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import gradio as gr
import pandas as pd

from math import ceil
import os

from data import player_df
from gradio_function import get_data
from translate import jp_pitch_to_en_pitch, max_pitch_types

os.makedirs('files', exist_ok=True)

css = '''
.pitch-usage {height: 256px}
.pitch-usage .js-plotly-plot {height: 100%}

.pitch-velo {height: 100px}
.pitch-velo .js-plotly-plot {height: 100%}

.pitch-loc {height: 256px}
.pitch-loc .js-plotly-plot {height: 100%}

.pitch-velo-summary {height: 384px}
.pitch-velo-summary .js-plotly-plot {height: 100%}
'''
  # display: flex;
  # align-items: center;
  # justify-content: center;

with gr.Blocks(css=css) as demo:
  gr.Markdown('''
  # NPB data visualization demo
  [Data from SportsNavi](https://sports.yahoo.co.jp/)
  ''')
  player = gr.Dropdown(value=None, choices=sorted(player_df['name'].dropna().tolist()), label='Player')
  player_info = gr.Markdown()
  download_file = gr.DownloadButton(label='Download player data')
  with gr.Group():
    with gr.Row():
      usage = gr.Plot(label='Pitch Distribution')#, elem_classes='pitch-usage')
      pitch_velo_summary = gr.Plot(label='Velocity Summary')#, elem_classes='pitch-velo-summary')
      pitch_loc_summary = gr.Plot(label='Overall Location')


  max_pitch_maps = len(jp_pitch_to_en_pitch)
  pitch_maps_per_row = 4
  max_rows = ceil(max_pitch_maps/pitch_maps_per_row)

  gr.Markdown('''
  ## Pitch Locations
  Pitcher's persective
  ''')
  pitch_groups = []
  pitch_names = []
  pitch_infos = []
  pitch_velos = []
  pitch_maps = []
  for row in range(max_rows):
    with gr.Row():
      _pitch_maps_per_row = pitch_maps_per_row if row < max_rows-1 else max_pitch_maps - pitch_maps_per_row * (max_rows - 1)
      visible = row==0
      for col in range(_pitch_maps_per_row):
        with gr.Column(elem_classes='pitch-col', min_width=256):
          pitch_group = gr.Group(visible=visible)
          pitch_groups.append(pitch_group)
          with pitch_group:
            pitch_names.append(gr.Markdown(f'### Pitch {col+1}', visible=visible))
            pitch_infos.append(gr.DataFrame(pd.DataFrame([{'Whiff%': None, 'CSW%': None}]), interactive=False, visible=visible))
            pitch_velos.append(gr.Plot(show_label=False, elem_classes='pitch-velo', visible=visible))
            pitch_maps.append(gr.Plot(label='Pitch Location', elem_classes='pitch-loc', visible=visible))

  gr.Markdown('## Pitch Velocity')
  velo_stats = gr.DataFrame(pd.DataFrame([{'Avg. Velo': None, 'League Avg. Velo': None}]), interactive=False, label='Pitch Velocity')

  gr.Markdown('## Bugs and other notes')
  with gr.Accordion('Click to open', open=False):
    gr.Markdown('''
    - Y axis ticks messy when no velocity distribution is plotted
    - Topmost distribution in summary velo distribution plot is clipped
    - DataFrame precision inconsistent
    '''
    )

  player.input(get_data, inputs=player, outputs=[player_info, download_file, usage, pitch_velo_summary, pitch_loc_summary, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, velo_stats])

demo.launch(
    share=True,
    debug=True
)