from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
import datasets
from datasets import load_dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib
from matplotlib.ticker import MaxNLocator
from matplotlib.gridspec import GridSpec
from scipy.stats import zscore
import math
import matplotlib
from adjustText import adjust_text
import matplotlib.ticker as mtick
from shinywidgets import output_widget, render_widget
import pandas as pd
#from configure import base_url
import shinyswatch
import inflect
from matplotlib.pyplot import text
import rolling_pitcher_functions as rpf

def percentile(n):
    def percentile_(x):
        return np.nanpercentile(x, n)
    percentile_.__name__ = 'percentile_%s' % n
    return percentile_

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



print('Starting Everything:')
# exit_velo_df = milb_a_ev_df.append([triple_a_ev_df,double_a_ev_df,a_high_a_ev_df,single_a_ev_df]).reset_index(drop=True)
# player_df_all = mlb_a_player_df.append([triple_a_player_df,double_a_player_df,a_high_a_player_df,single_a_player_df]).reset_index(drop=True)
# exit_velo_df = pd.read_csv('exit_velo_df_all.csv',index_col=[0])
# player_df_all = pd.read_csv('player_df_all.csv',index_col=[0])

# pa_df = pd.read_csv('pa_df_all.csv',index_col=[0])
# pa_df_full_na = pa_df.dropna()

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2024.csv',
                                                         ])
dataset_train = dataset['train']
exit_velo_df_mlb = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
#print(df_2023)
exit_velo_df_mlb['level'] = 'MLB'

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['aaa_pitch_data_2024.csv',
                                                         ])
dataset_train = dataset['train']
exit_velo_df_aaa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
#print(df_2023)
exit_velo_df_aaa['level'] = 'AAA'

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['aa_pitch_data_2024.csv',
                                                        ])
dataset_train = dataset['train']
exit_velo_df_aa = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
#print(df_2023)
exit_velo_df_aa['level'] = 'AA'

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['high_a_pitch_data_2024.csv',
                                                         ])
dataset_train = dataset['train']
exit_velo_df_ha = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
#print(df_2023)
exit_velo_df_ha['level'] = 'A+'

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2024.csv',
                                                         ])
dataset_train = dataset['train']
exit_velo_df_a = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
#print(df_2023)
exit_velo_df_a['level'] = 'A'

exit_velo_df = pd.concat([exit_velo_df_mlb,exit_velo_df_aaa,exit_velo_df_aa,exit_velo_df_ha,exit_velo_df_a])

# exit_velo_df_copy = exit_velo_df.copy()

# exit_velo_df = exit_velo_df_copy.copy()

end_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
       'double', 'sac_fly', 'force_out', 'home_run',
       'grounded_into_double_play', 'fielders_choice', 'field_error',
       'triple', 'sac_bunt', 'double_play', 'intent_walk',
       'fielders_choice_out', 'strikeout_double_play',
       'sac_fly_double_play', 'catcher_interf', 'other_out']



exit_velo_df['pa'] = exit_velo_df.event_type.isin(end_codes)
#exit_velo_df['pa'] = 1
exit_velo_df['k'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
exit_velo_df['bb'] = exit_velo_df.event_type.isin(list(filter(None, [x if 'walk' in x else '' for x in exit_velo_df.event_type.fillna('None').unique()])))
exit_velo_df['k_minus_bb'] = exit_velo_df['k'].astype(np.float32)-exit_velo_df['bb'].astype(np.float32)

exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])



swing_codes = ['Swinging Strike', 'In play, no out', 
        'Foul', 'In play, out(s)', 
       'In play, run(s)', 'Swinging Strike (Blocked)',
       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']

swings_in = ['Swinging Strike', 'In play, no out', 
        'Foul', 'In play, out(s)', 
       'In play, run(s)', 'Swinging Strike (Blocked)',
       'Foul Bunt','Foul Tip', 'Missed Bunt','Foul Pitchout','Swinging Pitchout']       

swing_strike_codes = ['Swinging Strike',
 'Swinging Strike (Blocked)','Missed Bunt','Foul Tip','Swinging Pitchout']


contact_codes = ['In play, no out', 
        'Foul', 'In play, out(s)', 
       'In play, run(s)', 
       'Foul Bunt']

codes_in = ['In play, out(s)',
'Swinging Strike',
'Ball',
'Foul',
'In play, no out',
'Called Strike',
'Foul Tip',
'In play, run(s)',
'Hit By Pitch',
'Ball In Dirt',
'Pitchout',
'Swinging Strike (Blocked)',
'Foul Bunt',
'Missed Bunt',
'Foul Pitchout',
'Intent Ball',
'Swinging Pitchout']

exit_velo_df['in_zone'] = exit_velo_df['zone'] < 10


exit_velo_df = exit_velo_df.drop_duplicates(subset=['play_id'])

exit_velo_df_codes = exit_velo_df[exit_velo_df.play_description.isin(codes_in)].dropna(subset=['in_zone'])

exit_velo_df_codes['bip'] = ~exit_velo_df_codes.launch_speed.isna()
conditions = [
    (exit_velo_df_codes['launch_speed'].isna()),
    (exit_velo_df_codes['launch_speed']*1.5 - exit_velo_df_codes['launch_angle'] >= 117 ) & (exit_velo_df_codes['launch_speed'] + exit_velo_df_codes['launch_angle'] >= 124) & (exit_velo_df_codes['launch_speed'] > 98) & (exit_velo_df_codes['launch_angle'] >= 8) & (exit_velo_df_codes['launch_angle'] <= 50)
]

choices = [False,True]
exit_velo_df_codes['barrel'] = np.select(conditions, choices, default=np.nan)

conditions_ss = [
    (exit_velo_df_codes['launch_angle'].isna()),
    (exit_velo_df_codes['launch_angle'] >= 8 ) * (exit_velo_df_codes['launch_angle'] <= 32 ) 
]

choices_ss = [False,True]
exit_velo_df_codes['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
conditions_hh = [
    (exit_velo_df_codes['launch_speed'].isna()),
    (exit_velo_df_codes['launch_speed'] >= 94.5 ) 
]

choices_hh = [False,True]
exit_velo_df_codes['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)


conditions_tb = [
    (exit_velo_df_codes['event_type']=='single'),
    (exit_velo_df_codes['event_type']=='double'),
    (exit_velo_df_codes['event_type']=='triple'),
    (exit_velo_df_codes['event_type']=='home_run'),
]

choices_tb = [1,2,3,4]

exit_velo_df_codes['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)

conditions_woba = [
    (exit_velo_df_codes['event_type']=='walk'),
    (exit_velo_df_codes['event_type']=='hit_by_pitch'),    
    (exit_velo_df_codes['event_type']=='single'),
    (exit_velo_df_codes['event_type']=='double'),
    (exit_velo_df_codes['event_type']=='triple'),
    (exit_velo_df_codes['event_type']=='home_run'),
]

choices_woba = [0.705,
                0.688,
                0.897,
                1.233,
                1.612,
                2.013]

exit_velo_df_codes['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)


woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
       'double', 'sac_fly', 'force_out', 'home_run',
       'grounded_into_double_play', 'fielders_choice', 'field_error',
       'triple', 'sac_bunt', 'double_play',
       'fielders_choice_out', 'strikeout_double_play',
       'sac_fly_double_play', 'other_out']





conditions_woba_code = [
    (exit_velo_df_codes['event_type'].isin(woba_codes))
]

choices_woba_code = [1]

exit_velo_df_codes['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)


#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)



#exit_velo_df_codes['barrel']  = (exit_velo_df_codes.launch_speed >= 98) & (exit_velo_df_codes.launch_angle >= (26 - (-98 + exit_velo_df_codes.launch_speed))) & (exit_velo_df_codes.launch_angle <= 30 + (-98 + exit_velo_df_codes.launch_speed)) & (exit_velo_df_codes.launch_angle >= 8) & (exit_velo_df_codes.launch_angle <= 50)
exit_velo_df_codes['pitches'] = 1
exit_velo_df_codes['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in exit_velo_df_codes.play_code]
exit_velo_df_codes['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in exit_velo_df_codes.play_code]
exit_velo_df_codes['swings'] = [1 if x in swings_in else 0 for x in exit_velo_df_codes.play_description]

exit_velo_df_codes['out_zone'] = exit_velo_df_codes.in_zone == False
exit_velo_df_codes['zone_swing'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)
exit_velo_df_codes['zone_contact'] = (exit_velo_df_codes.in_zone == True)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)
exit_velo_df_codes['ozone_swing'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)
exit_velo_df_codes['ozone_contact'] = (exit_velo_df_codes.in_zone==False)&(exit_velo_df_codes.swings == 1)&(exit_velo_df_codes.whiffs == 0)

exit_velo_df_codes_summ = exit_velo_df_codes.groupby(['pitcher_id','pitcher_name','level']).agg(
    pa = ('pa','sum'), 
    k = ('k','sum'), 
    bb = ('bb','sum'), 
    k_minus_bb = ('k_minus_bb','sum'), 
    csw = ('csw','sum'), 
    bip = ('bip','sum'), 
    tb = ('tb','sum'),
    woba = ('woba','sum'),
    woba_codes = ('woba_codes','sum'),
    hard_hit = ('hard_hit','sum'), 
    barrel = ('barrel','sum'), 
    sweet_spot = ('sweet_spot','sum'), 
    max_launch_speed = ('launch_speed','max'),
    launch_speed_90 =  ('launch_speed',percentile(90)),
    launch_speed = ('launch_speed','mean'),
    launch_angle = ('launch_angle','mean'),
    pitches = ('pitches','sum'),
    swings =  ('swings','sum'),
    in_zone = ('in_zone','sum'),
    out_zone = ('out_zone','sum'),
    whiffs = ('whiffs','sum'),
    zone_swing = ('zone_swing','sum'),
    zone_contact = ('zone_contact','sum'),
    ozone_swing = ('ozone_swing','sum'),
    ozone_contact = ('ozone_contact','sum'),
    ).reset_index()



exit_velo_df_codes_summ['k_percent'] = [exit_velo_df_codes_summ.k[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
exit_velo_df_codes_summ['bb_percent'] =[exit_velo_df_codes_summ.bb[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]
exit_velo_df_codes_summ['k_minus_bb_percent'] =[exit_velo_df_codes_summ.k_minus_bb[x]/exit_velo_df_codes_summ.pa[x] if exit_velo_df_codes_summ.pa[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]


exit_velo_df_codes_summ['sweet_spot_percent'] = [exit_velo_df_codes_summ.sweet_spot[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['woba_percent'] = [exit_velo_df_codes_summ.woba[x]/exit_velo_df_codes_summ.woba_codes[x] if exit_velo_df_codes_summ.woba_codes[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]


exit_velo_df_codes_summ['hard_hit_percent'] = [exit_velo_df_codes_summ.hard_hit[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]



exit_velo_df_codes_summ['csw_percent'] = [exit_velo_df_codes_summ.csw[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]


exit_velo_df_codes_summ['barrel_percent'] = exit_velo_df_codes_summ.barrel = [exit_velo_df_codes_summ.barrel[x]/exit_velo_df_codes_summ.bip[x] if exit_velo_df_codes_summ.bip[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['zone_contact_percent'] = [exit_velo_df_codes_summ.zone_contact[x]/exit_velo_df_codes_summ.zone_swing[x] if exit_velo_df_codes_summ.zone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['zone_swing_percent'] = [exit_velo_df_codes_summ.zone_swing[x]/exit_velo_df_codes_summ.in_zone[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['zone_percent'] = [exit_velo_df_codes_summ.in_zone[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['chase_percent'] = [exit_velo_df_codes_summ.ozone_swing[x]/(exit_velo_df_codes_summ.pitches[x] - exit_velo_df_codes_summ.in_zone[x]) if (exit_velo_df_codes_summ.pitches[x]- exit_velo_df_codes_summ.in_zone[x]) != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['chase_contact'] = [exit_velo_df_codes_summ.ozone_contact[x]/exit_velo_df_codes_summ.ozone_swing[x] if exit_velo_df_codes_summ.ozone_swing[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['swing_percent'] = [exit_velo_df_codes_summ.swings[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['whiff_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.swings[x] if exit_velo_df_codes_summ.swings[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ['swstr_rate'] = [exit_velo_df_codes_summ.whiffs[x]/exit_velo_df_codes_summ.pitches[x] if exit_velo_df_codes_summ.pitches[x] != 0 else np.nan for x in range(len(exit_velo_df_codes_summ))]

exit_velo_df_codes_summ = exit_velo_df_codes_summ.dropna(subset=['bip'])

print('whiffing')
print(exit_velo_df_codes_summ['whiff_rate'])

exit_velo_df_codes_summ.head()

woba_list = ['woba']
pa_list = ['k','bb','k_minus_bb','bb_minus_k']
balls_in_play_list = ['hard_hit','launch_speed','launch_speed_90','launch_angle','barrel','sweet_spot']
pitches_list = ['zone_percent','swing_percent','sw_str','csw']
swings_list = ['whiff_percent']
in_zone_pitches_list = ['zone_swing']
in_zone_swings_list = ['zone_contact']
out_zone_pitches_list = ['chase_percent']
out_zone_swings_list = ['chase_contact']

plot_dict = {
'k':{'x_axis':'Plate Appearances','y_axis':'K%','title':'K%','x_value':'k','x_range':[0.0,0.1,0.2,0.3,0.4],'percent':True,'percentile_label':'k_percent','flip_p':False,'percentile':False},
'bb':{'x_axis':'Plate Appearances','y_axis':'BB%','title':'BB%','x_value':'bb','x_range':[0.0,0.1,0.2,0.3],'percent':True,'percentile_label':'bb_percent','flip_p':True,'percentile':False},
'k_minus_bb':{'x_axis':'Plate Appearances','y_axis':'K-BB%','title':'K-BB%','x_value':'k_minus_bb','x_range':[-0.1,0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'k_minus_bb_percent','flip_p':False,'percentile':False,'avg_adjust':False},
'csw':{'x_axis':'Pitches','y_axis':'CSW%','title':'CSW%','x_value':'csw','x_range':[.2,.25,.3,.35,.4],'percent':True,'percentile_label':'csw_percent','flip_p':False,'percentile':False},
'woba':{'x_axis':'wOBA PA','y_axis':'wOBA','title':'wOBA','x_value':'woba','x_range':[.20,.30,.40,.50],'percent':False,'percentile_label':'woba_percent','flip_p':True,'percentile':False,'avg_adjust':True},
'launch_speed':{'x_axis':'Balls In Play','y_axis':'Exit Velocity','title':'Exit Velocity','x_value':'launch_speed','x_range':[85,90,95,100,105],'percent':False,'percentile_label':'launch_speed','flip_p':True,'percentile':False},
'launch_speed_90':{'x_axis':'Balls In Play','y_axis':'90th Percentile Exit Velocity','title':'90th Percentile Exit Velocity','x_value':'launch_speed','x_range':[90,95,100,105,110,115,120],'percent':False,'percentile_label':'launch_speed_90','flip_p':True,'percentile':True},              
'sweet_spot':{'x_axis':'Balls In Play','y_axis':'SweetSpot%','title':'SweetSpot%','x_value':'sweet_spot','x_range':[0.2,0.3,0.4,0.5,0.6],'percent':True,'percentile_label':'sweet_spot_percent','flip_p':True,'percentile':False},
'launch_angle':{'x_axis':'Balls In Play','y_axis':'Launch Angle','title':'Launch Angle','x_value':'launch_angle','x_range':[-20,-10,0,10,20],'percent':False,'percentile_label':'launch_angle','flip_p':True,'percentile':False},
'barrel':{'x_axis':'Balls In Play','y_axis':'Barrel%','title':'Barrel%','x_value':'barrel','x_range':[0,0.05,0.10,.15,.20,.25,.30],'percent':True,'percentile_label':'barrel_percent','flip_p':True,'percentile':False},
'zone_percent':{'x_axis':'Pitches','y_axis':'Zone%','title':'Zone%','x_value':'in_zone','x_range':[0.3,0.4,0.5,0.6,0.7],'percent':True,'percentile_label':'zone_percent','flip_p':False,'percentile':False},
'swing_percent':{'x_axis':'Pitches','y_axis':'Swing%','title':'Swing%','x_value':'swings','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'swing_percent','flip_p':False,'percentile':False},
'whiff_percent':{'x_axis':'Swings','y_axis':'Whiff%','title':'Whiff%','x_value':'whiffs','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'whiff_rate','flip_p':False,'percentile':False},
'sw_str':{'x_axis':'Pitches','y_axis':'SwStr%','title':'SwStr%','x_value':'whiffs','x_range':[0.0,0.05,0.1,0.15,0.2,0.25],'percent':True,'percentile_label':'swstr_rate','flip_p':False,'percentile':False},
'zone_swing':{'x_axis':'In-Zone Pitches','y_axis':'Z-Swing%','title':'Z-Swing%','x_value':'zone_swing','x_range':[0.3,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_swing_percent','flip_p':True,'percentile':False},
'zone_contact':{'x_axis':'In-Zone Swings','y_axis':'Z-Contact%','title':'Z-Contact%','x_value':'zone_contact','x_range':[0.5,0.6,0.7,0.8,0.9,1],'percent':True,'percentile_label':'zone_contact_percent','flip_p':True,'percentile':False},
'chase_percent':{'x_axis':'Out-of-Zone Pitches','y_axis':'O-Swing%','title':'O-Swing%','x_value':'ozone_swing','x_range':[0.0,0.1,0.2,0.3,0.4,0.5],'percent':True,'percentile_label':'chase_percent','flip_p':False,'percentile':False},
'chase_contact':{'x_axis':'Out-of-Zone Swings','y_axis':'O-Contact%','title':'O-Contact%','x_value':'ozone_contact','x_range':[0.2,0.3,0.4,0.5,0.6,0.7,0.8],'percent':True,'percentile_label':'chase_contact','flip_p':True,'percentile':False},}

test_df = exit_velo_df.sort_values(by='pitcher_name').drop_duplicates(subset='pitcher_id').reset_index(drop=True)[['pitcher_id','pitcher_name']]#['pitcher'].to_dict()
test_df = test_df.set_index('pitcher_id')

#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])

#batter_dict = test_df['pitcher_name'].to_dict()
batter_dict_mlb = exit_velo_df_mlb.sort_values(['pitcher_name']).set_index('pitcher_id')['pitcher_name'].to_dict()
batter_dict_aaa = exit_velo_df_aaa.sort_values(['pitcher_name']).set_index('pitcher_id')['pitcher_name'].to_dict()
batter_dict_aa = exit_velo_df_aa.sort_values(['pitcher_name']).set_index('pitcher_id')['pitcher_name'].to_dict()
batter_dict_ha = exit_velo_df_ha.sort_values(['pitcher_name']).set_index('pitcher_id')['pitcher_name'].to_dict()
batter_dict_a = exit_velo_df_a.sort_values(['pitcher_name']).set_index('pitcher_id')['pitcher_name'].to_dict()


level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A'}

plot_dict_small = {
'k':'K%',
'bb':'BB%',
'k_minus_bb':'K-BB%',
'csw':'CSW%',
'woba':'wOBA',
'launch_speed':'Exit Velocity',
'launch_speed_90':'90th Percentile Exit Velocity',
'sweet_spot':'SweetSpot%',
'launch_angle':'Launch Angle',
'zone_percent':'Zone%',
'barrel':'Barrel%',
'swing_percent':'Swing%',
'whiff_percent':'Whiff%',
'sw_str':'SwStr%',
'zone_swing':'Z-Swing%',
'zone_contact':'Z-Contact%',
'chase_percent':'O-Swing%',
'chase_contact':'O-Contact%',}


def server(input,output,session):

    @render.ui
    def test():
   
            # @reactive.Effect
            if input.my_tabs() == 'MLB':
                
                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
                #batter_dict = exit_velo_df_mlb.set_index('batter_id')['batter_name'].to_dict()
                return ui.input_select("id", "Select Player",batter_dict_mlb,selectize=True)
                

            if input.my_tabs() == 'AAA':
                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
                #batter_dict = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
                return ui.input_select("id", "Select Player",batter_dict_aaa,selectize=True)

            if input.my_tabs() == 'AA':
                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
                #batter_dict = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
                return ui.input_select("id", "Select Player",batter_dict_aa,selectize=True)

            if input.my_tabs() == 'A+':
                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
                #batter_dict = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
                return ui.input_select("id", "Select Player",batter_dict_ha,selectize=True)                

            if input.my_tabs() == 'A':
                #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
                #batter_dict = exit_velo_df_aaa.set_index('batter_id')['batter_name'].to_dict()
                return ui.input_select("id", "Select Player",batter_dict_a,selectize=True)    
    

    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot_mlb():
         

        rpf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
                         player_id = input.id(),
                         stat_id = input.stat_id(),
                         batter_dict = batter_dict_mlb,
                         window_select = input.n(),
                         level_id = input.my_tabs())
 

    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot_aaa():
         

        rpf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
                         player_id = input.id(),
                         stat_id = input.stat_id(),
                         batter_dict = batter_dict_aaa,
                         window_select = input.n(),
                         level_id = input.my_tabs())
 
    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot_aa():
         

        rpf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
                         player_id = input.id(),
                         stat_id = input.stat_id(),
                         batter_dict = batter_dict_aa,
                         window_select = input.n(),
                         level_id = input.my_tabs())

    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot_ha():
         

        rpf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
                         player_id = input.id(),
                         stat_id = input.stat_id(),
                         batter_dict = batter_dict_ha,
                         window_select = input.n(),
                         level_id = input.my_tabs())
     


    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot_a():
         

        rpf.rolling_plot(df = exit_velo_df_codes[exit_velo_df_codes['level']==input.my_tabs()],
                         df_summ = exit_velo_df_codes_summ[exit_velo_df_codes_summ['level']==input.my_tabs()],
                         player_id = input.id(),
                         stat_id = input.stat_id(),
                         batter_dict = batter_dict_a,
                         window_select = input.n(),
                         level_id = input.my_tabs())
 






app = App(ui.page_fluid(
#    ui.tags.base(href=base_url),
    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.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),

    # ui.navset_tab(
    #     ui.nav_control(
    #          ui.a(
    #             "Home",
    #             href="https://nesticot-tjstats-site.hf.space/home/"
    #         ),
    #     ),
    #     ui.nav_menu(
    #         "Batter Charts",
    #         ui.nav_control(
    #         ui.a(
    #             "Batting Rolling",
    #             href="https://nesticot-tjstats-site-rolling-batter.hf.space/"
    #         ),
    #         ui.a(
    #             "Spray",
    #             href="https://nesticot-tjstats-site-spray.hf.space/"
    #         ),
    #         ui.a(
    #             "Decision Value",
    #             href="https://nesticot-tjstats-site-decision-value.hf.space/"
    #         ),
    #         ui.a(
    #             "Damage Model",
    #             href="https://nesticot-tjstats-site-damage.hf.space/"
    #         ),
    #         ui.a(
    #             "Batter Scatter",
    #             href="https://nesticot-tjstats-site-batter-scatter.hf.space/"
    #         ),
    #         ui.a(
    #             "EV vs LA Plot",
    #             href="https://nesticot-tjstats-site-ev-angle.hf.space/"
    #         ),
    #         ui.a(
    #             "Statcast Compare",
    #             href="https://nesticot-tjstats-site-statcast-compare.hf.space/"
    #         ),
    #         ui.a(
    #             "MLB/MiLB Cards",
    #             href="https://nesticot-tjstats-site-mlb-cards.hf.space/"
    #         )
    #     ),
    #     ),
    #     ui.nav_menu(
    #         "Pitcher Charts",
    #         ui.nav_control(
    #          ui.a(
    #             "Pitcher Rolling",
    #             href="https://nesticot-tjstats-site-rolling-pitcher.hf.space/"
    #         ),
    #          ui.a(
    #             "Pitcher Summary",
    #             href="https://nesticot-tjstats-site-pitching-summary-graphic-new.hf.space/"
    #         ),
    #          ui.a(
    #             "Pitcher Scatter",
    #             href="https://nesticot-tjstats-site-pitcher-scatter.hf.space"
    #         )
    #     ),
    #     )),   
        ui.row(
    ui.layout_sidebar(

      ui.panel_sidebar(
        ui.output_ui('test','Select Player'),
        #ui.input_select("id", "Select Pitcher",batter_dict,selected=675911,width=1,size=1,selectize=True),
        #ui.input_select("level_id", "Select Level",level_dict,width=1,size=1),
        ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1,size=1),
        ui.input_numeric("n", "Rolling Window Size", value=50),
        ui.input_action_button("go", "Generate",class_="btn-primary"),
        ui.output_table("result")
      ),

      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("MLB",
                ui.output_plot("plot_mlb",height = "1000px",width="1000px")),
            ui.nav("AAA",
                ui.output_plot("plot_aaa",height = "1000px",width="1000px")),
            ui.nav("AA",
                ui.output_plot("plot_aa",height = "1000px",width="1000px")),
            ui.nav("A+",
                ui.output_plot("plot_ha",height = "1000px",width="1000px")),
            ui.nav("A",
                ui.output_plot("plot_a",height = "1000px",width="1000px"))



                ,id="my_tabs")))))),server)