app.py

from shiny import ui, render, App
import matplotlib.image as mpimg
import pandas as pd
import pygsheets
import pytz
from datetime import datetime
import numpy as np
import joblib

import datasets
from datasets import load_dataset
dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2024_spring.csv'])
dataset_train = dataset['train']
df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)


# print('Starting')
# df_2024 = pd.read_csv('2024_spring_data.csv',index_col=[0])
print('Starting')
spring_teams = df_2024.groupby(['pitcher_id']).tail(1)[['pitcher_id','pitcher_team']].set_index(['pitcher_id'])['pitcher_team'].to_dict()


df_2024['vy_f'] = -(df_2024['vy0']**2 - (2 * df_2024['ay'] * (df_2024['y0'] - 17/12)))**0.5
df_2024['t'] = (df_2024['vy_f'] - df_2024['vy0']) / df_2024['ay']
df_2024['vz_f'] = (df_2024['vz0']) + (df_2024['az'] * df_2024['t'])
df_2024['vaa'] = -np.arctan(df_2024['vz_f'] / df_2024['vy_f']) * (180 / np.pi)

#df_2024['vy_f'] = -(df_2024['vy0']**2 - (2 * df_2024['ay'] * (df_2024['y0'] - 17/12)))**0.5
#df_2024['t'] = (df_2024['vy_f'] - df_2024['vy0']) / df_2024['ay']
df_2024['vx_f'] = (df_2024['vx0']) + (df_2024['ax'] * df_2024['t'])
df_2024['haa'] = -np.arctan(df_2024['vx_f'] / df_2024['vy_f']) * (180 / np.pi)
grouped_ivb_2023 = pd.read_csv('2023_pitch_group_data.csv',index_col=[0,3])

model = joblib.load('tjstuff_model_20240123.joblib')


def percentile(n):
    def percentile_(x):
        return x.quantile(n)
    percentile_.__name__ = 'percentile_{:02.0f}'.format(n*100)
    return percentile_

def df_clean(df):
    df_copy = df.copy()
    df_copy.loc[df_copy['pitcher_hand'] == 'L','hb'] *= -1
    df_copy.loc[df_copy['pitcher_hand'] == 'L','x0'] *= -1
    df_copy.loc[df_copy['pitcher_hand'] == 'L','spin_direction'] = 360 - df_copy.loc[df_copy['pitcher_hand'] == 'L','spin_direction']

    df_copy['pitch_l'] = [1 if x == 'L' else 0 for x in df_copy['pitcher_hand']]
    df_copy['bat_l'] = [1 if x == 'L' else 0 for x in df_copy['batter_hand']]
    df_copy = df_copy[~df_copy.pitch_type.isin(["EP", "PO", "KN", "FO", "CS", "SC", "FA"])].reset_index(drop=True)
    df_copy['pitch_type'] = df_copy['pitch_type'].replace({'FT':'SI','KC':'CU','ST':'SL','SV':'SL'})

    # df_copy['des_new'] = df_copy['play_description'].map(des_dict)
    # df_copy['ev_new'] = df_copy.loc[df_copy['des_new'] == 'hit_into_play','event_type'].map(ev_dict)
    # df_copy.loc[df_copy['des_new']=='hit_into_play','des_new'] = df_copy.loc[df_copy['des_new']=='hit_into_play','ev_new']
    # df_copy = df_copy.dropna(subset=['des_new'])
    # des_values = df_copy.groupby(['des_new'])['delta_run_exp'].mean()
    # df_copy = df_copy.merge(des_values,left_on='des_new',right_on='des_new',suffixes=['','_mean'])

    df_copy_fb_sum = df_copy[df_copy.pitch_type.isin(["FF", "FC", "SI"])].groupby(['pitcher_id']).agg(
        fb_velo = ('start_speed','mean'),
        fb_max_ivb  = ('ivb',percentile(0.9)),
        fb_max_x  = ('hb',percentile(0.9)),
        fb_min_x  = ('hb',percentile(0.1)),
        fb_max_velo  = ('start_speed',percentile(0.9)),
        fb_axis  = ('spin_direction','mean'),
    )

    df_copy = df_copy.merge(df_copy_fb_sum,left_on='pitcher_id',right_index=True,how='left')

    df_copy['fb_velo_diff'] = df_copy['start_speed']- df_copy['fb_velo']
    df_copy['fb_max_ivb_diff'] = df_copy['ivb']- df_copy['fb_max_ivb']
    df_copy['fb_max_hb_diff'] = df_copy['hb']- df_copy['fb_max_x']
    df_copy['fb_min_hb_diff'] = df_copy['hb']- df_copy['fb_min_x']
    df_copy['fb_max_velo_diff'] = df_copy['start_speed']- df_copy['fb_max_velo']
    df_copy['fb_axis_diff'] = df_copy['spin_direction']- df_copy['fb_axis']

    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_velo_diff'] = 0
    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_ivb_diff'] = 0
    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_hb_diff'] = 0
    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_min_hb_diff'] = 0
    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_max_velo_diff'] = 0
    # df_copy.loc[df_copy.pitch_type.isin(["FF", "FC", "SI"]),'fb_axis_diff'] = 0


    df_copy['max_speed'] = df_copy.groupby(['pitcher_id'])['start_speed'].transform('max')
    df_copy['max_speed_diff'] = df_copy['start_speed'] - df_copy['max_speed']

    df_copy['max_ivb'] = df_copy.groupby(['pitcher_id'])['ivb'].transform('max')
    df_copy['max_ivb_diff'] = df_copy['ivb'] - df_copy['max_ivb']

    df_copy['vy_f'] = -(df_copy['vy0']**2 - (2 * df_copy['ay'] * (df_copy['y0'] - 17/12)))**0.5
    df_copy['t'] = (df_copy['vy_f'] - df_copy['vy0']) / df_copy['ay']
    df_copy['vz_f'] = (df_copy['vz0']) + (df_copy['az'] * df_copy['t'])
    df_copy['vaa'] = -np.arctan(df_copy['vz_f'] / df_copy['vy_f']) * (180 / np.pi)

    #df_copy['vy_f'] = -(df_copy['vy0']**2 - (2 * df_copy['ay'] * (df_copy['y0'] - 17/12)))**0.5
    #df_copy['t'] = (df_copy['vy_f'] - df_copy['vy0']) / df_copy['ay']
    df_copy['vx_f'] = (df_copy['vx0']) + (df_copy['ax'] * df_copy['t'])
    df_copy['haa'] = -np.arctan(df_copy['vx_f'] / df_copy['vy_f']) * (180 / np.pi)

    # df_copy['x_diff'] = df_copy['x0'] - df_copy['px']
    # df_copy['z_diff'] = df_copy['z0'] - df_copy['pz']

    # df_copy['vaa'] = np.arctan(df_copy['z_diff'] / df_copy['release_pos_y']) * 360 / np.pi
    # df_copy['haa'] = np.arctan(-df_copy['x_diff'] / df_copy['release_pos_y']) * 360 / np.pi

    df_copy = df_copy.dropna(subset=['pitch_type']).fillna(0)
    return df_copy


app_ui = ui.page_fluid(
    ui.layout_sidebar(
 
        ui.panel_sidebar(
                ui.input_date_range("date_range_id", "Date range input",start = df_2024.game_date.min(),
                                     end = df_2024.game_date.max(),width=2,min=df_2024.game_date.min(),
                                       max=df_2024.game_date.max()),width=2),
        ui.panel_main(     
            ui.navset_tab(
            # ui.nav("Raw Data",
            #     ui.output_data_frame("raw_table")),                
            ui.nav("Pitch Data",
                ui.output_data_frame("table")),
            ui.nav("Pitch Data (Daily)",
                ui.output_data_frame("table_daily")),          
            ui.nav("2023 vs Spring",
                ui.output_data_frame("table_2023")),          
            ui.nav("2023 vs Spring Difference",
                ui.output_data_frame("table_difference")),     
            # ui.nav("New Pitches",
            #     ui.output_data_frame("table_new")),     
            ui.nav("tjStuff+",
                ui.output_data_frame("table_stuff")),     
            ui.nav("tjStuff+ (Daily)",
                ui.output_data_frame("table_stuff_day")),     

                ))))


from urllib.request import Request, urlopen
from shiny import App, reactive, ui
from shiny.ui import h2, tags
# importing OpenCV(cv2) module




#print(app_ui)
def server(input, output, session):

    # @output
    # @render.data_frame
    # def raw_table():

    #     return render.DataGrid(
    #         df_2024,
    #         width='fit-content',
    #         height=750,
    #         filters=True,
    #     )        

    @output
    @render.data_frame
    def table():

        grouped_ivb = df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])].groupby(['pitcher_id','pitcher_name','pitcher_team','pitcher_hand','pitch_type']).agg(
        pitches = ('start_speed','count'),
        
        start_speed = ('start_speed','mean'),
        ivb = ('ivb','mean'),
        hb = ('hb','mean'),
        spin_rate = ('spin_rate','mean'),
        vaa = ('vaa','mean'),
        haa = ('haa','mean'),
        horizontal_release = ('x0','mean'),
        vertical_release = ('z0','mean'),
        extension = ('extension','mean')).round(1).reset_index()
        #grouped_ivb = grouped_ivb.set_index(['pitcher_id']).reset_index()
        # return     grouped_ivb    
        return render.DataGrid(
            grouped_ivb,
            width='fit-content',
            height=750,
            filters=True,
        )        

    @output
    @render.data_frame
    def table_daily():

        grouped_ivb = df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])].groupby(['pitcher_id','pitcher_name','pitcher_team','pitcher_hand','pitch_type','game_date']).agg(
        pitches = ('start_speed','count'),
        
        start_speed = ('start_speed','mean'),
        ivb = ('ivb','mean'),
        hb = ('hb','mean'),
        spin_rate = ('spin_rate','mean'),
        vaa = ('vaa','mean'),
        haa = ('haa','mean'),
        horizontal_release = ('x0','mean'),
        vertical_release = ('z0','mean'),
        extension = ('extension','mean')).round(1).reset_index()
        #grouped_ivb = grouped_ivb.set_index(['pitcher_id']).reset_index()
        # return     grouped_ivb    
        return render.DataGrid(
            grouped_ivb,
            width='fit-content',
            height=750,
            filters=True,
        )             
        
        #return grouped_ivb

    @output
    @render.data_frame
    def table_2023():
        grouped_ivb = df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])].groupby(['pitcher_id','pitcher_name','pitcher_hand','pitch_type']).agg(
            pitches = ('start_speed','count'),
            
            start_speed = ('start_speed','mean'),
            ivb = ('ivb','mean'),
            hb = ('hb','mean'),
            spin_rate = ('spin_rate','mean'),
            vaa = ('vaa','mean'),
            haa = ('haa','mean'),
            horizontal_release = ('x0','mean'),
            vertical_release = ('z0','mean'),
            extension = ('extension','mean')).round(1).reset_index()
        grouped_ivb = grouped_ivb.set_index(['pitcher_id','pitch_type'])

        

        #####
        ivb_merged = grouped_ivb_2023.merge(right=grouped_ivb,
                            left_index=True,
                            right_index=True,
                            how='right',suffixes=['_2023','_spring']).reset_index()

        ivb_merged['pitcher_name'] = ivb_merged['pitcher_name_spring']
        ivb_merged['pitcher_hand'] = ivb_merged['pitcher_hand_spring']
        #ivb_merged['pitch_type'] = ivb_merged['pitch_type_spring']


        # ivb_merged = ivb_merged[['pitcher_id', 'pitcher_name', 'pitcher_hand', 'pitch_type',
        #      'pitches_spring', 'start_speed_spring', 'ivb_spring',
        #        'hb_spring', 'spin_rate_spring', 'horizontal_release_spring',
        #        'vertical_release_spring', 'extension_spring']]
        ivb_merged['pitcher_team'] = ivb_merged['pitcher_id'].map(spring_teams)
        ivb_merged = ivb_merged.set_index(['pitcher_id', 'pitcher_name','pitcher_team', 'pitcher_hand', 'pitch_type',])

        return render.DataGrid(
            ivb_merged[['pitches_2023','start_speed_2023', 'ivb_2023', 'hb_2023',
       'spin_rate_2023', 'vaa_2023','haa_2023', 'horizontal_release_2023', 'vertical_release_2023',
       'extension_2023','pitches_spring','start_speed_spring', 'ivb_spring', 'hb_spring',
       'spin_rate_spring','vaa_spring','haa_spring', 'horizontal_release_spring', 'vertical_release_spring',
       'extension_spring',]].reset_index(),
            width='fit-content',
            height=750,
            filters=True,
        )             
               
    @output
    @render.data_frame
    def table_difference():
        grouped_ivb = df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])].groupby(['pitcher_id','pitcher_name','pitcher_hand','pitch_type']).agg(
            pitches = ('start_speed','count'),
            
            start_speed = ('start_speed','mean'),
            ivb = ('ivb','mean'),
            hb = ('hb','mean'),
            spin_rate = ('spin_rate','mean'),
            vaa = ('vaa','mean'),
            haa = ('haa','mean'),
            horizontal_release = ('x0','mean'),
            vertical_release = ('z0','mean'),
            extension = ('extension','mean')).round(1).reset_index()
        grouped_ivb = grouped_ivb.set_index(['pitcher_id','pitch_type'])

        

        #####
        ivb_merged = grouped_ivb_2023.merge(right=grouped_ivb,
                            left_index=True,
                            right_index=True,
                            how='right',suffixes=['_2023','_spring']).reset_index()

        ivb_merged['pitcher_name'] = ivb_merged['pitcher_name_spring']
        ivb_merged['pitcher_hand'] = ivb_merged['pitcher_hand_spring']
        #ivb_merged['pitch_type'] = ivb_merged['pitch_type_spring']


        # ivb_merged = ivb_merged[['pitcher_id', 'pitcher_name', 'pitcher_hand', 'pitch_type',
        #      'pitches_spring', 'start_speed_spring', 'ivb_spring',
        #        'hb_spring', 'spin_rate_spring', 'horizontal_release_spring',
        #        'vertical_release_spring', 'extension_spring']]
        ivb_merged['pitcher_team'] = ivb_merged['pitcher_id'].map(spring_teams)
        ivb_merged = ivb_merged.set_index(['pitcher_id', 'pitcher_name','pitcher_team', 'pitcher_hand', 'pitch_type',])

        ivb_merged[['start_speed_difference', 'ivb_difference', 'hb_difference','spin_rate_difference','vaa_difference','haa_difference',
                'horizontal_release_difference', 'vertical_release_difference',
            'extension_difference']] = ivb_merged[['start_speed_spring', 'ivb_spring', 'hb_spring',
            'spin_rate_spring', 'vaa_spring','haa_spring','horizontal_release_spring', 'vertical_release_spring',
            'extension_spring']].values - ivb_merged[['start_speed_2023', 'ivb_2023', 'hb_2023',
            'spin_rate_2023', 'vaa_2023','haa_2023','horizontal_release_2023', 'vertical_release_2023',
            'extension_2023']].values


        return render.DataGrid(
            ivb_merged[['start_speed_difference', 'ivb_difference', 'hb_difference',
       'spin_rate_difference', 
       'vaa_difference','haa_difference','horizontal_release_difference', 'vertical_release_difference',
       'extension_difference']].reset_index(),
            width='fit-content',
            height=750,
            filters=True,
        )                  

    # @output
    # @render.data_frame
    # def table_new():
    #     grouped_ivb = df_2024.groupby(['pitcher_id','pitcher_name','pitcher_hand','pitch_type']).agg(
    #         pitches = ('start_speed','count'),
            
    #         start_speed = ('start_speed','mean'),
    #         ivb = ('ivb','mean'),
    #         hb = ('hb','mean'),
    #         spin_rate = ('spin_rate','mean'),
    #         vaa = ('vaa','mean'),
    #         haa = ('haa','mean'),
    #         horizontal_release = ('x0','mean'),
    #         vertical_release = ('z0','mean'),
    #         extension = ('extension','mean')).round(1).reset_index()
    #     grouped_ivb = grouped_ivb.set_index(['pitcher_id','pitch_type'])

    #     grouped_ivb_2023 = pd.read_csv('2023_pitch_group_data.csv',index_col=[0,3])

    #     #####
    #     ivb_merged = grouped_ivb_2023.merge(right=grouped_ivb,
    #                         left_index=True,
    #                         right_index=True,
    #                         how='right',suffixes=['_2023','_spring']).reset_index()

    #     ivb_merged['pitcher_name'] = ivb_merged['pitcher_name_spring']
    #     ivb_merged['pitcher_hand'] = ivb_merged['pitcher_hand_spring']
    #     #ivb_merged['pitch_type'] = ivb_merged['pitch_type_spring']


    #     # ivb_merged = ivb_merged[['pitcher_id', 'pitcher_name', 'pitcher_hand', 'pitch_type',
    #     #      'pitches_spring', 'start_speed_spring', 'ivb_spring',
    #     #        'hb_spring', 'spin_rate_spring', 'horizontal_release_spring',
    #     #        'vertical_release_spring', 'extension_spring']]
    #     ivb_merged['pitcher_team'] = ivb_merged['pitcher_id'].map(spring_teams)
    #     ivb_merged = ivb_merged.set_index(['pitcher_id', 'pitcher_name','pitcher_team', 'pitcher_hand', 'pitch_type',])






    #     ivb_merged[['start_speed_difference', 'ivb_difference', 'hb_difference','spin_rate_difference','vaa_difference','haa_difference',
    #             'horizontal_release_difference', 'vertical_release_difference',
    #         'extension_difference']] = ivb_merged[['start_speed_spring', 'ivb_spring', 'hb_spring',
    #         'spin_rate_spring', 'vaa_spring','haa_spring','horizontal_release_spring', 'vertical_release_spring',
    #         'extension_spring']].values - ivb_merged[['start_speed_2023', 'ivb_2023', 'hb_2023',
    #         'spin_rate_2023', 'vaa_2023','haa_2023','horizontal_release_2023', 'vertical_release_2023',
    #         'extension_2023']].values
    #     ivb_merged_new = ivb_merged.reset_index()

    #     ivb_merged_new = ivb_merged_new[
    #         pd.isnull(ivb_merged_new['pitches_2023']) & 
    #         pd.notnull(ivb_merged_new['pitches_spring']) &
    #         ivb_merged_new['pitcher_id'].isin(ivb_merged_new[pd.notnull(ivb_merged_new['pitches_2023'])]['pitcher_id'])
    #     ][

    #         ['pitcher_id', 'pitcher_name', 'pitcher_hand', 'pitch_type',
    #         'pitches_spring', 'start_speed_spring', 'ivb_spring',
    #         'hb_spring', 'spin_rate_spring', 'vaa_spring','haa_spring', 'horizontal_release_spring',
    #         'vertical_release_spring', 'extension_spring']
    #     ]#.reset_index()

    #     # ivb_merged_new = ivb_merged.copy().reset_index()
    #     ivb_merged_new['pitcher_team'] = ivb_merged_new['pitcher_id'].map(spring_teams)
    #     ivb_merged_new = ivb_merged_new.set_index(['pitcher_id', 'pitcher_name','pitcher_team', 'pitcher_hand', 'pitch_type',])
    #     #ivb_merged_new.to_clipboard(header=False)
    #     df_2024_date_min = df_2024.groupby(['pitcher_id','pitcher_name','pitcher_hand','pitch_type','game_date'])[['game_date']].min()
    #     ivb_merged_new = ivb_merged_new.merge(right=df_2024_date_min,
    #                                         left_index=True,
    #                                         right_index=True)
    #     ivb_merged_new = ivb_merged_new.drop(columns=['game_date'])

    #     return render.DataGrid(
    #         ivb_merged_new.reset_index(),
    #         width='fit-content',
    #         height=750,
    #         filters=True,
    #     )                  

    @output
    @render.data_frame
    def table_stuff():
   

        df_2024_update = df_clean(df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])])

        features = ['start_speed','spin_rate','extension','ivb','hb','x0','z0','fb_max_velo_diff','fb_max_ivb_diff','fb_max_hb_diff']

        targets = ['delta_run_exp_mean']


        from scipy import stats
        df_2024_update['y_pred'] = model.predict(df_2024_update[features])


        y_pred_mean = -0.0023964706
        y_pred_std =0.0057581966
        # y_pred_mean = -0.0136602735
        # y_pred_std = 0.006434487

        ## tjStuff+
        df_2024_stuff = df_2024_update.groupby(['pitcher_id','pitcher_name','pitcher_team']).agg(
            pitches = ('y_pred','count'),
            run_exp = ('y_pred','mean'),)
        # run_exp_loc = ('y_pred_loc','mean'))

        df_2024_stuff['run_exp_mean'] = y_pred_mean
        df_2024_stuff['run_exp_std'] = y_pred_std

        df_2024_stuff_50 = df_2024_stuff[df_2024_stuff.pitches >= 1]

        df_2024_stuff_50['tj_stuff_plus'] = 100 + 10*((-df_2024_stuff_50.run_exp + df_2024_stuff_50.run_exp_mean) / df_2024_stuff_50.run_exp_std)


        df_2024_stuff_pitch = df_2024_update.groupby(['pitcher_id','pitcher_name','pitcher_team','pitch_type']).agg(
            pitches = ('y_pred','count'),
            run_exp = ('y_pred','mean'),)
        # run_exp_loc = ('y_pred_loc','mean'))

        df_2024_stuff_pitch['run_exp_mean'] = y_pred_mean
        df_2024_stuff_pitch['run_exp_std'] = y_pred_std

        df_2024_stuff_pitch_50 = df_2024_stuff_pitch[df_2024_stuff_pitch.pitches >= 1]

        df_2024_stuff_pitch_50['tj_stuff_plus'] = 100 + 10*((-df_2024_stuff_pitch_50.run_exp + df_2024_stuff_pitch_50.run_exp_mean) / df_2024_stuff_pitch_50.run_exp_std)

        df_2024_stuff_pitch_50_pivot = df_2024_stuff_pitch_50.reset_index().pivot(index=['pitcher_id','pitcher_name','pitcher_team'],
                                    columns=['pitch_type'],
                                    values=['tj_stuff_plus'])

        df_2024_stuff_pitch_50_pivot['all'] = df_2024_stuff_pitch_50_pivot.index.map(df_2024_stuff_50['tj_stuff_plus'].to_dict())
        ## Difference
        print('Sheet6')
        df_2024_stuff_pitch_50_pivot = df_2024_stuff_pitch_50_pivot.sort_index(level=[1])        
        df_2024_stuff_pitch_50_pivot.columns = df_2024_stuff_pitch_50_pivot.columns.droplevel()
        column_list = list(df_2024_stuff_pitch_50_pivot.columns[:-1])
        column_list.append('All')
        df_2024_stuff_pitch_50_pivot.columns = column_list
        df_2024_stuff_pitch_50_pivot = df_2024_stuff_pitch_50_pivot.applymap(lambda x: int(x) if not pd.isna(x) else x)
        df_2024_stuff_pitch_50_pivot = df_2024_stuff_pitch_50_pivot.reset_index()

        return render.DataGrid(
            df_2024_stuff_pitch_50_pivot,
            width='fit-content',
            height=750,
            filters=True)          
    
    @output
    @render.data_frame
    def table_stuff_day():


        df_2024_update = df_clean(df_2024[(pd.to_datetime(df_2024['game_date']).dt.date>=input.date_range_id()[0])&
                              (pd.to_datetime(df_2024['game_date']).dt.date<=input.date_range_id()[1])])
        print('made it here')

        features = ['start_speed','spin_rate','extension','ivb','hb','x0','z0','fb_max_velo_diff','fb_max_ivb_diff','fb_max_hb_diff']

        targets = ['delta_run_exp_mean']


        from scipy import stats
        df_2024_update['y_pred'] = model.predict(df_2024_update[features])


        y_pred_mean = -0.0023964706
        y_pred_std =0.0057581966
        # y_pred_mean = -0.0136602735
        # y_pred_std = 0.006434487

        ## tjStuff+
        df_2024_stuff_daily = df_2024_update.groupby(['pitcher_id','pitcher_name','pitcher_team','game_date']).agg(
            pitches = ('y_pred','count'),
            run_exp = ('y_pred','mean'),)
        # run_exp_loc = ('y_pred_loc','mean'))

        df_2024_stuff_daily['run_exp_mean'] = y_pred_mean
        df_2024_stuff_daily['run_exp_std'] = y_pred_std

        df_2024_stuff_daily_50 = df_2024_stuff_daily[df_2024_stuff_daily.pitches >= 1]

        df_2024_stuff_daily_50['tj_stuff_plus'] = 100 + 10*((-df_2024_stuff_daily_50.run_exp + df_2024_stuff_daily_50.run_exp_mean) / df_2024_stuff_daily_50.run_exp_std)


        df_2024_stuff_daily_pitch = df_2024_update.groupby(['pitcher_id','pitcher_name','pitcher_team','pitch_type','game_date']).agg(
            pitches = ('y_pred','count'),
            run_exp = ('y_pred','mean'),)
        # run_exp_loc = ('y_pred_loc','mean'))

        df_2024_stuff_daily_pitch['run_exp_mean'] = y_pred_mean
        df_2024_stuff_daily_pitch['run_exp_std'] = y_pred_std

        df_2024_stuff_daily_pitch_50 = df_2024_stuff_daily_pitch[df_2024_stuff_daily_pitch.pitches >= 1]

        df_2024_stuff_daily_pitch_50['tj_stuff_plus'] = 100 + 10*((-df_2024_stuff_daily_pitch_50.run_exp + df_2024_stuff_daily_pitch_50.run_exp_mean) / df_2024_stuff_daily_pitch_50.run_exp_std)
        df_2024_stuff_daily_pitch_50 = df_2024_stuff_daily_pitch_50.reset_index()
        df_2024_stuff_daily_pitch_50_pivot = df_2024_stuff_daily_pitch_50.pivot(index=['pitcher_id','pitcher_name','pitcher_team','game_date'],
                                    columns=['pitch_type'],
                                    values=['tj_stuff_plus'])
        print('made it here')
        df_2024_stuff_daily_pitch_50_pivot['all'] = df_2024_stuff_daily_pitch_50_pivot.index.map(df_2024_stuff_daily_50['tj_stuff_plus'].to_dict())
        df_2024_stuff_daily_pitch_50_pivot = df_2024_stuff_daily_pitch_50_pivot.sort_index(level=[1,3])   
        print(df_2024_stuff_daily_pitch_50_pivot)

        df_2024_stuff_daily_pitch_50_pivot.columns = df_2024_stuff_daily_pitch_50_pivot.columns.droplevel()
        column_list = list(df_2024_stuff_daily_pitch_50_pivot.columns[:-1])
        column_list.append('All')
        df_2024_stuff_daily_pitch_50_pivot.columns = column_list
        df_2024_stuff_daily_pitch_50_pivot = df_2024_stuff_daily_pitch_50_pivot.applymap(lambda x: int(x) if not pd.isna(x) else x)
        df_2024_stuff_daily_pitch_50_pivot = df_2024_stuff_daily_pitch_50_pivot.reset_index()

        return render.DataGrid(
            df_2024_stuff_daily_pitch_50_pivot,
            width='fit-content',
            height=750,
            filters=True)          









app = App(app_ui, server)