Refactor portfolio file loading to streamline assignment of DataFrames. The load_file function now returns both export and portfolio DataFrames in a single call, improving code clarity and efficiency.

app.py

@@ -603,9 +603,7 @@ if selected_tab == 'Data Load':
             if st.session_state['portfolio_loaded']:
                 if upload_toggle == 'Paydirt DB':
                     portfolio_file = st.session_state['db_portfolio_file']
-                    st.session_state['export_portfolio'] = portfolio_file
-                    st.session_state['portfolio'] = portfolio_file
-
+                    st.session_state['export_portfolio'], st.session_state['portfolio'] = load_file(portfolio_file, site_var, type_var, sport_var, 'portfolio')
                     st.session_state['export_portfolio'] = st.session_state['export_portfolio'].dropna(how='all')
                     st.session_state['export_portfolio'] = st.session_state['export_portfolio'].reset_index(drop=True)
 

global_func/load_file.py

@@ -90,15 +90,18 @@ sport_headers = {
 def load_file(upload, site_var, type_var, sport_var, upload_type):
     if upload is not None:
         try:
-            clean_name = re.sub(r' \(\d+\)', '', upload.name)
-            
-            if clean_name.endswith('.csv'):
-                df = pd.read_csv(upload)
-            elif clean_name.endswith(('.xls', '.xlsx')):
-                df = pd.read_excel(upload)
-            else:
-                st.error('Please upload either a CSV or Excel file')
-                return None, None
+            try:
+                clean_name = re.sub(r' \(\d+\)', '', upload.name)
+                
+                if clean_name.endswith('.csv'):
+                    df = pd.read_csv(upload)
+                elif clean_name.endswith(('.xls', '.xlsx')):
+                    df = pd.read_excel(upload)
+                else:
+                    st.error('Please upload either a CSV or Excel file')
+                    return None, None
+            except:
+                df = upload
             
             for col in df.columns:
                 if "Unnamed" in col: