Update app.py
Browse files
app.py
CHANGED
@@ -31,11 +31,9 @@ df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys()
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### PITCH COLOURS ###
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pitch_colours = {
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'Four-Seam Fastball':'#FF007D',#BC136F
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'Fastball':'#FF007D',#BC136F
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'Sinker':'#98165D',#DC267F
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'Cutter':'#BE5FA0',
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-
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'Changeup':'#F79E70',#F75233
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'Splitter':'#FE6100',#F75233
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'Screwball':'#F08223',
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@@ -50,7 +48,7 @@ pitch_colours = {
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'Slow Curve':'#274BFC',
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'Eephus':'#648FFF',
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'
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'Pitch Out':'#472C30',
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'Other':'#9C8975',
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@@ -70,7 +68,7 @@ season_fg=2024
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# 'Name':[x['PlayerName'] for x in chad_fg['data']],
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# })
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# mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict()
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@@ -101,7 +99,7 @@ df_2024_update['woba_pred'] = np.nan
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df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred'] = [sum(x) for x in xwoba_model.predict_proba(df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
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team_logos = pd.read_csv('team_logos.csv')
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### PITCH COLOURS ###
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pitch_colours = {
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'Four-Seam Fastball':'#FF007D',#BC136F
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'Sinker':'#98165D',#DC267F
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'Cutter':'#BE5FA0',
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'Changeup':'#F79E70',#F75233
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'Splitter':'#FE6100',#F75233
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'Screwball':'#F08223',
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'Slow Curve':'#274BFC',
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'Eephus':'#648FFF',
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+
'Knuckleball':'#867A08',
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'Pitch Out':'#472C30',
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'Other':'#9C8975',
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# 'Name':[x['PlayerName'] for x in chad_fg['data']],
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# })
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+
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# mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict()
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df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred'] = [sum(x) for x in xwoba_model.predict_proba(df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
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pitcher_dicts = df_2024_update.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict()
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team_logos = pd.read_csv('team_logos.csv')
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