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nesticot commited on Dec 23, 2024

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-import polars as pl
-import api_scraper
-import pandas as pd
-scrape = api_scraper.MLB_Scrape()
-# import df_update
-# update = df_update.df_update()
-from matplotlib.colors import LinearSegmentedColormap, Normalize
-import numpy as np
-import requests
-from io import BytesIO
-from PIL import Image
-from matplotlib.gridspec import GridSpec
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-import PIL
-level_dict =  {
-               '11':'AAA',
-               '14':'A',}
-def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
-    """
-    Display the player's bio information on the given axis.
-    Parameters
-    ----------
-    pitcher_id : str
-        The player's ID.
-    ax : plt.Axes
-        The axis to display the bio information on.
-    sport_id : int
-        The sport ID (1 for MLB, other for minor leagues).
-    year_input : int
-        The season year.
-    """
-    # Construct the URL to fetch player data
-    url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
-    # Send a GET request to the URL and parse the JSON response
-    data = requests.get(url).json()
-    # Extract player information from the JSON data
-    player_name = data['people'][0]['fullName']
-    position = data['people'][0]['primaryPosition']['abbreviation']
-    bat_side = data['people'][0]['batSide']['code']
-    pitcher_hand = data['people'][0]['pitchHand']['code']
-    age = data['people'][0]['currentAge']
-    height = data['people'][0]['height']
-    weight = data['people'][0]['weight']
-    # Display the player's name, handedness, age, height, and weight on the axis
-    ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=30)
-    ax.text(0.5, 0.65, f'{position}, B/T: {bat_side}/{pitcher_hand}, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=20)
-    if position == 'P':
-        ax.text(0.5, 0.38, f'Season Pitching Percentiles', va='top', ha='center', fontsize=16)
-    else:
-        ax.text(0.5, 0.41, f'Season Batting Percentiles', va='top', ha='center', fontsize=16)
-    # Make API call to retrieve sports information
-    response = requests.get(url='https://statsapi.mlb.com/api/v1/sports').json()
-    # Convert the JSON response into a Polars DataFrame
-    df_sport_id = pl.DataFrame(response['sports'])
-    abb = df_sport_id.filter(pl.col('id') == sport_id)['abbreviation'][0]
-    # Display the season and sport abbreviation
-    ax.text(0.5, 0.20, f'{year_input} {abb} Season', va='top', ha='center', fontsize=14, fontstyle='italic')
-    # Turn off the axis
-    ax.axis('off')
-df_teams = scrape.get_teams()
-team_dict = dict(zip(df_teams['team_id'],df_teams['parent_org_abbreviation']))
-# 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()
-# level_dict =  {'1':'MLB',
-#                '11':'AAA'}
-level_dict =  {
-               '11':'AAA',
-               '14':'A (FSL)',}
-level_dict_file =  {
-               '11':'aaa',
-               '14':'a',}
-year_list = [2024]
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-# Define the UI layout for the app
-app_ui = ui.page_fluid(
-    ui.tags.div(
-         {"style": "width:90%;margin: 0 auto;max-width: 1600px;"},
-        ui.tags.style(
-            """
-            h4 {
-                margin-top: 1em;font-size:35px;
-            }
-            h2{
-                font-size:25px;
-            }
-            """
-         ),
-    ui.tags.h4("TJStats"),
-    ui.tags.i("Baseball Analytics and Visualizations"),
-    ui.markdown("""<a href='https://x.com/TJStats'>Follow me on Twitter</a><sup>1</sup>"""),
-    ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),
-    ui.markdown("### 2024 MiLB Statcast Summaries"),
-    ui.markdown("""This Shiny App allows you to generate Baseball Savant-style percentile bars for MiLB players in the 2024 Season.
-                Currently, MiLB Statcast is only available for AAA and A (Florida State League) levels."""),
-    ui.layout_sidebar(
-        ui.panel_sidebar(
-            # Row for selecting season and level
-            ui.row(
-                ui.column(6, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
-                ui.column(6, ui.input_select('level_input', 'Select Level', level_dict)),
-            ),
-            # 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.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")),
-            width=3,
-        ),
-        ui.panel_main(
-            ui.navset_tab(
-                # Tab for game summary plot
-                ui.nav("Batter Summary",
-                       ui.output_text("status_batter"),
-                       ui.output_plot('batter_plot', width='1200px', height='1200px')
-                ),
-                ui.nav("Pitcher Summary",
-                       ui.output_text("status_pitcher"),
-                       ui.output_plot('pitcher_plot', width='1200px', height='1200px')
-                )
-                ,id="tabset"
-            )
-        )
-    )
-)
-)
-def server(input, output, session):
-    @render.ui
-    @reactive.event(input.player_button,input.tabset, ignore_none=False)
-    def player_select_ui():
-        if input.tabset() == "Batter Summary":
-            #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())).filter(
-                ~pl.col("position").is_in(['P','TWP'])).sort("name")
-            # Create a dictionary of pitcher IDs and names
-            batter_dict_pos = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['position']))
-            year = int(input.year_input())
-            sport_id = int(input.level_input())
-            batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv').sort('batter_name',descending=False)
-            # Map elements in Polars DataFrame from a dictionary
-            batter_summary = batter_summary.with_columns(
-                pl.col("batter_id").map_elements(lambda x: batter_dict_pos.get(x, x)).alias("position")
-            )
-            batter_dict_pos = dict(zip(batter_summary['batter_id'], batter_summary['batter_name']))
-            # Create a dictionary of pitcher IDs and names
-            batter_dict = dict(zip(batter_summary['batter_id'], batter_summary['batter_name'] + ' - ' + batter_summary['position']))
-            # Return a select input for choosing a pitcher
-            return ui.input_select("batter_id", "Select Batter", batter_dict, selectize=True)
-        if input.tabset() == "Pitcher Summary":
-            #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())).filter(
-                pl.col("position").is_in(['P','TWP'])).sort("name")
-            # Create a dictionary of pitcher IDs and names
-            batter_dict_pos = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['position']))
-            year = int(input.year_input())
-            sport_id = int(input.level_input())
-            batter_summary = pl.read_csv(f'data/statcast/pitcher_summary_{level_dict_file[str(sport_id)]}_{year}.csv').sort('pitcher_name',descending=False)
-            # Map elements in Polars DataFrame from a dictionary
-            batter_summary = batter_summary.with_columns(
-                pl.col("pitcher_id").map_elements(lambda x: batter_dict_pos.get(x, x)).alias("position")
-            )
-            batter_dict_pos = dict(zip(batter_summary['pitcher_id'], batter_summary['pitcher_name']))
-            # Create a dictionary of pitcher IDs and names
-            batter_dict = dict(zip(batter_summary['pitcher_id'], batter_summary['pitcher_name'] + ' - ' + batter_summary['position']))
-            # Return a select input for choosing a pitcher
-            return ui.input_select("pitcher_id", "Select Batter", batter_dict, selectize=True)
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)
-    def batter_plot():
-        merged_dict = {
-            "woba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "wOBA" },
-            "xwoba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "xwOBA" },
-            "launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Average EV"},
-            "launch_speed_90": { "format": '.1f', "percentile_flip": False, "stat_title": "90th% EV"},
-            "max_launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Max EV"},
-            "barrel_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Barrel%" },
-            "hard_hit_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Hard-Hit%" },
-            "sweet_spot_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "LA Sweet-Spot%" },
-            "zone_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Zone%" },
-            "zone_swing_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Z-Swing%" },
-            "chase_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "O-Swing%" },
-            "whiff_rate": { "format": '.1%', "percentile_flip": True, "stat_title": "Whiff%" },
-            "k_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "K%" },
-            "bb_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "BB%" },
-            "pull_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull%" },
-            "pulled_fly_ball_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull FB%" },
-        }
-        # Show progress/loading notification
-        with ui.Progress(min=0, max=1) as p:
-            def draw_baseball_savant_percentiles(new_player_metrics, new_player_percentiles, colors=None,
-                                                                                sport_id=None,
-                                            year_input=None):
-                """
-                Draw Baseball Savant-style percentile bars with proper alignment and scaling.
-                :param new_player_metrics: DataFrame containing new player metrics.
-                :param new_player_percentiles: DataFrame containing new player percentiles.
-                :param colors: List of colors for bars (optional, red/blue default).
-                """
-                # Extract player information
-                batter_id = new_player_metrics['batter_id'][0]
-                player_name = batter_name_id[batter_id]
-                stats = [merged_dict[x]['stat_title'] for x in merged_dict.keys()]
-                # Calculate percentiles and values
-                percentiles = [int((1 - x) * 100) if merged_dict[stat]["percentile_flip"] else int(x * 100) for x, stat in zip(new_player_percentiles.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
-                percentiles = np.clip(percentiles, 1, 100)
-                values = [str(f'{x:{merged_dict[stat]["format"]}}').strip('%') for x, stat in zip(new_player_metrics.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
-                # Get team logo URL
-                logo_url = image_dict[team_dict[player_team_dict[batter_id]]]
-                # Create a custom colormap
-                color_list = ['#3661AD', '#B4CFD1', '#D82129']
-                cmap = LinearSegmentedColormap.from_list("custom_cmap", color_list)
-                norm = Normalize(vmin=0.1, vmax=0.9)
-                norm_percentiles = norm(percentiles / 100)
-                colors = [cmap(p) for p in norm_percentiles]
-                # Figure setup
-                num_stats = len(stats)
-                bar_height = 4.5
-                spacing = 1
-                fig_height = (bar_height + spacing) * num_stats
-                fig = plt.figure(figsize=(12, 12))
-                gs = GridSpec(6, 5, height_ratios=[0.1, 1.5, 0.9, 0.9, 7.6, 0.1], width_ratios=[0.2, 1.5, 7, 1.5, 0.2])
-                # Define subplots
-                ax_title = fig.add_subplot(gs[1, 2])
-                ax_table = fig.add_subplot(gs[2, :])
-                ax_fv_table = fig.add_subplot(gs[3, :])
-                ax_fv_table.axis('off')
-                ax = fig.add_subplot(gs[4, :])
-                ax_logo = fig.add_subplot(gs[1, 3])
-                ax.set_xlim(-1, 99)
-                ax.set_ylim(-1, 99)
-                ax.set_aspect("equal")
-                ax.axis("off")
-                # Draw each bar
-                for i, (stat, percentile, value, color) in enumerate(zip(stats, percentiles, values, colors)):
-                    y = fig_height - (i + 1) * (bar_height + spacing)
-                    ax.add_patch(patches.Rectangle((0, y + bar_height / 4), 100, bar_height / 2, color="#C7DCDC", lw=0))
-                    ax.add_patch(patches.Rectangle((0, y), percentile, bar_height, color=color, lw=0))
-                    circle_y = y + bar_height - bar_height / 2
-                    circle = plt.Circle((percentile, circle_y), bar_height / 2, color=color, ec='white', lw=1.5, zorder=10)
-                    ax.add_patch(circle)
-                    fs = 14
-                    ax.text(percentile, circle_y, f"{percentile}", ha="center", va="center", fontsize=10, color='white', zorder=10, fontweight='bold')
-                    ax.text(-5, y + bar_height / 2, stat, ha="right", va="center", fontsize=fs)
-                    ax.text(115, y + bar_height / 2, str(value), ha="right", va="center", fontsize=fs, zorder=5)
-                    if i < len(stats) and i > 0:
-                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=-33, xmax=0)
-                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=100, xmax=115)
-                # Draw vertical lines for 10%, 50%, and 90% with labels
-                for x, label, align, color in zip([10, 50, 90], ["Poor", "Average", "Great"], ['center', 'center', 'center'], color_list):
-                    ax.axvline(x=x, ymin=0, ymax=1, color='#FFF', linestyle='-', lw=1, zorder=1, alpha=0.5)
-                    ax.text(x, fig_height + 4, label, ha=align, va='center', fontsize=12, fontweight='bold', color=color)
-                    triangle = patches.RegularPolygon((x, fig_height + 1), 3, radius=1, orientation=0, color=color, zorder=2)
-                    ax.add_patch(triangle)
-                # # Title
-                # ax_title.set_ylim(0, 1)
-                # ax_title.text(0.5, 0.5, f"{player_name} - {player_position_dict[batter_id]}\nPercentile Rankings - 2024 AAA", ha="center", va="center", fontsize=24)
-                # ax_title.axis("off")
-                player_bio(batter_id, ax=ax_title, sport_id=sport_id, year_input=year_input)
-                # Add team logo
-                #response = requests.get(logo_url)
-                if input.switch():
-                    response = requests.get(input.logo_select())
-                else:
-                    response = requests.get(logo_url)
-                img = Image.open(BytesIO(response.content))
-                ax_logo.imshow(img)
-                ax_logo.axis("off")
-                ax.axis('equal')
-                # Metrics data table
-                metrics_data = {
-                    "Pitches": new_player_metrics['pitches'][0],
-                    "PA": new_player_metrics['pa'][0],
-                    "BIP": new_player_metrics['bip'][0],
-                    "HR": f"{new_player_metrics['home_run'][0]:.0f}",
-                    "AVG": f"{new_player_metrics['avg'][0]:.3f}",
-                    "OBP": f"{new_player_metrics['obp'][0]:.3f}",
-                    "SLG": f"{new_player_metrics['slg'][0]:.3f}",
-                    "OPS": f"{new_player_metrics['obp'][0] + new_player_metrics['slg'][0]:.3f}",
-                }
-                df_table = pd.DataFrame(metrics_data, index=[0])
-                ax_table.axis('off')
-                table = ax_table.table(cellText=df_table.values, colLabels=df_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
-                for key, cell in table.get_celld().items():
-                    if key[0] == 0:
-                        cell.set_text_props(fontweight='bold')
-                table.auto_set_font_size(False)
-                table.set_fontsize(12)
-                table.scale(1, 1.5)
-                # Additional subplots for spacing
-                ax_top = fig.add_subplot(gs[0, :])
-                ax_bot = fig.add_subplot(gs[-1, :])
-                ax_top.axis('off')
-                ax_bot.axis('off')
-                ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14)
-                ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14)
-                fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-                # Player headshot
-                ax_headshot = fig.add_subplot(gs[1, 1])
-                try:
-                    url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{batter_id}/headshot/milb/current.png'
-                    response = requests.get(url)
-                    img = Image.open(BytesIO(response.content))
-                    ax_headshot.set_xlim(0, 1)
-                    ax_headshot.set_ylim(0, 1)
-                    ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper')
-                except PIL.UnidentifiedImageError:
-                    ax_headshot.axis('off')
-                    #return
-                ax_headshot.axis('off')
-                ax_table.set_title('Season Summary', style='italic')
-                # Fangraphs scouting grades table
-                print(batter_id)
-                if batter_id not in dict_mlb_fg.keys():
-                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    return
-                df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[batter_id])][['cFV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']].reset_index(drop=True)
-                ax_fv_table.axis('off')
-                if df_fv_table.empty:
-                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    return
-                df_fv_table.columns = ['FV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']
-                table_fv = ax_fv_table.table(cellText=df_fv_table.values, colLabels=df_fv_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
-                for key, cell in table_fv.get_celld().items():
-                    if key[0] == 0:
-                        cell.set_text_props(fontweight='bold')
-                table_fv.auto_set_font_size(False)
-                table_fv.set_fontsize(12)
-                table_fv.scale(1, 1.5)
-                ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic')
-                #plt.show()
-            def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered):
-                """
-                Calculate percentiles for a new player's metrics.
-                :param player_id: ID of the player.
-                :param new_player_metrics: DataFrame containing new player metrics.
-                :param player_summary_filtered: Filtered player summary DataFrame.
-                :return: DataFrame containing new player percentiles.
-                """
-                filtered_summary_clone = player_summary_filtered[['batter_id'] + stat_list].filter(pl.col('batter_id') != player_id).clone()
-                combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas()
-                combined_percentiles = pl.DataFrame(pd.concat([combined_data['batter_id'], combined_data[stat_list].rank(pct=True)], axis=1))
-                new_player_percentiles = combined_percentiles.filter(pl.col('batter_id') == player_id)
-                return new_player_percentiles
-            p.set(message="Generating plot", detail="This may take a while...")
-            p.set(0.3, "Gathering data...")
-            # Example: New player's metrics
-            year = int(input.year_input())
-            sport_id = int(input.level_input())
-            batter_id = int(input.batter_id())
-            df_player = scrape.get_players(sport_id=sport_id,season=year)
-            batter_name_id = dict(zip(df_player['player_id'],df_player['name']))
-            player_team_dict = dict(zip(df_player['player_id'],df_player['team']))
-            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
-            batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv')
-            df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv')
-            df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv')
-            df_small = df_rosters[['minorbamid','minormasterid']].dropna()
-            dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid']))
-            batter_summary_filter = batter_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0))
-            stat_list = batter_summary.columns[2:]
-            batter_summary_filter_pd = batter_summary_filter.to_pandas()
-            new_player_metrics = batter_summary.filter(pl.col('batter_id') == batter_id)[['batter_id'] + stat_list]
-            # Get percentiles for the new player
-            new_player_percentiles = calculate_new_player_percentiles(batter_id, new_player_metrics, batter_summary_filter)
-            p.set(0.6, "Creating plot...")
-            # Draw Baseball Savant-style percentile bars
-            draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics,
-                                            new_player_percentiles=new_player_percentiles,
-                                            sport_id=sport_id,
-                                            year_input=year)
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)
-    def pitcher_plot():
-        merged_dict = {
-                "woba_percent": { "format": '.3f', "percentile_flip": True, "stat_title": "wOBA" },
-                "xwoba_percent": { "format": '.3f', "percentile_flip": True, "stat_title": "xwOBA" },
-                "launch_speed": { "format": '.1f', "percentile_flip": True, "stat_title": "Average EV"},
-                "barrel_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Barrel%" },
-                "hard_hit_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Hard-Hit%" },
-                "whiff_rate": { "format": '.1%', "percentile_flip": False, "stat_title": "Whiff%" },
-                "zone_contact_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Z-Contact%" },
-                "zone_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Zone%" },
-                "chase_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "O-Swing%" },
-                "csw_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "CSW%" },
-                "k_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "K%" },
-                "bb_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "BB%" },
-                "k_minus_bb_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "K - BB%" },
-                "ground_ball_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "GB%" },
-            }
-        with ui.Progress(min=0, max=1) as p:
-            def draw_baseball_savant_percentiles(new_player_metrics, new_player_percentiles, colors=None,
-                                                                                sport_id=None,
-                                            year_input=None):
-                """
-                Draw Baseball Savant-style percentile bars with proper alignment and scaling.
-                :param new_player_metrics: DataFrame containing new player metrics.
-                :param new_player_percentiles: DataFrame containing new player percentiles.
-                :param colors: List of colors for bars (optional, red/blue default).
-                """
-                # Extract player information
-                pitcher_id = new_player_metrics['pitcher_id'][0]
-                player_name = pitcher_name_id[pitcher_id]
-                stats = [merged_dict[x]['stat_title'] for x in merged_dict.keys()]
-                # Calculate percentiles and values
-                percentiles = [int((1 - x) * 100) if merged_dict[stat]["percentile_flip"] else int(x * 100) for x, stat in zip(new_player_percentiles.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
-                percentiles = np.clip(percentiles, 1, 100)
-                values = [str(f'{x:{merged_dict[stat]["format"]}}').strip('%') for x, stat in zip(new_player_metrics.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
-                # Get team logo URL
-                logo_url = image_dict[team_dict[player_team_dict[pitcher_id]]]
-                # Create a custom colormap
-                color_list = ['#3661AD', '#B4CFD1', '#D82129']
-                cmap = LinearSegmentedColormap.from_list("custom_cmap", color_list)
-                norm = Normalize(vmin=0.1, vmax=0.9)
-                norm_percentiles = norm(percentiles / 100)
-                colors = [cmap(p) for p in norm_percentiles]
-                # Figure setup
-                num_stats = len(stats)
-                bar_height = 4.5
-                spacing = 1
-                fig_height = (bar_height + spacing) * num_stats
-                fig = plt.figure(figsize=(12, 12))
-                gs = GridSpec(7, 5, height_ratios=[0.1, 1.7, 0.9, 0.9,0.9, 7.4, 0.1], width_ratios=[0.2, 1.5, 7, 1.5, 0.2])
-                # Define subplots
-                ax_title = fig.add_subplot(gs[1, 2])
-                ax_table = fig.add_subplot(gs[2, :])
-                ax_fv_table = fig.add_subplot(gs[3, :])
-                ax_fv_table.axis('off')
-                ax_stuff = fig.add_subplot(gs[4, :])
-                ax = fig.add_subplot(gs[5, :])
-                ax_logo = fig.add_subplot(gs[1, 3])
-                ax.set_xlim(-1, 99)
-                ax.set_ylim(-1, 99)
-                ax.set_aspect("equal")
-                ax.axis("off")
-                # Draw each bar
-                for i, (stat, percentile, value, color) in enumerate(zip(stats, percentiles, values, colors)):
-                    y = fig_height - (i + 1) * (bar_height + spacing)
-                    ax.add_patch(patches.Rectangle((0, y + bar_height / 4), 100, bar_height / 2, color="#C7DCDC", lw=0))
-                    ax.add_patch(patches.Rectangle((0, y), percentile, bar_height, color=color, lw=0))
-                    circle_y = y + bar_height - bar_height / 2
-                    circle = plt.Circle((percentile, circle_y), bar_height / 2, color=color, ec='white', lw=1.5, zorder=10)
-                    ax.add_patch(circle)
-                    fs = 14
-                    ax.text(percentile, circle_y, f"{percentile}", ha="center", va="center", fontsize=10, color='white', zorder=10, fontweight='bold')
-                    ax.text(-5, y + bar_height / 2, stat, ha="right", va="center", fontsize=fs)
-                    ax.text(115, y + bar_height / 2, str(value), ha="right", va="center", fontsize=fs, zorder=5)
-                    if i < len(stats) and i > 0:
-                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=-33, xmax=0)
-                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=100, xmax=115)
-                # Draw vertical lines for 10%, 50%, and 90% with labels
-                for x, label, align, color in zip([10, 50, 90], ["Poor", "Average", "Great"], ['center', 'center', 'center'], color_list):
-                    ax.axvline(x=x, ymin=0, ymax=1, color='#FFF', linestyle='-', lw=1, zorder=1, alpha=0.5)
-                    ax.text(x, fig_height + 4, label, ha=align, va='center', fontsize=12, fontweight='bold', color=color)
-                    triangle = patches.RegularPolygon((x, fig_height + 1), 3, radius=1, orientation=0, color=color, zorder=2)
-                    ax.add_patch(triangle)
-                # # Title
-                # ax_title.set_ylim(0, 1)
-                # ax_title.text(0.5, 0.5, f"{player_name} - {player_position_dict[pitcher_id]}\nPercentile Rankings - 2024 AAA", ha="center", va="center", fontsize=24)
-                # ax_title.axis("off")
-                player_bio(pitcher_id, ax=ax_title, sport_id=sport_id, year_input=year_input)
-                # Add team logo
-                #response = requests.get(logo_url)
-                #######if input.switch():
-                ########    response = requests.get(input.logo_select())
-                ######else:
-                response = requests.get(logo_url)
-                img = Image.open(BytesIO(response.content))
-                ax_logo.imshow(img)
-                ax_logo.axis("off")
-                ax.axis('equal')
-                lg_dict = {
-                    11:'all',
-                    14:10
-                    }
-                levelt = {
-                    11:1,
-                    14:4
-                    }
-                fg_api = f'https://www.fangraphs.com/api/leaders/minor-league/data?pos=all&level={levelt[sport_id]}&lg={lg_dict[sport_id]}&stats=pit&qual=0&type=2&team=&season=2024&seasonEnd=2024&org=&ind=0&splitTeam=false'
-                response = requests.get(fg_api)
-                data = response.json()
-                df_fg = pl.DataFrame(data)
-                if pitcher_id not in dict_mlb_fg.keys():
-                    #ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    metrics_data = {
-                        "Pitches": new_player_metrics['pitches'][0],
-                        "PA": new_player_metrics['pa'][0],
-                        "BIP": new_player_metrics['bip'][0],
-                        "HR": f"{new_player_metrics['home_run'][0]:.0f}",
-                        "K": f"{new_player_metrics['k'][0]:.0f}",
-                        "BB": f"{new_player_metrics['bb'][0]:.0f}",
-                    }
-                else:
-                    df_fg_filter = df_fg.filter(pl.col('minormasterid') == dict_mlb_fg[pitcher_id])
-                    # Metrics data table
-                    metrics_data = {
-                        "G": f"{df_fg_filter['G'][0]:.0f}",
-                        "IP": f"{df_fg_filter['IP'][0]:.1f}",
-                        "Pitches": f"{new_player_metrics['pitches'][0]:.0f}",
-                        "PA": f"{df_fg_filter['TBF'][0]:.0f}",
-                        "BIP": new_player_metrics['bip'][0],
-                        "ERA": f"{df_fg_filter['ERA'][0]:.2f}",
-                        "FIP": f"{df_fg_filter['FIP'][0]:.2f}",
-                        "WHIP": f"{df_fg_filter['WHIP'][0]:.2f}",
-                    }
-                df_table = pd.DataFrame(metrics_data, index=[0])
-                ax_table.axis('off')
-                table = ax_table.table(cellText=df_table.values, colLabels=df_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
-                for key, cell in table.get_celld().items():
-                    if key[0] == 0:
-                        cell.set_text_props(fontweight='bold')
-                table.auto_set_font_size(False)
-                table.set_fontsize(12)
-                table.scale(1, 1.5)
-                # Additional subplots for spacing
-                ax_top = fig.add_subplot(gs[0, :])
-                ax_bot = fig.add_subplot(gs[-1, :])
-                ax_top.axis('off')
-                ax_bot.axis('off')
-                ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14)
-                ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14)
-                # Player headshot
-                ax_headshot = fig.add_subplot(gs[1, 1])
-                try:
-                    url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{pitcher_id}/headshot/milb/current.png'
-                    response = requests.get(url)
-                    img = Image.open(BytesIO(response.content))
-                    ax_headshot.set_xlim(0, 1)
-                    ax_headshot.set_ylim(0, 1)
-                    ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper')
-                except PIL.UnidentifiedImageError:
-                    ax_headshot.axis('off')
-                    #return
-                ax_headshot.axis('off')
-                ax_table.set_title('Season Summary', style='italic')
-                # Fangraphs scouting grades table
-                print(pitcher_id)
-                if pitcher_id not in dict_mlb_fg.keys():
-                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    #return
-                try:
-                    df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[pitcher_id])][['cFV','FB', 'SL', 'CB', 'CH', 'SPL', 'CT','CMD']].dropna(axis=1).reset_index(drop=True)
-                    df_fv_table.columns = ['FV']+[x.upper() for x in df_fv_table.columns[1:]]
-                    table_fv = ax_fv_table.table(cellText=df_fv_table.values, colLabels=df_fv_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
-                    for key, cell in table_fv.get_celld().items():
-                        if key[0] == 0:
-                            cell.set_text_props(fontweight='bold')
-                    table_fv.auto_set_font_size(False)
-                    table_fv.set_fontsize(12)
-                    table_fv.scale(1, 1.5)
-                    ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic')
-                except KeyError:
-                    #ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    #return
-                # ax_fv_table.axis('off')
-                # if df_fv_table.empty:
-                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
-                    #return
-                # df_stuff_filter = df_stuff.filter(pl.col('pitcher_id')==pitcher_id)
-                stuff_table = ax_stuff.table(cellText=[df_stuff_filter['tj_stuff_plus']],
-                                    colLabels=df_stuff_filter['pitch_type'],
-                                    cellLoc='center',
-                                    loc='center', bbox=[0.07, 0, 0.86, 1])
-                stuff_table.auto_set_font_size(False)
-                stuff_table.set_fontsize(12)
-                stuff_table.scale(1, 1.5)
-                ax_stuff.axis('off')
-                ax_stuff.set_title('tjStuff+', style='italic')
-                for key, cell in stuff_table.get_celld().items():
-                    if key[0] == 0:
-                        cell.set_text_props(fontweight='bold')
-                # Color the stuff_table values based on the cmap defined
-                for (i, j), cell in stuff_table.get_celld().items():
-                    if i == 0:
-                        cell.set_text_props(fontweight='bold')
-                    else:
-                        norm = Normalize(vmin=90, vmax=110)
-                        value = float(cell.get_text().get_text())
-                        color = cmap(norm(value))
-                        cell.set_facecolor(color)
-                        #cell.set_text_props(color='white' if value < 100 else 'black')
-                fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-            def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered):
-                """
-                Calculate percentiles for a new player's metrics.
-                :param player_id: ID of the player.
-                :param new_player_metrics: DataFrame containing new player metrics.
-                :param player_summary_filtered: Filtered player summary DataFrame.
-                :return: DataFrame containing new player percentiles.
-                """
-                filtered_summary_clone = player_summary_filtered[['pitcher_id'] + stat_list].filter(pl.col('pitcher_id') != player_id).clone()
-                combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas()
-                combined_percentiles = pl.DataFrame(pd.concat([combined_data['pitcher_id'], combined_data[stat_list].rank(pct=True)], axis=1))
-                new_player_percentiles = combined_percentiles.filter(pl.col('pitcher_id') == player_id)
-                return new_player_percentiles
-            p.set(message="Generating plot", detail="This may take a while...")
-            p.set(0.3, "Gathering data...")
-            df_teams = scrape.get_teams()
-            team_dict = dict(zip(df_teams['team_id'],df_teams['parent_org_abbreviation']))
-            # Example: New player's metrics
-            # Example: New player's metrics
-            year = int(input.year_input())
-            sport_id = int(input.level_input())
-            pitcher_id = int(input.pitcher_id())
-            df_player = scrape.get_players(sport_id=sport_id,season=2024)
-            pitcher_name_id = dict(zip(df_player['player_id'],df_player['name']))
-            player_team_dict = dict(zip(df_player['player_id'],df_player['team']))
-            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
-            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
-            pitcher_summary = pl.read_csv(f'data/statcast/pitcher_summary_{level_dict_file[str(sport_id)]}_{year}.csv')
-            df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv')
-            df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv')
-            df_small = df_rosters[['minorbamid','minormasterid']].dropna()
-            dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid']))
-            df_stuff = pl.read_csv(f'data/stuff/stuff_{level_dict_file[str(sport_id)]}_{year}.csv')
-            # Filter out the "All" row
-            filtered_df = df_stuff.filter(pl.col("pitch_type") != "All")
-            filtered_all_df = df_stuff.filter(pl.col("pitch_type") == "All")
-            # Calculate total pitches for each pitcher and proportion of each pitch type
-            result_df = (
-                filtered_df
-                .with_columns([
-                    # Total pitches for each pitcher
-                    pl.col("pitches").sum().over("pitcher_id").alias("total_pitches"),
-                    # Proportion of pitches
-                    (pl.col("pitches") / pl.col("pitches").sum().over("pitcher_id")).alias("pitch_proportion"),
-                ])
-            ).filter(pl.col("pitch_proportion") > 0.05)
-            df_stuff = pl.concat([filtered_all_df.with_columns(
-                [pl.col("pitches").sum().over("pitcher_id").alias("total_pitches"),
-                (pl.col("pitches") / pl.col("pitches").sum().over("pitcher_id")).alias("pitch_proportion")]
-            ), result_df])
-            df_stuff_filter = df_stuff.filter(pl.col('pitcher_id')==pitcher_id)
-            pitcher_summary_filter = pitcher_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0))
-            stat_list = pitcher_summary.columns[2:]
-            pitcher_summary_filter_pd = pitcher_summary_filter.to_pandas()
-            new_player_metrics = pitcher_summary.filter(pl.col('pitcher_id') == pitcher_id)[['pitcher_id'] + stat_list]
-            # Get percentiles for the new player
-            new_player_percentiles = calculate_new_player_percentiles(pitcher_id, new_player_metrics, pitcher_summary_filter)
-            p.set(0.6, "Creating plot...")
-            # Draw Baseball Savant-style percentile bars
-            draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics,
-                                            new_player_percentiles=new_player_percentiles,
-                                            sport_id=sport_id,
-                                            year_input=year)
 app = App(app_ui, server)

+import polars as pl
+import api_scraper
+import pandas as pd
+scrape = api_scraper.MLB_Scrape()
+# import df_update
+# update = df_update.df_update()
+from matplotlib.colors import LinearSegmentedColormap, Normalize
+import numpy as np
+import requests
+from io import BytesIO
+from PIL import Image
+from matplotlib.gridspec import GridSpec
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import PIL
+level_dict =  {
+               '11':'AAA',
+               '14':'A',}
+def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
+    """
+    Display the player's bio information on the given axis.
+    Parameters
+    ----------
+    pitcher_id : str
+        The player's ID.
+    ax : plt.Axes
+        The axis to display the bio information on.
+    sport_id : int
+        The sport ID (1 for MLB, other for minor leagues).
+    year_input : int
+        The season year.
+    """
+    # Construct the URL to fetch player data
+    url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
+    # Send a GET request to the URL and parse the JSON response
+    data = requests.get(url).json()
+    # Extract player information from the JSON data
+    player_name = data['people'][0]['fullName']
+    position = data['people'][0]['primaryPosition']['abbreviation']
+    bat_side = data['people'][0]['batSide']['code']
+    pitcher_hand = data['people'][0]['pitchHand']['code']
+    age = data['people'][0]['currentAge']
+    height = data['people'][0]['height']
+    weight = data['people'][0]['weight']
+    # Display the player's name, handedness, age, height, and weight on the axis
+    ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=30)
+    ax.text(0.5, 0.65, f'{position}, B/T: {bat_side}/{pitcher_hand}, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=20)
+    if position == 'P':
+        ax.text(0.5, 0.38, f'Season Pitching Percentiles', va='top', ha='center', fontsize=16)
+    else:
+        ax.text(0.5, 0.41, f'Season Batting Percentiles', va='top', ha='center', fontsize=16)
+    # Make API call to retrieve sports information
+    response = requests.get(url='https://statsapi.mlb.com/api/v1/sports').json()
+    # Convert the JSON response into a Polars DataFrame
+    df_sport_id = pl.DataFrame(response['sports'])
+    abb = df_sport_id.filter(pl.col('id') == sport_id)['abbreviation'][0]
+    # Display the season and sport abbreviation
+    ax.text(0.5, 0.20, f'{year_input} {abb} Season', va='top', ha='center', fontsize=14, fontstyle='italic')
+    # Turn off the axis
+    ax.axis('off')
+df_teams = scrape.get_teams()
+team_dict = dict(zip(df_teams['team_id'],df_teams['parent_org_abbreviation']))
+# 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()
+# level_dict =  {'1':'MLB',
+#                '11':'AAA'}
+level_dict =  {
+               '11':'AAA',
+               '14':'A (FSL)',}
+level_dict_file =  {
+               '11':'aaa',
+               '14':'a',}
+year_list = [2024]
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+# Define the UI layout for the app
+app_ui = ui.page_fluid(
+    ui.tags.div(
+         {"style": "width:90%;margin: 0 auto;max-width: 1600px;"},
+        ui.tags.style(
+            """
+            h4 {
+                margin-top: 1em;font-size:35px;
+            }
+            h2{
+                font-size:25px;
+            }
+            """
+         ),
+    ui.tags.h4("TJStats"),
+    ui.tags.i("Baseball Analytics and Visualizations"),
+    ui.markdown("""<a href='https://x.com/TJStats'>Follow me on Twitter</a><sup>1</sup>"""),
+    ui.markdown("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),
+    ui.markdown("### MiLB Statcast Batting Summaries"),
+    ui.markdown("""This Shiny App allows you to generate Baseball Savant-style percentile bars for MiLB players in the 2024 Season.
+                Currently, MiLB Statcast is only available for AAA and A (Florida State League) levels."""),
+    ui.layout_sidebar(
+        ui.panel_sidebar(
+            # Row for selecting season and level
+            ui.row(
+                ui.column(6, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
+                ui.column(6, ui.input_select('level_input', 'Select Level', level_dict)),
+            ),
+            # 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.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")),
+            width=3,
+        ),
+        ui.panel_main(
+            ui.navset_tab(
+                # Tab for game summary plot
+                ui.nav("Batter Summary",
+                       ui.output_text("status_batter"),
+                       ui.output_plot('batter_plot', width='1200px', height='1200px')
+                ),
+                ui.nav("Pitcher Summary",
+                       ui.output_text("status_pitcher"),
+                       ui.output_plot('pitcher_plot', width='1200px', height='1200px')
+                )
+                ,id="tabset"
+            )
+        )
+    )
+)
+)
+def server(input, output, session):
+    @render.ui
+    @reactive.event(input.player_button,input.tabset, ignore_none=False)
+    def player_select_ui():
+        if input.tabset() == "Batter Summary":
+            #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())).filter(
+                ~pl.col("position").is_in(['P','TWP'])).sort("name")
+            # Create a dictionary of pitcher IDs and names
+            batter_dict_pos = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['position']))
+            year = int(input.year_input())
+            sport_id = int(input.level_input())
+            batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv').sort('batter_name',descending=False)
+            # Map elements in Polars DataFrame from a dictionary
+            batter_summary = batter_summary.with_columns(
+                pl.col("batter_id").map_elements(lambda x: batter_dict_pos.get(x, x)).alias("position")
+            )
+            batter_dict_pos = dict(zip(batter_summary['batter_id'], batter_summary['batter_name']))
+            # Create a dictionary of pitcher IDs and names
+            batter_dict = dict(zip(batter_summary['batter_id'], batter_summary['batter_name'] + ' - ' + batter_summary['position']))
+            # Return a select input for choosing a pitcher
+            return ui.input_select("batter_id", "Select Batter", batter_dict, selectize=True)
+        if input.tabset() == "Pitcher Summary":
+            #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())).filter(
+                pl.col("position").is_in(['P','TWP'])).sort("name")
+            # Create a dictionary of pitcher IDs and names
+            batter_dict_pos = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['position']))
+            year = int(input.year_input())
+            sport_id = int(input.level_input())
+            batter_summary = pl.read_csv(f'data/statcast/pitcher_summary_{level_dict_file[str(sport_id)]}_{year}.csv').sort('pitcher_name',descending=False)
+            # Map elements in Polars DataFrame from a dictionary
+            batter_summary = batter_summary.with_columns(
+                pl.col("pitcher_id").map_elements(lambda x: batter_dict_pos.get(x, x)).alias("position")
+            )
+            batter_dict_pos = dict(zip(batter_summary['pitcher_id'], batter_summary['pitcher_name']))
+            # Create a dictionary of pitcher IDs and names
+            batter_dict = dict(zip(batter_summary['pitcher_id'], batter_summary['pitcher_name'] + ' - ' + batter_summary['position']))
+            # Return a select input for choosing a pitcher
+            return ui.input_select("pitcher_id", "Select Batter", batter_dict, selectize=True)
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)
+    def batter_plot():
+        merged_dict = {
+            "woba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "wOBA" },
+            "xwoba_percent": { "format": '.3f', "percentile_flip": False, "stat_title": "xwOBA" },
+            "launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Average EV"},
+            "launch_speed_90": { "format": '.1f', "percentile_flip": False, "stat_title": "90th% EV"},
+            "max_launch_speed": { "format": '.1f', "percentile_flip": False, "stat_title": "Max EV"},
+            "barrel_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Barrel%" },
+            "hard_hit_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Hard-Hit%" },
+            "sweet_spot_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "LA Sweet-Spot%" },
+            "zone_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Zone%" },
+            "zone_swing_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Z-Swing%" },
+            "chase_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "O-Swing%" },
+            "whiff_rate": { "format": '.1%', "percentile_flip": True, "stat_title": "Whiff%" },
+            "k_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "K%" },
+            "bb_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "BB%" },
+            "pull_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull%" },
+            "pulled_fly_ball_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Pull FB%" },
+        }
+        # Show progress/loading notification
+        with ui.Progress(min=0, max=1) as p:
+            def draw_baseball_savant_percentiles(new_player_metrics, new_player_percentiles, colors=None,
+                                                                                sport_id=None,
+                                            year_input=None):
+                """
+                Draw Baseball Savant-style percentile bars with proper alignment and scaling.
+                :param new_player_metrics: DataFrame containing new player metrics.
+                :param new_player_percentiles: DataFrame containing new player percentiles.
+                :param colors: List of colors for bars (optional, red/blue default).
+                """
+                # Extract player information
+                batter_id = new_player_metrics['batter_id'][0]
+                player_name = batter_name_id[batter_id]
+                stats = [merged_dict[x]['stat_title'] for x in merged_dict.keys()]
+                # Calculate percentiles and values
+                percentiles = [int((1 - x) * 100) if merged_dict[stat]["percentile_flip"] else int(x * 100) for x, stat in zip(new_player_percentiles.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
+                percentiles = np.clip(percentiles, 1, 100)
+                values = [str(f'{x:{merged_dict[stat]["format"]}}').strip('%') for x, stat in zip(new_player_metrics.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
+                # Get team logo URL
+                logo_url = image_dict[team_dict[player_team_dict[batter_id]]]
+                # Create a custom colormap
+                color_list = ['#3661AD', '#B4CFD1', '#D82129']
+                cmap = LinearSegmentedColormap.from_list("custom_cmap", color_list)
+                norm = Normalize(vmin=0.1, vmax=0.9)
+                norm_percentiles = norm(percentiles / 100)
+                colors = [cmap(p) for p in norm_percentiles]
+                # Figure setup
+                num_stats = len(stats)
+                bar_height = 4.5
+                spacing = 1
+                fig_height = (bar_height + spacing) * num_stats
+                fig = plt.figure(figsize=(12, 12))
+                gs = GridSpec(6, 5, height_ratios=[0.1, 1.5, 0.9, 0.9, 7.6, 0.1], width_ratios=[0.2, 1.5, 7, 1.5, 0.2])
+                # Define subplots
+                ax_title = fig.add_subplot(gs[1, 2])
+                ax_table = fig.add_subplot(gs[2, :])
+                ax_fv_table = fig.add_subplot(gs[3, :])
+                ax_fv_table.axis('off')
+                ax = fig.add_subplot(gs[4, :])
+                ax_logo = fig.add_subplot(gs[1, 3])
+                ax.set_xlim(-1, 99)
+                ax.set_ylim(-1, 99)
+                ax.set_aspect("equal")
+                ax.axis("off")
+                # Draw each bar
+                for i, (stat, percentile, value, color) in enumerate(zip(stats, percentiles, values, colors)):
+                    y = fig_height - (i + 1) * (bar_height + spacing)
+                    ax.add_patch(patches.Rectangle((0, y + bar_height / 4), 100, bar_height / 2, color="#C7DCDC", lw=0))
+                    ax.add_patch(patches.Rectangle((0, y), percentile, bar_height, color=color, lw=0))
+                    circle_y = y + bar_height - bar_height / 2
+                    circle = plt.Circle((percentile, circle_y), bar_height / 2, color=color, ec='white', lw=1.5, zorder=10)
+                    ax.add_patch(circle)
+                    fs = 14
+                    ax.text(percentile, circle_y, f"{percentile}", ha="center", va="center", fontsize=10, color='white', zorder=10, fontweight='bold')
+                    ax.text(-5, y + bar_height / 2, stat, ha="right", va="center", fontsize=fs)
+                    ax.text(115, y + bar_height / 2, str(value), ha="right", va="center", fontsize=fs, zorder=5)
+                    if i < len(stats) and i > 0:
+                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=-33, xmax=0)
+                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=100, xmax=115)
+                # Draw vertical lines for 10%, 50%, and 90% with labels
+                for x, label, align, color in zip([10, 50, 90], ["Poor", "Average", "Great"], ['center', 'center', 'center'], color_list):
+                    ax.axvline(x=x, ymin=0, ymax=1, color='#FFF', linestyle='-', lw=1, zorder=1, alpha=0.5)
+                    ax.text(x, fig_height + 4, label, ha=align, va='center', fontsize=12, fontweight='bold', color=color)
+                    triangle = patches.RegularPolygon((x, fig_height + 1), 3, radius=1, orientation=0, color=color, zorder=2)
+                    ax.add_patch(triangle)
+                # # Title
+                # ax_title.set_ylim(0, 1)
+                # ax_title.text(0.5, 0.5, f"{player_name} - {player_position_dict[batter_id]}\nPercentile Rankings - 2024 AAA", ha="center", va="center", fontsize=24)
+                # ax_title.axis("off")
+                player_bio(batter_id, ax=ax_title, sport_id=sport_id, year_input=year_input)
+                # Add team logo
+                #response = requests.get(logo_url)
+                if input.switch():
+                    response = requests.get(input.logo_select())
+                else:
+                    response = requests.get(logo_url)
+                img = Image.open(BytesIO(response.content))
+                ax_logo.imshow(img)
+                ax_logo.axis("off")
+                ax.axis('equal')
+                # Metrics data table
+                metrics_data = {
+                    "Pitches": new_player_metrics['pitches'][0],
+                    "PA": new_player_metrics['pa'][0],
+                    "BIP": new_player_metrics['bip'][0],
+                    "HR": f"{new_player_metrics['home_run'][0]:.0f}",
+                    "AVG": f"{new_player_metrics['avg'][0]:.3f}",
+                    "OBP": f"{new_player_metrics['obp'][0]:.3f}",
+                    "SLG": f"{new_player_metrics['slg'][0]:.3f}",
+                    "OPS": f"{new_player_metrics['obp'][0] + new_player_metrics['slg'][0]:.3f}",
+                }
+                df_table = pd.DataFrame(metrics_data, index=[0])
+                ax_table.axis('off')
+                table = ax_table.table(cellText=df_table.values, colLabels=df_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
+                for key, cell in table.get_celld().items():
+                    if key[0] == 0:
+                        cell.set_text_props(fontweight='bold')
+                table.auto_set_font_size(False)
+                table.set_fontsize(12)
+                table.scale(1, 1.5)
+                # Additional subplots for spacing
+                ax_top = fig.add_subplot(gs[0, :])
+                ax_bot = fig.add_subplot(gs[-1, :])
+                ax_top.axis('off')
+                ax_bot.axis('off')
+                ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14)
+                ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14)
+                fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
+                # Player headshot
+                ax_headshot = fig.add_subplot(gs[1, 1])
+                try:
+                    url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{batter_id}/headshot/milb/current.png'
+                    response = requests.get(url)
+                    img = Image.open(BytesIO(response.content))
+                    ax_headshot.set_xlim(0, 1)
+                    ax_headshot.set_ylim(0, 1)
+                    ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper')
+                except PIL.UnidentifiedImageError:
+                    ax_headshot.axis('off')
+                    #return
+                ax_headshot.axis('off')
+                ax_table.set_title('Season Summary', style='italic')
+                # Fangraphs scouting grades table
+                print(batter_id)
+                if batter_id not in dict_mlb_fg.keys():
+                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
+                    return
+                df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[batter_id])][['cFV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']].reset_index(drop=True)
+                ax_fv_table.axis('off')
+                if df_fv_table.empty:
+                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
+                    return
+                df_fv_table.columns = ['FV', 'Hit', 'Game', 'Raw', 'Spd', 'Fld']
+                table_fv = ax_fv_table.table(cellText=df_fv_table.values, colLabels=df_fv_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
+                for key, cell in table_fv.get_celld().items():
+                    if key[0] == 0:
+                        cell.set_text_props(fontweight='bold')
+                table_fv.auto_set_font_size(False)
+                table_fv.set_fontsize(12)
+                table_fv.scale(1, 1.5)
+                ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic')
+                #plt.show()
+            def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered):
+                """
+                Calculate percentiles for a new player's metrics.
+                :param player_id: ID of the player.
+                :param new_player_metrics: DataFrame containing new player metrics.
+                :param player_summary_filtered: Filtered player summary DataFrame.
+                :return: DataFrame containing new player percentiles.
+                """
+                filtered_summary_clone = player_summary_filtered[['batter_id'] + stat_list].filter(pl.col('batter_id') != player_id).clone()
+                combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas()
+                combined_percentiles = pl.DataFrame(pd.concat([combined_data['batter_id'], combined_data[stat_list].rank(pct=True)], axis=1))
+                new_player_percentiles = combined_percentiles.filter(pl.col('batter_id') == player_id)
+                return new_player_percentiles
+            p.set(message="Generating plot", detail="This may take a while...")
+            p.set(0.3, "Gathering data...")
+            # Example: New player's metrics
+            year = int(input.year_input())
+            sport_id = int(input.level_input())
+            batter_id = int(input.batter_id())
+            df_player = scrape.get_players(sport_id=sport_id,season=year)
+            batter_name_id = dict(zip(df_player['player_id'],df_player['name']))
+            player_team_dict = dict(zip(df_player['player_id'],df_player['team']))
+            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
+            batter_summary = pl.read_csv(f'data/statcast/batter_summary_{level_dict_file[str(sport_id)]}_{year}.csv')
+            df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv')
+            df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv')
+            df_small = df_rosters[['minorbamid','minormasterid']].dropna()
+            dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid']))
+            batter_summary_filter = batter_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0))
+            stat_list = batter_summary.columns[2:]
+            batter_summary_filter_pd = batter_summary_filter.to_pandas()
+            new_player_metrics = batter_summary.filter(pl.col('batter_id') == batter_id)[['batter_id'] + stat_list]
+            # Get percentiles for the new player
+            new_player_percentiles = calculate_new_player_percentiles(batter_id, new_player_metrics, batter_summary_filter)
+            p.set(0.6, "Creating plot...")
+            # Draw Baseball Savant-style percentile bars
+            draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics,
+                                            new_player_percentiles=new_player_percentiles,
+                                            sport_id=sport_id,
+                                            year_input=year)
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)
+    def pitcher_plot():
+        merged_dict = {
+                "avg_start_speed_ff": { "format": '.1f', "percentile_flip": False, "stat_title": "Fastball Velocity" },
+                "extension": { "format": '.1f', "percentile_flip": False, "stat_title": "Extension" },
+                "woba_percent": { "format": '.3f', "percentile_flip": True, "stat_title": "wOBA" },
+                "xwoba_percent": { "format": '.3f', "percentile_flip": True, "stat_title": "xwOBA" },
+                "launch_speed": { "format": '.1f', "percentile_flip": True, "stat_title": "Average EV"},
+                "barrel_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Barrel%" },
+                "hard_hit_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Hard-Hit%" },
+                "whiff_rate": { "format": '.1%', "percentile_flip": False, "stat_title": "Whiff%" },
+                "zone_contact_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "Z-Contact%" },
+                "zone_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "Zone%" },
+                "chase_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "O-Swing%" },
+                "csw_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "CSW%" },
+                "k_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "K%" },
+                "bb_percent": { "format": '.1%', "percentile_flip": True, "stat_title": "BB%" },
+                "k_minus_bb_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "K - BB%" },
+                "ground_ball_percent": { "format": '.1%', "percentile_flip": False, "stat_title": "GB%" },
+            }
+        with ui.Progress(min=0, max=1) as p:
+            def draw_baseball_savant_percentiles(new_player_metrics, new_player_percentiles, colors=None,
+                                                                                sport_id=None,
+                                            year_input=None):
+                """
+                Draw Baseball Savant-style percentile bars with proper alignment and scaling.
+                :param new_player_metrics: DataFrame containing new player metrics.
+                :param new_player_percentiles: DataFrame containing new player percentiles.
+                :param colors: List of colors for bars (optional, red/blue default).
+                """
+                # Extract player information
+                pitcher_id = new_player_metrics['pitcher_id'][0]
+                player_name = pitcher_name_id[pitcher_id]
+                stats = [merged_dict[x]['stat_title'] for x in merged_dict.keys()]
+                # Calculate percentiles and values
+                percentiles = [int((1 - x) * 100) if merged_dict[stat]["percentile_flip"] else int(x * 100) for x, stat in zip(new_player_percentiles.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
+                percentiles = np.clip(percentiles, 1, 100)
+                values = [str(f'{x:{merged_dict[stat]["format"]}}').strip('%') for x, stat in zip(new_player_metrics.select(merged_dict.keys()).to_numpy()[0], merged_dict.keys())]
+                # Get team logo URL
+                logo_url = image_dict[team_dict[player_team_dict[pitcher_id]]]
+                # Create a custom colormap
+                color_list = ['#3661AD', '#B4CFD1', '#D82129']
+                cmap = LinearSegmentedColormap.from_list("custom_cmap", color_list)
+                norm = Normalize(vmin=0.1, vmax=0.9)
+                norm_percentiles = norm(percentiles / 100)
+                colors = [cmap(p) for p in norm_percentiles]
+                # Figure setup
+                num_stats = len(stats)
+                bar_height = 4.4
+                spacing = 0.7
+                fig_height = (bar_height + spacing) * num_stats
+                fig = plt.figure(figsize=(12, 12))
+                gs = GridSpec(7, 5, height_ratios=[0.05, 1.5, 0.75, 0.75,0.75, 7.7, 0.1], width_ratios=[0.2, 1.5, 7, 1.5, 0.2])
+                # Define subplots
+                ax_title = fig.add_subplot(gs[1, 2])
+                ax_table = fig.add_subplot(gs[2, :])
+                ax_fv_table = fig.add_subplot(gs[3, :])
+                ax_fv_table.axis('off')
+                ax_stuff = fig.add_subplot(gs[4, :])
+                ax = fig.add_subplot(gs[5, :])
+                ax_logo = fig.add_subplot(gs[1, 3])
+                ax.set_xlim(-1, 99)
+                ax.set_ylim(-1, 99)
+                ax.set_aspect("equal")
+                ax.axis("off")
+                # Draw each bar
+                for i, (stat, percentile, value, color) in enumerate(zip(stats, percentiles, values, colors)):
+                    y = fig_height - (i + 1) * (bar_height + spacing)
+                    ax.add_patch(patches.Rectangle((0, y + bar_height / 4), 100, bar_height / 2, color="#C7DCDC", lw=0))
+                    ax.add_patch(patches.Rectangle((0, y), percentile, bar_height, color=color, lw=0))
+                    circle_y = y + bar_height - bar_height / 2
+                    circle = plt.Circle((percentile, circle_y), bar_height / 2, color=color, ec='white', lw=1.5, zorder=10)
+                    ax.add_patch(circle)
+                    fs = 14
+                    ax.text(percentile, circle_y, f"{percentile}", ha="center", va="center", fontsize=10, color='white', zorder=10, fontweight='bold')
+                    ax.text(-5, y + bar_height / 2, stat, ha="right", va="center", fontsize=fs)
+                    ax.text(115, y + bar_height / 2, str(value), ha="right", va="center", fontsize=fs, zorder=5)
+                    if i < len(stats) and i > 0:
+                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=-33, xmax=0)
+                        ax.hlines(y=y + bar_height + spacing / 2, color='#399098', linestyle=(0, (5, 5)), linewidth=1, xmin=100, xmax=115)
+                # Draw vertical lines for 10%, 50%, and 90% with labels
+                for x, label, align, color in zip([10, 50, 90], ["Poor", "Average", "Great"], ['center', 'center', 'center'], color_list):
+                    ax.axvline(x=x, ymin=0, ymax=1, color='#FFF', linestyle='-', lw=1, zorder=1, alpha=0.5)
+                    ax.text(x, fig_height + 4, label, ha=align, va='center', fontsize=12, fontweight='bold', color=color)
+                    triangle = patches.RegularPolygon((x, fig_height + 1), 3, radius=1, orientation=0, color=color, zorder=2)
+                    ax.add_patch(triangle)
+                # # Title
+                # ax_title.set_ylim(0, 1)
+                # ax_title.text(0.5, 0.5, f"{player_name} - {player_position_dict[pitcher_id]}\nPercentile Rankings - 2024 AAA", ha="center", va="center", fontsize=24)
+                # ax_title.axis("off")
+                player_bio(pitcher_id, ax=ax_title, sport_id=sport_id, year_input=year_input)
+                # Add team logo
+                #response = requests.get(logo_url)
+                #######if input.switch():
+                ########    response = requests.get(input.logo_select())
+                ######else:
+                response = requests.get(logo_url)
+                img = Image.open(BytesIO(response.content))
+                ax_logo.imshow(img)
+                ax_logo.axis("off")
+                ax.axis('equal')
+                lg_dict = {
+                    11:'all',
+                    14:10
+                    }
+                levelt = {
+                    11:1,
+                    14:4
+                    }
+                fg_api = f'https://www.fangraphs.com/api/leaders/minor-league/data?pos=all&level={levelt[sport_id]}&lg={lg_dict[sport_id]}&stats=pit&qual=0&type=2&team=&season=2024&seasonEnd=2024&org=&ind=0&splitTeam=false'
+                response = requests.get(fg_api)
+                data = response.json()
+                df_fg = pl.DataFrame(data)
+                if pitcher_id not in dict_mlb_fg.keys():
+                    #ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
+                    metrics_data = {
+                        "Pitches": new_player_metrics['pitches'][0],
+                        "PA": new_player_metrics['pa'][0],
+                        "BIP": new_player_metrics['bip'][0],
+                        "HR": f"{new_player_metrics['home_run'][0]:.0f}",
+                        "K": f"{new_player_metrics['k'][0]:.0f}",
+                        "BB": f"{new_player_metrics['bb'][0]:.0f}",
+                    }
+                else:
+                    df_fg_filter = df_fg.filter(pl.col('minormasterid') == dict_mlb_fg[pitcher_id])
+                    # Metrics data table
+                    metrics_data = {
+                        "G": f"{df_fg_filter['G'][0]:.0f}",
+                        "IP": f"{df_fg_filter['IP'][0]:.1f}",
+                        "Pitches": f"{new_player_metrics['pitches'][0]:.0f}",
+                        "PA": f"{df_fg_filter['TBF'][0]:.0f}",
+                        "BIP": new_player_metrics['bip'][0],
+                        "ERA": f"{df_fg_filter['ERA'][0]:.2f}",
+                        "FIP": f"{df_fg_filter['FIP'][0]:.2f}",
+                        "WHIP": f"{df_fg_filter['WHIP'][0]:.2f}",
+                    }
+                df_table = pd.DataFrame(metrics_data, index=[0])
+                ax_table.axis('off')
+                table = ax_table.table(cellText=df_table.values, colLabels=df_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
+                for key, cell in table.get_celld().items():
+                    if key[0] == 0:
+                        cell.set_text_props(fontweight='bold')
+                table.auto_set_font_size(False)
+                table.set_fontsize(12)
+                table.scale(1, 1.5)
+                # Additional subplots for spacing
+                ax_top = fig.add_subplot(gs[0, :])
+                ax_bot = fig.add_subplot(gs[-1, :])
+                ax_top.axis('off')
+                ax_bot.axis('off')
+                ax_bot.text(0.05, 2, "By: Thomas Nestico (@TJStats)", ha="left", va="center", fontsize=14)
+                ax_bot.text(0.95, 2, "Data: MLB, Fangraphs", ha="right", va="center", fontsize=14)
+                # Player headshot
+                ax_headshot = fig.add_subplot(gs[1, 1])
+                try:
+                    url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{pitcher_id}/headshot/milb/current.png'
+                    response = requests.get(url)
+                    img = Image.open(BytesIO(response.content))
+                    ax_headshot.set_xlim(0, 1)
+                    ax_headshot.set_ylim(0, 1)
+                    ax_headshot.imshow(img, extent=[1/6, 5/6, 0, 1], origin='upper')
+                except PIL.UnidentifiedImageError:
+                    ax_headshot.axis('off')
+                    #return
+                ax_headshot.axis('off')
+                ax_table.set_title('Season Summary', style='italic')
+                # Fangraphs scouting grades table
+                print(pitcher_id)
+                if pitcher_id not in dict_mlb_fg.keys():
+                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
+                    #return
+                df_fv_table = df_prospects[(df_prospects['minorMasterId'] == dict_mlb_fg[pitcher_id])][['cFV','FB', 'SL', 'CB', 'CH', 'SPL', 'CT','CMD']].dropna(axis=1).reset_index(drop=True)
+                ax_fv_table.axis('off')
+                if df_fv_table.empty:
+                    ax_fv_table.text(x=0.5, y=0.5, s='No Scouting Data', style='italic', ha='center', va='center', fontsize=20, bbox=dict(facecolor='white', alpha=1, pad=10))
+                    #return
+                else:
+                    df_fv_table.columns = ['FV']+[x.upper() for x in df_fv_table.columns[1:]]
+                    table_fv = ax_fv_table.table(cellText=df_fv_table.values, colLabels=df_fv_table.columns, cellLoc='center', loc='bottom', bbox=[0.07, 0, 0.86, 1])
+                    for key, cell in table_fv.get_celld().items():
+                        if key[0] == 0:
+                            cell.set_text_props(fontweight='bold')
+                    table_fv.auto_set_font_size(False)
+                    table_fv.set_fontsize(12)
+                    table_fv.scale(1, 1.5)
+                    ax_fv_table.set_title('Fangraphs Scouting Grades', style='italic')
+                # df_stuff_filter = df_stuff.filter(pl.col('pitcher_id')==pitcher_id)
+                stuff_table = ax_stuff.table(cellText=[df_stuff_filter['tj_stuff_plus']],
+                                    colLabels=df_stuff_filter['pitch_type'],
+                                    cellLoc='center',
+                                    loc='center', bbox=[0.07, 0, 0.86, 1])
+                stuff_table.auto_set_font_size(False)
+                stuff_table.set_fontsize(12)
+                stuff_table.scale(1, 1.5)
+                ax_stuff.axis('off')
+                ax_stuff.set_title('tjStuff+', style='italic')
+                for key, cell in stuff_table.get_celld().items():
+                    if key[0] == 0:
+                        cell.set_text_props(fontweight='bold')
+                # Color the stuff_table values based on the cmap defined
+                for (i, j), cell in stuff_table.get_celld().items():
+                    if i == 0:
+                        cell.set_text_props(fontweight='bold')
+                    else:
+                        norm = Normalize(vmin=90, vmax=110)
+                        value = float(cell.get_text().get_text())
+                        color = cmap(norm(value))
+                        cell.set_facecolor(color)
+                        #cell.set_text_props(color='white' if value < 100 else 'black')
+                fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
+            def calculate_new_player_percentiles(player_id, new_player_metrics, player_summary_filtered):
+                """
+                Calculate percentiles for a new player's metrics.
+                :param player_id: ID of the player.
+                :param new_player_metrics: DataFrame containing new player metrics.
+                :param player_summary_filtered: Filtered player summary DataFrame.
+                :return: DataFrame containing new player percentiles.
+                """
+                filtered_summary_clone = player_summary_filtered[['pitcher_id'] + stat_list].filter(pl.col('pitcher_id') != player_id).clone()
+                combined_data = pl.concat([filtered_summary_clone, new_player_metrics], how="vertical").to_pandas()
+                combined_percentiles = pl.DataFrame(pd.concat([combined_data['pitcher_id'], combined_data[stat_list].rank(pct=True)], axis=1))
+                new_player_percentiles = combined_percentiles.filter(pl.col('pitcher_id') == player_id)
+                return new_player_percentiles
+            p.set(message="Generating plot", detail="This may take a while...")
+            p.set(0.3, "Gathering data...")
+            df_teams = scrape.get_teams()
+            team_dict = dict(zip(df_teams['team_id'],df_teams['parent_org_abbreviation']))
+            # Example: New player's metrics
+            # Example: New player's metrics
+            year = int(input.year_input())
+            sport_id = int(input.level_input())
+            pitcher_id = int(input.pitcher_id())
+            df_player = scrape.get_players(sport_id=sport_id,season=2024)
+            pitcher_name_id = dict(zip(df_player['player_id'],df_player['name']))
+            player_team_dict = dict(zip(df_player['player_id'],df_player['team']))
+            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
+            player_position_dict = dict(zip(df_player['player_id'],df_player['position']))
+            pitcher_summary = pl.read_csv(f'data/statcast/pitcher_summary_{level_dict_file[str(sport_id)]}_{year}.csv')
+            df_prospects = pd.read_csv(f'data/prospects/prospects_{year}.csv')
+            df_rosters = pd.read_csv(f'data/rosters/fangraphs_rosters_{year}.csv')
+            df_small = df_rosters[['minorbamid','minormasterid']].dropna()
+            dict_mlb_fg=dict(zip(df_small['minorbamid'].astype(int),df_small['minormasterid']))
+            df_stuff = pl.read_csv(f'data/stuff/stuff_{level_dict_file[str(sport_id)]}_{year}.csv')
+            # Filter out the "All" row
+            filtered_df = df_stuff.filter(pl.col("pitch_type") != "All")
+            filtered_all_df = df_stuff.filter(pl.col("pitch_type") == "All")
+            # Calculate total pitches for each pitcher and proportion of each pitch type
+            result_df = (
+                filtered_df
+                .with_columns([
+                    # Total pitches for each pitcher
+                    pl.col("pitches").sum().over("pitcher_id").alias("total_pitches"),
+                    # Proportion of pitches
+                    (pl.col("pitches") / pl.col("pitches").sum().over("pitcher_id")).alias("pitch_proportion"),
+                ])
+            ).filter(pl.col("pitch_proportion") > 0.05)
+            df_stuff = pl.concat([filtered_all_df.with_columns(
+                [pl.col("pitches").sum().over("pitcher_id").alias("total_pitches"),
+                (pl.col("pitches") / pl.col("pitches").sum().over("pitcher_id")).alias("pitch_proportion")]
+            ), result_df])
+            df_stuff_filter = df_stuff.filter(pl.col('pitcher_id')==pitcher_id)
+            pitcher_summary_filter = pitcher_summary.filter((pl.col('pa') >= 300) & (pl.col('launch_speed') >= 0))
+            stat_list = pitcher_summary.columns[2:]
+            pitcher_summary_filter_pd = pitcher_summary_filter.to_pandas()
+            new_player_metrics = pitcher_summary.filter(pl.col('pitcher_id') == pitcher_id)[['pitcher_id'] + stat_list]
+            # Get percentiles for the new player
+            new_player_percentiles = calculate_new_player_percentiles(pitcher_id, new_player_metrics, pitcher_summary_filter)
+            p.set(0.6, "Creating plot...")
+            # Draw Baseball Savant-style percentile bars
+            draw_baseball_savant_percentiles(new_player_metrics=new_player_metrics,
+                                            new_player_percentiles=new_player_percentiles,
+                                            sport_id=sport_id,
+                                            year_input=year)
 app = App(app_ui, server)