Update salary input limits in app.py: increase the maximum acceptable salary from 60,000 to 100,000 to accommodate a wider range of salary options for users. Adjust conditional checks in predict_dupes.py to use 'elif' for improved clarity in type_var evaluations.

app.py

@@ -886,7 +886,7 @@ with tab2:
                 with st.form(key='macro_filter_form'):
                     max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
                     min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
-                    max_salary = st.number_input("Max acceptable salary?", value=60000, min_value=1000, step=100)
+                    max_salary = st.number_input("Max acceptable salary?", value=100000, min_value=1000, step=100)
                     max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
                     min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
                     if stack_dict is not None:

global_func/predict_dupes.py

@@ -90,7 +90,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 0, 
                 np.round(portfolio['dupes_calc'], 0) - 1
             )
-        if type_var == 'Classic':
+        elif type_var == 'Classic':
             num_players = len([col for col in portfolio.columns if col not in ['salary', 'median', 'Own']])
             dup_count_columns = [f'player_{i}_percent_rank' for i in range(1, num_players + 1)]
             own_columns = [f'player_{i}_own' for i in range(1, num_players + 1)]
@@ -157,7 +157,7 @@ def predict_dupes(portfolio, maps_dict, site_var, type_var, Contest_Size, streng
                 0, 
                 np.round(portfolio['dupes_calc'], 0) - 1
             )
-        if type_var == 'Classic':
+        elif type_var == 'Classic':
             if sport_var == 'CS2':
                 dup_count_columns = ['CPT_Own_percent_rank', 'FLEX1_Own_percent_rank', 'FLEX2_Own_percent_rank', 'FLEX3_Own_percent_rank', 'FLEX4_Own_percent_rank', 'FLEX5_Own_percent_rank']
                 own_columns = ['CPT_Own', 'FLEX1_Own', 'FLEX2_Own', 'FLEX3_Own', 'FLEX4_Own', 'FLEX5_Own']