Refactor reassess_edge function signature in global_func/reassess_edge.py to simplify parameters and update app.py to utilize the new signature, enhancing edge reassessment logic for player data processing.

app.py

@@ -1607,7 +1607,8 @@ with tab2:
                         st.session_state['export_base']['salary'] = st.session_state['export_base']['salary'].astype('uint16')
                         
                         print(st.session_state['export_base'].head(10))
-                        st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
+                        # st.session_state['export_base'] = predict_dupes(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
+                        st.session_state['export_base'] = reassess_edge(st.session_state['export_base'], prior_frame, Contest_Size, salary_max)
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
                         
         with st.container():

global_func/reassess_edge.py

@@ -65,7 +65,7 @@ def reassess_lineup_edge(row: pd.Series, Contest_Size: int) -> float:
     
     return row['Lineup Edge'] - row['Lineup Edge'].mean()
 
-def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame, maps_dict: dict, site_var: str, type_var: str, Contest_Size: int, strength_var: str, sport_var: str, salary_max: int) -> pd.DataFrame:
+def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame, Contest_Size: int, salary_max: int) -> pd.DataFrame:
     orig_df = original_frame.copy()
     orig_df = orig_df.reset_index(drop=True)
     refactored_df = refactored_frame.copy()