif view_var == 'Advanced':
+        final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX']]
+        disp_proj = final_Proj.set_index('Player')
+    st.dataframe(disp_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), height=750, use_container_width = True)
+    st.download_button(
+        label="Export Tables",
+        data=convert_df_to_csv(final_Proj),
+        file_name='NFL_ROO_export.csv',
+        mime='text/csv',
+    )
+
+with tab3:
+    st.header("Optimals")
+    with st.expander("Info and Filters"):
+        st.info("These filters will display various optimals in the table below to pick from. If you want to export the entire set of 10,000 optimals, hit the 'Prepare full data export' button. If you would like to apply the filters here to the 10,000 optimals before you export, use the 'Prepare full data export (Filter)' button.")
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            slate_type_var3 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var3')
+            if slate_type_var3 == 'Regular':
+                if site_var == 'Draftkings':
+                    raw_baselines = dk_roo_raw
+                elif site_var == 'Fanduel':
+                    raw_baselines = fd_roo_raw
+            elif slate_type_var3 == 'Showdown':
+                if site_var == 'Draftkings':
+                    raw_baselines = dk_sd_roo_raw
+                elif site_var == 'Fanduel':
+                    raw_baselines = fd_sd_roo_raw
+            slate_var3 = st.radio("Which slate data are you loading?", ('Main', 'Secondary', 'Auxiliary'), key='slate_var3')
+            if slate_type_var3 == 'Regular':
+                if site_var == 'Draftkings':
+                    dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
+                elif site_var == 'Fanduel':
+                    fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
+            elif slate_type_var3 == 'Showdown':
+                if site_var == 'Draftkings':
+                    dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
+                elif site_var == 'Fanduel':
+                    fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
+        with col2:
+            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+            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 = raw_baselines['Player'].unique())
+            elif player_var1 == 'Full Slate':
+                    player_var2 = raw_baselines.Player.values.tolist()
+        with col3:
+            if site_var == 'Draftkings':
+                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 50000, value = 49000, step = 100, key = 'salary_min_var')
+                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var')
+            elif site_var == 'Fanduel':
+                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 60000, value = 59000, step = 100, key = 'salary_min_var')
+                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 60000, value = 60000, step = 100, key = 'salary_max_var')
+        with col4:
+            if site_var == 'Draftkings':
+                min_stacks_var = st.number_input("Minimum stacks used", min_value = 0, max_value = 5, value = 1, step = 1, key = 'min_stacks_var')
+                max_stacks_var = st.number_input("Maximum stacks used", min_value = 0, max_value = 5, value = 5, step = 1, key = 'max_stacks_var')
+            elif site_var == 'Fanduel':
+                min_stacks_var = st.number_input("Minimum stacks used", min_value = 0, max_value = 4, value = 1, step = 1, key = 'min_stacks_var')
+                max_stacks_var = st.number_input("Maximum stacks used", min_value = 0, max_value = 4, value = 4, step = 1, key = 'max_stacks_var')
+        
+        
+        if site_var == 'Draftkings':
+            raw_baselines = dk_roo_raw
+            if slate_type_var3 == 'Regular':
+                ROO_slice = raw_baselines
+                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
+                column_names = dk_columns
+            elif slate_type_var3 == 'Showdown':
+                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
+                column_names = dk_sd_columns
+            
+                    
+        elif site_var == 'Fanduel':
+            raw_baselines = fd_roo_raw
+            if slate_type_var3 == 'Regular':
+                ROO_slice = raw_baselines
+                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
+                column_names = fd_columns
+            elif slate_type_var3 == 'Showdown':
+                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
+                column_names = fd_sd_columns
+
+        reg_dl_col, filtered_dl_col, blank_dl_col = st.columns([2, 2, 6])
+        with reg_dl_col:
+            if st.button("Prepare full data export", key='data_export'):
+                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                if site_var == 'Draftkings':
+                    if slate_type_var3 == 'Regular':
+                        map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(dk_id_map)
+                    elif slate_type_var3 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
+                elif site_var == 'Fanduel':
+                    if slate_type_var3 == 'Regular':
+                        map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(fd_id_map)
+                    elif slate_type_var3 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
+                reg_opt_col, pm_opt_col = st.columns(2)
+                with reg_opt_col:
+                    st.download_button(
+                        label="Export optimals set (IDs)",
+                        data=convert_df(data_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    st.download_button(
+                        label="Export optimals set (Names)",
+                        data=convert_df(name_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                with pm_opt_col:
+                    if site_var == 'Draftkings':
+                        if slate_type_var3 == 'Regular':
+                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var == 'Fanduel':
+                        if slate_type_var3 == 'Regular':
+                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (IDs)",
+                        data=convert_pm_df(data_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    
+                    if site_var == 'Draftkings':
+                        if slate_type_var3 == 'Regular':
+                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var == 'Fanduel':
+                        if slate_type_var3 == 'Regular':
+                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (Names)",
+                        data=convert_pm_df(name_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+        with filtered_dl_col:
+            if st.button("Prepare full data export (Filtered)", key='data_export_filtered'):
+                name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
+                if site_var == 'Draftkings':
+                    if slate_type_var3 == 'Regular':
+                        map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                    elif slate_type_var3 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                    for col_idx in map_columns:
+                        data_export[col_idx] = data_export[col_idx].map(dk_id_map)
+                elif site_var == 'Fanduel':
+                    if slate_type_var3 == 'Regular':
+                        map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+                    elif slate_type_var3 == 'Showdown':
+                        map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+                    for col_idx in map_columns:
+                        data_export[col_idx] = data_export[col_idx].map(fd_id_map)
+                data_export = data_export[data_export['salary'] >= salary_min_var]
+                data_export = data_export[data_export['salary'] <= salary_max_var]
+                data_export = data_export[data_export['Team_count'] >= min_stacks_var]
+                data_export = data_export[data_export['Team_count'] <= max_stacks_var]
+
+                name_export = name_export[name_export['salary'] >= salary_min_var]
+                name_export = name_export[name_export['salary'] <= salary_max_var]
+                name_export = name_export[name_export['Team_count'] >= min_stacks_var]
+                name_export = name_export[name_export['Team_count'] <= max_stacks_var]
+                
+                reg_opt_col, pm_opt_col = st.columns(2)
+                with reg_opt_col:
+                    st.download_button(
+                        label="Export optimals set (IDs)",
+                        data=convert_df(data_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    st.download_button(
+                        label="Export optimals set (Names)",
+                        data=convert_df(name_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                with pm_opt_col:
+                    if site_var == 'Draftkings':
+                        if slate_type_var3 == 'Regular':
+                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var == 'Fanduel':
+                        if slate_type_var3 == 'Regular':
+                            data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (IDs)",
+                        data=convert_pm_df(data_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+                    
+                    if site_var == 'Draftkings':
+                        if slate_type_var3 == 'Regular':
+                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    elif site_var == 'Fanduel':
+                        if slate_type_var3 == 'Regular':
+                            name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                        elif slate_type_var3 == 'Showdown':
+                            name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
+                    st.download_button(
+                        label="Portfolio Manager Export (Names)",
+                        data=convert_pm_df(name_export),
+                        file_name='NFL_optimals_export.csv',
+                        mime='text/csv',
+                    )
+        
+    if site_var == '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_var == '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)
+    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] >= salary_min_var]
+    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] <= salary_max_var]
+    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['Team_count'] >= min_stacks_var]
+    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['Team_count'] <= max_stacks_var]
+    export_file = st.session_state.data_export_display.copy()
+    name_export = st.session_state.data_export_display.copy()
+    if site_var == 'Draftkings':
+        if slate_type_var3 == 'Regular':
+            map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+        elif slate_type_var3 == 'Showdown':
+            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+        for col_idx in map_columns:
+            export_file[col_idx] = export_file[col_idx].map(dk_id_map)
+    elif site_var == 'Fanduel':
+        if slate_type_var3 == 'Regular':
+            map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+        elif slate_type_var3 == 'Showdown':
+            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
+        for col_idx in map_columns:
+            export_file[col_idx] = export_file[col_idx].map(fd_id_map)
+            
+    with st.container():
+        if st.button("Reset Optimals", key='reset3'):
+            for key in st.session_state.keys():
+                del st.session_state[key]
+            if site_var == 'Draftkings':
+                st.session_state.working_seed = dk_lineups.copy()
+            elif site_var == '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 (IDs)",
+            data=convert_df(export_file),
+            file_name='NFL_display_optimals.csv',
+            mime='text/csv',
+        )
+        st.download_button(
+            label="Export display optimals (Names)",
+            data=convert_df(name_export),
+            file_name='NFL_display_optimals.csv',
+            mime='text/csv',
+        )
+    
+    with st.container():
+        if slate_type_var3 == 'Regular':
+            if 'working_seed' in st.session_state:
+                # Create a new dataframe with summary statistics
+                if site_var == 'Draftkings':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,9]),
+                            np.mean(st.session_state.working_seed[:,9]),
+                            np.max(st.session_state.working_seed[:,9]),
+                            np.std(st.session_state.working_seed[:,9])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,10]),
+                            np.mean(st.session_state.working_seed[:,10]),
+                            np.max(st.session_state.working_seed[:,10]),
+                            np.std(st.session_state.working_seed[:,10])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,15]),
+                            np.mean(st.session_state.working_seed[:,15]),
+                            np.max(st.session_state.working_seed[:,15]),
+                            np.std(st.session_state.working_seed[:,15])
+                        ]
+                    })
+                elif site_var == '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])
+                        ]
+                    })
+        elif slate_type_var3 == 'Showdown':
+            if 'working_seed' in st.session_state:
+                # Create a new dataframe with summary statistics
+                if site_var == 'Draftkings':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,12]),
+                            np.mean(st.session_state.working_seed[:,12]),
+                            np.max(st.session_state.working_seed[:,12]),
+                            np.std(st.session_state.working_seed[:,12])
+                        ]
+                    })
+                elif site_var == 'Fanduel':
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,12]),
+                            np.mean(st.session_state.working_seed[:,12]),
+                            np.max(st.session_state.working_seed[:,12]),
+                            np.std(st.session_state.working_seed[:,12])
+                        ]
+                    })
+
+        # 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_var == 'Draftkings':
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
+                elif site_var == 'Fanduel':
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
+                
+                # Flatten the DataFrame and count unique values
+                value_counts = player_columns.values.flatten().tolist()
+                value_counts = pd.Series(value_counts).value_counts()
+                
+                percentages = (value_counts / lineup_num_var * 100).round(2)
+                
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values
+                })
+                
+                # Sort by frequency in descending order
+                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
+                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                summary_df = summary_df.set_index('Player')
+                
+                # Display the table
+                st.write("Player Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export player frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NFL_player_frequency.csv',
+                    mime='text/csv',
+                )
+        with tab2:
+            if 'working_seed' in st.session_state:
+                if site_var == 'Draftkings':
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.working_seed[:, :6]
+                elif site_var == 'Fanduel':
+                    if slate_type_var3 == 'Regular':
+                        player_columns = st.session_state.working_seed[:, :9]
+                    elif slate_type_var3 == 'Showdown':
+                        player_columns = st.session_state.working_seed[:, :6]
+                
+                # Flatten the DataFrame and count unique values
+                value_counts = player_columns.flatten().tolist()
+                value_counts = pd.Series(value_counts).value_counts()
+                
+                percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
+                # Create a DataFrame with the results
+                summary_df = pd.DataFrame({
+                    'Player': value_counts.index,
+                    'Frequency': value_counts.values,
+                    'Percentage': percentages.values
+                })
+                
+                # Sort by frequency in descending order
+                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
+                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
+                summary_df = summary_df.sort_values('Frequency', ascending=False)
+                summary_df = summary_df.set_index('Player')
+                
+                # Display the table
+                st.write("Seed Frame Frequency Table:")
+                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
+            
+                st.download_button(
+                    label="Export seed frame frequency",
+                    data=convert_df_to_csv(summary_df),
+                    file_name='NFL_seed_frame_frequency.csv',
+                    mime='text/csv',
+                )
+
+with tab4:
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Handbuilder")
+    with col2:
+        slate_var3 = st.selectbox("Slate Selection", options=['Main', 'Secondary', 'Auxiliary'])
+        if site_var == 'Draftkings':
+            if slate_var3 == 'Main':
+                handbuild_roo = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
+            elif slate_var3 == 'Secondary':
+                handbuild_roo = dk_roo_raw[dk_roo_raw['slate'] == 'Secondary Slate']
+            elif slate_var3 == 'Auxiliary':
+                handbuild_roo = dk_roo_raw[dk_roo_raw['slate'] == 'Late Slate']
+        elif site_var == 'Fanduel':
+            if slate_var3 == 'Main':
+                handbuild_roo = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
+            elif slate_var3 == 'Secondary':
+                handbuild_roo = fd_roo_raw[fd_roo_raw['slate'] == 'Secondary Slate']
+            elif slate_var3 == 'Auxiliary':
+                handbuild_roo = fd_roo_raw[fd_roo_raw['slate'] == 'Late Slate']
+
+    # --- POSITION LIMITS ---
+    if site_var == 'Draftkings':
+        position_limits = {
+            'QB': 1,
+            'RB': 2,
+            'WR': 3,
+            'TE': 1,
+            'UTIL': 1,
+            'DST': 1,
+            # Add more as needed
+        }
+        max_salary = 50000
+        max_players = 9
+    else:
+        position_limits = {
+            'QB': 1,
+            'RB': 2,
+            'WR': 3,
+            'TE': 1,
+            'UTIL': 1,
+            'DST': 1,
+            # Add more as needed
+        }
+        max_salary = 60000
+        max_players = 9
+
+    # --- LINEUP STATE ---
+    if 'handbuilder_lineup' not in st.session_state:
+        st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own'])
+    if 'handbuilder_select_key' not in st.session_state:
+        st.session_state['handbuilder_select_key'] = 0
+
+    # Count positions in the current lineup
+    lineup = st.session_state['handbuilder_lineup']
+    slot_counts = lineup['Slot'].value_counts() if not lineup.empty else {}
+
+    # --- PLAYER FILTERS ---
+    with st.expander("Player Filters"):
+        col1, col2 = st.columns(2)
+        with col1:
+            pos_select3 = st.multiselect("Select your position(s)", options=['QB', 'RB', 'WR', 'TE', 'UTIL', 'DST'], key='pos_select3')
+        with col2:
+            salary_var = st.number_input("Salary Max", min_value = 0, max_value = 20000, value = 20000, step = 100)
+
+    # --- TEAM FILTER UI ---
+    with st.expander("Team Filters"):
+        all_teams = sorted(handbuild_roo['Team'].unique())
+        st.markdown("**Toggle teams to include:**")
+        team_cols = st.columns(len(all_teams) // 2 + 1)
+    
+    selected_teams = []
+    for idx, team in enumerate(all_teams):
+        col = team_cols[idx % len(team_cols)]
+        if f"handbuilder_team_{team}" not in st.session_state:
+            st.session_state[f"handbuilder_team_{team}"] = False
+        checked = col.toggle(team, value=st.session_state[f"handbuilder_team_{team}"], key=f"handbuilder_team_{team}")
+        if checked:
+            selected_teams.append(team)
+
+    # If no teams selected, show all teams
+    if selected_teams:
+        player_select_df = handbuild_roo[
+            handbuild_roo['Team'].isin(selected_teams)
+        ][['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+    else:
+        player_select_df = handbuild_roo[['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+    
+    # If no teams selected, show all teams
+    if pos_select3:
+        position_mask_2 = handbuild_roo['Position'].apply(lambda x: any(pos in x for pos in pos_select3))
+        player_select_df = player_select_df[position_mask_2][['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+    else:
+        player_select_df = player_select_df[['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own']].drop_duplicates(subset=['Player', 'Team']).copy()
+    
+    player_select_df = player_select_df[player_select_df['Salary'] <= salary_var]
+
+    
+    with st.expander("Quick Fill Options"):
+        auto_team_var = st.selectbox("Auto Fill Team", options=all_teams)
+        auto_size_var = st.selectbox("Auto Fill Size", options=[3, 4, 5])
+        auto_range_var = st.selectbox("Auto Fill Options", options=['QB/WR', 'RB/WR/TE', 'QB/WR/TE/RB'])
+        # --- QUICK FILL LOGIC ---
+        if st.button("Quick Fill", key="quick_fill"):
+            # 1. Get all eligible players from the selected team, not already in the lineup
+            current_players = set(st.session_state['handbuilder_lineup']['Player'])
+            team_players = player_select_df[
+                (player_select_df['Team'] == auto_team_var) &
+                (~player_select_df['Player'].isin(current_players))
+            ].copy()
+
+            # 2. Sort by Order
+            team_players = team_players.sort_values(by='Median', ascending=False)
+
+            # 3. Select the order range
+            if auto_range_var == 'QB/WR':
+                selected_players = team_players[team_players['Position'] == 'QB'].head(1)
+                selected_players = pd.concat([selected_players, team_players[team_players['Position'] == 'WR'].head(auto_size_var - 1)])
+                if len(selected_players) < auto_size_var:
+                    team_players = player_select_df[
+                        (player_select_df['Team'] == auto_team_var) &
+                        (~player_select_df['Player'].isin(current_players))
+                    ].copy()
+
+                    # 2. Sort by Order
+                    team_players = team_players.sort_values(by='Median', ascending=False)
+                    selected_players = team_players.head(auto_size_var + 1)
+            elif auto_range_var == 'QB/WR/TE':
+                selected_players = team_players[team_players['Position'] == 'QB'].head(1)
+                selected_players = pd.concat([selected_players, team_players[team_players['Position'].isin(['WR', 'TE'])].head(auto_size_var - 1)])
+                if len(selected_players) < auto_size_var:
+                    team_players = player_select_df[
+                        (player_select_df['Team'] == auto_team_var) &
+                        (~player_select_df['Player'].isin(current_players))
+                    ].copy()
+
+                    # 2. Sort by Order
+                    team_players = team_players.sort_values(by='Median', ascending=False)
+                    selected_players = team_players.head(auto_size_var + 1)
+            elif auto_range_var == 'QB/WR/TE/RB':
+                selected_players = team_players[team_players['Position'] == 'QB'].head(1)
+                selected_players = pd.concat([selected_players, team_players[team_players['Position'].isin(['RB', 'WR', 'TE'])].head(auto_size_var - 1)])
+                if len(selected_players) < auto_size_var:
+                    team_players = player_select_df[
+                        (player_select_df['Team'] == auto_team_var) &
+                        (~player_select_df['Player'].isin(current_players))
+                    ].copy()
+
+                    # 2. Sort by Order
+                    team_players = team_players.sort_values(by='Median', ascending=False)
+                    selected_players = team_players.head(auto_size_var + 1)
+            else:
+                selected_players = team_players.head(auto_size_var)
+
+            # 4. Add each player to the lineup, filling the first available eligible slot
+            for _, player_row in selected_players.iterrows():
+                eligible_positions = re.split(r'[/, ]+', player_row['Position'])
+                slot_to_fill = None
+
+                for slot in ['QB', 'RB', 'WR', 'TE', 'UTIL', 'DST']:
+                    if slot_counts.get(slot, 0) < position_limits.get(slot, 0):
+                        if slot == 'UTIL':
+                            if 'DST' not in eligible_positions and 'QB' not in eligible_positions:
+                                slot_to_fill = slot
+                                break
+                        elif slot in eligible_positions:
+                            slot_to_fill = slot
+                            break
+
+                if slot_to_fill is not None:
+                    # Avoid duplicates
+                    if player_row['Player'] not in st.session_state['handbuilder_lineup']['Player'].values:
+                        add_row = player_row.copy()
+                        add_row['Slot'] = slot_to_fill
+                        st.session_state['handbuilder_lineup'] = pd.concat(
+                            [st.session_state['handbuilder_lineup'], pd.DataFrame([add_row[[
+                                'Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own', 'Slot'
+                            ]]])],
+                            ignore_index=True
+                        )
+                        # Update slot_counts for next player
+                        slot_counts[slot_to_fill] = slot_counts.get(slot_to_fill, 0) + 1
+            st.rerun()
+
+    # --- FILTER OUT PLAYERS WHOSE ALL ELIGIBLE POSITIONS ARE FILLED ---
+    def is_player_eligible(row):
+        eligible_positions = re.split(r'[/, ]+', row['Position'])
+        # Player is eligible if at least one of their positions is not at max
+        for pos in eligible_positions:
+            if slot_counts.get(pos, 0) < position_limits.get(pos, 0):
+                return True
+        return False
+
+    # player_select_df = player_select_df[player_select_df.apply(is_player_eligible, axis=1)]
+    print(player_select_df.head(10))
+
+    col1, col2 = st.columns([1, 2])
+    with col2:
+        st.subheader("Player Select")
+        event = st.dataframe(
+            player_select_df.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Salary', 'Own']).format(precision=2),
+            on_select="rerun",
+            selection_mode=["single-row"],
+            key=f"handbuilder_select_{st.session_state['handbuilder_select_key']}",
+            height=500,
+            hide_index=True
+        )
+        # If a row is selected, add that player to the lineup and reset selection
+        if event and "rows" in event.selection and len(event.selection["rows"]) > 0:
+            idx = event.selection["rows"][0]
+            player_row = player_select_df.iloc[[idx]]
+            eligible_positions = re.split(r'[/, ]+', player_row['Position'].iloc[0])
+            # Find the first eligible slot that is not full
+            slot_to_fill = None
+
+            for slot in ['QB', 'RB', 'WR', 'TE', 'UTIL', 'DST']:
+                if slot_counts.get(slot, 0) < position_limits.get(slot, 0):
+                    if slot == 'UTIL':
+                        if 'DST' not in eligible_positions and 'QB' not in eligible_positions:
+                            slot_to_fill = slot
+                            break
+                    elif slot in eligible_positions:
+                        slot_to_fill = slot
+                        break
+
+            if slot_to_fill is not None:
+                # Avoid duplicates
+                if not player_row['Player'].iloc[0] in st.session_state['handbuilder_lineup']['Player'].values:
+                    # Add the slot info
+                    player_row = player_row.assign(Slot=slot_to_fill)
+                    st.session_state['handbuilder_lineup'] = pd.concat(
+                        [st.session_state['handbuilder_lineup'], player_row[['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own', 'Slot']]],
+                        ignore_index=True
+                    )
+                st.session_state['handbuilder_select_key'] += 1
+                st.rerun()
+
+
+    with col1:
+        st.subheader("Lineup Build")
+
+        # --- EXPLICIT LINEUP ORDER ---
+        if site_var == 'Draftkings':
+            lineup_slots = ['QB', 'RB', 'RB', 'WR', 'WR', 'WR', 'TE', 'UTIL', 'DST']
+        else:
+            lineup_slots = ['QB', 'RB', 'RB', 'WR', 'WR', 'WR', 'TE', 'UTIL', 'DST']
+        display_columns = ['Slot', 'Player', 'Position', 'Team', 'Salary', 'Median', 'Own']
+
+        filled_lineup = st.session_state['handbuilder_lineup']
+        display_rows = []
+        used_indices = set()
+        if not filled_lineup.empty:
+            for slot in lineup_slots:
+                match = filled_lineup[(filled_lineup['Slot'] == slot) & (~filled_lineup.index.isin(used_indices))]
+                if not match.empty:
+                    row = match.iloc[0]
+                    used_indices.add(match.index[0])
+                    display_rows.append({
+                        'Slot': slot,
+                        'Player': row['Player'],
+                        'Position': row['Position'],
+                        'Team': row['Team'],
+                        'Salary': row['Salary'],
+                        'Median': row['Median'],
+                        '2x%': row['2x%'],
+                        'Own': row['Own']
+                    })
+                else:
+                    display_rows.append({
+                        'Slot': slot,
+                        'Player': '',
+                        'Position': '',
+                        'Team': '',
+                        'Salary': np.nan,
+                        'Median': np.nan,
+                        '2x%': np.nan,
+                        'Own': np.nan
+                    })
+
+        lineup_display_df = pd.DataFrame(display_rows, columns=display_columns)
+
+        # Show the lineup table with single-row selection for removal
+        event_remove = st.dataframe(
+            lineup_display_df.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn', subset=['Median']).background_gradient(cmap='RdYlGn_r', subset=['Salary', 'Own']).format(precision=2),
+            on_select="rerun",
+            selection_mode=["single-row"],
+            key="lineup_remove",
+            height=445,
+            hide_index=True
+        )
+
+        # If a row is selected and not blank, remove that player from the lineup
+        if event_remove and "rows" in event_remove.selection and len(event_remove.selection["rows"]) > 0:
+            idx = event_remove.selection["rows"][0]
+            player_to_remove = lineup_display_df.iloc[idx]['Player']
+            slot_to_remove = lineup_display_df.iloc[idx]['Slot']
+            if player_to_remove:  # Only remove if not blank
+                st.session_state['handbuilder_lineup'] = filled_lineup[
+                    ~((filled_lineup['Player'] == player_to_remove) & (filled_lineup['Slot'] == slot_to_remove))
+                ]
+                st.rerun()
+
+        # --- SUMMARY ROW ---
+        if not filled_lineup.empty:
+            total_salary = filled_lineup['Salary'].sum()
+            total_median = filled_lineup['Median'].sum()
+            avg_2x = filled_lineup['2x%'].mean()
+            total_own = filled_lineup['Own'].sum()
+            most_common_team = filled_lineup['Team'].mode()[0] if not filled_lineup['Team'].mode().empty else ""
+
+            summary_row = pd.DataFrame({
+                'Slot': [''],
+                'Player': ['TOTAL'],
+                'Position': [''],
+                'Team': [most_common_team],
+                'Salary': [total_salary],
+                'Median': [total_median],
+                '2x%': [avg_2x],
+                'Own': [total_own]
+            })
+            summary_row = summary_row[['Salary', 'Median', 'Own']].head(max_players)
+            
+            col1, col3 = st.columns([2, 3])
+
+            with col1:
+                if (max_players - len(filled_lineup)) > 0:
+                    st.markdown(f"""
+                            <div style='text-align: left; vertical-align: top; margin-top: 0; padding-top: 0;''>
+                                <b>💰 Per Player:</b> ${round((max_salary - total_salary) / (max_players - len(filled_lineup)), 0)} &nbsp;
+                            </div>
+                            """,
+                            unsafe_allow_html=True)
+                else:
+                    st.markdown(f"""
+                            <div style='text-align: left; vertical-align: top; margin-top: 0; padding-top: 0;''>
+                                <b>💰 Leftover:</b> ${round(max_salary - total_salary, 0)} &nbsp;
+                            </div>
+                            """,
+                            unsafe_allow_html=True)
+
+            with col3:
+                if total_salary <= max_salary:
+                    st.markdown(
+                        f"""
+                        <div style='text-align: right; vertical-align: top; margin-top: 0; padding-top: 0;''>
+                            <b>💰 Salary:</b> ${round(total_salary, 0)} &nbsp;
+                            <b>🔥 Median:</b> {round(total_median, 2)} &nbsp;
+                        </div>
+                        """,
+                        unsafe_allow_html=True
+                    )
+                else:
+                    st.markdown(
+                        f"""
+                        <div style='text-align: right; vertical-align: top; margin-top: 0; padding-top: 0;''>
+                            <b>❌ Salary:</b> ${round(total_salary, 0)} &nbsp;
+                            <b>🔥 Median:</b> {round(total_median, 2)} &nbsp;
+                        </div>
+                        """,
+                        unsafe_allow_html=True
+                    )
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    # Optionally, add a button to clear the lineup
+    clear_col, save_col, export_col, clear_saved_col, blank_col = st.columns([2, 2, 2, 2, 12])
+    with clear_col:
+        if st.button("Clear Lineup", key='clear_lineup'):
+            st.session_state['handbuilder_lineup'] = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Median', '2x%', 'Own', 'Slot'])
+            st.rerun()
+    with save_col:
+        if st.button("Save Lineup", key='save_lineup'):
+            if 'saved_lineups' in st.session_state:
+                st.session_state['saved_lineups'].append(lineup_display_df['Player'].tolist())
+                print(st.session_state['saved_lineups'])
+            else:
+                st.session_state['saved_lineups'] = [lineup_display_df['Player'].tolist()]
+                print(st.session_state['saved_lineups'])
+            st.rerun()
+    with export_col:
+        if 'saved_lineups' in st.session_state and st.session_state['saved_lineups']:
+            # Convert list of lists to numpy array
+            saved_lineups_array = np.array(st.session_state['saved_lineups'])
+            st.download_button(
+                label="Export Handbuilds",
+                data=convert_hb_df(saved_lineups_array, dk_hb_columns if site_var == 'Draftkings' else fd_hb_columns),
+                file_name='handbuilds_export.csv',
+                mime='text/csv',
+            )
+        else:
+            st.write("No saved lineups to export")
+    
+    if 'saved_lineups' in st.session_state:
+        st.table(pd.DataFrame(st.session_state['saved_lineups'], columns=dk_hb_columns if site_var == 'Draftkings' else fd_hb_columns))
+    else:
+        st.write("No saved lineups")
+    
+    with clear_saved_col:
+        if st.button("Clear Saved", key='clear_saved'):
+            if 'saved_lineups' in st.session_state:
+                del st.session_state['saved_lineups']
+            st.rerun()