app.py

import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import time
import random
import scipy.stats

@st.cache_resource
def init_conn():
          scope = ['https://www.googleapis.com/auth/spreadsheets',
                    "https://www.googleapis.com/auth/drive"]
          
          credentials = {
            "type": "service_account",
            "project_id": "sheets-api-connect-378620",
            "private_key_id": st.secrets['sheets_api_connect_pk'],
            "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
            "client_id": "106625872877651920064",
            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
            "token_uri": "https://oauth2.googleapis.com/token",
            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
            "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
          }

          gc = gspread.service_account_from_dict(credentials)
          
          all_dk_player_projections = st.secrets['NFL_Data']
          
          return gc, all_dk_player_projections

st.set_page_config(layout="wide")

gc, all_dk_player_projections = init_conn()

game_format = {'Dropback% Proj': '{:.2%}', 'DesRush%': '{:.2%}', 'Rush%': '{:.2%}'}

rb_util = {'Player Snaps%': '{:.2%}','Rush Att%': '{:.2%}', 'Routes%': '{:.2%}', 'Targets%': '{:.2%}', 'SDD Snaps%': '{:.2%}', 'i5 Rush%': '{:.2%}',
                   'LDD Snaps%': '{:.2%}','2-min%': '{:.2%}'}

wr_te_util = {'Routes%': '{:.2%}','Targets%': '{:.2%}', 'Air Yards%': '{:.2%}', 'Endzone Targets%': '{:.2%}', 'Third/Fourth%': '{:.2%}', 'Third/Fourth Targets%': '{:.2%}',
                   'Play Action Targets%': '{:.2%}','2-min%': '{:.2%}'}

wr_matchups_form = {'Opp Man%': '{:.2%}','Opp Zone%': '{:.2%}'}

trending_form = {'Trend': '{:.2%}'}

@st.cache_resource(ttl = 600)
def pull_baselines():
    sh = gc.open_by_url(all_dk_player_projections)
    worksheet = sh.worksheet('RB_Util')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per',
                               'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
                               'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    rb_search = raw_display.sort_values(by='Utilization Rank', ascending=True)
    
    worksheet = sh.worksheet('WR_TE_Util')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'week', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per',
                               'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
                               'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    wr_search = raw_display.sort_values(by='Utilization Rank', ascending=True)
    
    worksheet = sh.worksheet('RB_Util_Season')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per',
                               'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
                               'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    rb_season = raw_display.sort_values(by='Utilization Rank', ascending=True)
    
    worksheet = sh.worksheet('WR_TE_Util_Season')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)      
    raw_display = raw_display[['player_name', 'position', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per',
                               'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
    raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
                               'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
    wr_season = raw_display.sort_values(by='Utilization Rank', ascending=True)
    
    worksheet = sh.worksheet('Defensive Matchups')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)
    raw_display = raw_display.dropna(subset='Weighted Targets')
    raw_display = raw_display[raw_display['Weighted Targets'] != '#DIV/0!']
    raw_display = raw_display[raw_display['Weighted Targets'] != '#N/A']
    wr_matchups = raw_display.sort_values(by='Weighted Targets', ascending=False)
    
    worksheet = sh.worksheet('FL_Macro')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)
    raw_display = raw_display[raw_display['Active'] == 1]
    raw_display = raw_display.dropna(subset='Team')
    macro_data = raw_display.drop('Active', axis=1)
    macro_data = macro_data.sort_values(by='Team Total', ascending=False)
    
    worksheet = sh.worksheet('Ownership Trend')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.replace('', np.nan)
    raw_display = raw_display.dropna(subset='Team')
    trending_data = raw_display.sort_values(by='Trend', ascending=False)
    
    return rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data

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

rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
pos_list = ['RB', 'WR', 'TE']

tab1, tab2 = st.tabs(["Slate Specific", "Season Long Research"])
with tab1:
    col1, col2 = st.columns([1, 8])
    
    with col1:
        if st.button("Load/Reset Data", key='reset2'):
              st.cache_data.clear()
              rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
        stat_type_var2 = st.radio("What table are you loading?", ('Macro Stats', 'WR/TE Coverage Matchups', 'Ownership Trends', 'Nothing idk lol'))
        if stat_type_var2 == 'WR/TE Coverage Matchups':
            routes_var2 = st.slider("Is there a certain range of routes you want to include?", 0, 50, (10, 50), key='sal_var2')
        split_var2 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'))
        pos_split2 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'))      
        if pos_split2 == 'Specific Positions':
            if stat_type_var2 == 'WR/TE Coverage Matchups':
                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
            elif stat_type_var2 == 'Ownership Trends':
                pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE', 'DST'])
        elif pos_split2 == 'All Positions':
            pos_var2 = pos_list 
        if split_var2 == 'Specific Teams':
            team_var2 = st.multiselect('Which teams would you like to include in the Table?', options = wr_matchups['Team'].unique())
        elif split_var2 == 'All Teams':
            team_var2 = wr_matchups['Team'].unique().tolist()
        if stat_type_var2 == 'Macro Stats':
            slate_table_instance = macro_data
            slate_table_instance = slate_table_instance.set_index('Team')  
        elif stat_type_var2 == 'WR/TE Coverage Matchups':
            slate_table_instance = wr_matchups
            slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)]  
            slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
            slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] >= routes_var2[0]]
            slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] <= routes_var2[1]]
            slate_table_instance = slate_table_instance.set_index('name')
        elif stat_type_var2 == 'Ownership Trends':
            slate_table_instance = trending_data
            slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)]  
            slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
        elif stat_type_var2 == 'Nothing idk lol':
            slate_table_instance = wr_matchups
    
    with col2:
        if stat_type_var2 == 'Macro Stats':
            st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(game_format, precision=2), height=1000, use_container_width = True)
        elif stat_type_var2 == 'WR/TE Coverage Matchups':
            st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(wr_matchups_form, precision=2), height=1000, use_container_width = True)
        elif stat_type_var2 == 'Ownership Trends':
            st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(trending_form, precision=2), height=1000, use_container_width = True)
        elif stat_type_var2 == 'Nothing idk lol':
            st.write('lol same bro but yo the vibes immaculate')
        if stat_type_var2 == 'WR/TE Coverage Matchups':
            st.download_button(
                            label="Export Tables",
                            data=convert_df_to_csv(slate_table_instance),
                            file_name='NFL_Slate_Research_export.csv',
                            mime='text/csv',
            )

with tab2:
    col1, col2 = st.columns([1, 8])
    
    with col1:
        if st.button("Load/Reset Data", key='reset1'):
              st.cache_data.clear()
              rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
        stat_type_var1 = st.radio("What table are you loading?", ('RB Usage (Weekly)', 'WR/TE Usage (Weekly)', 'RB Usage (Season)', 'WR/TE Usage (Season)'), key='stat_type_var1')
        split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1')
        pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')      
        week_split1 = st.radio("Are you viewing all weeks or specific weeks?", ('All Weeks', 'Specific Weeks'), key='week_split1')
        if pos_split1 == 'Specific Positions':
            pos_var1 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
        elif pos_split1 == 'All Positions':
            pos_var1 = pos_list 
        if split_var1 == 'Specific Teams':
            team_var1 = st.multiselect('Which teams would you like to include in the Table?', options = rb_search['Team-Season'].unique(), key='team_var1')
        elif split_var1 == 'All Teams':
            team_var1 = rb_search['Team-Season'].unique().tolist()
        if week_split1 == 'Specific Weeks':
            week_var1 = st.multiselect('Which weeks would you like to include in the Table?', options = rb_search['Week'].unique(), key='week_var1')
        elif week_split1 == 'All Weeks':
            week_var1 = rb_search['Week'].unique().tolist()
        if stat_type_var1 == 'RB Usage (Weekly)':
            table_instance = rb_search
            table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
            table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
            table_instance = table_instance[table_instance['Week'].isin(week_var1)]
            table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR']
        elif stat_type_var1 == 'WR/TE Usage (Weekly)':
            table_instance = wr_search    
            table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
            table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
            table_instance = table_instance[table_instance['Week'].isin(week_var1)]
            table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR']
        elif stat_type_var1 == 'RB Usage (Season)':
            table_instance = rb_season
            table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
            table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
            table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR']
        elif stat_type_var1 == 'WR/TE Usage (Season)':
            table_instance = wr_season
            table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
            table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
            table_instance['PPR_Diff'] = table_instance['Expected PPR'] - table_instance['PPR']
    with col2:
        if stat_type_var1 == 'RB Usage (Weekly)':
            st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), height=1000, use_container_width = True)
        elif stat_type_var1 == 'WR/TE Usage (Weekly)':
            st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), height=1000, use_container_width = True)
        elif stat_type_var1 == 'RB Usage (Season)':
            st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), height=1000, use_container_width = True)
        elif stat_type_var1 == 'WR/TE Usage (Season)':
            st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), height=1000, use_container_width = True)
    
        st.download_button(
                        label="Export Tables",
                        data=convert_df_to_csv(table_instance),
                        file_name='NFL_Research_export.csv',
                        mime='text/csv',
        )