app.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pitch_summary_functions as psf
import requests
import matplotlib
from api_scraper import MLB_Scrape
from shinywidgets import output_widget, render_widget
import shinyswatch



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

import datasets
from datasets import load_dataset
### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2024.csv' ])
dataset_train = dataset['train']
df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True).drop_duplicates(subset=['play_id'],keep='last')


df_2024.loc[(df_2024['pitcher_id']==804636)&(df_2024['pitch_type'].isin(['FF','FC']),'start_speed'] = df_2024.loc[(df_2024['pitcher_id']==804636)&(df_2024['pitch_type'].isin(['FF','FC']),'start_speed'] + 3
# ### Import Datasets
# import datasets
# from datasets import load_dataset
# dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2020.csv' ])
# dataset_train = dataset['train']
# df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)

### PITCH COLOURS ###
pitch_colours = {
        'Four-Seam Fastball':'#FF007D',#BC136F
        'Fastball':'#FF007D',
        'Sinker':'#98165D',#DC267F
        'Cutter':'#BE5FA0',

        'Changeup':'#F79E70',#F75233
        'Splitter':'#FE6100',#F75233
        'Screwball':'#F08223',
        'Forkball':'#FFB000',
        
        'Slider':'#67E18D',#1BB999#785EF0
        'Sweeper':'#1BB999',#37CD85#904039
        'Slurve':'#376748',#785EF0#549C07#BEABD8

        'Knuckle Curve':'#311D8B',  
        'Curveball':'#3025CE',
        'Slow Curve':'#274BFC',
        'Eephus':'#648FFF',

        'Knuckleball':'#867A08',

        'Pitch Out':'#472C30',
        'Other':'#9C8975',
        }

spring_teams = df_2024.groupby(['pitcher_id']).tail(1)[['pitcher_id','pitcher_team']].set_index(['pitcher_id'])['pitcher_team'].to_dict()

season_start = '2024-03-20'
season_end = '2024-09-29'
season_fg=2024 
#chad_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season={season_fg}&season={season_fg}&month=1000&season1={season_fg}&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching').json()


# chadwick_df_small = pd.DataFrame(data={
# 'key_mlbam':[x['xMLBAMID'] for x in chad_fg['data']],
# 'key_fangraphs':[x['playerid'] for x in chad_fg['data']],
# 'Name':[x['PlayerName'] for x in chad_fg['data']],
# })    


# mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict()


statcast_pitch_summary = pd.read_csv('statcast_pitch_summary.csv')
cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])


df_2024_codes = psf.df_update_code(df_2024)

df_2024_update = psf.df_clean(df_2024_codes)
import joblib
model = joblib.load('joblib_model/tjstuff_model_20240318.joblib')
y_pred_mean = 0.0011434511
y_pred_std = 0.006554768

xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')

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']


df_2024_update['y_pred'] = model.predict(df_2024_update[features])

df_2024_update['tj_stuff_plus'] = 100 + 10*((-df_2024_update.y_pred +y_pred_mean) / y_pred_std)

df_2024_update['woba_pred'] = np.nan

df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']  = [sum(x) for x in xwoba_model.predict_proba(df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]

pitcher_dicts = df_2024_update.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict()

team_logos = pd.read_csv('team_logos.csv')

mlb_stats = MLB_Scrape()
teams_df = mlb_stats.get_teams()
team_logo_dict = teams_df.set_index(['team_id'])['parent_org_id'].to_dict()

font_properties = {'family': 'calibi', 'size': 12}
font_properties_titles = {'family': 'calibi', 'size': 20}
font_properties_axes = {'family': 'calibi', 'size': 16}
df_plot = []
ax2_loc = []
gs = []
fig = []

function_dict={
    'velocity_kde':'Velocity Distributions',
    'break_plot':'Pitch Movement',
    'rolling_tj_stuff':'Rolling tjStuff+',
    'location_lhb':'Locations vs LHB',
    'location_rhb':'Locations vs RHB',
}

split_dict =  {'all':'All',
               'left':'LHB',
               'right':'RHB'}

split_dict_hand =  {'all':['L','R'],
               'left':['L'],
               'right':['R']}

ball_dict =  {'0':'0',
               '1':'1',
               '2':'2',
               '3':'3'}

strike_dict =  {'0':'0',
               '1':'1',
               '2':'2'}

# count_dict = {'0_0':'Through 0-0',
#                  '0_1':'Through 0-1',
#                  '0_2':'Through 0-2',
#                  '1_0':'Through 1-0',
#                  '1_1':'Through 1-1',
#                  '1_2':'Through 1-2',
#                  '2_1':'Through 2-1',
#                  '2_0':'Through 2-0',
#                  '3_0':'Through 3-0',
#                  '3_1':'Through 3-1',
#                  '2_2':'Through 2-2',
#                  '3_2':'Through 3-2'}

# count_dict_fg = {'0_0':'',
#                  '0_1':'61',
#                  '0_2':'62',
#                  '1_0':'63',
#                  '1_1':'64',
#                  '1_2':'65',
#                  '2_1':'66',
#                  '2_0':'67',
#                  '3_0':'68',
#                  '3_1':'69',
#                  '2_2':'70',
#                  '3_2':'71'}

from urllib.request import Request, urlopen
from shiny import App, reactive, ui, render
from shiny.ui import h2, tags
# importing OpenCV(cv2) module
app_ui = ui.page_fluid(
    ui.tags.div(
         {"style": "width:90%;margin: 0 auto;max-width: 1600px;"},
        ui.tags.style(
            """
            h4 {
                margin-top: 1em;font-size:35px;
            }
            h2{
                font-size:25px;
            }
            """
         ),
    shinyswatch.theme.simplex(),
    ui.tags.h4("TJStats"),
    ui.tags.i("Baseball Analytics and Visualizations"),
    ui.row(

    
    ui.layout_sidebar(
 
        ui.panel_sidebar(
                ui.row(
                ui.column(6,
                    ui.input_select('player_id','Select Player',pitcher_dicts,selectize=True,multiple=False)),
                ui.column(6,    ui.output_ui('test','Select Game'))),
                
                ui.row(
                ui.column(4,
                    ui.input_select('plot_id_1','Plot Left',function_dict,multiple=False,selected='velocity_kde')),
                ui.column(4,
                    ui.input_select('plot_id_2','Plot Middle',function_dict,multiple=False,selected='rolling_tj_stuff')),
                ui.column(4,
                    ui.input_select('plot_id_3','Plot Right',function_dict,multiple=False,selected='break_plot'))),

                # ui.input_select('count_id','Count',count_dict,multiple=True,selectize=True,selected='0_0'),

                ui.row(
                ui.column(6,
                        ui.input_select('ball_id','Balls',ball_dict,multiple=False,selected='0'),
                                                ui.input_radio_buttons(
                                                    "count_id_balls",
                                                    "Count Filter Balls",
                                                    {
                                                        "exact": "Exact Balls",
                                                        "greater": ">= Balls",
                                                        "lesser": "<= Balls",
                                                    },selected='greater')),
                ui.column(6,                                    
                        ui.input_select('strike_id','Strikes',strike_dict,multiple=False,selected='0'),
                        ui.input_radio_buttons(
                                                    "count_id_strikes",
                                                    "Count Filter Strikes",
                                                    {
                                                        "exact": "Exact Strikes",
                                                        "greater": ">= Strikes",
                                                        "lesser": "<= Strikes",
                                                    },selected='greater'))),
                ui.row(
                    ui.column(6,
                        ui.input_select('split_id','Select Split',split_dict,multiple=False)),
                    ui.column(6,
                        ui.input_numeric('rolling_window','Rolling Window (for tjStuff+ Plot)',min=1,value=10))),


                
                ui.input_action_button("go", "Generate",class_="btn-primary"),
                               
                                       
                                       width=4)
                ,
        ui.panel_main(     
            ui.navset_tab(
            # ui.nav("Raw Data",
            #     ui.output_data_frame("raw_table")),                
            ui.nav("Season Summary",
                   ui.output_plot('plot',
                                  width='2000px',
                                  height='2000px')),
            ui.nav("Game Summary",
                   ui.output_plot('plot_game',
                                  width='2000px',
                                  height='2000px'))
                ,id="my_tabs"))))))










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

    @render.ui
    def test():

            # @reactive.Effect
            if input.my_tabs() == 'Season Summary':   

                return 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()),        
            # @reactive.Effect
            if input.my_tabs() == 'Game Summary':
                pitcher_id_select = int(input.player_id())
                df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)]
                

                # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
                df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str)
                #print(df_plot['game_opp'])

                date_dict = pd.concat([df_plot.drop_duplicates(subset=['pitcher_id','game_id','game_opp'])[['game_id','game_opp']]]).set_index('game_id').to_dict()
                return ui.input_select("game_id", "Select Game",date_dict,selectize=True)
            
    @output
    @render.plot
    @reactive.event(input.go, ignore_none=False)
    def plot():
        #fig, ax = plt.subplots(3, 2, figsize=(9, 9))

        font_properties = {'family': 'calibi', 'size': 12}
        font_properties_titles = {'family': 'calibi', 'size': 20}
        font_properties_axes = {'family': 'calibi', 'size': 16}
                
        if len((input.player_id()))<1:
            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
            ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center')
            ax.grid('off')
            return                
        
        pitcher_id_select = int(input.player_id())


        df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)]
        df_plot = df_plot[(pd.to_datetime(df_plot['game_date']).dt.date>=input.date_range_id()[0])&
                                            (pd.to_datetime(df_plot['game_date']).dt.date<=input.date_range_id()[1])]
        
        df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])]

        if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0:
            ball_title = ''
            strike_title = ''
        else: 
            if input.count_id_balls()=='exact':
                df_plot = df_plot[df_plot['balls']==int(input.ball_id())]
                ball_title = str(f'{(input.ball_id())} Ball Count; ')                                                   
            elif input.count_id_balls()=='greater':
                df_plot = df_plot[df_plot['balls']>=int(input.ball_id())]
                ball_title = str(f'At Least {(input.ball_id())} Ball Count; ')                         
            elif input.count_id_balls()=='lesser':  
                df_plot = df_plot[df_plot['balls']<=int(input.ball_id())]
                ball_title = str(f'At Most {(input.ball_id())} Ball Count; ')                                        

            if input.count_id_strikes()=='exact':
                df_plot = df_plot[df_plot['strikes']==int(input.strike_id())]     
                strike_title = str(f'{(input.strike_id())} Strike Count; ')                           
            elif input.count_id_strikes()=='greater':
                df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())]     
                strike_title = str(f'At Least {(input.strike_id())} Strike Count; ')            
            elif input.count_id_strikes()=='lesser':
                df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())]  
                strike_title = str(f'At Most {(input.strike_id())} Strike Count; ')                                        
                                      


        if input.split_id() == 'all':
            split_title = ''

        elif input.split_id() == 'left':
            split_title = 'vs. LHH'
            
        elif input.split_id() == 'right':
            split_title = 'vs. RHH'


        if len(df_plot)<1:
            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
            ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center')
            ax.grid('off')
            return

        df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1
        df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1
        #df_plot = df_plot.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left')
        df_plot = df_plot.sort_values(by=['pitch_description'])
        df_plot = df_plot.sort_values(by=['start_time'])

        grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description'])
        grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand'])



        from matplotlib.gridspec import GridSpec
        plt.rcParams['font.family'] = 'Calibri'
        df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum")
        label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()

        #plt.rcParams["figure.figsize"] = [10,10]
        fig = plt.figure(figsize=(20, 20))
        plt.rcParams.update({'figure.autolayout': True})
        fig.set_facecolor('white')
        sns.set_theme(style="whitegrid", palette=colour_palette)
        print('this is the one plot')
        # gs = GridSpec(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5])
        gs = GridSpec(5, 5,  height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1])
        #### NO FG
        ####gs = GridSpec(5, 5,  height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1])
        #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4])

        gs.update(hspace=0.2, wspace=0.3)

        # Add subplots to the grid
        ax0 = fig.add_subplot(gs[0, :]) 
        ax1_table = fig.add_subplot(gs[1, :])
        ax2_left = fig.add_subplot(gs[2, 1]) 
        ax2_middle = fig.add_subplot(gs[2, 2]) 
        ax2_right = fig.add_subplot(gs[2, 3])
        ax3 = fig.add_subplot(gs[-2, :])
        #axfooter = fig.add_subplot(gs[-1, :])

        ax1_table.axis('off')

        sns.set_theme(style="whitegrid", palette=colour_palette)
        fig.set_facecolor('white')

        font_properties = {'family': 'calibi', 'size': 12}
        font_properties_titles = {'family': 'calibi', 'size': 20}
        font_properties_axes = {'family': 'calibi', 'size': 16}

        # ## FANGRAPHS TABLE ###
        # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select,
        #                     split=input.split_id(),
        #                     start_date=input.date_range_id()[0],
        #                     end_date=input.date_range_id()[1])


        # psf.fangraphs_table(data=data_pull,
        #                     stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'],
        #                     ax=ax1_table)

        start_date = str(pd.to_datetime(input.date_range_id()[0]).strftime('%m/%d/%Y'))
        end_date = str(pd.to_datetime(input.date_range_id()[1]).strftime('%m/%d/%Y'))


        pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json()

        pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
        pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
        pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0])
        pitcher_stats_call_df['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced']
        pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced']
        pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent']
        pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','era','whip','k_percent','bb_percent','k_bb_percent']]

        pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
        pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
        pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 

        table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center',
                        bbox=[0.04, 0.2, 0.92, 0.8])

        min_font_size = 20
        table_fg.set_fontsize(min_font_size)


        new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ERA}$','$\\bf{WHIP}$','$\\bf{K\%}$','$\\bf{BB\%}$','$\\bf{K-BB\%}$']
        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
        for i, col_name in enumerate(new_column_names):
            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)

        ax1_table.axis('off')



        for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax2_left,ax2_middle,ax2_right],[1,2,3]):
            if x == 'velocity_kde':
                psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig)
            if x == 'rolling_tj_stuff':
                psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y)
            if x == 'break_plot':
                psf.break_plot(df=df_plot,ax=y) 
            if x == 'location_lhb':
                psf.location_plot(df=df_plot,ax=y,hand='L')
            if x == 'location_rhb':
                psf.location_plot(df=df_plot,ax=y,hand='R')

        pitches_list = df_plot['pitch_description'].unique()
        colour_pitches = [pitch_colours[x] for x in pitches_list]
        
    #     handles, labels = ax2_right.get_legend_handles_labels()

    #   # Manually create handles and labels for each pitch-color pair
        handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
        labels = pitches_list  
        
                


        ### FANGRAPHS TABLE ###
        psf.table_summary(df=df_plot.copy(),
                        pitcher_id=pitcher_id_select,
                        ax=ax3,
                        df_group=grouped_ivb.copy(),
                        df_group_all=grouped_ivb_all.copy(),
                        statcast_pitch_summary=statcast_pitch_summary.copy())


        # ############ FOOTER ################
        # #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
        # axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
        # axfooter.text(x=1-0.05,y=1,s='Data: MLB, Fangraphs',ha='right',fontname='Calibri',fontsize=24,va='top')


        # axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
        #             ha='center',va='center',fontname='Calibri',fontsize=16)
        # axfooter.axis('off')
        # #fig.tight_layout()

        

        # Get value counts of the column and sort in descending order
        sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False)

        # Get the list of items ordered from most to least frequent
        items_in_order = sorted_value_counts.index.tolist()
        # Create a dictionary to map names to colors
        name_to_color = dict(zip(labels, handles))

        # Order the colors based on the correct order of names
        ordered_colors = [name_to_color[name] for name in items_in_order]


        ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5,
                fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20})


        ################## Title ##########
        title_spot = f'{df_plot.pitcher_name.values[0]}'


        ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top')
        ax0.text(x=0.5,y=0.35,s='A Season Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic')

        player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
        #ax0.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
        ax0.axis('off')
        ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top')

        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
        # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')
        # ax0.axis('off')


        ax0.text(x=0.5,y=0.15,s=f'{input.date_range_id()[0]} to {input.date_range_id()[1]}',fontname='Calibri',ha='center',fontsize=30,va='top',fontstyle='italic')
        
        ax0.text(x=0.5,y=0.0,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
        ax0.axis('off')

        from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)
        import urllib
        import urllib.request
        import urllib.error
        from urllib.error import HTTPError

        try: 
            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png'
            test_mage = plt.imread(url)
        except urllib.error.HTTPError as err:
            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png'                
            test_mage = plt.imread(url)    
        imagebox = OffsetImage(test_mage, zoom = 0.5)
        ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False)
        ax0.add_artist(ab)

        #player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()




        if 'currentTeam' in player_bio['people'][0]:
            try:
                url = team_logos[team_logos['id'] == team_logo_dict[player_bio['people'][0]['currentTeam']['id']]]['imageLink'].values[0]

                im = plt.imread(url)
                # response = requests.get(url)
                # im = Image.open(BytesIO(response.content))
                # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0])
                # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
                imagebox = OffsetImage(im, zoom = 0.4)
                ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False)
                ax0.add_artist(ab)
            except IndexError:
                print()
            

        ############ FOOTER ################
        #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
        axfooter = fig.add_subplot(gs[-1, :])
        axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
        axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top')


        axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
                    ha='center',va='center',fontname='Calibri',fontsize=16)
        axfooter.axis('off')
        #fig.tight_layout()

        fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)


    @output
    @render.plot
    @reactive.event(input.go, ignore_none=False)
    def plot_game():
        #fig, ax = plt.subplots(3, 2, figsize=(9, 9))

        font_properties = {'family': 'calibi', 'size': 12}
        font_properties_titles = {'family': 'calibi', 'size': 20}
        font_properties_axes = {'family': 'calibi', 'size': 16}
                
        if len((input.player_id()))<1:
            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
            ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center')
            ax.grid('off')
            return        
        
        pitcher_id_select = int(input.player_id())




        df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)&(df_2024_update['game_id']==int(input.game_id()))]
        df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])]

        if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0:
            ball_title = ''
            strike_title = ''
        else: 
            if input.count_id_balls()=='exact':
                df_plot = df_plot[df_plot['balls']==int(input.ball_id())]
                ball_title = str(f'{(input.ball_id())} Ball Count; ')                                                   
            elif input.count_id_balls()=='greater':
                df_plot = df_plot[df_plot['balls']>=int(input.ball_id())]
                ball_title = str(f'At Least {(input.ball_id())} Ball Count; ')                         
            elif input.count_id_balls()=='lesser':  
                df_plot = df_plot[df_plot['balls']<=int(input.ball_id())]
                ball_title = str(f'At Most {(input.ball_id())} Ball Count; ')                                        

            if input.count_id_strikes()=='exact':
                df_plot = df_plot[df_plot['strikes']==int(input.strike_id())]     
                strike_title = str(f'{(input.strike_id())} Strike Count; ')                           
            elif input.count_id_strikes()=='greater':
                df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())]     
                strike_title = str(f'At Least {(input.strike_id())} Strike Count; ')            
            elif input.count_id_strikes()=='lesser':
                df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())]  
                strike_title = str(f'At Most {(input.strike_id())} Strike Count; ')                                        
                                      

                                    


        if input.split_id() == 'all':
            split_title = ''

        elif input.split_id() == 'left':
            split_title = 'vs. LHH'
            
        elif input.split_id() == 'right':
            split_title = 'vs. RHH'

        if len(df_plot)<1:
            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
            ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center')
            ax.grid('off')
            return


        df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1
        df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1
        #df_plot = df_plot.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left')
        df_plot = df_plot.sort_values(by=['pitch_description'])
        df_plot = df_plot.sort_values(by=['start_time'])

        # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
        df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str)
        #print(df_plot['game_opp'])

        #date_dict = pd.concat([df_plot.drop_duplicates(subset=['pitcher_id','game_id','game_opp'])[['game_id','game_opp']]]).set_index('game_id').to_dict()

        grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description'])
        grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand'])



        from matplotlib.gridspec import GridSpec
        plt.rcParams['font.family'] = 'Calibri'
        df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum")
        label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()

        #plt.rcParams["figure.figsize"] = [10,10]
        fig = plt.figure(figsize=(20, 20))
        plt.rcParams.update({'figure.autolayout': True})
        fig.set_facecolor('white')
        sns.set_theme(style="whitegrid", palette=colour_palette)
        print('this is the one plot')
        # gs = GridSpec(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5])
        gs = GridSpec(5, 5,  height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1])
        #### NO FG
        ####gs = GridSpec(5, 5,  height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1])
        #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4])

        gs.update(hspace=0.2, wspace=0.3)

        # Add subplots to the grid
        ax0 = fig.add_subplot(gs[0, :]) 
        ax1_table = fig.add_subplot(gs[1, :])
        ax2_left = fig.add_subplot(gs[2, 1]) 
        ax2_middle = fig.add_subplot(gs[2, 2]) 
        ax2_right = fig.add_subplot(gs[2, 3])
        ax3 = fig.add_subplot(gs[-2, :])
        # axfooter = fig.add_subplot(gs[-1, :])

        ax1_table.axis('off')

        sns.set_theme(style="whitegrid", palette=colour_palette)
        fig.set_facecolor('white')

        font_properties = {'family': 'calibi', 'size': 12}
        font_properties_titles = {'family': 'calibi', 'size': 20}
        font_properties_axes = {'family': 'calibi', 'size': 16}

        print(df_2024_update['game_date'].values[0])
        # ## FANGRAPHS TABLE ###
        # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select,
        #                     split=input.split_id(),
        #                     start_date=df_plot['game_date'].values[0],
        #                     end_date=df_plot['game_date'].values[0])

        start_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y'))
        end_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y'))


        pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json()
        
        pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
        pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
        pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0])
        # pitcher_stats_call_df['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced']
        # pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced']
        # pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent']
        pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','earnedRuns','hits','strikeOuts','baseOnBalls','hitByPitch','homeRuns']]
        pitcher_stats_call_df_small['whiffs'] = int(df_plot['is_whiff'].sum())
        # pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
        # pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
        # pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 

        table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center',
                        bbox=[0.04, 0.2, 0.92, 0.8])

        min_font_size = 20
        table_fg.set_fontsize(min_font_size)


        new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ER}$','$\\bf{H}$','$\\bf{K}$','$\\bf{BB}$','$\\bf{HBP}$','$\\bf{HR}$','$\\bf{Whiffs}$']
        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
        for i, col_name in enumerate(new_column_names):
            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)

        ax1_table.axis('off')


        # psf.fangraphs_table(data=data_pull,
        #                     stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'],
        #                     ax=ax1_table)


        # psf.velocity_kdes(df=df_plot,
        #                 ax=ax2_loc,
        #                 gs=gs,
        #                 fig=fig)

        # # psf.tj_stuff_roling(df = df_plot,
        # #                     window = 5,
        # #                     ax=ax2_velo)
        # psf.location_plot(df=df_plot,ax=ax2_velo,hand='L')

        # psf.location_plot(df=df_plot,ax=ax2_loc,hand='R')
        # # # ## Break Plot
        # psf.break_plot(df=df_plot,ax=ax2) 
        for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax2_left,ax2_middle,ax2_right],[1,2,3]):
            if x == 'velocity_kde':
                psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig)
            if x == 'rolling_tj_stuff':
                psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y)
            if x == 'break_plot':
                psf.break_plot(df=df_plot,ax=y) 
            if x == 'location_lhb':
                psf.location_plot(df=df_plot,ax=y,hand='L')
            if x == 'location_rhb':
                psf.location_plot(df=df_plot,ax=y,hand='R')

        pitches_list = df_plot['pitch_description'].unique()
        colour_pitches = [pitch_colours[x] for x in pitches_list]
        
    #     handles, labels = ax2_right.get_legend_handles_labels()

    #   # Manually create handles and labels for each pitch-color pair
        handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
        labels = pitches_list  


        ### FANGRAPHS TABLE ###
        psf.table_summary(df=df_plot.copy(),
                        pitcher_id=pitcher_id_select,
                        ax=ax3,
                        df_group=grouped_ivb.copy(),
                        df_group_all=grouped_ivb_all.copy(),
                        statcast_pitch_summary=statcast_pitch_summary.copy())





        # Get value counts of the column and sort in descending order
        sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False)

        # Get the list of items ordered from most to least frequent
        items_in_order = sorted_value_counts.index.tolist()
        # Create a dictionary to map names to colors
        name_to_color = dict(zip(labels, handles))

        # Order the colors based on the correct order of names
        ordered_colors = [name_to_color[name] for name in items_in_order]


        ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5,
                fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20})


        ################## Title ##########
        title_spot = f'{df_plot.pitcher_name.values[0]}'


        ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top')
        ax0.text(x=0.5,y=0.35,s='A Game Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic')


        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
        #ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')
        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
        # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')

        ax0.text(x=0.5,y=0.15,s= df_plot['game_opp'].values[0],fontname='Calibri',ha='center',fontstyle='italic',fontsize=30,va='top')
        
        ax0.text(x=0.5,y=0.00,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
        ax0.axis('off')


        from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)
        import urllib
        import urllib.request
        import urllib.error
        from urllib.error import HTTPError

        try: 
            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png'
            test_mage = plt.imread(url)
        except urllib.error.HTTPError as err:
            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png'                
            test_mage = plt.imread(url)
        imagebox = OffsetImage(test_mage, zoom = 0.5)
        ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False)
        ax0.add_artist(ab)

        #player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()

        player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
        #ax0.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
        ax0.axis('off')
        ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top')



        if 'currentTeam' in player_bio['people'][0]:
            try:
                url = team_logos[team_logos['id'] == team_logo_dict[player_bio['people'][0]['currentTeam']['id']]]['imageLink'].values[0]

                im = plt.imread(url)
                # response = requests.get(url)
                # im = Image.open(BytesIO(response.content))
                # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0])
                # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
                imagebox = OffsetImage(im, zoom = 0.4)
                ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False)
                ax0.add_artist(ab)
            except IndexError:
                print()
            
        ############ FOOTER ################
        #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
        axfooter = fig.add_subplot(gs[-1, :])
        axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
        axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top')


        axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
                    ha='center',va='center',fontname='Calibri',fontsize=16)
        axfooter.axis('off')
        #fig.tight_layout()


        fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)

app = App(app_ui, server)