Update app.py

app.py

@@ -1,676 +1,685 @@
-import polars as pl
-import numpy as np
-import pandas as pd
-import api_scraper
-scrape = api_scraper.MLB_Scrape()
-from functions import df_update
-from functions import pitch_summary_functions
-update = df_update.df_update()
-import requests
-import joblib
-from matplotlib.gridspec import GridSpec
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import seaborn as sns
-from functions.pitch_summary_functions import *
-from functions.df_update import *
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-from functions.heat_map_functions import *
-
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-
-
-year_list = [2017,2018,2019,2020,2021,2022,2023,2024,2025]
-
-
-
-level_dict =  {'1':'MLB',
-               '11':'AAA',
-               '12':'AA',
-               '13':'A+',
-               '14':'A',
-               '17':'AFL',
-               '22':'College',
-               '21':'Prospects',
-               '51':'International' }
-
-function_dict={
-    'velocity_kdes':'Velocity Distributions',
-    'break_plot':'Pitch Movement',
-    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
-    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
-    'location_plot_lhb':'Locations vs LHB',
-    'location_plot_rhb':'Locations vs RHB',
-}
-
-
-split_dict =  {'all':'All',
-               'left':'LHH',
-               'right':'RHH'}
-
-split_dict_hand =  {'all':['L','R'],
-               'left':['L'],
-               'right':['R']}
-
-
-type_dict =  {'R':'Regular Season',
-               'S':'Spring',
-               'P':'Playoffs' }
-
-format_dict = {
-    'pitch_percent': '{:.1%}',
-    'pitches': '{:.0f}',
-    'heart_zone_percent': '{:.1%}',
-    'shadow_zone_percent': '{:.1%}',
-    'chase_zone_percent': '{:.1%}',
-    'waste_zone_percent': '{:.1%}',
-    'csw_percent': '{:.1%}',
-    'whiff_rate': '{:.1%}',
-    'zone_whiff_percent': '{:.1%}',
-    'chase_percent': '{:.1%}',
-    'bip': '{:.0f}',
-    'xwoba_percent_contact': '{:.3f}'
-}
-
-format_dict = {
-    'pitch_percent': '{:.1%}',
-    'pitches': '{:.0f}',
-    'heart_zone_percent': '{:.1%}',
-    'shadow_zone_percent': '{:.1%}',
-    'chase_zone_percent': '{:.1%}',
-    'waste_zone_percent': '{:.1%}',
-    'csw_percent': '{:.1%}',
-    'whiff_rate': '{:.1%}',
-    'zone_whiff_percent': '{:.1%}',
-    'chase_percent': '{:.1%}',
-    'bip': '{:.0f}',
-    'xwoba_percent_contact': '{:.3f}'
-}
-label_translation_dict = {
-    'pitch_percent': 'Pitch%',
-    'pitches': 'Pitches',
-    'heart_zone_percent': 'Heart%',
-    'shadow_zone_percent': 'Shado%',
-    'chase_zone_percent': 'Chas%',
-    'waste_zone_percent': 'Waste%',
-    'csw_percent': 'CSW%',
-    'whiff_rate': 'Whiff%',
-    'zone_whiff_percent': 'Z-Whiff%',
-    'chase_percent': 'O-Swing%',
-    'bip': 'BBE',
-    'xwoba_percent_contact': 'xwOBACON'
-}
-
-cmap_sum22 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFB000',])
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-cmap_sum2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
-cmap_sum_r = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFB000','#FFFFFF','#648FFF',])
-
-
-import requests
-
-import os
-CAMPAIGN_ID = os.getenv("CAMPAIGN_ID")
-ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
-BACKUP_PW = os.getenv("BACKUP_PW")
-ADMIN_PW = os.getenv("ADMIN_PW")
-
-url = f"https://www.patreon.com/api/oauth2/v2/campaigns/{CAMPAIGN_ID}/members"
-
-headers = {
-    "Authorization": f"Bearer {ACCESS_TOKEN}"
-}
-
-# Simple parameters, requesting the member's email and currently entitled tiers
-params = {
-    "fields[member]": "full_name,email",  # Request the member's email
-    "include": "currently_entitled_tiers",  # Include the currently entitled tiers
-    "page[size]": 1000  # Fetch up to 1000 patrons per request
-}
-
-response = requests.get(url, headers=headers, params=params)
-
-
-VALID_PASSWORDS = []
-if response.status_code == 200:
-    data = response.json()
-    for patron in data['data']:
-        try:
-            tiers = patron['relationships']['currently_entitled_tiers']['data']
-            if any(tier['id'] == '9078921' for tier in tiers):
-                full_name = patron['attributes']['email']
-                VALID_PASSWORDS.append(full_name)
-        except KeyError:
-            continue
-VALID_PASSWORDS.append(BACKUP_PW)
-VALID_PASSWORDS.append(ADMIN_PW)
-# VALID_PASSWORDS.append('')
-
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-# Define the login UI
-login_ui = ui.page_fluid(
-    ui.card(
-        ui.h2([
-            "TJStats Pitching Heat Maps App ",
-            ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank")
-        ]),
-        ui.p(
-            "This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", 
-            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/117909954", target="_blank"),
-            "."
-        ),
-        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="25%"),
-        ui.tags.input(
-            type="checkbox",
-            id="authenticated",
-            value=False,
-            disabled=True
-        ),
-        ui.input_action_button("login", "Login", class_="btn-primary"),
-        ui.output_text("login_message"),
-    )
-)
-
-
-main_ui = ui.page_sidebar(
-    ui.sidebar(
-        # Row for selecting season and level
-        ui.row(
-            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
-            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
-            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
-        ),
-        # Row for the action button to get player list
-        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
-        # Row for selecting the player
-        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
-
-
-        ui.row(ui.input_action_button("get_pitches", "Get Pitch Types", class_="btn-secondary")),
-
-        
-        # Rows for selecting plots and split options
-        ui.row(ui.column(12, ui.output_ui('pitch_type_ui', 'Select Pitch Type'))),
-        ui.row(ui.column(12, ui.input_select('plot_type', 'Select Plot', ['Pitch%','Whiff%','xwOBACON']))),
-        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
-        
-        # Row for the action button to generate plot
-        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
-        width="400px"  # Added this parameter to control sidebar width
-    ),
-    
-    # Main content (former panel_main content)
-    ui.navset_tab(
-        # Tab for game summary plot
-        ui.nav("Pitching Summary",
-               ui.output_text("status"),
-               ui.output_plot('plot', width='1440px', height=f'{900/1600*1440}px')
-        ),
-    )
-)
-
-
-# Combined UI with conditional panel
-app_ui = ui.page_fluid(
-    ui.tags.head(
-        ui.tags.script(src="script.js")
-    ),
-
-    ui.panel_conditional(
-        "!input.authenticated",
-        login_ui
-    ),
-    ui.panel_conditional(
-        "input.authenticated",
-        main_ui
-    )
-)
-
-
-def server(input, output, session):
-
-    @reactive.Effect
-    @reactive.event(input.login)
-    def check_password():
-        if input.password() in VALID_PASSWORDS:
-            ui.update_checkbox("authenticated", value=True)
-            ui.update_text("login_message", value="")
-        else:
-            ui.update_text("login_message", value="Invalid password!")
-            ui.update_text("password", value="")
-
-    @output
-    @render.text
-    def login_message():
-        return ""
-
-    # Instead of using @reactive.calc with @reactive.event
-    cached_data_value = reactive.value(None)  # Initialize with None
-
-    @reactive.calc
-    @reactive.event(input.date_id,input.pitcher_id)
-    def cached_data():
-
-        if not hasattr(input, 'pitcher_id') or input.pitcher_id() is None or not hasattr(input, 'date_id') or input.date_id() is None:
-            return  # Exit early if required inputs aren't ready
-        year_input = int(input.year_input())
-        sport_id = int(input.level_input())
-        player_input = int(input.pitcher_id())
-        start_date = str(input.date_id()[0])
-        end_date = str(input.date_id()[1])
-        # Simulate an expensive data operation
-        game_list  = scrape.get_player_games_list(sport_id = sport_id,
-                                                season = year_input, 
-                                                player_id = player_input,
-                                                start_date = start_date,
-                                                end_date = end_date,
-                                                game_type = [input.type_input()])
-
-        data_list = scrape.get_data(game_list_input = game_list[:])
-        df = (update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("pitcher_id") == player_input)&
-                            (pl.col("is_pitch") == True)
-                            
-
-                            ))).with_columns(
-                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            )
-        return df
-
-
-    @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
-    def player_select_ui():
-        # Get the list of pitchers for the selected level and season
-        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
-            pl.col("position").is_in(['P','TWP'])).sort("name")
-        
-        # Create a dictionary of pitcher IDs and names
-        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
-        
-        # Return a select input for choosing a pitcher
-        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
-
-    is_loading = reactive.value(False)
-    data_result = reactive.value(None)
-
-    @reactive.effect
-    @reactive.event(input.get_pitches)
-    def load_data():
-        is_loading.set(True)
-        data_result.set(None)  # Clear any previous data
-        try:
-            # This will fetch the data
-            result = cached_data()
-            data_result.set(result)
-        except Exception as e:
-            # Handle any errors
-            print(f"Error loading data: {e}")
-        finally:
-            is_loading.set(False)
-
-    @output
-    @render.ui
-    def pitch_type_ui():
-        # Make sure to add dependencies on both values
-        input.get_pitches()
-        loading = is_loading()
-        data = data_result()
-        
-        # If loading, show spinner
-        if loading:
-            return ui.div(
-                ui.span("Loading pitch types... ", class_="me-2"),
-                ui.tags.div(class_="spinner-border spinner-border-sm text-primary"),
-                style="padding: 10px; background-color: #f8f9fa; border-radius: 5px;"
-            )
-        
-        # If data is loaded, show dropdown
-        elif data is not None:
-            df = data
-            df = df.clone() if hasattr(df, 'clone') else df.copy()
-            pitch_dict = dict(zip(df['pitch_type'], df['pitch_description']))
-            return ui.input_select(
-                "pitch_type_input", 
-                "Select Pitch Type", 
-                pitch_dict, 
-                selectize=True
-            )
-        
-        # Initial state or after reset
-        else:
-            return ui.div(
-                ui.p("Click 'Get Pitch Types' to load the dropdown.", class_="text-muted"),
-                style="text-align: center; padding: 10px;"
-            )  # Empty div with instructions
-    @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
-    def date_id():
-        # Create a date range input for selecting the date range within the selected year
-        return ui.input_date_range("date_id", "Select Date Range",
-                                   start=f"{int(input.year_input())}-01-01",
-                                   end=f"{int(input.year_input())}-12-31",
-                                   min=f"{int(input.year_input())}-01-01",
-                                    max=f"{int(input.year_input())}-12-31")
-    
-
-
-    @output
-    @render.text
-    def status():
-        # Only show status when generating
-        if input.generate == 0:
-            return ""
-        return ""   
-    
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def plot():     
-        # Show progress/loading notification
-        with ui.Progress(min=0, max=1) as p:
-            p.set(message="Generating plot", detail="This may take a while...")
-
-            
-            p.set(0.3, "Gathering data...")
-            year_input = int(input.year_input())
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-            start_date = str(input.date_id()[0])
-            end_date = str(input.date_id()[1])
-            
-
-            print(year_input, sport_id, player_input, start_date, end_date)
-
-            df = cached_data()
-            df = df.clone()
-
-            pitch_input = input.pitch_type_input()
-
-            df_plot = pitch_heat_map(pitch_input, df)
-            pivot_table_l = pitch_prop(df=df_plot, hand = 'L')
-            pivot_table_r = pitch_prop(df=df_plot, hand = 'R')
-
-
-            table_left = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'L'), selection=['pitcher_hand'])
-            table_left = table_left.with_columns(
-                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'L'))).alias('pitch_percent')
-            )
-
-            table_right = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'R'), selection=['pitcher_hand'])
-            table_right = table_right.with_columns(
-                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'R'))).alias('pitch_percent')
-            )
-            try:
-                normalize = mcolors.Normalize(vmin=table_left['pitch_percent']*0.5, 
-                                            vmax=table_left['pitch_percent']*1.5) # Define the range of values
-    
-    
-                df_colour_left = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_l[0]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[1]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[2]]])
-                df_colour_left[0] = '#ffffff'
-            except ValueError:
-                normalize = mcolors.Normalize(vmin=0, 
-                                            vmax=1) # Define the range of values
-                df_colour_left = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
-                    
-            try: 
-                normalize = mcolors.Normalize(vmin=table_right['pitch_percent']*0.5, 
-                                            vmax=table_right['pitch_percent']*1.5) # Define the range of values
-    
-    
-                df_colour_right = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_r[0]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[1]],
-                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[2]]])
-                df_colour_right[0] = '#ffffff'
-                
-            except ValueError:
-                normalize = mcolors.Normalize(vmin=0, 
-                                            vmax=1) # Define the range of values
-                df_colour_right = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
-                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
-
-            table_left = table_left.select(
-                'pitch_percent',
-                'pitches',
-                'heart_zone_percent',
-                'shadow_zone_percent',
-                'chase_zone_percent',
-                'waste_zone_percent',
-                'csw_percent',
-                'whiff_rate',
-                'zone_whiff_percent',
-                'chase_percent',
-                'bip',
-                'xwoba_percent_contact').to_pandas().T
-
-            table_right = table_right.select(
-                'pitch_percent',
-                'pitches',
-                'heart_zone_percent',
-                'shadow_zone_percent',
-                'chase_zone_percent',
-                'waste_zone_percent',
-                'csw_percent',
-                'whiff_rate',
-                'zone_whiff_percent',
-                'chase_percent',
-                'bip',
-                'xwoba_percent_contact').to_pandas().T
-
-            table_right = table_right.replace({'nan%':'—'})
-            table_right = table_right.replace({'nan':'—'})
-
-
-
-
-
-            p.set(0.6, "Creating plot...")
-
-            import matplotlib.pyplot as plt
-            fig = plt.figure(figsize=(16, 9))
-            fig.set_facecolor('white')
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            gs = GridSpec(3, 5,  height_ratios=[2,9,1],width_ratios=[1,9,1,9,1])
-            gs.update(hspace=0.2, wspace=0.3)
-
-            # Add subplots to the grid
-            ax_header = fig.add_subplot(gs[0, :]) 
-            ax_left = fig.add_subplot(gs[1, 1]) 
-            ax_right = fig.add_subplot(gs[1, 3]) 
-
-            axfooter = fig.add_subplot(gs[-1, :])
-
-
-            if input.plot_type() == 'Pitch%':
-                heat_map_plot(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum2,
-                                hand='L')
-
-                heat_map_plot(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum2,
-                                hand='R')
-
-
-            if input.plot_type() == 'Whiff%':
-                heat_map_plot_hex_whiff(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum,
-                                hand='L')
-
-                heat_map_plot_hex_whiff(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum,
-                                hand='R')
-
-            if input.plot_type() == 'xwOBACON':
-                heat_map_plot_hex_damage(df=df_plot, 
-                                ax=ax_left,
-                                cmap=cmap_sum,
-                                hand='L')
-
-                heat_map_plot_hex_damage(df=df_plot, 
-                                ax=ax_right,
-                                cmap=cmap_sum,
-                                hand='R')
-
-
-            # Load the image
-            img = mpimg.imread('images/left.png')
-            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
-            ab = AnnotationBbox(imagebox, (1.25, -0.5), box_alignment=(0, 0), frameon=False)
-            ax_left.add_artist(ab)
-
-
-            # Load the image
-            img = mpimg.imread('images/right.png')
-            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
-            # Create an AnnotationBbox
-            ab = AnnotationBbox(imagebox, (-1.25, -0.5), box_alignment=(1, 0), frameon=False)
-
-            ax_right.add_artist(ab)
-
-
-            table_plot(ax=ax_left,
-                        table=table_left,
-                            hand='L')
-
-            table_plot_pivot(ax=ax_left,
-                        pivot_table=pivot_table_l,
-                        df_colour=df_colour_left)
-
-
-            table_plot(ax=ax_right,
-                        table=table_right,
-                            hand='R')
-
-            table_plot_pivot(ax=ax_right,
-                        pivot_table=pivot_table_r,
-                        df_colour=df_colour_right)
-
-
-            from matplotlib.cm import ScalarMappable
-            from matplotlib.colors import Normalize
-            # Create a ScalarMappable with the same colormap and normalization
-            if input.plot_type() == 'Pitch%':
-                sm = ScalarMappable(cmap=cmap_sum2, norm=Normalize(vmin=0, vmax=1))
-
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
-
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
-
-                cbar.ax.set_xticklabels(['Least', 'Most'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
-
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
-
-
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
-
-            if input.plot_type() == 'Whiff%':
-                sm = ScalarMappable(cmap=cmap_sum, norm=Normalize(vmin=0.15, vmax=0.35))
-
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
-
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
-
-                cbar.ax.set_xticklabels(['15%', '35%'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
-
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
-
-
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
-
-
-            if input.plot_type() == 'xwOBACON':
-                sm = ScalarMappable(cmap=cmap_sum_r, norm=Normalize(vmin=0.25, vmax=0.5))
-
-                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
-                cbar.set_ticks([])
-
-                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
-
-                cbar.ax.set_xticklabels(['.000', '.500'])
-                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
-                labels = cbar.ax.get_xticklabels()
-
-                labels[0].set_horizontalalignment('left')
-                labels[-1].set_horizontalalignment('right')
-                labels = cbar.ax.get_xticklabels()
-
-
-                cbar.ax.set_xticklabels(labels)
-                cbar.ax.tick_params(length=0)
-
-
-            axfooter.text(x=0.02,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=14,va='top')
-            axfooter.text(x=1-0.02,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=14,va='top')
-
-            axfooter.axis('off')
-
-            # Display the image on the axis
-            ax_header.set_xlim(-12,12)
-            ax_header.set_ylim(0, 2)
-
-
-            if input.plot_type() == 'Pitch%':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Pitch Frequency",ha='center',fontsize=24,va='top')
-            if input.plot_type() == 'Whiff%':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Whiff%",ha='center',fontsize=24,va='top')
-            if input.plot_type() == 'xwOBACON':
-                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} xwOBACON",ha='center',fontsize=24,va='top')
-
-            ax_header.text(x=0,y=0.7,s=f"{year_input} {level_dict[str(sport_id)]} Season",ha='center',fontsize=16,va='top')            
-            ax_header.text(x=0,y=0.3,s=f"{df_plot['game_date'][0]} to {df_plot['game_date'][-1]}",ha='center',fontsize=16,va='top',fontstyle='italic')
-
-            ax_header.axis('off')
-
-
-            import urllib
-            import urllib.request
-            import urllib.error
-            from urllib.error import HTTPError
-
-
-            plot_header(pitcher_id=player_input,
-                    ax=ax_header, 
-                    df_team=scrape.get_teams(), 
-                    df_players=scrape.get_players(sport_id,year_input),
-                    sport_id=sport_id,)
-
-
-
-
-
-
-            fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
-
-
-    
-
-app = App(app_ui, server)
-
-            
+import polars as pl
+import numpy as np
+import pandas as pd
+import api_scraper
+scrape = api_scraper.MLB_Scrape()
+from functions import df_update
+from functions import pitch_summary_functions
+update = df_update.df_update()
+import requests
+import joblib
+from matplotlib.gridspec import GridSpec
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import seaborn as sns
+from functions.pitch_summary_functions import *
+from functions.df_update import *
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+from functions.heat_map_functions import *
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+
+year_list = [2017,2018,2019,2020,2021,2022,2023,2024,2025]
+
+
+
+level_dict =  {'1':'MLB',
+               '11':'AAA',
+               '12':'AA',
+               '13':'A+',
+               '14':'A',
+               '17':'AFL',
+               '22':'College',
+               '21':'Prospects',
+               '51':'International' }
+
+function_dict={
+    'velocity_kdes':'Velocity Distributions',
+    'break_plot':'Pitch Movement',
+    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
+    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
+    'location_plot_lhb':'Locations vs LHB',
+    'location_plot_rhb':'Locations vs RHB',
+}
+
+
+split_dict =  {'all':'All',
+               'left':'LHH',
+               'right':'RHH'}
+
+split_dict_hand =  {'all':['L','R'],
+               'left':['L'],
+               'right':['R']}
+
+
+type_dict =  {'R':'Regular Season',
+               'S':'Spring',
+               'P':'Playoffs' }
+
+format_dict = {
+    'pitch_percent': '{:.1%}',
+    'pitches': '{:.0f}',
+    'heart_zone_percent': '{:.1%}',
+    'shadow_zone_percent': '{:.1%}',
+    'chase_zone_percent': '{:.1%}',
+    'waste_zone_percent': '{:.1%}',
+    'csw_percent': '{:.1%}',
+    'whiff_rate': '{:.1%}',
+    'zone_whiff_percent': '{:.1%}',
+    'chase_percent': '{:.1%}',
+    'bip': '{:.0f}',
+    'xwoba_percent_contact': '{:.3f}'
+}
+
+format_dict = {
+    'pitch_percent': '{:.1%}',
+    'pitches': '{:.0f}',
+    'heart_zone_percent': '{:.1%}',
+    'shadow_zone_percent': '{:.1%}',
+    'chase_zone_percent': '{:.1%}',
+    'waste_zone_percent': '{:.1%}',
+    'csw_percent': '{:.1%}',
+    'whiff_rate': '{:.1%}',
+    'zone_whiff_percent': '{:.1%}',
+    'chase_percent': '{:.1%}',
+    'bip': '{:.0f}',
+    'xwoba_percent_contact': '{:.3f}'
+}
+label_translation_dict = {
+    'pitch_percent': 'Pitch%',
+    'pitches': 'Pitches',
+    'heart_zone_percent': 'Heart%',
+    'shadow_zone_percent': 'Shado%',
+    'chase_zone_percent': 'Chas%',
+    'waste_zone_percent': 'Waste%',
+    'csw_percent': 'CSW%',
+    'whiff_rate': 'Whiff%',
+    'zone_whiff_percent': 'Z-Whiff%',
+    'chase_percent': 'O-Swing%',
+    'bip': 'BBE',
+    'xwoba_percent_contact': 'xwOBACON'
+}
+
+cmap_sum22 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFB000',])
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
+cmap_sum2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
+cmap_sum_r = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFB000','#FFFFFF','#648FFF',])
+
+
+import requests
+
+import os
+CAMPAIGN_ID = os.getenv("CAMPAIGN_ID")
+ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
+BACKUP_PW = os.getenv("BACKUP_PW")
+ADMIN_PW = os.getenv("ADMIN_PW")
+
+url = f"https://www.patreon.com/api/oauth2/v2/campaigns/{CAMPAIGN_ID}/members"
+
+headers = {
+    "Authorization": f"Bearer {ACCESS_TOKEN}"
+}
+
+# Simple parameters, requesting the member's email and currently entitled tiers
+params = {
+    "fields[member]": "full_name,email",  # Request the member's email
+    "include": "currently_entitled_tiers",  # Include the currently entitled tiers
+    "page[size]": 1000  # Fetch up to 1000 patrons per request
+}
+
+response = requests.get(url, headers=headers, params=params)
+
+
+VALID_PASSWORDS = []
+if response.status_code == 200:
+    data = response.json()
+    for patron in data['data']:
+        try:
+            tiers = patron['relationships']['currently_entitled_tiers']['data']
+            if any(tier['id'] == '9078921' for tier in tiers):
+                full_name = patron['attributes']['email']
+                VALID_PASSWORDS.append(full_name)
+        except KeyError:
+            continue
+VALID_PASSWORDS.append(BACKUP_PW)
+VALID_PASSWORDS.append(ADMIN_PW)
+# VALID_PASSWORDS.append('')
+
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+
+# Define the login UI
+login_ui = ui.page_fluid(
+    ui.card(
+        ui.h2([
+            "TJStats Pitching Heat Maps App ",
+            ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank")
+        ]),
+        ui.p(
+            "This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", 
+            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/117909954", target="_blank"),
+            "."
+        ),
+        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="25%"),
+        ui.tags.input(
+            type="checkbox",
+            id="authenticated",
+            value=False,
+            disabled=True
+        ),
+        ui.input_action_button("login", "Login", class_="btn-primary"),
+        ui.output_text("login_message"),
+    )
+)
+
+
+main_ui = ui.page_sidebar(
+    ui.sidebar(
+        # Row for selecting season and level
+        ui.row(
+            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
+            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
+            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
+        ),
+        # Row for the action button to get player list
+        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
+        # Row for selecting the player
+        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
+
+
+        ui.row(ui.input_action_button("get_pitches", "Get Pitch Types", class_="btn-secondary")),
+
+        
+        # Rows for selecting plots and split options
+        ui.row(ui.column(12, ui.output_ui('pitch_type_ui', 'Select Pitch Type'))),
+        ui.row(ui.column(6, ui.input_select('plot_type', 'Select Plot', ['Pitch%','Whiff%','xwOBACON'])),
+               ui.column(6, ui.input_switch('scatter_switch', 'Show Pitches', value=False))),
+        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
+        
+        # Row for the action button to generate plot
+        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
+        width="400px"  # Added this parameter to control sidebar width
+    ),
+    
+    # Main content (former panel_main content)
+    ui.navset_tab(
+        # Tab for game summary plot
+        ui.nav("Pitching Summary",
+               ui.output_text("status"),
+               ui.output_plot('plot', width='1440px', height=f'{900/1600*1440}px')
+        ),
+    )
+)
+
+
+# Combined UI with conditional panel
+app_ui = ui.page_fluid(
+    ui.tags.head(
+        ui.tags.script(src="script.js")
+    ),
+
+    ui.panel_conditional(
+        "!input.authenticated",
+        login_ui
+    ),
+    ui.panel_conditional(
+        "input.authenticated",
+        main_ui
+    )
+)
+
+
+def server(input, output, session):
+
+    @reactive.Effect
+    @reactive.event(input.login)
+    def check_password():
+        if input.password() in VALID_PASSWORDS:
+            ui.update_checkbox("authenticated", value=True)
+            ui.update_text("login_message", value="")
+        else:
+            ui.update_text("login_message", value="Invalid password!")
+            ui.update_text("password", value="")
+
+    @output
+    @render.text
+    def login_message():
+        return ""
+
+    # Instead of using @reactive.calc with @reactive.event
+    cached_data_value = reactive.value(None)  # Initialize with None
+
+    @reactive.calc
+    @reactive.event(input.date_id,input.pitcher_id)
+    def cached_data():
+
+        if not hasattr(input, 'pitcher_id') or input.pitcher_id() is None or not hasattr(input, 'date_id') or input.date_id() is None:
+            return  # Exit early if required inputs aren't ready
+        year_input = int(input.year_input())
+        sport_id = int(input.level_input())
+        player_input = int(input.pitcher_id())
+        start_date = str(input.date_id()[0])
+        end_date = str(input.date_id()[1])
+        # Simulate an expensive data operation
+        game_list  = scrape.get_player_games_list(sport_id = sport_id,
+                                                season = year_input, 
+                                                player_id = player_input,
+                                                start_date = start_date,
+                                                end_date = end_date,
+                                                game_type = [input.type_input()])
+
+        data_list = scrape.get_data(game_list_input = game_list[:])
+        df = (update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("pitcher_id") == player_input)&
+                            (pl.col("is_pitch") == True)
+                            
+
+                            ))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            )
+        return df
+
+
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def player_select_ui():
+        # Get the list of pitchers for the selected level and season
+        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
+            pl.col("position").is_in(['P','TWP'])).sort("name")
+        
+        # Create a dictionary of pitcher IDs and names
+        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
+        
+        # Return a select input for choosing a pitcher
+        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
+
+    is_loading = reactive.value(False)
+    data_result = reactive.value(None)
+
+    @reactive.effect
+    @reactive.event(input.get_pitches)
+    def load_data():
+        is_loading.set(True)
+        data_result.set(None)  # Clear any previous data
+        try:
+            # This will fetch the data
+            result = cached_data()
+            data_result.set(result)
+        except Exception as e:
+            # Handle any errors
+            print(f"Error loading data: {e}")
+        finally:
+            is_loading.set(False)
+
+    @output
+    @render.ui
+    def pitch_type_ui():
+        # Make sure to add dependencies on both values
+        input.get_pitches()
+        loading = is_loading()
+        data = data_result()
+        
+        # If loading, show spinner
+        if loading:
+            return ui.div(
+                ui.span("Loading pitch types... ", class_="me-2"),
+                ui.tags.div(class_="spinner-border spinner-border-sm text-primary"),
+                style="padding: 10px; background-color: #f8f9fa; border-radius: 5px;"
+            )
+        
+        # If data is loaded, show dropdown
+        elif data is not None:
+            df = data
+            df = df.clone() if hasattr(df, 'clone') else df.copy()
+            pitch_dict = dict(zip(df['pitch_type'], df['pitch_description']))
+            return ui.input_select(
+                "pitch_type_input", 
+                "Select Pitch Type", 
+                pitch_dict, 
+                selectize=True
+            )
+        
+        # Initial state or after reset
+        else:
+            return ui.div(
+                ui.p("Click 'Get Pitch Types' to load the dropdown.", class_="text-muted"),
+                style="text-align: center; padding: 10px;"
+            )  # Empty div with instructions
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def date_id():
+        # Create a date range input for selecting the date range within the selected year
+        return ui.input_date_range("date_id", "Select Date Range",
+                                   start=f"{int(input.year_input())}-01-01",
+                                   end=f"{int(input.year_input())}-12-31",
+                                   min=f"{int(input.year_input())}-01-01",
+                                    max=f"{int(input.year_input())}-12-31")
+    
+
+
+    @output
+    @render.text
+    def status():
+        # Only show status when generating
+        if input.generate == 0:
+            return ""
+        return ""   
+    
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def plot():     
+        # Show progress/loading notification
+        with ui.Progress(min=0, max=1) as p:
+            p.set(message="Generating plot", detail="This may take a while...")
+
+            
+            p.set(0.3, "Gathering data...")
+            year_input = int(input.year_input())
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+            start_date = str(input.date_id()[0])
+            end_date = str(input.date_id()[1])
+
+            scatter_bool = input.scatter_switch()
+            
+
+            print(year_input, sport_id, player_input, start_date, end_date)
+
+            df = cached_data()
+            df = df.clone()
+
+            pitch_input = input.pitch_type_input()
+
+            df_plot = pitch_heat_map(pitch_input, df)
+            pivot_table_l = pitch_prop(df=df_plot, hand = 'L')
+            pivot_table_r = pitch_prop(df=df_plot, hand = 'R')
+
+
+            table_left = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'L'), selection=['pitcher_hand'])
+            table_left = table_left.with_columns(
+                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'L'))).alias('pitch_percent')
+            )
+
+            table_right = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'R'), selection=['pitcher_hand'])
+            table_right = table_right.with_columns(
+                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'R'))).alias('pitch_percent')
+            )
+            try:
+                normalize = mcolors.Normalize(vmin=table_left['pitch_percent']*0.5, 
+                                            vmax=table_left['pitch_percent']*1.5) # Define the range of values
+    
+    
+                df_colour_left = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_l[0]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[1]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[2]]])
+                df_colour_left[0] = '#ffffff'
+            except ValueError:
+                normalize = mcolors.Normalize(vmin=0, 
+                                            vmax=1) # Define the range of values
+                df_colour_left = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
+                    
+            try: 
+                normalize = mcolors.Normalize(vmin=table_right['pitch_percent']*0.5, 
+                                            vmax=table_right['pitch_percent']*1.5) # Define the range of values
+    
+    
+                df_colour_right = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_r[0]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[1]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[2]]])
+                df_colour_right[0] = '#ffffff'
+                
+            except ValueError:
+                normalize = mcolors.Normalize(vmin=0, 
+                                            vmax=1) # Define the range of values
+                df_colour_right = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
+
+            table_left = table_left.select(
+                'pitch_percent',
+                'pitches',
+                'heart_zone_percent',
+                'shadow_zone_percent',
+                'chase_zone_percent',
+                'waste_zone_percent',
+                'csw_percent',
+                'whiff_rate',
+                'zone_whiff_percent',
+                'chase_percent',
+                'bip',
+                'xwoba_percent_contact').to_pandas().T
+
+            table_right = table_right.select(
+                'pitch_percent',
+                'pitches',
+                'heart_zone_percent',
+                'shadow_zone_percent',
+                'chase_zone_percent',
+                'waste_zone_percent',
+                'csw_percent',
+                'whiff_rate',
+                'zone_whiff_percent',
+                'chase_percent',
+                'bip',
+                'xwoba_percent_contact').to_pandas().T
+
+            table_right = table_right.replace({'nan%':'—'})
+            table_right = table_right.replace({'nan':'—'})
+
+
+
+
+
+            p.set(0.6, "Creating plot...")
+
+            import matplotlib.pyplot as plt
+            fig = plt.figure(figsize=(16, 9))
+            fig.set_facecolor('white')
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            gs = GridSpec(3, 5,  height_ratios=[2,9,1],width_ratios=[1,9,1,9,1])
+            gs.update(hspace=0.2, wspace=0.3)
+
+            # Add subplots to the grid
+            ax_header = fig.add_subplot(gs[0, :]) 
+            ax_left = fig.add_subplot(gs[1, 1]) 
+            ax_right = fig.add_subplot(gs[1, 3]) 
+
+            axfooter = fig.add_subplot(gs[-1, :])
+
+
+            if input.plot_type() == 'Pitch%':
+                heat_map_plot(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum2,
+                                hand='L',
+                                scatter=scatter_bool)
+
+                heat_map_plot(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum2,
+                                hand='R',
+                                scatter=scatter_bool))
+
+
+            if input.plot_type() == 'Whiff%':
+                heat_map_plot_hex_whiff(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum,
+                                hand='L',
+                                scatter=scatter_bool))
+
+                heat_map_plot_hex_whiff(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum,
+                                hand='R',
+                                scatter=scatter_bool))
+
+            if input.plot_type() == 'xwOBACON':
+                heat_map_plot_hex_damage(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum,
+                                hand='L',
+                                scatter=scatter_bool))
+
+                heat_map_plot_hex_damage(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum,
+                                hand='R',
+                                scatter=scatter_bool))
+
+
+            # Load the image
+            img = mpimg.imread('images/left.png')
+            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
+            ab = AnnotationBbox(imagebox, (1.25, -0.5), box_alignment=(0, 0), frameon=False)
+            ax_left.add_artist(ab)
+
+
+            # Load the image
+            img = mpimg.imread('images/right.png')
+            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
+            # Create an AnnotationBbox
+            ab = AnnotationBbox(imagebox, (-1.25, -0.5), box_alignment=(1, 0), frameon=False)
+
+            ax_right.add_artist(ab)
+
+
+            table_plot(ax=ax_left,
+                        table=table_left,
+                            hand='L')
+
+            table_plot_pivot(ax=ax_left,
+                        pivot_table=pivot_table_l,
+                        df_colour=df_colour_left)
+
+
+            table_plot(ax=ax_right,
+                        table=table_right,
+                            hand='R')
+
+            table_plot_pivot(ax=ax_right,
+                        pivot_table=pivot_table_r,
+                        df_colour=df_colour_right)
+
+
+            from matplotlib.cm import ScalarMappable
+            from matplotlib.colors import Normalize
+            # Create a ScalarMappable with the same colormap and normalization
+            if input.plot_type() == 'Pitch%':
+                sm = ScalarMappable(cmap=cmap_sum2, norm=Normalize(vmin=0, vmax=1))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['Least', 'Most'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+            if input.plot_type() == 'Whiff%':
+                sm = ScalarMappable(cmap=cmap_sum, norm=Normalize(vmin=0.15, vmax=0.35))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['15%', '35%'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+
+            if input.plot_type() == 'xwOBACON':
+                sm = ScalarMappable(cmap=cmap_sum_r, norm=Normalize(vmin=0.25, vmax=0.5))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['.000', '.500'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+
+            axfooter.text(x=0.02,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=14,va='top')
+            axfooter.text(x=1-0.02,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=14,va='top')
+
+            axfooter.axis('off')
+
+            # Display the image on the axis
+            ax_header.set_xlim(-12,12)
+            ax_header.set_ylim(0, 2)
+
+
+            if input.plot_type() == 'Pitch%':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Pitch Frequency",ha='center',fontsize=24,va='top')
+            if input.plot_type() == 'Whiff%':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Whiff%",ha='center',fontsize=24,va='top')
+            if input.plot_type() == 'xwOBACON':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} xwOBACON",ha='center',fontsize=24,va='top')
+
+            ax_header.text(x=0,y=0.7,s=f"{year_input} {level_dict[str(sport_id)]} Season",ha='center',fontsize=16,va='top')            
+            ax_header.text(x=0,y=0.3,s=f"{df_plot['game_date'][0]} to {df_plot['game_date'][-1]}",ha='center',fontsize=16,va='top',fontstyle='italic')
+
+            ax_header.axis('off')
+
+
+            import urllib
+            import urllib.request
+            import urllib.error
+            from urllib.error import HTTPError
+
+
+            plot_header(pitcher_id=player_input,
+                    ax=ax_header, 
+                    df_team=scrape.get_teams(), 
+                    df_players=scrape.get_players(sport_id,year_input),
+                    sport_id=sport_id,)
+
+
+
+
+
+
+            fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
+
+
+    
+
+app = App(app_ui, server)
+
+