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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
)
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