Enhance reassess_edge function by adding max_salary parameter to improve salary handling in the predict_dupes call, ensuring more accurate edge reassessment.

global_func/reassess_edge.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from global_func.predict_dupes import predict_dupes
 
-def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_dict: dict, site_var: str, type_var: str, Contest_Size: int, strength_var: str, sport_var: str) -> pd.DataFrame:
+def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_dict: dict, site_var: str, type_var: str, Contest_Size: int, strength_var: str, sport_var: str, max_salary: int) -> pd.DataFrame:
     """
     Reassess edge by concatenating modified frame with base frame, running predict_dupes,
     and then extracting the first N rows (where N is the length of modified_frame).
@@ -15,6 +15,7 @@ def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_d
         Contest_Size: Contest size for calculations
         strength_var: Strength variable (Weak/Average/Sharp)
         sport_var: Sport variable
+        max_salary: Maximum salary for the contest
         
     Returns:
         DataFrame: Updated modified_frame with recalculated metrics
@@ -26,7 +27,7 @@ def reassess_edge(modified_frame: pd.DataFrame, base_frame: pd.DataFrame, maps_d
     combined_frame = pd.concat([modified_frame, base_frame], ignore_index=True)
     
     # Run predict_dupes on the combined frame
-    updated_combined_frame = predict_dupes(combined_frame, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
+    updated_combined_frame = predict_dupes(combined_frame, maps_dict, site_var, type_var, Contest_Size, strength_var, sport_var, max_salary)
     
     # Extract the first N rows (which correspond to our modified frame)
     result_frame = updated_combined_frame.head(num_modified_rows).copy()