src/streamlit_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
from database import gcservice_account
import os

NBA_Data = os.getenv('NBA_Data')

@st.cache_resource(ttl = 600)
def init_baselines():
    sh = gcservice_account.open_by_url(NBA_Data)
    
    worksheet = sh.worksheet('Trending')
    raw_display = pd.DataFrame(worksheet.get_values())
    raw_display.columns = raw_display.iloc[0]
    raw_display = raw_display[1:]
    raw_display = raw_display.reset_index(drop=True)
    trend_table = raw_display[raw_display['PLAYER_NAME'] != ""]
    trend_table.replace('', np.nan, inplace=True)
    trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'Season MIN', 'Season Fantasy', 'Season FPPM', 'Season Ceiling', 'Season FD_Fantasy', 'Season FD_Ceiling', 'L10 MIN', 'L10 Fantasy', 'L10 FPPM', 'L10 Ceiling', 'L10 FD_Fantasy',
                               'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 FPPM', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
                               'L3 FPPM', 'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
                               'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value',
                               'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
    trend_table['DK_Salary'] = trend_table['DK_Salary'].str.replace(',', '').astype(float)
    trend_table['FD_Salary'] = trend_table['FD_Salary'].str.replace(',', '').astype(float)
    trend_table = trend_table.dropna(subset=['Position'])
    data_cols = trend_table.columns.drop(['PLAYER_NAME', 'Team', 'Position', 'FD_Position'])
    trend_table[data_cols] = trend_table[data_cols].apply(pd.to_numeric, errors='coerce')
    
    dk_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
    
    fd_minutes_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min']]
    
    dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
    
    fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]

    dk_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
    
    fd_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
    
    dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']]
    
    fd_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'FD_Salary', 'FD_Proj', 'Adj FD_Median', 'FD_Avg_Val', 'Adj FD_Ceiling', 'FD_Ceiling_Value']]

    return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table

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

trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()

col1, col2 = st.columns([1, 9])
with col1:
    if st.button("Reset Data", key='reset1'):
              st.cache_data.clear()
              trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
    split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'FPPM Trends', 'Slate specific', 'Overall'), key='split_var1')
    site_var1 = st.radio("What site would you like to view?", ('Draftkings', 'Fanduel'), key='site_var1')
    if site_var1 == 'Draftkings':
        trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
                                   'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
                                   'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']]
        minutes_table = dk_minutes_table
        medians_table = dk_medians_table
        fppm_table = dk_fppm_table
        proj_medians_table = dk_proj_medians_table
    elif site_var1 == 'Fanduel':
        trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season FD_Fantasy', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy',
                                   'Trend FD_Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling',
                                   'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
        minutes_table = fd_minutes_table
        medians_table = fd_medians_table
        fppm_table = fd_fppm_table
        proj_medians_table = fd_proj_medians_table
    trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy',
                                        'Trend Median', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
                                        'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
    minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season MIN', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
    medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season Fantasy', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
    fppm_table = fppm_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Season FPPM', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM'], axis=1)
    proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj',
                                                      'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1)
    if split_var1 == 'Overall':
        view_var1 = trend_table.Team.values.tolist()
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = view_var1
        
        split_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var2')
        if split_var2 == 'Specific Positions':
            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
        elif split_var2 == 'All':
            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
        
        proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')
            
    elif split_var1 == 'Minutes Trends':
        view_var1 = trend_table.Team.values.tolist()
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = view_var1
        
        pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
        if pos_var2 == 'Specific Positions':
            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
        elif pos_var2 == 'All':
            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
        
    elif split_var1 == 'Fantasy Trends':
        view_var1 = trend_table.Team.values.tolist()
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = view_var1
        
        pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
        if pos_var2 == 'Specific Positions':
            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
        elif pos_var2 == 'All':
            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
            
    elif split_var1 == 'FPPM Trends':
        view_var1 = trend_table.Team.values.tolist()
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = view_var1
        
        pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
        if pos_var2 == 'Specific Positions':
            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
        elif pos_var2 == 'All':
            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
            
    elif split_var1 == 'Slate specific':
        view_var1 = trend_table.Team.values.tolist()
        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
        
        if split_var2 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
        elif split_var2 == 'All':
            team_var1 = view_var1
        
        pos_var2 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='pos_var2')
        if pos_var2 == 'Specific Positions':
            pos_var1 = st.multiselect('Which positions would you like to include in the tables?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var1')
        elif pos_var2 == 'All':
            pos_var1 = ['PG', 'SG', 'SF', 'PF', 'C']
        
        proj_var1 = st.slider("Is there a certain projection range you want to view?", 0, 100, (10, 100), key='proj_var1')

with col2:
    if split_var1 == 'Overall':
        table_display = trend_table[trend_table['Proj'] >= proj_var1[0]]
        table_display = table_display[table_display['Proj'] <= proj_var1[1]]
        table_display = table_display[table_display['Team'].isin(team_var1)]
        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
        table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
        table_display = table_display.set_index('PLAYER_NAME')
        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        st.download_button(
            label="Export Trending Numbers",
            data=convert_df_to_csv(table_display),
            file_name='Trending_export.csv',
            mime='text/csv',
        )
            
    elif split_var1 == 'Minutes Trends':
        table_display = minutes_table[minutes_table['Team'].isin(team_var1)]
        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
        table_display = table_display.set_index('PLAYER_NAME')
        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        st.download_button(
            label="Export Trending Numbers",
            data=convert_df_to_csv(table_display),
            file_name='Trending_export.csv',
            mime='text/csv',
        )
        
    elif split_var1 == 'Fantasy Trends':
        table_display = medians_table[medians_table['Team'].isin(team_var1)]
        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
        table_display = table_display.set_index('PLAYER_NAME')
        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        st.download_button(
            label="Export Trending Numbers",
            data=convert_df_to_csv(table_display),
            file_name='Trending_export.csv',
            mime='text/csv',
        )
        
    elif split_var1 == 'FPPM Trends':
        table_display = fppm_table[fppm_table['Team'].isin(team_var1)]
        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
        table_display = table_display.set_index('PLAYER_NAME')
        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        st.download_button(
            label="Export Trending Numbers",
            data=convert_df_to_csv(table_display),
            file_name='Trending_export.csv',
            mime='text/csv',
        )
        
    elif split_var1 == 'Slate specific':
        table_display = proj_medians_table[proj_medians_table['Proj'] >= proj_var1[0]]
        table_display = table_display[table_display['Proj'] <= proj_var1[1]]
        table_display = table_display[table_display['Team'].isin(team_var1)]
        table_display = table_display[table_display['Position'].str.contains('|'.join(pos_var1))]
        table_display = table_display.sort_values(by='Adj Ceiling', ascending=False)
        table_display = table_display.set_index('PLAYER_NAME')
        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
        st.download_button(
            label="Export Trending Numbers",
            data=convert_df_to_csv(table_display),
            file_name='NBA_Trending_export.csv',
            mime='text/csv',
        )