app.py

import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]

import numpy as np
import pandas as pd
import streamlit as st
import gspread
import pymongo

@st.cache_resource
def init_conn():
        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["MLB_Database"]
        db2 = client["MLB_DFS"]

        return db, db2
    
db, db2 = init_conn()

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                   '4x%': '{:.2%}'}

dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']
fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', '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;
    }
</style>""", unsafe_allow_html=True)

@st.cache_resource(ttl = 60)
def init_baselines():
    collection = db["Player_Range_Of_Outcomes"] 
    cursor = collection.find()
    player_frame = pd.DataFrame(cursor)
    
    timestamp = player_frame['Timestamp'][0]

    roo_data = player_frame.drop(columns=['_id', 'index', 'timestamp'])
    roo_data['Salary'] = roo_data['Salary'].astype(int)

    collection = db["Player_SD_Range_Of_Outcomes"] 
    cursor = collection.find()
    player_frame = pd.DataFrame(cursor)

    sd_roo_data = player_frame.drop(columns=['_id', 'index'])
    sd_roo_data['Salary'] = sd_roo_data['Salary'].astype(int)

    collection = db["Scoring_Percentages"] 
    cursor = collection.find()
    team_frame = pd.DataFrame(cursor)
    scoring_percentages = team_frame.drop(columns=['_id', 'index'])
    
    return roo_data, sd_roo_data, scoring_percentages

@st.cache_data(ttl = 60)
def init_DK_lineups():  
        
        collection = db['DK_MLB_SD1_seed_frame']
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']]
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_data(ttl = 60)
def init_FD_lineups():  
        
        collection = db['FD_MLB_SD1_seed_frame']
        cursor = collection.find().limit(10000)
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']]
        FD_seed = raw_display.to_numpy()

        return FD_seed

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

@st.cache_data
def convert_df(array):
    array = pd.DataFrame(array, columns=column_names)
    return array.to_csv().encode('utf-8')

roo_data, sd_roo_data, scoring_percentages = init_baselines()
hold_display = roo_data

view_var = st.radio("Select view", ["Simple", "Advanced"])

tab1, tab2, tab3 = st.tabs(["Scoring Percentages", "Player ROO", "Optimals"])

with tab1:
    st.title("Scoring Percentages")
    st.dataframe(scoring_percentages)

with tab2:
    st.title("Player ROO")
    st.dataframe(sd_roo_data)

with tab3:
    with st.expander("Info and Filters"):
        if st.button("Load/Reset Data", key='reset2'):
            st.cache_data.clear()
            roo_data, sd_roo_data, scoring_percentages = init_baselines()
            hold_display = roo_data
            dk_lineups = init_DK_lineups()
            fd_lineups = init_FD_lineups()
            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
            for key in st.session_state.keys():
                del st.session_state[key]
              
        slate_var1 = st.radio("Which data are you loading?", ('Regular', 'Showdown'))
        
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
        if slate_var1 == 'Regular':
            if site_var1 == 'Draftkings':
                dk_lineups = init_DK_lineups()
            elif site_var1 == 'Fanduel':
                fd_lineups = init_FD_lineups()
        elif slate_var1 == 'Showdown':
            if site_var1 == 'Draftkings':
                dk_lineups = init_DK_lineups()
            elif site_var1 == 'Fanduel':
                fd_lineups = init_FD_lineups()
        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
        
        if slate_var1 == 'Regular':
                raw_baselines = roo_data
        elif slate_var1 == 'Showdown':
            raw_baselines = sd_roo_data
        
        if site_var1 == 'Draftkings':
            if slate_var1 == 'Regular':
                ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings']
                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
            elif slate_var1 == 'Showdown':
                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
            # Get the minimum and maximum ownership values from dk_lineups
            min_own = np.min(dk_lineups[:,8])
            max_own = np.max(dk_lineups[:,8])
            column_names = dk_columns
            
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = raw_baselines.Player.values.tolist()
                    
        elif site_var1 == 'Fanduel':
            raw_baselines = hold_display
            if slate_var1 == 'Regular':
                ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel']
                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
            elif slate_var1 == 'Showdown':
                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
            min_own = np.min(fd_lineups[:,8])
            max_own = np.max(fd_lineups[:,8])
            column_names = fd_columns
            
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = raw_baselines.Player.values.tolist()

        if st.button("Prepare data export", key='data_export'):
            data_export = st.session_state.working_seed.copy()
            # if site_var1 == 'Draftkings':
            #     for col_idx in range(6):
            #         data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            # elif site_var1 == 'Fanduel':
            #     for col_idx in range(6):
            #         data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
            st.download_button(
                label="Export optimals set",
                data=convert_df(data_export),
                file_name='MLB_optimals_export.csv',
                mime='text/csv',
            )
        
    if site_var1 == 'Draftkings':
        if 'working_seed' in st.session_state:
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = dk_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        elif 'working_seed' not in st.session_state:
            st.session_state.working_seed = dk_lineups.copy()
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = dk_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        
    elif site_var1 == 'Fanduel':
        if 'working_seed' in st.session_state:
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = fd_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
        elif 'working_seed' not in st.session_state:
            st.session_state.working_seed = fd_lineups.copy()
            st.session_state.working_seed = st.session_state.working_seed
            if player_var1 == 'Specific Players':
                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
            elif player_var1 == 'Full Slate':
                st.session_state.working_seed = fd_lineups.copy()
            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)

    export_file = st.session_state.data_export_display.copy()
    # if site_var1 == 'Draftkings':
    #     for col_idx in range(6):
    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
    # elif site_var1 == 'Fanduel':
    #     for col_idx in range(6):
    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
            
    with st.container():
        if st.button("Reset Optimals", key='reset3'):
            for key in st.session_state.keys():
                del st.session_state[key]
            if site_var1 == 'Draftkings':
                st.session_state.working_seed = dk_lineups.copy()
            elif site_var1 == 'Fanduel':
                st.session_state.working_seed = fd_lineups.copy()
        if 'data_export_display' in st.session_state:
            st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
        st.download_button(
            label="Export display optimals",
            data=convert_df(export_file),
            file_name='MLB_display_optimals.csv',
            mime='text/csv',
        )
    
    with st.container():
        if 'working_seed' in st.session_state:
            # Create a new dataframe with summary statistics
            if site_var1 == 'Draftkings':
                summary_df = pd.DataFrame({
                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                    'Salary': [
                        np.min(st.session_state.working_seed[:,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[:,8]),
                        np.mean(st.session_state.working_seed[:,8]),
                        np.max(st.session_state.working_seed[:,8]),
                        np.std(st.session_state.working_seed[:,8])
                    ]
                })
            elif site_var1 == '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[:,8]),
                        np.mean(st.session_state.working_seed[:,8]),
                        np.max(st.session_state.working_seed[:,8]),
                        np.std(st.session_state.working_seed[:,8])
                    ]
                })

            # Set the index of the summary dataframe as the "Metric" column
            summary_df = summary_df.set_index('Metric')

            # Display the summary dataframe
            st.subheader("Optimal Statistics")
            st.dataframe(summary_df.style.format({
                'Salary': '{:.2f}',
                'Proj': '{:.2f}',
                'Own': '{:.2f}'
            }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)

    with st.container():
        tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
        with tab1:
            if 'data_export_display' in st.session_state:
                if site_var1 == 'Draftkings':
                    player_columns = st.session_state.data_export_display.iloc[:, :6]
                elif site_var1 == 'Fanduel':
                    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='MLB_player_frequency.csv',
                    mime='text/csv',
                )
        with tab2:
            if 'working_seed' in st.session_state:
                if site_var1 == 'Draftkings':
                    player_columns = st.session_state.working_seed[:, :6]
                elif site_var1 == 'Fanduel':
                    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='MLB_seed_frame_frequency.csv',
                    mime='text/csv',
                )