Refactor salary mapping logic in app.py to differentiate between NFL and NBA players for DraftKings and FanDuel. Updated the salary calculation to apply a specific divisor for NFL while maintaining the original mapping for NBA, enhancing the accuracy of player salary projections based on sport selection.

app.py

@@ -560,9 +560,12 @@ with tab1:
             flex_working['Freq'] = flex_working['Freq'].astype(int)
             flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
             if sim_site_var1 == 'Draftkings':
-                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                if sim_sport_var1 == 'NFL':
+                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+                elif sim_sport_var1 == 'NBA':
+                    freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
             elif sim_site_var1 == 'Fanduel':
-                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
+                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
             flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100)
             flex_working['Exposure'] = flex_working['Freq']/(1000)
             flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']