app.py

from shiny import App, ui, render, reactive
import polars as pl
import numpy as np
import pandas as pd
import api_scraper
scrape = api_scraper.MLB_Scrape()
from functions import df_update
from functions import pitch_summary_functions
update = df_update.df_update()
from stuff_model import feature_engineering as fe
from stuff_model import stuff_apply
import requests
import joblib
from matplotlib.gridspec import GridSpec
import math
from pytabulator import TableOptions, Tabulator, output_tabulator, render_tabulator, theme
theme.tabulator_site()

colour_palette = ['#FFB000','#648FFF','#785EF0',
                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']

# df = pl.read_csv("data.csv")
# df = pl.read_parquet("data_small.parquet")[:]
# df = pl.read_parquet("data.parquet")[:]
# print('df')
season = 2025


# df_a = pl.read_parquet("data/data_a_2024.parquet")[:]

# df_mlb = pl.read_parquet("data_small.parquet")[:]
# df_aaa = pl.read_parquet("data_small_aaa.parquet")[:]
# df_a = pl.read_parquet("data_small_a.parquet")[:]


df_mlb = pl.read_parquet(f"hf://datasets/TJStatsApps/mlb_data/data/mlb_pitch_data_2025_spring.parquet")
        

def df_final(df:pl.dataframe,year_input:int,sport_id:int):

    #df_schedule = scrape.get_schedule(year_input=[year_input],sport_id=[sport_id],game_type=['S','E'])
   # df = df.join(df_schedule, on='game_id', how='left')


    print('schedule')
    # Calculate mound_to_release as 60.5 - extension
    df = df.with_columns([
        (60.5 - df["extension"]).alias("release_pos_y")
    ])
    
    # Calculate delta time (Δt)
    delta_t = (df["release_pos_y"] - df["y0"]) / df["vy0"]

    # Corrected back-calculation of release_pos_x and release_pos_z
    df = df.with_columns([
        (df["x0"] + df["vx0"] * delta_t + 0.5 * df["ax"] * delta_t ** 2).alias("release_pos_x"),
        (df["z0"] + df["vz0"] * delta_t + 0.5 * df["az"] * delta_t ** 2).alias("release_pos_z")
    ])
    
    df_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df))
    print('stuff')
    df_up = update.update(df)
    print('update')
    df_total = df_up.join(df_stuff[['play_id','tj_stuff_plus']], on='play_id', how='left')
    print('total')



    
    return df_total
        # df_aaa = pl.read_parquet("data/data_aaa_2024.parquet")[:]
df_mlb_total = df_final(df=df_mlb,year_input=season,sport_id=1)

df_max_ev = pl.read_csv("max_ev.csv")

df_max_ev = df_max_ev.filter(pl.col('max_launch_speed') >= 100)
# df_aaa_total = df_final(df=df_aaa,year_input=season,sport_id=11)
# df_a_total = df_final(df=df_a.drop_nulls(subset=['start_speed']),year_input=season,sport_id=14)

rounding_dict = {
    'pa': 0,
    'bip': 0,
    'hits': 0,
    'k': 0,
    'bb': 0,
    'max_launch_speed': 1,
    'launch_speed_90': 1,
    'launch_speed': 1,
    'pitches': 0,
    'tj_stuff_plus_avg': 0,
    'avg': 3,
    'obp': 3,
    'slg': 3,
    'ops': 3,
    'k_percent': 3,
    'bb_percent': 3,
    'k_minus_bb_percent': 3,
    'sweet_spot_percent': 3,
    'woba_percent': 3,
    'xwoba_percent': 3,
    'woba_percent_contact': 3,
    'xwoba_percent_contact': 3,
    'hard_hit_percent': 3,
    'barrel_percent': 3,
    'zone_contact_percent': 3,
    'zone_swing_percent': 3,
    'zone_percent': 3,
    'chase_percent': 3,
    'chase_contact': 3,
    'swing_percent': 3,
    'whiff_rate': 3,
    'swstr_rate': 3,
    'ground_ball_percent': 3,
    'line_drive_percent': 3,
    'fly_ball_percent': 3,
    'pop_up_percent': 3,
    'pulled_fly_ball_percent': 3,
    'heart_zone_swing_percent': 3,
    'shadow_zone_swing_percent': 3,
    'chase_zone_swing_percent': 3,
    'waste_zone_swing_percent': 3,
    'heart_zone_whiff_percent': 3,
    'shadow_zone_whiff_percent': 3,
    'chase_zone_whiff_percent': 3,
    'waste_zone_whiff_percent': 3,
    'start_speed_avg': 1,
    'vb_avg': 1,
    'ivb_avg': 1,
    'hb_avg': 1,
    'z0_avg': 1,
    'x0_avg': 1,
    'vaa_avg': 1,
    'haa_avg': 1,
    'spin_rate_avg': 0,
    'extension_avg': 1   
}

columns = [
    { "title": "PA", "field": "pa", "width": 150},
    { "title": "BBE", "field": "bip", "width": 150 },
    { "title": "H", "field": "hits", "width": 150 },
    { "title": "K", "field": "k", "width": 150 },
    { "title": "BB", "field": "bb", "width": 150 },
    { "title": "Max EV", "field": "max_launch_speed", "width": 150 },
    { "title": "90th% EV", "field": "launch_speed_90", "width": 150 },
    { "title": "EV", "field": "launch_speed", "width": 150 },
    { "title": "Pitches", "field": "pitches", "width": 150 },
    { "title": "AVG", "field": "avg", "width": 150 },
    { "title": "OBP", "field": "obp", "width": 150 },
    { "title": "SLG", "field": "slg", "width": 150 },
    { "title": "OPS", "field": "ops", "width": 150 },
    { "title": "K%", "field": "k_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "BB%", "field": "bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "K-BB%", "field": "k_minus_bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "SwSpot%", "field": "sweet_spot_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "wOBA", "field": "woba_percent", "width": 150 },
    { "title": "xwOBA", "field": "xwoba_percent", "width": 150 },
    { "title": "wOBACON", "field": "woba_percent_contact", "width": 150 },
    { "title": "xwOBACON", "field": "xwoba_percent_contact", "width": 150 },
    { "title": "HardHit%", "field": "hard_hit_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Barrel%", "field": "barrel_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Z-Contact%", "field": "zone_contact_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Z-Swing%", "field": "zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Zone%", "field": "zone_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "O-Swing%", "field": "chase_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "O-Contact%", "field": "chase_contact", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Swing%", "field": "swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Whiff%", "field": "whiff_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "SwStr%", "field": "swstr_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "GB%", "field": "ground_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "LD%", "field": "line_drive_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "FB%", "field": "fly_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "PU%", "field": "pop_up_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},

    { "title": "Pull LD+FB%", "field": "pulled_fly_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},


    { "title": "Heart Swing%", "field": "heart_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Shadow Swing%", "field": "shadow_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Chase Swing%", "field": "chase_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Waste Swing%", "field": "waste_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Heart Whiff%", "field": "heart_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Shadow Whiff%", "field": "shadow_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Chase Whiff%", "field": "chase_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "Waste Whiff%", "field": "waste_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
    { "title": "tjStuff+", "field": "tj_stuff_plus_avg", "width": 150 },
    { "title": "Velocity", "field": "start_speed_avg", "width": 150 },
    { "title": "Extension", "field": "extension_avg", "width": 150 },
    { "title": "VB", "field": "vb_avg", "width": 150 },
    { "title": "iVB", "field": "ivb_avg", "width": 150 },
    { "title": "HB", "field": "hb_avg", "width": 150 },
    { "title": "vRel", "field": "z0_avg", "width": 150 },
    { "title": "hRel", "field": "x0_avg", "width": 150 },
    { "title": "VAA", "field": "vaa_avg", "width": 150 },
    { "title": "HAA", "field": "haa_avg", "width": 150 },
    { "title": "Spin Rate", "field": "spin_rate_avg", "width": 150 },
    { "title": "Extension", "field": "extension_avg", "width": 150 },

]

stat_titles = dict(zip([col["field"] for col in columns],[col["title"] for col in columns]))

stat_selection = [key for key in stat_titles.keys()]

agg_titles = {'batter_id':'Batter ID',
    'batter_name':'Batter Name',
    'batter_team':'Batter Team',
    'batter_hand':'Batter Hand',
    'pitcher_id':'Pitcher ID',
    'pitcher_name':'Pitcher Name',
    'pitcher_team':'Pitcher Team',
    'pitcher_hand':'Pitcher Hand',
    'pitch_type':'Pitch Type',
    'pitch_group':'Pitch Group',
    # 'home_away_batter':'Home/Away Batter',
    # 'home_away_pitcher':'Home/Away Pitcher',
    'is_swing':'Is Swing?',
    'is_bip':'Is BIP?',
    'in_zone_final':'In Zone?',
    'attack_zone_final':'Attack Zone'}


columns_group = [
    { "title": "Batter ID", "field": "batter_id", "width": 150, "headerFilter":"input","frozen":True,},
    { "title": "Batter Name", "field": "batter_name", "width": 200,"frozen":True, "headerFilter":"input"  },
    { "title": "Batter Team", "field": "batter_team", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Batter Hand", "field": "batter_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitcher ID", "field": "pitcher_id", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitcher Name", "field": "pitcher_name", "width": 200,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitcher Team", "field": "pitcher_team", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitcher Hand", "field": "pitcher_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitch Type", "field": "pitch_type", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Pitch Group", "field": "pitch_group", "width": 150,"frozen":True, "headerFilter":"input"  },
    # { "title": "Home/Away Batter", "field": "home_away_batter", "width": 150,"frozen":True, "headerFilter":"input"  },
    # { "title": "Home/Away Pitcher", "field": "home_away_pitcher", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Is Swing?", "field": "is_swing", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Is BIP?", "field": "is_bip", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "In Zone?", "field": "in_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  },
    { "title": "Attack Zone", "field": "attack_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  } 
    ]


agg_titles_mev = [{ "title": "Batter ID", "field": "batter_id", "width": 150, "headerFilter":"input","frozen":True,},
{ "title": "Batter Name", "field": "batter_name", "width": 150, "headerFilter":"input","frozen":True,},
{ "title": "BBE", "field": "bip", "width": 150, "headerFilter":"input","frozen":True,},
{ "title": "Max EV", "field": "max_launch_speed", "width": 150, "headerFilter":"input","frozen":False,},
{ "title": "Previous Max EV", "field": "max_launch_speed_old", "width": 200, "headerFilter":"input","frozen":False,},
{ "title": "Difference", "field": "diff", "width": 150, "headerFilter":"input","frozen":False,},
]

app_ui = ui.page_sidebar(
    ui.sidebar(

        ui.row(
                ui.markdown("## Spring Training Statcast Leaderboard"),
                ui.markdown("This app generates a table with Spring Training Statcast metrics."),
                ui.column(4,ui.div(
                    "By: ",
                    ui.tags.a(
                        "@TJStats",
                        href="https://x.com/TJStats",
                        target="_blank"
                    )
                ),
                ui.tags.p("Data: MLB")),
                ui.column(8,
                ui.tags.p(
                    ui.tags.a(
                        "Support me on Patreon for more apps",
                        href="https://www.patreon.com/TJ_Stats",
                        target="_blank"
                    )))),  
        
        ui.input_selectize(
            "level_input",
            "Select Level:",
            choices=['MLB'],
            multiple=False,
            selected=['MLB']
        ),
        ui.input_selectize(
            "list_input",
            "Select Aggregation:",
            choices=agg_titles,
            multiple=True,
            selected=['batter_id', 'batter_name']
        ),
        ui.input_selectize(
            "list_stats",
            "Select Stats:",
            choices=stat_titles,
            multiple=True,
            selected=['pa']
        ),
        ui.input_date_range(
            "date_id", 
            "Select Date Range",
            start=f'{season}-01-01',
            end=f'{season}-12-01',
            min=f'{season}-01-01',
            max=f'{season}-12-01',
        ),
        ui.hr(),
        ui.h4("Filters"),
        ui.div(
            {"id": "filter-container"},
            ui.div(
                {"class": "filter-row", "id": "filter_row_1"},  # Add id for deletion
                ui.row(
                    ui.column(5,  # Adjusted column widths to make room for delete button
                        ui.input_select(
                            "filter_column_1",
                            "Metric",
                            choices={}
                        )
                    ),
                    ui.column(3,
                        ui.input_select(
                            "filter_operator_1",
                            "Operator",
                            choices=[">=", "<="]
                        ),
                    ),
                    ui.column(3,
                        ui.input_numeric(
                            "filter_value_1",
                            "Value",
                            value=0
                        )
                    ),
                    ui.column(1,
                        ui.markdown("&nbsp;"),
                 

                        ui.input_action_button(
                            f"delete_filter_1",
                            "",
                            class_="btn-danger btn-sm",
                            style="padding: 3px 6px;",
                            icon='✖'
                        
                    )
                    )
                )
            )
        ),
        ui.input_action_button(
            "add_filter",
            "Add Filter",
            class_="btn-secondary"
        ),
        ui.br(),
        ui.br(),
        ui.input_action_button(
            "generate_table",
            "Generate Table",
            class_="btn-primary"
        ),
        width="400px"
    ),
    ui.navset_tab(
        ui.nav_panel("Leaderboard",
            ui.card(
                #ui.card_header("Leaderboard"),
                output_tabulator("tabulator")
            )
        ),
        ui.nav_panel("Max EV",
            ui.card(
                #ui.card_header("Leaderboard"),
                output_tabulator("tabulator_max_ev")
            )
        ),
        id="tabset"
    )
)

def server(input, output, session):
    # Store the number of active filters
    filter_count = reactive.value(1)
    # Store active filter IDs
    active_filters = reactive.value([1])

    @reactive.effect
    @reactive.event(input.list_stats)
    def _():
        stat_choices = {k: k for k in input.list_stats()}
        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
        ui.update_select("filter_column_1", choices=filtered_stat_choices)

    @reactive.effect
    @reactive.event(input.add_filter)
    def _():
        current_count = filter_count.get()
        new_count = current_count + 1
        
        stat_choices = {k: k for k in input.list_stats()}
        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
        
        ui.insert_ui(
            selector="#filter-container",
            where="beforeEnd",
            ui=ui.div(
                {"class": "filter-row", "id": f"filter_row_{new_count}"},
                ui.row(
                    ui.column(5,
                        ui.input_select(
                            f"filter_column_{new_count}",
                            "Metric",
                            choices=filtered_stat_choices
                        ),
                    ),
                    ui.column(3,
                        ui.input_select(
                            f"filter_operator_{new_count}",
                            "Operator",
                            choices=[">=", "<="]
                        ),
                    ),
                    ui.column(3,
                        ui.input_numeric(
                            f"filter_value_{new_count}",
                            "Value",
                            value=0
                        )
                    ),
                    ui.column(1,
                        ui.markdown("&nbsp;"),
                 

                        ui.input_action_button(
                            f"delete_filter_{new_count}",
                            "",
                            class_="btn-danger btn-sm",
                            style="padding: 3px 6px;",
                            icon='✖'
                        
                    )
                    )
                )
            )
        )
        filter_count.set(new_count)
        current_filters = active_filters.get()
        current_filters.append(new_count)
        active_filters.set(current_filters)

    @reactive.effect
    def _():
        # Monitor all possible delete buttons
        for i in range(1, filter_count.get() + 1):
            try:
                if getattr(input, f"delete_filter_{i}")() > 0:
                    # Remove the filter row
                    ui.remove_ui(f"#filter_row_{i}")
                    # Update active filters
                    current_filters = active_filters.get()
                    if i in current_filters:
                        current_filters.remove(i)
                        active_filters.set(current_filters)
            except:
                continue

    @output
    @render_tabulator
    @reactive.event(input.generate_table, ignore_none=False) 
    def tabulator():
        columns_c = columns.copy()
        selection_list = list(input.list_input())
        start_date = str(input.date_id()[0])
        end_date = str(input.date_id()[1])



        # import datetime

        # date = (datetime.datetime.now() - datetime.timedelta(hours=8)).date()
        # print(datetime.datetime.now())

        # date_str = date.strftime('%Y-%m-%d')
        # # Initialize the scraper


        # game_list_input = (scrape.get_schedule(year_input=[int(date_str[0:4])], sport_id=[1], game_type=['S'])
        #             .filter(pl.col('date') == date)['game_id'])

        # data = scrape.get_data(game_list_input)
        # df_new = scrape.get_data_df(data)

        # df_mlb = pl.concat([df_mlb_old, df_new]).sort('game_date', descending=True)        
        
        
        


        if input.level_input() == "MLB":
            df_agg = update.update_summary_select(df=df_mlb_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)&(pl.col('start_speed')>0)),
                                    selection=selection_list)

        # elif input.level_input() == "AAA":
        #     df_agg = update.update_summary_select(df=df_aaa_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
        #                             selection=selection_list)
        
        # elif input.level_input() == "A":
        #     df_agg = update.update_summary_select(df=df_a_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
        #                             selection=selection_list)
                
        
        df_agg = df_agg.select(selection_list + list(input.list_stats()))#.sort('pa', descending=True)
        
        # Apply filters - only for active filters
        for i in active_filters.get():
            try:
                col_name = getattr(input, f"filter_column_{i}")()
                if col_name:  # Only apply filter if column is selected
                    operator = getattr(input, f"filter_operator_{i}")()
                    if col_name in [col["field"] for col in columns_c if col.get("formatter") == "money"]:
                        value =  getattr(input, f"filter_value_{i}")()/100
                    else:
                        value = getattr(input, f"filter_value_{i}")()
                
                    if operator == ">=":
                        df_agg = df_agg.filter(pl.col(col_name) >= value)
                    elif operator == "<=":
                        df_agg = df_agg.filter(pl.col(col_name) <= value)
            except:
                continue
        
        for col in df_agg.columns[len(selection_list):]:
            if col in rounding_dict:
                df_agg = df_agg.with_columns(pl.col(col).round(rounding_dict[col]))

        for column in columns_c:
            if column.get("formatter") == "money" and column.get("field") in df_agg.columns:
                df_agg = df_agg.with_columns(pl.col(column.get("field"))*100)

        col_group = []
        for column in columns_group:
            if column.get("field") in df_agg.columns:
                col_group.append(column)

        col_group_stats = []
        for column in columns_c:
            if column.get("field") in df_agg.columns:
                col_group_stats.append(column)

        columns_c = col_group + col_group_stats

        # Replace all boolean columns with 0 and 1
        df_agg = df_agg.with_columns(
            [df_agg[col].cast(pl.Int8) for col in df_agg.columns if df_agg[col].dtype == pl.Boolean]
        )

        return Tabulator(
            df_agg.to_pandas(),
            
            table_options=TableOptions(
                height=800,
                
                columns=columns_c,
            )
        )




    @output
    @render_tabulator
    def tabulator_max_ev():
        if input.tabset() == "Max EV":
        
            start_date = str(input.date_id()[0])
            end_date = str(input.date_id()[1])
    
            
            df_agg = update.update_summary_select(df=df_mlb_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
                                        selection=['batter_id', 'batter_name'])
    
    
            df_agg = df_agg.join(df_max_ev, on='batter_id', how='left',suffix='_old')
    
            df_agg = df_agg.with_columns([
                (pl.col('max_launch_speed')-pl.col('max_launch_speed_old')).alias('diff')
                ])
            
            df_agg = df_agg.select(['batter_id', 'batter_name','bip','max_launch_speed','max_launch_speed_old','diff'])#.sort('pa', descending=True)
            df_agg = df_agg.drop_nulls(subset=['max_launch_speed'])
    
            df_agg = df_agg.filter(pl.col("diff").is_not_null()).with_columns(
                pl.col("diff").round(1).map_elements(lambda x: f"{x:+.1f}")
            ).sort("max_launch_speed", descending=True)
            
                
            
            # print(df_agg.to_pandas()) 
            return Tabulator(
                df_agg.to_pandas(),
                
                table_options=TableOptions(
                    height=800,
                    
                    columns=agg_titles_mev,
                )
            )

# ui.brush_opts()


app = App(app_ui, server)