Add initial Streamlit application with data loading, portfolio management, and optimization features

app.py

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+import streamlit as st
+st.set_page_config(layout="wide")
+import numpy as np
+import pandas as pd
+import time
+from fuzzywuzzy import process
+import random
+
+## import global functions
+from global_func.clean_player_name import clean_player_name
+from global_func.load_file import load_file
+from global_func.load_ss_file import load_ss_file
+from global_func.find_name_mismatches import find_name_mismatches
+from global_func.predict_dupes import predict_dupes
+from global_func.highlight_rows import highlight_changes, highlight_changes_winners, highlight_changes_losers
+from global_func.load_csv import load_csv
+from global_func.find_csv_mismatches import find_csv_mismatches
+
+freq_format = {'Finish_percentile': '{:.2%}', 'Lineup Edge': '{:.2%}', 'Win%': '{:.2%}'}
+player_wrong_names_mlb = ['Enrique Hernandez']
+player_right_names_mlb = ['Kike Hernandez']
+
+tab1, tab2, tab3 = st.tabs(["Data Load", "Late Swap", "Manage Portfolio"])
+with tab1:
+    if st.button('Clear data', key='reset1'):
+        st.session_state.clear()
+    # Add file uploaders to your app
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.subheader("Draftkings/Fanduel CSV")
+        st.info("Upload the player pricing CSV from the site you are playing on. This is used in late swap exporting and/or with SaberSim portfolios, but is not necessary for the portfolio management functions.")
+
+        upload_csv_col, csv_template_col = st.columns([3, 1])
+        with upload_csv_col:
+            csv_file = st.file_uploader("Upload CSV File", type=['csv'])
+            if 'csv_file' in st.session_state:
+                del st.session_state['csv_file']
+        with csv_template_col:
+
+            csv_template_df = pd.DataFrame(columns=['Name', 'ID', 'Roster Position', 'Salary'])
+
+            st.download_button(
+                label="CSV Template",
+                data=csv_template_df.to_csv(index=False),
+                file_name="csv_template.csv",
+                mime="text/csv"
+            )
+        st.session_state['csv_file'] = load_csv(csv_file)
+        try:
+            st.session_state['csv_file']['Salary'] = st.session_state['csv_file']['Salary'].astype(str).str.replace(',', '').astype(int)
+        except:
+            pass
+            
+        if csv_file:
+            st.session_state['csv_file'] = st.session_state['csv_file'].drop_duplicates(subset=['Name'])
+            st.success('Projections file loaded successfully!')
+            st.dataframe(st.session_state['csv_file'].head(10))
+    
+    with col2:
+        st.subheader("Portfolio File")
+        st.info("Go ahead and upload a portfolio file here. Only include player columns and an optional 'Stack' column if you are playing MLB.")
+        saber_toggle = st.radio("Are you uploading from SaberSim?", options=['No', 'Yes'])
+        st.info("If you are uploading from SaberSim, you will need to upload a CSV file for the slate for name matching.")
+        if saber_toggle == 'Yes':
+            if csv_file is not None:
+                portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
+                if 'portfolio' in st.session_state:
+                    del st.session_state['portfolio']
+                if 'export_portfolio' in st.session_state:
+                    del st.session_state['export_portfolio']
+
+        else:
+            portfolio_file = st.file_uploader("Upload Portfolio File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
+            if 'portfolio' in st.session_state:
+                del st.session_state['portfolio']
+            if 'export_portfolio' in st.session_state:
+                del st.session_state['export_portfolio']
+
+        if portfolio_file:
+            if saber_toggle == 'Yes':
+                st.session_state['export_portfolio'], st.session_state['portfolio'] = load_ss_file(portfolio_file, st.session_state['csv_file'])
+                st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
+                st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
+                st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
+                st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
+            else:
+                st.session_state['export_portfolio'], st.session_state['portfolio'] = load_file(portfolio_file)
+                st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
+                st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
+                st.session_state['portfolio'] = st.session_state['portfolio'].dropna(how='all')
+                st.session_state['portfolio'] = st.session_state['portfolio'].reset_index(drop=True)
+            # Check if Stack column exists in the portfolio
+            if 'Stack' in st.session_state['portfolio'].columns:
+                # Create dictionary mapping index to Stack values
+                stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
+                st.write(f"Found {len(stack_dict)} stack assignments")
+                st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['Stack'])
+            else:
+                stack_dict = None
+                st.info("No Stack column found in portfolio")
+            if st.session_state['portfolio'] is not None:
+                st.success('Portfolio file loaded successfully!')
+                st.session_state['portfolio'] = st.session_state['portfolio'].apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
+                st.dataframe(st.session_state['portfolio'].head(10))
+
+    with col3:
+        st.subheader("Projections File")
+        st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' (Needed for Showdown) columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.")
+        
+        # Create two columns for the uploader and template button
+        upload_col, template_col = st.columns([3, 1])
+        
+        with upload_col:
+            projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
+            if 'projections_df' in st.session_state:
+                del st.session_state['projections_df']
+        
+        with template_col:
+            # Create empty DataFrame with required columns
+            template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership'])
+            # Add download button for template
+            st.download_button(
+                label="Template",
+                data=template_df.to_csv(index=False),
+                file_name="projections_template.csv",
+                mime="text/csv"
+            )
+            
+        if projections_file:
+            export_projections, projections = load_file(projections_file)
+            if projections is not None:
+                st.success('Projections file loaded successfully!')
+                projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
+                st.dataframe(projections.head(10))
+
+    if portfolio_file and projections_file:
+        if st.session_state['portfolio'] is not None and projections is not None:
+            st.subheader("Name Matching Analysis")
+            # Initialize projections_df in session state if it doesn't exist
+            if 'projections_df' not in st.session_state:
+                st.session_state['projections_df'] = projections.copy()
+                st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int))
+            
+            # Update projections_df with any new matches
+            st.session_state['projections_df'] = find_name_mismatches(st.session_state['portfolio'], st.session_state['projections_df'])
+            if csv_file is not None and 'export_dict' not in st.session_state:
+                    # Create a dictionary of Name to Name+ID from csv_file
+                    try:
+                        name_id_map = dict(zip(
+                            st.session_state['csv_file']['Name'], 
+                            st.session_state['csv_file']['Name + ID']
+                        ))
+                    except:
+                        name_id_map = dict(zip(
+                            st.session_state['csv_file']['Nickname'], 
+                            st.session_state['csv_file']['Id']
+                        ))
+                    
+                    # Function to find best match
+                    def find_best_match(name):
+                        best_match = process.extractOne(name, name_id_map.keys())
+                        if best_match and best_match[1] >= 85:  # 85% match threshold
+                            return name_id_map[best_match[0]]
+                        return name  # Return original name if no good match found
+                    
+                    # Apply the matching
+                    projections['upload_match'] = projections['player_names'].apply(find_best_match)
+                    st.session_state['export_dict'] = dict(zip(projections['player_names'], projections['upload_match']))
+
+with tab2:
+    if st.button('Clear data', key='reset2'):
+        st.session_state.clear()
+    
+    if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
+
+        optimized_df = None
+
+        map_dict = {
+                    'pos_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                    st.session_state['projections_df']['position'])),
+                    'salary_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['salary'])),
+                    'proj_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                    st.session_state['projections_df']['median'])),
+                    'own_map': dict(zip(st.session_state['projections_df']['player_names'],
+                                    st.session_state['projections_df']['ownership'])),
+                    'team_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['team']))
+                }
+        # Calculate new stats for optimized lineups
+        st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+            lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
+        )
+        st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+            lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
+        )
+
+        st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+            lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
+        )
+
+        options_container = st.container()
+        with options_container:
+            col1, col2, col3, col4, col5, col6 = st.columns(6)
+            with col1:
+                curr_site_var = st.selectbox("Select your current site", options=['DraftKings', 'FanDuel'])
+            with col2:
+                curr_sport_var = st.selectbox("Select your current sport", options=['NBA', 'MLB', 'NFL', 'NHL', 'MMA'])
+            with col3:
+                swap_var = st.multiselect("Select late swap strategy", options=['Optimize', 'Increase volatility', 'Decrease volatility'])
+            with col4:
+                remove_teams_var = st.multiselect("What teams have already played?", options=st.session_state['projections_df']['team'].unique())
+            with col5:
+                winners_var = st.multiselect("Are there any players doing exceptionally well?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
+            with col6:
+                losers_var = st.multiselect("Are there any players doing exceptionally poorly?", options=st.session_state['projections_df']['player_names'].unique(), max_selections=3)
+        if st.button('Clear Late Swap'):
+            if 'optimized_df' in st.session_state:
+                del st.session_state['optimized_df']
+
+            map_dict = {
+                        'pos_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['position'])),
+                        'salary_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                            st.session_state['projections_df']['salary'])),
+                        'proj_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['median'])),
+                        'own_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['ownership'])),
+                        'team_map': dict(zip(st.session_state['projections_df']['player_names'], 
+                                        st.session_state['projections_df']['team']))
+                    }
+            # Calculate new stats for optimized lineups
+            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+                lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
+            )
+            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+                lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
+            )
+            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+                lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
+            )
+
+        if st.button('Run Late Swap'):
+            st.session_state['portfolio'] = st.session_state['portfolio'].drop(columns=['salary', 'median', 'Own'])
+            if curr_sport_var == 'NBA':
+                if curr_site_var == 'DraftKings':
+                    st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL'], axis=1)
+                else:
+                    st.session_state['portfolio'] = st.session_state['portfolio'].set_axis(['PG', 'PG', 'SG', 'SG', 'SF', 'SF', 'PF', 'PF', 'C'], axis=1)
+            
+            # Define roster position rules
+            if curr_site_var == 'DraftKings':
+                position_rules = {
+                    'PG': ['PG'],
+                    'SG': ['SG'],
+                    'SF': ['SF'],
+                    'PF': ['PF'],
+                    'C': ['C'],
+                    'G': ['PG', 'SG'],
+                    'F': ['SF', 'PF'],
+                    'UTIL': ['PG', 'SG', 'SF', 'PF', 'C']
+                    }
+            else:
+                position_rules = {
+                    'PG': ['PG'],
+                    'SG': ['SG'],
+                    'SF': ['SF'],
+                    'PF': ['PF'],
+                    'C': ['C'],
+                }
+            # Create position groups from projections data
+            position_groups = {}
+            for _, player in st.session_state['projections_df'].iterrows():
+                positions = player['position'].split('/')
+                for pos in positions:
+                    if pos not in position_groups:
+                        position_groups[pos] = []
+                    position_groups[pos].append({
+                        'player_names': player['player_names'],
+                        'salary': player['salary'],
+                        'median': player['median'],
+                        'ownership': player['ownership'],
+                        'positions': positions  # Store all eligible positions
+                    })
+
+            def optimize_lineup(row):
+                current_lineup = []
+                total_salary = 0
+                if curr_site_var == 'DraftKings':
+                    salary_cap = 50000
+                else:
+                    salary_cap = 60000
+                used_players = set()
+
+                # Convert row to dictionary with roster positions
+                roster = {}
+                for col, player in zip(row.index, row):
+                    if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
+                        roster[col] = {
+                            'name': player,
+                            'position': map_dict['pos_map'].get(player, '').split('/'),
+                            'team': map_dict['team_map'].get(player, ''),
+                            'salary': map_dict['salary_map'].get(player, 0),
+                            'median': map_dict['proj_map'].get(player, 0),
+                            'ownership': map_dict['own_map'].get(player, 0)
+                        }
+                        total_salary += roster[col]['salary']
+                        used_players.add(player)
+
+                # Optimize each roster position in random order
+                roster_positions = list(roster.items())
+                random.shuffle(roster_positions)
+                
+                for roster_pos, current in roster_positions:
+                    # Skip optimization for players from removed teams
+                    if current['team'] in remove_teams_var:
+                        continue
+                    
+                    valid_positions = position_rules[roster_pos]
+                    better_options = []
+
+                    # Find valid replacements for this roster position
+                    for pos in valid_positions:
+                        if pos in position_groups:
+                            pos_options = [
+                                p for p in position_groups[pos]
+                                if p['median'] > current['median']
+                                and (total_salary - current['salary'] + p['salary']) <= salary_cap
+                                and p['player_names'] not in used_players
+                                and any(valid_pos in p['positions'] for valid_pos in valid_positions)
+                                and map_dict['team_map'].get(p['player_names']) not in remove_teams_var  # Check team restriction
+                            ]
+                            better_options.extend(pos_options)
+
+                    if better_options:
+                        # Remove duplicates
+                        better_options = {opt['player_names']: opt for opt in better_options}.values()
+                        
+                        # Sort by median projection and take the best one
+                        best_replacement = max(better_options, key=lambda x: x['median'])
+                        
+                        # Update the lineup and tracking variables
+                        used_players.remove(current['name'])
+                        used_players.add(best_replacement['player_names'])
+                        total_salary = total_salary - current['salary'] + best_replacement['salary']
+                        roster[roster_pos] = {
+                            'name': best_replacement['player_names'],
+                            'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
+                            'team': map_dict['team_map'][best_replacement['player_names']],
+                            'salary': best_replacement['salary'],
+                            'median': best_replacement['median'],
+                            'ownership': best_replacement['ownership']
+                        }
+
+                # Return optimized lineup maintaining original column order
+                return [roster[pos]['name'] for pos in row.index if pos in roster]
+
+            def optimize_lineup_winners(row):
+                current_lineup = []
+                total_salary = 0
+                if curr_site_var == 'DraftKings':
+                    salary_cap = 50000
+                else:
+                    salary_cap = 60000
+                used_players = set()
+
+                # Check if any winners are in the lineup and count them
+                winners_in_lineup = sum(1 for player in row if player in winners_var)
+                changes_needed = min(winners_in_lineup, 3) if winners_in_lineup > 0 else 0
+                changes_made = 0
+
+                # Convert row to dictionary with roster positions
+                roster = {}
+                for col, player in zip(row.index, row):
+                    if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
+                        roster[col] = {
+                            'name': player,
+                            'position': map_dict['pos_map'].get(player, '').split('/'),
+                            'team': map_dict['team_map'].get(player, ''),
+                            'salary': map_dict['salary_map'].get(player, 0),
+                            'median': map_dict['proj_map'].get(player, 0),
+                            'ownership': map_dict['own_map'].get(player, 0)
+                        }
+                        total_salary += roster[col]['salary']
+                        used_players.add(player)
+
+                # Only proceed with ownership-based optimization if we have winners in the lineup
+                if changes_needed > 0:
+                    # Randomize the order of positions to optimize
+                    roster_positions = list(roster.items())
+                    random.shuffle(roster_positions)
+                    
+                    for roster_pos, current in roster_positions:
+                        # Stop if we've made enough changes
+                        if changes_made >= changes_needed:
+                            break
+                            
+                        # Skip optimization for players from removed teams or if the current player is a winner
+                        if current['team'] in remove_teams_var or current['name'] in winners_var:
+                            continue
+                        
+                        valid_positions = list(position_rules[roster_pos])
+                        random.shuffle(valid_positions)
+                        better_options = []
+
+                        # Find valid replacements with higher ownership
+                        for pos in valid_positions:
+                            if pos in position_groups:
+                                pos_options = [
+                                    p for p in position_groups[pos]
+                                    if p['ownership'] > current['ownership']
+                                    and p['median'] >= current['median'] - 3
+                                    and (total_salary - current['salary'] + p['salary']) <= salary_cap
+                                    and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
+                                    and p['player_names'] not in used_players
+                                    and any(valid_pos in p['positions'] for valid_pos in valid_positions)
+                                    and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
+                                ]
+                                better_options.extend(pos_options)
+
+                        if better_options:
+                            # Remove duplicates
+                            better_options = {opt['player_names']: opt for opt in better_options}.values()
+                            
+                            # Sort by ownership and take the highest owned option
+                            best_replacement = max(better_options, key=lambda x: x['ownership'])
+                            
+                            # Update the lineup and tracking variables
+                            used_players.remove(current['name'])
+                            used_players.add(best_replacement['player_names'])
+                            total_salary = total_salary - current['salary'] + best_replacement['salary']
+                            roster[roster_pos] = {
+                                'name': best_replacement['player_names'],
+                                'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
+                                'team': map_dict['team_map'][best_replacement['player_names']],
+                                'salary': best_replacement['salary'],
+                                'median': best_replacement['median'],
+                                'ownership': best_replacement['ownership']
+                            }
+                            changes_made += 1
+
+                # Return optimized lineup maintaining original column order
+                return [roster[pos]['name'] for pos in row.index if pos in roster]
+            
+            def optimize_lineup_losers(row):
+                current_lineup = []
+                total_salary = 0
+                if curr_site_var == 'DraftKings':
+                    salary_cap = 50000
+                else:
+                    salary_cap = 60000
+                used_players = set()
+
+                # Check if any winners are in the lineup and count them
+                losers_in_lineup = sum(1 for player in row if player in losers_var)
+                changes_needed = min(losers_in_lineup, 3) if losers_in_lineup > 0 else 0
+                changes_made = 0
+
+                # Convert row to dictionary with roster positions
+                roster = {}
+                for col, player in zip(row.index, row):
+                    if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Lineup Edge']:
+                        roster[col] = {
+                            'name': player,
+                            'position': map_dict['pos_map'].get(player, '').split('/'),
+                            'team': map_dict['team_map'].get(player, ''),
+                            'salary': map_dict['salary_map'].get(player, 0),
+                            'median': map_dict['proj_map'].get(player, 0),
+                            'ownership': map_dict['own_map'].get(player, 0)
+                        }
+                        total_salary += roster[col]['salary']
+                        used_players.add(player)
+
+                # Only proceed with ownership-based optimization if we have winners in the lineup
+                if changes_needed > 0:
+                    # Randomize the order of positions to optimize
+                    roster_positions = list(roster.items())
+                    random.shuffle(roster_positions)
+                    
+                    for roster_pos, current in roster_positions:
+                        # Stop if we've made enough changes
+                        if changes_made >= changes_needed:
+                            break
+                            
+                        # Skip optimization for players from removed teams or if the current player is a winner
+                        if current['team'] in remove_teams_var or current['name'] in losers_var:
+                            continue
+                        
+                        valid_positions = list(position_rules[roster_pos])
+                        random.shuffle(valid_positions)
+                        better_options = []
+
+                        # Find valid replacements with higher ownership
+                        for pos in valid_positions:
+                            if pos in position_groups:
+                                pos_options = [
+                                    p for p in position_groups[pos]
+                                    if p['ownership'] < current['ownership']
+                                    and p['median'] >= current['median'] - 3
+                                    and (total_salary - current['salary'] + p['salary']) <= salary_cap
+                                    and (total_salary - current['salary'] + p['salary']) >= salary_cap - 1000
+                                    and p['player_names'] not in used_players
+                                    and any(valid_pos in p['positions'] for valid_pos in valid_positions)
+                                    and map_dict['team_map'].get(p['player_names']) not in remove_teams_var
+                                ]
+                                better_options.extend(pos_options)
+
+                        if better_options:
+                            # Remove duplicates
+                            better_options = {opt['player_names']: opt for opt in better_options}.values()
+                            
+                            # Sort by ownership and take the highest owned option
+                            best_replacement = max(better_options, key=lambda x: x['ownership'])
+                            
+                            # Update the lineup and tracking variables
+                            used_players.remove(current['name'])
+                            used_players.add(best_replacement['player_names'])
+                            total_salary = total_salary - current['salary'] + best_replacement['salary']
+                            roster[roster_pos] = {
+                                'name': best_replacement['player_names'],
+                                'position': map_dict['pos_map'][best_replacement['player_names']].split('/'),
+                                'team': map_dict['team_map'][best_replacement['player_names']],
+                                'salary': best_replacement['salary'],
+                                'median': best_replacement['median'],
+                                'ownership': best_replacement['ownership']
+                            }
+                            changes_made += 1
+
+                # Return optimized lineup maintaining original column order
+                return [roster[pos]['name'] for pos in row.index if pos in roster]
+
+            # Create a progress bar
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+            
+            # Process each lineup
+            optimized_lineups = []
+            total_lineups = len(st.session_state['portfolio'])
+            
+            for idx, row in st.session_state['portfolio'].iterrows():
+                # First optimization pass
+                first_pass = optimize_lineup(row)
+                first_pass_series = pd.Series(first_pass, index=row.index)
+
+                second_pass = optimize_lineup(first_pass_series)
+                second_pass_series = pd.Series(second_pass, index=row.index)
+
+                third_pass = optimize_lineup(second_pass_series)
+                third_pass_series = pd.Series(third_pass, index=row.index)
+
+                fourth_pass = optimize_lineup(third_pass_series)
+                fourth_pass_series = pd.Series(fourth_pass, index=row.index)
+
+                fifth_pass = optimize_lineup(fourth_pass_series)
+                fifth_pass_series = pd.Series(fifth_pass, index=row.index)
+                
+                # Second optimization pass
+                final_lineup = optimize_lineup(fifth_pass_series)
+                optimized_lineups.append(final_lineup)
+                
+                if 'Optimize' in swap_var:
+                    progress = (idx + 1) / total_lineups
+                    progress_bar.progress(progress)
+                    status_text.text(f'Optimizing Lineups {idx + 1} of {total_lineups}')
+                else:
+                    pass
+            
+            # Create new dataframe with optimized lineups
+            if 'Optimize' in swap_var:
+                st.session_state['optimized_df_medians'] = pd.DataFrame(optimized_lineups, columns=st.session_state['portfolio'].columns)
+            else:
+                st.session_state['optimized_df_medians'] = st.session_state['portfolio']
+
+            # Create a progress bar
+            progress_bar_winners = st.progress(0)
+            status_text_winners = st.empty()
+            
+            # Process each lineup
+            optimized_lineups_winners = []
+            total_lineups = len(st.session_state['optimized_df_medians'])
+            
+            for idx, row in st.session_state['optimized_df_medians'].iterrows():
+
+                final_lineup = optimize_lineup_winners(row)
+                optimized_lineups_winners.append(final_lineup)
+                
+                if 'Decrease volatility' in swap_var:
+                    progress_winners = (idx + 1) / total_lineups
+                    progress_bar_winners.progress(progress_winners)
+                    status_text_winners.text(f'Lowering Volatility around Winners {idx + 1} of {total_lineups}')
+                else:
+                    pass
+            
+            # Create new dataframe with optimized lineups
+            if 'Decrease volatility' in swap_var:
+                st.session_state['optimized_df_winners'] = pd.DataFrame(optimized_lineups_winners, columns=st.session_state['optimized_df_medians'].columns)
+            else:
+                st.session_state['optimized_df_winners'] = st.session_state['optimized_df_medians']
+
+            # Create a progress bar
+            progress_bar_losers = st.progress(0)
+            status_text_losers = st.empty()
+            
+            # Process each lineup
+            optimized_lineups_losers = []
+            total_lineups = len(st.session_state['optimized_df_winners'])
+            
+            for idx, row in st.session_state['optimized_df_winners'].iterrows():
+
+                final_lineup = optimize_lineup_losers(row)
+                optimized_lineups_losers.append(final_lineup)
+                
+                if 'Increase volatility' in swap_var:
+                    progress_losers = (idx + 1) / total_lineups
+                    progress_bar_losers.progress(progress_losers)
+                    status_text_losers.text(f'Increasing Volatility around Losers {idx + 1} of {total_lineups}')
+                else:
+                    pass
+            
+            # Create new dataframe with optimized lineups
+            if 'Increase volatility' in swap_var:
+                st.session_state['optimized_df'] = pd.DataFrame(optimized_lineups_losers, columns=st.session_state['optimized_df_winners'].columns)
+            else:
+                st.session_state['optimized_df'] = st.session_state['optimized_df_winners']
+            
+            # Calculate new stats for optimized lineups
+            st.session_state['optimized_df']['salary'] = st.session_state['optimized_df'].apply(
+                lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row if player in map_dict['salary_map']), axis=1
+            )
+            st.session_state['optimized_df']['median'] = st.session_state['optimized_df'].apply(
+                lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row if player in map_dict['proj_map']), axis=1
+            )
+            st.session_state['optimized_df']['Own'] = st.session_state['optimized_df'].apply(
+                lambda row: sum(map_dict['own_map'].get(player, 0) for player in row if player in map_dict['own_map']), axis=1
+            )
+
+            # Display results
+            st.success('Optimization complete!')
+
+        if 'optimized_df' in st.session_state:
+            st.write("Increase in median highlighted in yellow, descrease in volatility highlighted in blue, increase in volatility highlighted in red:")
+            st.dataframe(
+                st.session_state['optimized_df'].style
+                .apply(highlight_changes, axis=1)
+                .apply(highlight_changes_winners, axis=1)
+                .apply(highlight_changes_losers, axis=1)
+                .background_gradient(axis=0)
+                .background_gradient(cmap='RdYlGn')
+                .format(precision=2),
+                height=1000,
+                use_container_width=True
+            )
+            
+            # Option to download optimized lineups
+            if st.button('Prepare Late Swap Export'):
+                export_df = st.session_state['optimized_df'].copy()
+                
+                # Map player names to their export IDs for all player columns
+                for col in export_df.columns:
+                    if col not in ['salary', 'median', 'Own']:
+                        export_df[col] = export_df[col].map(st.session_state['export_dict'])
+                
+                csv = export_df.to_csv(index=False)
+                st.download_button(
+                    label="Download CSV",
+                    data=csv,
+                    file_name="optimized_lineups.csv",
+                    mime="text/csv"
+                )
+        else:
+            st.write("Current Portfolio")
+            st.dataframe(
+                st.session_state['portfolio'].style
+                .background_gradient(axis=0)
+                .background_gradient(cmap='RdYlGn')
+                .format(precision=2),
+                height=1000,
+                use_container_width=True
+            )
+
+with tab3:
+    if st.button('Clear data', key='reset3'):
+        st.session_state.clear()
+    if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
+        col1, col2, col3 = st.columns([1, 8, 1])
+        excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Win%', 'Lineup Edge']
+        with col1:
+            site_var = st.selectbox("Select Site", ['Draftkings', 'Fanduel'])
+            sport_var = st.selectbox("Select Sport", ['NFL', 'MLB', 'NBA', 'NHL', 'MMA'])
+            st.info("It currently does not matter what sport you select, it may matter in the future.")
+            type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
+            Contest_Size = st.number_input("Enter Contest Size", value=25000, min_value=1, step=1)
+            strength_var = st.selectbox("Select field strength", ['Average', 'Sharp', 'Weak'])
+        if site_var == 'Draftkings':
+            if type_var == 'Classic':
+                map_dict = {
+                    'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
+                    'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
+                    'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                    'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
+                    'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
+                    'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
+                    'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                    'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
+                    'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
+                }
+            elif type_var == 'Showdown':
+                if sport_var == 'NFL':
+                    map_dict = {
+                        'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
+                        'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
+                        'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                        'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
+                        'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
+                        'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
+                        'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] * 1.5)),
+                        'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
+                        'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
+                    }
+                elif sport_var != 'NFL':
+                    map_dict = {
+                        'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
+                        'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
+                        'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'] / 1.5)),
+                        'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
+                        'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
+                        'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
+                        'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                        'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
+                        'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
+                    }
+        elif site_var == 'Fanduel':
+            map_dict = {
+                'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
+                'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
+                'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
+                'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
+                'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
+                'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+                'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
+                'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
+            }
+        
+        if type_var == 'Classic':
+            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+            if stack_dict is not None:
+                st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
+        elif type_var == 'Showdown':
+            # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            
+            # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            
+            # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+        with col3:
+            with st.form(key='filter_form'):
+                max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
+                min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
+                max_salary = st.number_input("Max acceptable salary?", value=60000, min_value=1000, step=100)
+                max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
+                player_names = set()
+                for col in st.session_state['portfolio'].columns:
+                    if col not in excluded_cols:
+                        player_names.update(st.session_state['portfolio'][col].unique())
+                player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[])
+                player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
+                if stack_dict is not None:
+                    stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
+                    stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
+                    stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
+                
+                submitted = st.form_submit_button("Submit")
+
+        with col2:
+            st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var)
+            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
+            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
+            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
+            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
+            if stack_dict is not None:
+                if stack_toggle == 'All Stacks':
+                    st.session_state['portfolio'] = st.session_state['portfolio']
+                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+                else:
+                    st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
+                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+            if player_remove:
+                # Create mask for lineups that contain any of the removed players
+                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                remove_mask = st.session_state['portfolio'][player_columns].apply(
+                    lambda row: not any(player in list(row) for player in player_remove), axis=1
+                )
+                st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
+            
+            if player_lock:
+                # Create mask for lineups that contain all locked players
+                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                
+                lock_mask = st.session_state['portfolio'][player_columns].apply(
+                    lambda row: all(player in list(row) for player in player_lock), axis=1
+                )
+                st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
+            export_file = st.session_state['portfolio'].copy()
+            st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
+            if csv_file is not None:
+                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                for col in player_columns:
+                    export_file[col] = export_file[col].map(st.session_state['export_dict'])
+            with st.expander("Download options"):
+                if stack_dict is not None:
+                    with st.form(key='stack_form'):
+                        st.subheader("Stack Count Adjustments")
+                        st.info("This allows you to fine tune the stacks that you wish to export. If you want to make sure you don't export any of a specific stack you can 0 it out.")
+                        # Create a container for stack value inputs
+                        sort_container = st.container()
+                        with sort_container:
+                            sort_var = st.selectbox("Sort export portfolio by:", options=['median', 'Lineup Edge', 'Own'])
+                        
+                        # Get unique stack values
+                        unique_stacks = sorted(list(set(stack_dict.values())))
+                        
+                        # Create a dictionary to store stack multipliers
+                        if 'stack_multipliers' not in st.session_state:
+                            st.session_state.stack_multipliers = {stack: 0.0 for stack in unique_stacks}
+                        
+                        # Create columns for the stack inputs
+                        num_cols = 6  # Number of columns to display
+                        for i in range(0, len(unique_stacks), num_cols):
+                            cols = st.columns(num_cols)
+                            for j, stack in enumerate(unique_stacks[i:i+num_cols]):
+                                with cols[j]:
+                                    # Create a unique key for each number input
+                                    key = f"stack_count_{stack}"
+                                    # Get the current count of this stack in the portfolio
+                                    current_stack_count = len(st.session_state['portfolio'][st.session_state['portfolio']['Stack'] == stack])
+                                    # Create number input with current value and max value based on actual count
+                                    st.session_state.stack_multipliers[stack] = st.number_input(
+                                        f"{stack} count",
+                                        min_value=0.0,
+                                        max_value=float(current_stack_count),
+                                        value=0.0,
+                                        step=1.0,
+                                        key=key
+                                    )
+                        
+                        # Create a copy of the portfolio
+                        portfolio_copy = st.session_state['portfolio'].copy()
+                        
+                        # Create a list to store selected rows
+                        selected_rows = []
+                        
+                        # For each stack, select the top N rows based on the count value
+                        for stack in unique_stacks:
+                            if stack in st.session_state.stack_multipliers:
+                                count = int(st.session_state.stack_multipliers[stack])
+                                # Get rows for this stack
+                                stack_rows = portfolio_copy[portfolio_copy['Stack'] == stack]
+                                # Sort by median and take top N rows
+                                top_rows = stack_rows.nlargest(count, sort_var)
+                                selected_rows.append(top_rows)
+                        
+                        # Combine all selected rows
+                        portfolio_copy = pd.concat(selected_rows)
+                        
+                        # Update export_file with filtered data
+                        export_file = portfolio_copy.copy()
+                        for col in export_file.columns:
+                            if col not in excluded_cols:
+                                export_file[col] = export_file[col].map(st.session_state['export_dict'])
+                        
+                        submitted = st.form_submit_button("Submit")
+                        if submitted:
+                            st.write('Export portfolio updated!')
+                
+            st.download_button(label="Download Portfolio", data=export_file.to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
+            # Display the paginated dataframe first
+            st.dataframe(
+                st.session_state['portfolio'].style
+                .background_gradient(axis=0)
+                .background_gradient(cmap='RdYlGn')
+                .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
+                .format(freq_format, precision=2), 
+                height=1000,
+                use_container_width=True
+            )
+
+            # Add pagination controls below the dataframe
+            total_rows = len(st.session_state['portfolio'])
+            rows_per_page = 500
+            total_pages = (total_rows + rows_per_page - 1) // rows_per_page  # Ceiling division
+
+            # Initialize page number in session state if not exists
+            if 'current_page' not in st.session_state:
+                st.session_state.current_page = 1
+
+            # Display current page range info and pagination control in a single line
+            st.write(
+                f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} "
+                f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}"
+            )
+            
+            # Add page number input
+            st.session_state.current_page = st.number_input(
+                f"Page (1-{total_pages})", 
+                min_value=1, 
+                max_value=total_pages,
+                value=st.session_state.current_page
+            )
+
+            # Calculate start and end indices for current page
+            start_idx = (st.session_state.current_page - 1) * rows_per_page
+            end_idx = min(start_idx + rows_per_page, total_rows)
+
+            # Get the subset of data for the current page
+            current_page_data = st.session_state['portfolio'].iloc[start_idx:end_idx]
+            
+            # Create player summary dataframe
+            player_stats = []
+            player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+            
+            if type_var == 'Showdown':
+                # Handle Captain positions
+                for player in player_names:
+                    # Create mask for lineups where this player is Captain (first column)
+                    cpt_mask = st.session_state['portfolio'][player_columns[0]] == player
+                    
+                    if cpt_mask.any():
+                        player_stats.append({
+                            'Player': f"{player} (CPT)",
+                            'Lineup Count': cpt_mask.sum(),
+                            'Avg Median': st.session_state['portfolio'][cpt_mask]['median'].mean(),
+                            'Avg Own': st.session_state['portfolio'][cpt_mask]['Own'].mean(),
+                            'Avg Dupes': st.session_state['portfolio'][cpt_mask]['Dupes'].mean(),
+                            'Avg Finish %': st.session_state['portfolio'][cpt_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': st.session_state['portfolio'][cpt_mask]['Lineup Edge'].mean(),
+                        })
+                    
+                    # Create mask for lineups where this player is FLEX (other columns)
+                    flex_mask = st.session_state['portfolio'][player_columns[1:]].apply(
+                        lambda row: player in list(row), axis=1
+                    )
+                    
+                    if flex_mask.any():
+                        player_stats.append({
+                            'Player': f"{player} (FLEX)",
+                            'Lineup Count': flex_mask.sum(),
+                            'Avg Median': st.session_state['portfolio'][flex_mask]['median'].mean(),
+                            'Avg Own': st.session_state['portfolio'][flex_mask]['Own'].mean(),
+                            'Avg Dupes': st.session_state['portfolio'][flex_mask]['Dupes'].mean(),
+                            'Avg Finish %': st.session_state['portfolio'][flex_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': st.session_state['portfolio'][flex_mask]['Lineup Edge'].mean(),
+                        })
+            else:
+                # Original Classic format processing
+                for player in player_names:
+                    player_mask = st.session_state['portfolio'][player_columns].apply(
+                        lambda row: player in list(row), axis=1
+                    )
+                    
+                    if player_mask.any():
+                        player_stats.append({
+                            'Player': player,
+                            'Lineup Count': player_mask.sum(),
+                            'Avg Median': st.session_state['portfolio'][player_mask]['median'].mean(),
+                            'Avg Own': st.session_state['portfolio'][player_mask]['Own'].mean(),
+                            'Avg Dupes': st.session_state['portfolio'][player_mask]['Dupes'].mean(),
+                            'Avg Finish %': st.session_state['portfolio'][player_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': st.session_state['portfolio'][player_mask]['Lineup Edge'].mean(),
+                        })
+            
+            player_summary = pd.DataFrame(player_stats)
+            player_summary = player_summary.sort_values('Lineup Count', ascending=False)
+            
+            st.subheader("Player Summary")
+            st.dataframe(
+                player_summary.style
+                .background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Avg Finish %', 'Avg Own', 'Avg Dupes'])
+                .format({
+                    'Avg Median': '{:.2f}',
+                    'Avg Own': '{:.2f}',
+                    'Avg Dupes': '{:.2f}',
+                    'Avg Finish %': '{:.2%}',
+                    'Avg Lineup Edge': '{:.2%}'
+                }),
+                height=400,
+                use_container_width=True
+            )

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: 200

requirements.txt

@@ -0,0 +1,10 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+fuzzywuzzy
+pulp
+docker
+plotly
+scipy
+pymongo