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nesticot commited on Apr 1, 2025

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7aedbde

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1 Parent(s): 298ba6a

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api_scraper.py +0 -0
app.py +947 -956
functions/__pycache__/df_update.cpython-39.pyc +0 -0
functions/__pycache__/pitch_summary_functions.cpython-39.pyc +0 -0
functions/pitch_summary_functions.py +0 -0
requirements.txt +16 -16
stuff_model/__pycache__/calculate_arm_angles.cpython-39.pyc +0 -0
stuff_model/__pycache__/feature_engineering.cpython-39.pyc +0 -0
stuff_model/__pycache__/stuff_apply.cpython-39.pyc +0 -0

api_scraper.py CHANGED Viewed

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app.py CHANGED Viewed

@@ -1,957 +1,948 @@
-import polars as pl
-import numpy as np
-import pandas as pd
-import api_scraper
-scrape = api_scraper.MLB_Scrape()
-from functions import df_update
-from functions import pitch_summary_functions
-update = df_update.df_update()
-from stuff_model import feature_engineering as fe
-from stuff_model import stuff_apply
-import requests
-import joblib
-from matplotlib.gridspec import GridSpec
-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 shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-import datetime
-import matplotlib.colors
-import pandas as pd
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#648FFF","#FFFFFF","#FFB000"])
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-year_list = [2017,2018,2019,2020,2021,2022,2023,2024]
-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',
-    'break_plot_rhh':'Pitch Movement LHH',
-    'break_plot_lhh':'Pitch Movement RHH',
-    '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']}
-# List of MLB teams and their corresponding ESPN logo URLs
-mlb_teams = [
-    {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
-    {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
-    {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
-    {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
-    {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
-    {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
-    {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
-    {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
-    {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
-    {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
-    {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
-    {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
-    {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
-    {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
-    {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
-    {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
-    {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
-    {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
-    {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
-    {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
-    {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
-    {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
-    {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
-    {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
-    {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
-    {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
-    {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
-    {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
-    {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
-    {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
-    {"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"}
-]
-df_image = pd.DataFrame(mlb_teams)
-image_dict = df_image.set_index('team')['logo_url'].to_dict()
-image_dict_flip = df_image.set_index('logo_url')['team'].to_dict()
-import requests
-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)
-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 Daily Pitching Summary 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/122860440", 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"),
-    )
-)
-# Define the UI layout for the app
-main_ui = ui.page_fluid(
-    ui.layout_sidebar(
-        ui.panel_sidebar(
-            # Row for selecting season and level
-            ui.row(
-                ui.column(6, ui.input_date('date_input', 'Select Date')),
-                ui.column(6, ui.input_select('level_input', 'Select Level', level_dict))
-            ),
-            ui.row(ui.input_action_button("game_button", "Get Games", class_="btn-primary")),
-            ui.row(
-                ui.row(ui.column(12, ui.output_ui('game_select_ui', 'Select Game'))),
-                ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
-            ),
-            # Rows for selecting plots and split options
-            ui.row(
-                ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='location_plot_lhb')),
-                ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='break_plot')),
-                ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='location_plot_rhb'))
-            ),
-            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=50))
-            ),
-        ui.row(
-            ui.column(6, ui.input_switch("switch", "Custom Team?", False)),
-            ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False))
-        ),
-            # Row for the action button to generate plot
-            ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary"))
-        ),
-        ui.panel_main(
-    # Main content area with tabs (placed directly in page_sidebar)
-            ui.navset_tab(
-                ui.nav_panel("Pitching Summary",
-                    ui.output_text("status"),
-                    ui.output_plot('plot', width='2100px', height='2100px')
-                ),
-                    ui.nav_panel("Table Summary",
-                                        ui.output_data_frame("grid_summary")),
-                    ui.nav_panel("Daily Table",
-                                        ui.output_data_frame("grid")),
-                    ui.nav_panel("Daily Table Style",
-                                ui.input_numeric('head', 'Table Limit', min=0, value=10),
-                                ui.input_numeric('pitch_min', 'Pitch Min.', min=0, value=10),
-                                ui.card(
-                                    {"style": "width: 1560px;"},
-                                    ui.head_content(
-                                        ui.tags.script(src="https://html2canvas.hertzen.com/dist/html2canvas.min.js"),
-                                        ui.tags.script("""
-                                        async function downloadPNG() {
-                                            const content = document.getElementById('capture-section');
-                                            try {
-                                                // Create a wrapper div with right margin only
-                                                const wrapper = document.createElement('div');
-                                                wrapper.style.paddingRight = '20px';
-                                                wrapper.style.paddingLeft = '20px';
-                                                wrapper.style.paddingTop = '20px';
-                                                wrapper.style.backgroundColor = 'white';
-                                                // Clone the content
-                                                const clonedContent = content.cloneNode(true);
-                                                wrapper.appendChild(clonedContent);
-                                                // Add wrapper to document temporarily
-                                                document.body.appendChild(wrapper);
-                                                const canvas = await html2canvas(wrapper, {
-                                                    backgroundColor: 'white',
-                                                    scale: 2,
-                                                    useCORS: true,
-                                                    logging: false,
-                                                    width: content.offsetWidth + 20,
-                                                    height: content.offsetHeight + 50
-                                                });
-                                                // Remove temporary wrapper
-                                                document.body.removeChild(wrapper);
-                                                // Convert canvas to blob
-                                                canvas.toBlob(function(blob) {
-                                                    const url = URL.createObjectURL(blob);
-                                                    const link = document.createElement('a');
-                                                    link.href = url;
-                                                    link.download = 'stats_card.png';
-                                                    document.body.appendChild(link);
-                                                    link.click();
-                                                    document.body.removeChild(link);
-                                                    URL.revokeObjectURL(url);
-                                                }, 'image/png');
-                                            } catch (error) {
-                                                console.error('Error generating PNG:', error);
-                                            }
-                                        }
-                                        $(document).on('click', '#capture_png_btn', function() {
-                                            downloadPNG();
-                                        });
-                                        """)
-                                    ),
-                                    ui.div(
-                                        {
-                                            "id": "capture-section",
-                                            "style": "background-color: white; padding: 0; margin-left: 20px; margin-right: 20px; margin-top: 20px; margin-bottom: 20px;"
-                                        },
-                                        ui.div({"style": "font-size:3em;"}, ui.output_text("style_title")),
-                                        ui.div({"style": "font-size:1.5em;"}, ui.output_text("min_title")),
-                                        ui.br(),
-                                        ui.output_table("grid_style"),
-                                        ui.br(),
-                                    ui.row(
-                                        ui.column(8,
-                                            ui.div(
-                                                {"style": "text-align: left;"},
-                                                ui.markdown("### By: @TJStats"),
-                                                ui.markdown("### Data: MLB"),
-                                            )
-                                        ),
-                                        ui.column(4,
-                                            ui.div(
-                                                {"style": "text-align: left; height: 86px; display: flex; justify-content: flex-end;"},
-                                                ui.output_image("logo", height="86px")
-                                            )
-                                        )
-                                    ),
-                                        ui.div({"style": "height: 20px;"})
-                                    ),
-                                    ui.div(
-                                        {"style": "display: flex; gap: 10px;"},
-                                        ui.input_action_button("capture_png_btn", "Save as PNG", class_="btn-success"),
-                                    ),
-                                )
-            )
-        )
-    )
-)
-)
-# 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 ""
-    @render.image
-    def logo():
-        # You'll need to provide the actual image path or URL here
-        return {"src": "tjstats_logo.jpg"}
-    @render.text
-    def style_title():
-            return f"Daily {level_dict[input.level_input()]} tjStuff+ Leaders - {str(input.date_input())}"
-    @render.text
-    def min_title():
-            return f"(Min. {int(input.pitch_min())} Pitches)"
-    @render.ui
-    @reactive.event(input.game_button,input.date_input,input.level_input, ignore_none=False)
-    def game_select_ui():
-        df = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
-                            sport_id=[int(input.level_input())],
-                            game_type=['S','R','P','E','A','I','W','F','L'])
-                          .filter(pl.col('gameday_type').is_in(['P','E']))
-                          .filter(pl.col('state').is_in(['I','M','N','O','F','T','U','Q','R']))
-                          .with_columns(pl.col('date').cast(pl.Utf8))
-                          .filter(pl.col('date') == str(input.date_input()))).with_columns(
-                                (pl.col('away')+' @ '+pl.col('home')+' - '+pl.col('state')).alias('matchup')).sort('time')
-        game_dict = dict(zip(df['game_id'], df['matchup']))
-        print('GAMES')
-        print(game_dict)
-        return ui.input_select("game_id", "Select Game", game_dict)
-    @render.ui
-    @reactive.event(input.game_id)
-    def player_select_ui():
-        try:
-            # Get the list of pitchers for the selected level and season
-            print('GAMEDATA')
-            print(input.game_id())
-            print(type(input.game_id()))
-            data_list = scrape.get_data(game_list_input = [int(input.game_id())])
-            print('GAME')
-            print(input.game_id())
-            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                                (pl.col("is_pitch") == True)&
-                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-                                )))).with_columns(
-                    pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-                ).with_columns(
-                    (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
-                )
-            print('DATAFRAME')
-            # df = scrape.get_data_df(data_list = data_list)
-            print(df)
-            pitcher_dict = dict(zip(df['pitcher_id'], df['pitcher_name']))
-            print('PITCHERS')
-            print(pitcher_dict)
-            return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict)
-        except Exception as e:
-            print(e)
-            return ui.output_text('pitcher_id',"No pitchers available for this game")
-    @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...")
-            data_list = scrape.get_data(game_list_input = [int(input.game_id())])
-            # df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-            #                     (pl.col("pitcher_id") == int(input.pitcher_id()))&
-            #                     (pl.col("is_pitch") == True)&
-            #                     (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-            #                     )))).with_columns(
-            #         pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            #     ))
-            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                                (pl.col("pitcher_id") == int(input.pitcher_id()))&
-                                (pl.col("is_pitch") == True)&
-                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-                                ))).with_columns(
-                            pl.col("extension").fill_null(6.2)
-                        )).with_columns(
-                                            pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-                                        ))
-            df = df.clone()
-            # df = df.with_columns(
-            #     pl.lit(6.2).alias("extension")
-            # )
-            p.set(0.6, "Creating plot...")
-            #plt.rcParams["figure.figsize"] = [10,10]
-            fig = plt.figure(figsize=(26,26))
-            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.GridSpec(6, 8,
-                                height_ratios=[6,20,12,36,36,6],
-                                width_ratios=[4,18,18,18,18,18,18,4])
-            gs.update(hspace=0.2, wspace=0.5)
-            # Define the positions of each subplot in the grid
-            ax_headshot = fig.add_subplot(gs[1,1:3])
-            ax_bio = fig.add_subplot(gs[1,3:5])
-            ax_logo = fig.add_subplot(gs[1,5:7])
-            ax_season_table = fig.add_subplot(gs[2,1:7])
-            ax_plot_1 = fig.add_subplot(gs[3,1:3])
-            ax_plot_2 = fig.add_subplot(gs[3,3:5])
-            ax_plot_3 = fig.add_subplot(gs[3,5:7])
-            ax_table = fig.add_subplot(gs[4,1:7])
-            ax_footer = fig.add_subplot(gs[-1,1:7])
-            ax_header = fig.add_subplot(gs[0,1:7])
-            ax_left = fig.add_subplot(gs[:,0])
-            ax_right = fig.add_subplot(gs[:,-1])
-            # Hide axes for footer, header, left, and right
-            ax_footer.axis('off')
-            ax_header.axis('off')
-            ax_left.axis('off')
-            ax_right.axis('off')
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            fig.set_facecolor('white')
-            df_teams = scrape.get_teams()
-            year_input = int(str(input.date_input())[:4])
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-            team_id = df['pitcher_team_id'][0]
-            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
-            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
-            # plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
-            if input.switch():
-                # Get the logo URL from the image dictionary using the team abbreviation
-                logo_url = input.logo_select()
-                # Send a GET request to the logo URL
-                response = requests.get(logo_url)
-                # Open the image from the response content
-                img = Image.open(BytesIO(response.content))
-                # Display the image on the axis
-                ax_logo.set_xlim(0, 1.3)
-                ax_logo.set_ylim(0, 1)
-                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
-                # Turn off the axis
-                ax_logo.axis('off')
-            else:
-                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input),team_id=team_id)
-            # stat_summary_table(df=df,
-            #                 ax=ax_season_table,
-            #                 player_input=player_input,
-            #                 split=input.split_id(),
-            #                 sport_id=sport_id)
-            stat_daily_summary(df=df,
-                        data=data_list,
-                        player_input=int(input.pitcher_id()),
-                        sport_id=int(input.level_input()),
-                        ax=ax_season_table)
-            # 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()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
-                if x == 'velocity_kdes':
-                            velocity_kdes(df,
-                            ax=y,
-                            gs=gs,
-                            gs_x=[3,4],
-                            gs_y=[z,z+2],
-                            fig=fig)
-                if x == 'tj_stuff_roling':
-                    tj_stuff_roling(df=df,
-                                window=int(input.rolling_window()),
-                                ax=y)
-                if x == 'tj_stuff_roling_game':
-                    tj_stuff_roling_game(df=df,
-                                        window=int(input.rolling_window()),
-                                        ax=y)
-                if x == 'break_plot':
-                    break_plot(df = df,ax=y)
-                if x == 'location_plot_lhb':
-                    location_plot(df = df,ax=y,hand='L')
-                if x == 'location_plot_rhb':
-                    location_plot(df = df,ax=y,hand='R')
-                if x == 'break_plot_rhh':
-                    break_plot(df = df.filter(pl.col('batter_hand')=='R'),ax=y)
-                if x == 'break_plot_lhh':
-                    break_plot(df = df.filter(pl.col('batter_hand')=='L'),ax=y)
-            summary_table(df=df,
-                        ax=ax_table)
-            plot_footer(ax_footer)
-            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1)
-            # Hide axes ticks and labels
-            ax_watermark.set_xticks([])
-            ax_watermark.set_yticks([])
-            ax_watermark.set_frame_on(False)  # Optional: Hide border
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Display the image
-            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.06)
-            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1)
-            ax_watermark2.set_xlim(0,1)
-            ax_watermark2.set_ylim(0,1)
-            # Hide axes ticks and labels
-            ax_watermark2.set_xticks([])
-            ax_watermark2.set_yticks([])
-            ax_watermark2.set_frame_on(False)  # Optional: Hide border
-            # Open the image
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Get the original size
-            width, height = img.size
-            # Calculate the new size (50% larger)
-            new_width = int(width * 0.5)
-            new_height = int(height * 0.5)
-            # Resize the image
-            img_resized = img.resize((new_width, new_height))
-            # Display the image
-            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
-            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-    @output
-    @render.data_frame
-    @reactive.event(input.generate_plot, ignore_none=False)
-    def grid_summary():
-        data_list = scrape.get_data(game_list_input = [int(input.game_id())])
-        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("pitcher_id") == int(input.pitcher_id()))&
-                            (pl.col("is_pitch") == True)&
-                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-                            ))).with_columns(
-                        pl.col("extension").fill_null(6.2)
-                    )).with_columns(
-                                        pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-                                    ))
-        df = df.clone()
-        features_table = ['start_speed',
-            'spin_rate',
-            'extension',
-            'ivb',
-            'hb',
-            'x0',
-            'z0']
-        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
-                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
-        return render.DataGrid(
-                df.select(selection).to_pandas().round(1),
-                row_selection_mode='multiple',
-                 height='700px',
-                width='fit-content',
-                filters=True,
-            )
-    @output
-    @render.data_frame
-    @reactive.event(input.generate_plot, ignore_none=False)
-    def grid():
-        df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
-                            sport_id=[int(input.level_input())],
-                            game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)).
-                            filter(pl.col('date') == str(input.date_input()))).with_columns(
-                                (pl.col('away')+' @ '+pl.col('home')).alias('matchup'))
-        game_list = df_games['game_id'].unique().to_list()
-            # Get the list of pitchers for the selected level and season
-        data_list = scrape.get_data(game_list)
-        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("is_pitch") == True)&
-                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-                            )))).with_columns(
-                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            ).with_columns(
-                (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
-            )
-        # game_list = game_list_df['game_id'].unique().to_list()
-        data = scrape.get_data(game_list[:])
-        df = scrape.get_data_df(data)
-        pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team']))
-        df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter(
-                                    (pl.col("is_pitch") == True)))))
-        df_test = df_test.with_columns(
-            (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')
-        )
-        # Aggregate tj_stuff_plus by pitcher_id and year
-        df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg(
-            pl.col('tj_stuff_plus').len().alias('count'),
-            pl.col('tj_stuff_plus').mean()
-        )
-        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
-        df_weighted_avg = df_agg_2024_pitch.with_columns(
-            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
-        ).group_by(['pitcher_id', 'pitcher_name']).agg(
-            pl.col('count').sum().alias('total_count'),
-            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
-        ).with_columns(
-            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
-        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])
-        # Add the 'pitch_type' column with value "All"
-        df_weighted_avg = df_weighted_avg.with_columns(
-            pl.lit("All").alias('pitch_type')
-        )
-        # Select and rename columns to match the original DataFrame
-        df_weighted_avg = df_weighted_avg.select([
-            'pitcher_id',
-            'pitcher_name',
-            'pitch_type',
-            pl.col('total_count').alias('count'),
-            'tj_stuff_plus'
-        ])
-        # Concatenate the new rows with the original DataFrame
-        df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg])
-        df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus'])
-        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
-        df_small.filter(pl.col('pitch_type')=='All')['count']))
-        # Check if 'FS' column exists, if not create it and fill with None
-        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
-                        columns='pitch_type',
-                        values='tj_stuff_plus').with_columns(
-                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))
-        # Check if 'FS' column exists, if not create it and fill with None
-        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
-            if col not in df_small_pivot.columns:
-                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))
-        df_small_pivot = df_small_pivot.with_columns(
-                        pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team"))
-        df_small_pivot = df_small_pivot.select(['pitcher_id','pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
-        df_small_pivot = df_small_pivot.with_columns(
-            pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']
-        )
-        return render.DataGrid(
-                df_small_pivot,
-                row_selection_mode='multiple',
-                 height='700px',
-                width='fit-content',
-                filters=True,
-            )
-    @output
-    @render.table
-    @reactive.event(input.generate_plot, input.pitch_min,input.head,ignore_none=False)
-    def grid_style():
-        row_limit = int(input.head())
-        pitch_limit = int(input.pitch_min())
-        df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
-                            sport_id=[int(input.level_input())],
-                            game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)).
-                            filter(pl.col('date') == str(input.date_input()))).with_columns(
-                                (pl.col('away')+' @ '+pl.col('home')).alias('matchup'))
-        game_list = df_games['game_id'].unique().to_list()
-            # Get the list of pitchers for the selected level and season
-        data_list = scrape.get_data(game_list)
-        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                            (pl.col("is_pitch") == True)&
-                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-                            )))).with_columns(
-                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-            ).with_columns(
-                (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
-            )
-        # game_list = game_list_df['game_id'].unique().to_list()
-        data = scrape.get_data(game_list[:])
-        df = scrape.get_data_df(data)
-        pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team']))
-        df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter(
-                                    (pl.col("is_pitch") == True)))))
-        # df_test = df_test.with_columns(
-        #     (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')
-        # )
-        # Aggregate tj_stuff_plus by pitcher_id and year
-        df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg(
-            pl.col('tj_stuff_plus').len().alias('count'),
-            pl.col('tj_stuff_plus').mean()
-        )
-        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
-        df_weighted_avg = df_agg_2024_pitch.with_columns(
-            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
-        ).group_by(['pitcher_id', 'pitcher_name']).agg(
-            pl.col('count').sum().alias('total_count'),
-            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
-        ).with_columns(
-            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
-        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])
-        # Add the 'pitch_type' column with value "All"
-        df_weighted_avg = df_weighted_avg.with_columns(
-            pl.lit("All").alias('pitch_type')
-        )
-        # Select and rename columns to match the original DataFrame
-        df_weighted_avg = df_weighted_avg.select([
-            'pitcher_id',
-            'pitcher_name',
-            'pitch_type',
-            pl.col('total_count').alias('count'),
-            'tj_stuff_plus'
-        ])
-        # Concatenate the new rows with the original DataFrame
-        df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg])
-        df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus'])
-        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
-        df_small.filter(pl.col('pitch_type')=='All')['count']))
-        # Check if 'FS' column exists, if not create it and fill with None
-        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
-                        columns='pitch_type',
-                        values='tj_stuff_plus').with_columns(
-                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))
-        # Check if 'FS' column exists, if not create it and fill with None
-        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
-            if col not in df_small_pivot.columns:
-                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))
-        df_small_pivot = df_small_pivot.with_columns(
-                        pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team"))
-        df_small_pivot = df_small_pivot.select(['pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
-        df_small_pivot = df_small_pivot.with_columns(
-            pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']
-        )
-        df_export = df_small_pivot.filter(pl.col('count')>=pitch_limit).to_pandas().head(row_limit)
-        df_export.columns = ['Name', 'Team', 'Pitches', 'CH', 'CU', 'FC',
-            'FF', 'FS', 'SI', 'SL', 'ST', 'All']
-        df_style = df_export.style
-        df_style = df_style.set_properties(**{'border': '1.0 px'},overwrite=False).set_table_styles([{'selector' :'th',
-                                                                                        'props':[('text-align', 'center'),('font-size', '22px'),('Height','30px'),('border', '1px black solid !important')]},
-                                                                                        {'selector' :'td', 'props':[('text-align', 'center'),('font-size', '22px')]}],overwrite=False).set_table_styles(
-                [{'selector': 'tr', 'props': [('line-height', '1px')]}],overwrite=False).set_properties(
-                **{'Height': '60px'},**{'text-align': 'center'},overwrite=False).hide_index()
-        #cmap_sum_2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
-        df_style = df_style.format('{:.0f}',subset=df_export.columns[3:], na_rep='')
-        # df_style
-        df_style = df_style.background_gradient(cmap=cmap_sum,subset = ((list(df_export.index[:]),df_export.columns[3:])),vmin=80,vmax=120)#.applymap(lambda x: 'color: white' if pd.isnull(x) else '')
-        #df_style = df_style.applymap(background_gradient_ignore_nan)
-        #df_style = df_style
-        df_style = df_style.applymap(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else '')
-        df_style = df_style.set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(df_style.index[:-1]),df_style.columns[:]))).set_properties(
-                    **{'min-width':'325px'},subset = ((list(df_style.index[:-1]),df_style.columns[0])),overwrite=False).set_properties(
-                    **{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[1:3])),overwrite=False).set_properties(
-                    **{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[3:])),overwrite=False).set_properties(
-                    # **{'min-width':'125px'},subset = ((list(df_style.index[:-1]),df_style.columns[-1])),overwrite=False).set_properties(
-                    **{'border': '1px black solid !important'},subset = ((list(df_style.index[:]),df_style.columns[:])))
-        # df_style = df_style.set_table_styles([{'selector' :'th',
-        #                                        'props':[('text-align', 'center'),('font-size', '22px'),('Height','30px'),('border', '1px black solid !important')]},
-        #                                       {'selector' :'td', 'props':[('text-align', 'center'),('font-size', '22px')]}], overwrite=False)
-        return df_style
 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()
+from stuff_model import feature_engineering as fe
+from stuff_model import stuff_apply
+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 shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+import datetime
+import matplotlib.colors
+import pandas as pd
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#648FFF","#FFFFFF","#FFB000"])
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+year_list = [2017,2018,2019,2020,2021,2022,2023,2024]
+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',
+    'break_plot_rhh':'Pitch Movement LHH',
+    'break_plot_lhh':'Pitch Movement RHH',
+    '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']}
+# List of MLB teams and their corresponding ESPN logo URLs
+mlb_teams = [
+    {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
+    {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
+    {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
+    {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
+    {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
+    {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
+    {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
+    {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
+    {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
+    {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
+    {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
+    {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
+    {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
+    {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
+    {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
+    {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
+    {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
+    {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
+    {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
+    {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
+    {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
+    {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
+    {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
+    {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
+    {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
+    {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
+    {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
+    {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
+    {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
+    {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
+    {"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"}
+]
+df_image = pd.DataFrame(mlb_teams)
+image_dict = df_image.set_index('team')['logo_url'].to_dict()
+image_dict_flip = df_image.set_index('logo_url')['team'].to_dict()
+import requests
+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 Daily Pitching Summary 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/122860440", 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"),
+    )
+)
+# Define the UI layout for the app
+main_ui = ui.page_fluid(
+    ui.layout_sidebar(
+        ui.panel_sidebar(
+            # Row for selecting season and level
+            ui.row(
+                ui.column(6, ui.input_date('date_input', 'Select Date')),
+                ui.column(6, ui.input_select('level_input', 'Select Level', level_dict))
+            ),
+            ui.row(ui.input_action_button("game_button", "Get Games", class_="btn-primary")),
+            ui.row(
+                ui.row(ui.column(12, ui.output_ui('game_select_ui', 'Select Game'))),
+                ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
+            ),
+            # Rows for selecting plots and split options
+            ui.row(
+                ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='location_plot_lhb')),
+                ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='break_plot')),
+                ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='location_plot_rhb'))
+            ),
+            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=50))
+            ),
+        ui.row(
+            ui.column(6, ui.input_switch("switch", "Custom Team?", False)),
+            ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False))
+        ),
+            # Row for the action button to generate plot
+            ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary"))
+        ),
+        ui.panel_main(
+    # Main content area with tabs (placed directly in page_sidebar)
+            ui.navset_tab(
+                ui.nav_panel("Pitching Summary",
+                    ui.output_text("status"),
+                    ui.output_plot('plot', width='2100px', height='2100px')
+                ),
+                    ui.nav_panel("Table Summary",
+                                        ui.output_data_frame("grid_summary")),
+                    ui.nav_panel("Daily Table",
+                                        ui.output_data_frame("grid")),
+                    ui.nav_panel("Daily Table Style",
+                                ui.input_numeric('head', 'Table Limit', min=0, value=10),
+                                ui.input_numeric('pitch_min', 'Pitch Min.', min=0, value=10),
+                                ui.card(
+                                    {"style": "width: 1560px;"},
+                                    ui.head_content(
+                                        ui.tags.script(src="https://html2canvas.hertzen.com/dist/html2canvas.min.js"),
+                                        ui.tags.script("""
+                                        async function downloadPNG() {
+                                            const content = document.getElementById('capture-section');
+                                            try {
+                                                // Create a wrapper div with right margin only
+                                                const wrapper = document.createElement('div');
+                                                wrapper.style.paddingRight = '20px';
+                                                wrapper.style.paddingLeft = '20px';
+                                                wrapper.style.paddingTop = '20px';
+                                                wrapper.style.backgroundColor = 'white';
+                                                // Clone the content
+                                                const clonedContent = content.cloneNode(true);
+                                                wrapper.appendChild(clonedContent);
+                                                // Add wrapper to document temporarily
+                                                document.body.appendChild(wrapper);
+                                                const canvas = await html2canvas(wrapper, {
+                                                    backgroundColor: 'white',
+                                                    scale: 2,
+                                                    useCORS: true,
+                                                    logging: false,
+                                                    width: content.offsetWidth + 20,
+                                                    height: content.offsetHeight + 50
+                                                });
+                                                // Remove temporary wrapper
+                                                document.body.removeChild(wrapper);
+                                                // Convert canvas to blob
+                                                canvas.toBlob(function(blob) {
+                                                    const url = URL.createObjectURL(blob);
+                                                    const link = document.createElement('a');
+                                                    link.href = url;
+                                                    link.download = 'stats_card.png';
+                                                    document.body.appendChild(link);
+                                                    link.click();
+                                                    document.body.removeChild(link);
+                                                    URL.revokeObjectURL(url);
+                                                }, 'image/png');
+                                            } catch (error) {
+                                                console.error('Error generating PNG:', error);
+                                            }
+                                        }
+                                        $(document).on('click', '#capture_png_btn', function() {
+                                            downloadPNG();
+                                        });
+                                        """)
+                                    ),
+                                    ui.div(
+                                        {
+                                            "id": "capture-section",
+                                            "style": "background-color: white; padding: 0; margin-left: 20px; margin-right: 20px; margin-top: 20px; margin-bottom: 20px;"
+                                        },
+                                        ui.div({"style": "font-size:3em;"}, ui.output_text("style_title")),
+                                        ui.div({"style": "font-size:1.5em;"}, ui.output_text("min_title")),
+                                        ui.br(),
+                                        ui.output_table("grid_style"),
+                                        ui.br(),
+                                    ui.row(
+                                        ui.column(8,
+                                            ui.div(
+                                                {"style": "text-align: left;"},
+                                                ui.markdown("### By: @TJStats"),
+                                                ui.markdown("### Data: MLB"),
+                                            )
+                                        ),
+                                        ui.column(4,
+                                            ui.div(
+                                                {"style": "text-align: left; height: 86px; display: flex; justify-content: flex-end;"},
+                                                ui.output_image("logo", height="86px")
+                                            )
+                                        )
+                                    ),
+                                        ui.div({"style": "height: 20px;"})
+                                    ),
+                                    ui.div(
+                                        {"style": "display: flex; gap: 10px;"},
+                                        ui.input_action_button("capture_png_btn", "Save as PNG", class_="btn-success"),
+                                    ),
+                                )
+            )
+        )
+    )
+)
+)
+# 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 ""
+    @render.image
+    def logo():
+        # You'll need to provide the actual image path or URL here
+        return {"src": "tjstats_logo.jpg"}
+    @render.text
+    def style_title():
+            return f"Daily {level_dict[input.level_input()]} tjStuff+ Leaders - {str(input.date_input())}"
+    @render.text
+    def min_title():
+            return f"(Min. {int(input.pitch_min())} Pitches)"
+    @render.ui
+    @reactive.event(input.game_button,input.date_input,input.level_input, ignore_none=False)
+    def game_select_ui():
+        df = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
+                            sport_id=[int(input.level_input())],
+                            game_type=['S','R','P','E','A','I','W','F','L'])
+                          .filter(pl.col('gameday_type').is_in(['P','E']))
+                          .filter(pl.col('state').is_in(['I','M','N','O','F','T','U','Q','R']))
+                          .with_columns(pl.col('date').cast(pl.Utf8))
+                          .filter(pl.col('date') == str(input.date_input()))).with_columns(
+                                (pl.col('away')+' @ '+pl.col('home')+' - '+pl.col('state')).alias('matchup')).sort('time')
+        game_dict = dict(zip(df['game_id'], df['matchup']))
+        print('GAMES')
+        print(game_dict)
+        return ui.input_select("game_id", "Select Game", game_dict)
+    @render.ui
+    @reactive.event(input.game_id)
+    def player_select_ui():
+        try:
+            # Get the list of pitchers for the selected level and season
+            data_list = scrape.get_data(game_list_input = [int(input.game_id())])
+            print('DATALIST')
+            print(input.game_id())
+            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                                (pl.col("is_pitch") == True)&
+                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+                                )))).with_columns(
+                    pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+                ).with_columns(
+                    (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
+                )
+            # print('DATAFRAME')
+            # print(df)
+            pitcher_dict = dict(zip(df['pitcher_id'], df['pitcher_name']))
+            print('PITCHERS')
+            print(pitcher_dict)
+            return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict)
+        except Exception as e:
+            print(e)
+            return ui.output_text('pitcher_id',"No pitchers available for this game")
+    @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...")
+            data_list = scrape.get_data(game_list_input = [int(input.game_id())])
+            # df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+            #                     (pl.col("pitcher_id") == int(input.pitcher_id()))&
+            #                     (pl.col("is_pitch") == True)&
+            #                     (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+            #                     )))).with_columns(
+            #         pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            #     ))
+            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                                (pl.col("pitcher_id") == int(input.pitcher_id()))&
+                                (pl.col("is_pitch") == True)&
+                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+                                ))).with_columns(
+                            pl.col("extension").fill_null(6.2)
+                        )).with_columns(
+                                            pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+                                        ))
+            df = df.clone()
+            # df = df.with_columns(
+            #     pl.lit(6.2).alias("extension")
+            # )
+            p.set(0.6, "Creating plot...")
+            #plt.rcParams["figure.figsize"] = [10,10]
+            fig = plt.figure(figsize=(26,26))
+            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.GridSpec(6, 8,
+                                height_ratios=[6,20,12,36,36,6],
+                                width_ratios=[4,18,18,18,18,18,18,4])
+            gs.update(hspace=0.2, wspace=0.5)
+            # Define the positions of each subplot in the grid
+            ax_headshot = fig.add_subplot(gs[1,1:3])
+            ax_bio = fig.add_subplot(gs[1,3:5])
+            ax_logo = fig.add_subplot(gs[1,5:7])
+            ax_season_table = fig.add_subplot(gs[2,1:7])
+            ax_plot_1 = fig.add_subplot(gs[3,1:3])
+            ax_plot_2 = fig.add_subplot(gs[3,3:5])
+            ax_plot_3 = fig.add_subplot(gs[3,5:7])
+            ax_table = fig.add_subplot(gs[4,1:7])
+            ax_footer = fig.add_subplot(gs[-1,1:7])
+            ax_header = fig.add_subplot(gs[0,1:7])
+            ax_left = fig.add_subplot(gs[:,0])
+            ax_right = fig.add_subplot(gs[:,-1])
+            # Hide axes for footer, header, left, and right
+            ax_footer.axis('off')
+            ax_header.axis('off')
+            ax_left.axis('off')
+            ax_right.axis('off')
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            fig.set_facecolor('white')
+            df_teams = scrape.get_teams()
+            year_input = int(str(input.date_input())[:4])
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+            team_id = df['pitcher_team_id'][0]
+            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
+            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
+            # plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
+            if input.switch():
+                # Get the logo URL from the image dictionary using the team abbreviation
+                logo_url = input.logo_select()
+                # Send a GET request to the logo URL
+                response = requests.get(logo_url)
+                # Open the image from the response content
+                img = Image.open(BytesIO(response.content))
+                # Display the image on the axis
+                ax_logo.set_xlim(0, 1.3)
+                ax_logo.set_ylim(0, 1)
+                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
+                # Turn off the axis
+                ax_logo.axis('off')
+            else:
+                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input),team_id=team_id)
+            # stat_summary_table(df=df,
+            #                 ax=ax_season_table,
+            #                 player_input=player_input,
+            #                 split=input.split_id(),
+            #                 sport_id=sport_id)
+            stat_daily_summary(df=df,
+                        data=data_list,
+                        player_input=int(input.pitcher_id()),
+                        sport_id=int(input.level_input()),
+                        ax=ax_season_table)
+            # 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()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
+                if x == 'velocity_kdes':
+                            velocity_kdes(df,
+                            ax=y,
+                            gs=gs,
+                            gs_x=[3,4],
+                            gs_y=[z,z+2],
+                            fig=fig)
+                if x == 'tj_stuff_roling':
+                    tj_stuff_roling(df=df,
+                                window=int(input.rolling_window()),
+                                ax=y)
+                if x == 'tj_stuff_roling_game':
+                    tj_stuff_roling_game(df=df,
+                                        window=int(input.rolling_window()),
+                                        ax=y)
+                if x == 'break_plot':
+                    break_plot(df = df,ax=y)
+                if x == 'location_plot_lhb':
+                    location_plot(df = df,ax=y,hand='L')
+                if x == 'location_plot_rhb':
+                    location_plot(df = df,ax=y,hand='R')
+                if x == 'break_plot_rhh':
+                    break_plot(df = df.filter(pl.col('batter_hand')=='R'),ax=y)
+                if x == 'break_plot_lhh':
+                    break_plot(df = df.filter(pl.col('batter_hand')=='L'),ax=y)
+            summary_table(df=df,
+                        ax=ax_table)
+            plot_footer(ax_footer)
+            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1)
+            # Hide axes ticks and labels
+            ax_watermark.set_xticks([])
+            ax_watermark.set_yticks([])
+            ax_watermark.set_frame_on(False)  # Optional: Hide border
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Display the image
+            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.06)
+            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1)
+            ax_watermark2.set_xlim(0,1)
+            ax_watermark2.set_ylim(0,1)
+            # Hide axes ticks and labels
+            ax_watermark2.set_xticks([])
+            ax_watermark2.set_yticks([])
+            ax_watermark2.set_frame_on(False)  # Optional: Hide border
+            # Open the image
+            img = Image.open('tj stats circle-01_new.jpg')
+            # Get the original size
+            width, height = img.size
+            # Calculate the new size (50% larger)
+            new_width = int(width * 0.5)
+            new_height = int(height * 0.5)
+            # Resize the image
+            img_resized = img.resize((new_width, new_height))
+            # Display the image
+            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
+            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
+    @output
+    @render.data_frame
+    @reactive.event(input.generate_plot, ignore_none=False)
+    def grid_summary():
+        data_list = scrape.get_data(game_list_input = [int(input.game_id())])
+        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("pitcher_id") == int(input.pitcher_id()))&
+                            (pl.col("is_pitch") == True)&
+                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+                            ))).with_columns(
+                        pl.col("extension").fill_null(6.2)
+                    )).with_columns(
+                                        pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+                                    ))
+        df = df.clone()
+        features_table = ['start_speed',
+            'spin_rate',
+            'extension',
+            'ivb',
+            'hb',
+            'x0',
+            'z0']
+        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
+                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
+        return render.DataGrid(
+                df.select(selection).to_pandas().round(1),
+                row_selection_mode='multiple',
+                 height='700px',
+                width='fit-content',
+                filters=True,
+            )
+    @output
+    @render.data_frame
+    @reactive.event(input.generate_plot, ignore_none=False)
+    def grid():
+        df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
+                            sport_id=[int(input.level_input())],
+                            game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)).
+                            filter(pl.col('date') == str(input.date_input()))).with_columns(
+                                (pl.col('away')+' @ '+pl.col('home')).alias('matchup'))
+        game_list = df_games['game_id'].unique().to_list()
+            # Get the list of pitchers for the selected level and season
+        data_list = scrape.get_data(game_list)
+        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("is_pitch") == True)&
+                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+                            )))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            ).with_columns(
+                (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
+            )
+        # game_list = game_list_df['game_id'].unique().to_list()
+        data = scrape.get_data(game_list[:])
+        df = scrape.get_data_df(data)
+        pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team']))
+        df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter(
+                                    (pl.col("is_pitch") == True)))))
+        df_test = df_test.with_columns(
+            (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')
+        )
+        # Aggregate tj_stuff_plus by pitcher_id and year
+        df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg(
+            pl.col('tj_stuff_plus').len().alias('count'),
+            pl.col('tj_stuff_plus').mean()
+        )
+        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
+        df_weighted_avg = df_agg_2024_pitch.with_columns(
+            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
+        ).group_by(['pitcher_id', 'pitcher_name']).agg(
+            pl.col('count').sum().alias('total_count'),
+            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
+        ).with_columns(
+            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
+        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])
+        # Add the 'pitch_type' column with value "All"
+        df_weighted_avg = df_weighted_avg.with_columns(
+            pl.lit("All").alias('pitch_type')
+        )
+        # Select and rename columns to match the original DataFrame
+        df_weighted_avg = df_weighted_avg.select([
+            'pitcher_id',
+            'pitcher_name',
+            'pitch_type',
+            pl.col('total_count').alias('count'),
+            'tj_stuff_plus'
+        ])
+        # Concatenate the new rows with the original DataFrame
+        df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg])
+        df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus'])
+        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
+        df_small.filter(pl.col('pitch_type')=='All')['count']))
+        # Check if 'FS' column exists, if not create it and fill with None
+        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
+                        columns='pitch_type',
+                        values='tj_stuff_plus').with_columns(
+                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))
+        # Check if 'FS' column exists, if not create it and fill with None
+        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
+            if col not in df_small_pivot.columns:
+                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))
+        df_small_pivot = df_small_pivot.with_columns(
+                        pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team"))
+        df_small_pivot = df_small_pivot.select(['pitcher_id','pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
+        df_small_pivot = df_small_pivot.with_columns(
+            pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']
+        )
+        return render.DataGrid(
+                df_small_pivot,
+                row_selection_mode='multiple',
+                 height='700px',
+                width='fit-content',
+                filters=True,
+            )
+    @output
+    @render.table
+    @reactive.event(input.generate_plot, input.pitch_min,input.head,ignore_none=False)
+    def grid_style():
+        row_limit = int(input.head())
+        pitch_limit = int(input.pitch_min())
+        df_games = (scrape.get_schedule(year_input=[int(str(input.date_input())[:4])],
+                            sport_id=[int(input.level_input())],
+                            game_type=['S','R','P','E','A','I','W','F','L']).with_columns(pl.col('date').cast(pl.Utf8)).
+                            filter(pl.col('date') == str(input.date_input()))).with_columns(
+                                (pl.col('away')+' @ '+pl.col('home')).alias('matchup'))
+        game_list = df_games['game_id'].unique().to_list()
+            # Get the list of pitchers for the selected level and season
+        data_list = scrape.get_data(game_list)
+        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("is_pitch") == True)&
+                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+                            )))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            ).with_columns(
+                (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name'))
+            )
+        # game_list = game_list_df['game_id'].unique().to_list()
+        data = scrape.get_data(game_list[:])
+        df = scrape.get_data_df(data)
+        pitcher_team_dict = dict(zip(df['pitcher_id'], df['pitcher_team']))
+        df_test = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(df).filter(
+                                    (pl.col("is_pitch") == True)))))
+        # df_test = df_test.with_columns(
+        #     (pl.col('pitcher_name')+' - '+pl.col('pitcher_team')).alias('pitcher_name')
+        # )
+        # Aggregate tj_stuff_plus by pitcher_id and year
+        df_agg_2024_pitch = df_test.group_by(['pitcher_id','pitcher_name','pitch_type']).agg(
+            pl.col('tj_stuff_plus').len().alias('count'),
+            pl.col('tj_stuff_plus').mean()
+        )
+        # Calculate the weighted average of 'tj_stuff_plus' for each pitcher
+        df_weighted_avg = df_agg_2024_pitch.with_columns(
+            (pl.col('tj_stuff_plus') * pl.col('count')).alias('weighted_tj_stuff_plus')
+        ).group_by(['pitcher_id', 'pitcher_name']).agg(
+            pl.col('count').sum().alias('total_count'),
+            pl.col('weighted_tj_stuff_plus').sum().alias('total_weighted_tj_stuff_plus')
+        ).with_columns(
+            (pl.col('total_weighted_tj_stuff_plus') / pl.col('total_count')).alias('tj_stuff_plus')
+        ).select(['pitcher_id', 'pitcher_name', 'tj_stuff_plus', 'total_count'])
+        # Add the 'pitch_type' column with value "All"
+        df_weighted_avg = df_weighted_avg.with_columns(
+            pl.lit("All").alias('pitch_type')
+        )
+        # Select and rename columns to match the original DataFrame
+        df_weighted_avg = df_weighted_avg.select([
+            'pitcher_id',
+            'pitcher_name',
+            'pitch_type',
+            pl.col('total_count').alias('count'),
+            'tj_stuff_plus'
+        ])
+        # Concatenate the new rows with the original DataFrame
+        df_agg_2024_pitch = pl.concat([df_agg_2024_pitch, df_weighted_avg])
+        df_small = df_agg_2024_pitch.select(['pitcher_id','pitcher_name','pitch_type','count','tj_stuff_plus'])
+        count_dict = dict(zip(df_small.filter(pl.col('pitch_type')=='All')['pitcher_id'],
+        df_small.filter(pl.col('pitch_type')=='All')['count']))
+        # Check if 'FS' column exists, if not create it and fill with None
+        df_small_pivot = (df_small.pivot(index=['pitcher_id','pitcher_name'],
+                        columns='pitch_type',
+                        values='tj_stuff_plus').with_columns(
+                        pl.col("pitcher_id").replace_strict(count_dict, default=None).alias("count")))
+        # Check if 'FS' column exists, if not create it and fill with None
+        for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']:
+            if col not in df_small_pivot.columns:
+                df_small_pivot = df_small_pivot.with_columns(pl.lit(None).alias(col))
+        df_small_pivot = df_small_pivot.with_columns(
+                        pl.col("pitcher_id").replace_strict(pitcher_team_dict, default=None).alias("pitcher_team"))
+        df_small_pivot = df_small_pivot.select(['pitcher_name','pitcher_team','count','CH','CU','FC','FF','FS','SI','SL','ST','All']).sort('All',descending=True)#.head(10)#.write_clipboard()
+        df_small_pivot = df_small_pivot.with_columns(
+            pl.col(col).cast(pl.Int32, strict=False) for col in ['CH', 'CU', 'FC', 'FF', 'FS', 'SI', 'SL', 'ST', 'All']
+        )
+        df_export = df_small_pivot.filter(pl.col('count')>=pitch_limit).to_pandas().head(row_limit)
+        df_export.columns = ['Name', 'Team', 'Pitches', 'CH', 'CU', 'FC',
+            'FF', 'FS', 'SI', 'SL', 'ST', 'All']
+        df_style = df_export.style
+        df_style = df_style.set_properties(**{'border': '1.0 px'},overwrite=False).set_table_styles([{'selector' :'th',
+                                                                                        'props':[('text-align', 'center'),('font-size', '22px'),('Height','30px'),('border', '1px black solid !important')]},
+                                                                                        {'selector' :'td', 'props':[('text-align', 'center'),('font-size', '22px')]}],overwrite=False).set_table_styles(
+                [{'selector': 'tr', 'props': [('line-height', '1px')]}],overwrite=False).set_properties(
+                **{'Height': '60px'},**{'text-align': 'center'},overwrite=False).hide_index()
+        #cmap_sum_2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
+        df_style = df_style.format('{:.0f}',subset=df_export.columns[3:], na_rep='')
+        # df_style
+        df_style = df_style.background_gradient(cmap=cmap_sum,subset = ((list(df_export.index[:]),df_export.columns[3:])),vmin=80,vmax=120)#.applymap(lambda x: 'color: white' if pd.isnull(x) else '')
+        #df_style = df_style.applymap(background_gradient_ignore_nan)
+        #df_style = df_style
+        df_style = df_style.applymap(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else '')
+        df_style = df_style.set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(df_style.index[:-1]),df_style.columns[:]))).set_properties(
+                    **{'min-width':'325px'},subset = ((list(df_style.index[:-1]),df_style.columns[0])),overwrite=False).set_properties(
+                    **{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[1:3])),overwrite=False).set_properties(
+                    **{'min-width':'100px'},subset = ((list(df_style.index[:-1]),df_style.columns[3:])),overwrite=False).set_properties(
+                    # **{'min-width':'125px'},subset = ((list(df_style.index[:-1]),df_style.columns[-1])),overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(df_style.index[:]),df_style.columns[:])))
+        # df_style = df_style.set_table_styles([{'selector' :'th',
+        #                                        'props':[('text-align', 'center'),('font-size', '22px'),('Height','30px'),('border', '1px black solid !important')]},
+        #                                       {'selector' :'td', 'props':[('text-align', 'center'),('font-size', '22px')]}], overwrite=False)
+        return df_style
 app = App(app_ui, server)

functions/__pycache__/df_update.cpython-39.pyc ADDED Viewed

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functions/__pycache__/pitch_summary_functions.cpython-39.pyc ADDED Viewed

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functions/pitch_summary_functions.py CHANGED Viewed

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requirements.txt CHANGED Viewed

@@ -1,16 +1,16 @@
-joblib==1.3.2
-lightgbm
-matplotlib==3.5.1
-numpy==1.23.5
-pandas==1.5.2
-polars==1.12.0
-Requests==2.31.0
-scipy==1.11.1
-seaborn==0.11.1
-scikit-learn==1.0.1
-shiny==0.6.1
-Jinja2==3.1.4
-tqdm==4.62.3
-pyarrow
-cairosvg

+joblib==1.3.2
+lightgbm
+matplotlib==3.5.1
+numpy==1.23.5
+pandas==1.5.2
+polars==1.12.0
+Requests==2.31.0
+scipy==1.11.1
+seaborn==0.11.1
+scikit-learn==1.0.1
+shiny==0.6.1
+Jinja2==3.1.4
+tqdm==4.62.3
+pyarrow
+cairosvg

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