Update app.py

app.py

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