Update app.py

app.py

@@ -183,8 +183,8 @@ def merge_with_master(processed_files):
     1. Use the earnings file as master and drop its inaccurate 'tin' column.
     2. Merge template info onto earnings using 'employee_name' (the key provided by "Employee Name").
        The trusted 'tin' comes from the template file.
-       In the template file, force the first column to be the trusted 'tin'.
-    3. Check that the merged earnings-template data has a 'tin' column.
+       For the template file, force its first column (which is "Personal ID of Employee") to be 'tin'.
+    3. Check that the merged earnings-template data has a 'tin' column populated.
        If present, merge the resulting DataFrame with the PAYE file using 'tin'.
     """
     earnings_file = None
@@ -210,7 +210,7 @@ def merge_with_master(processed_files):
     # Drop the inaccurate 'tin' column from earnings if it exists.
     if 'tin' in earnings_df.columns:
         earnings_df = earnings_df.drop(columns=['tin'])
-    # Double-check removal of any middle_name column.
+    # Remove any middle_name column.
     if 'middle_name' in earnings_df.columns:
         earnings_df = earnings_df.drop(columns=['middle_name'])
     
@@ -219,18 +219,21 @@ def merge_with_master(processed_files):
     # Merge template info onto earnings using 'employee_name'
     if template_file is not None:
         st.write(f"Merging template info from '{template_file['filename']}' using key 'employee_name'.")
-        template_df = template_file["df"]
+        template_df = template_file["df"].copy()
         # Force the first column of the template file to be 'tin'
         if not template_df.empty:
             cols = list(template_df.columns)
             cols[0] = "tin"
             template_df.columns = cols
-        # Drop any middle_name column from the template file.
+        
+        # Remove any middle_name column from the template file.
         if 'middle_name' in template_df.columns:
             template_df = template_df.drop(columns=['middle_name'])
-        # Ensure template has an 'employee_name' column (construct if necessary).
+        # Ensure the template has an 'employee_name' column.
         if 'employee_name' not in template_df.columns and 'first_name' in template_df.columns and 'last_name' in template_df.columns:
             template_df['employee_name'] = template_df['first_name'].astype(str).str.strip() + ' ' + template_df['last_name'].astype(str).str.strip()
+        # If after standardization the template still doesn't have employee_name,
+        # you may need to construct it manually if possible.
         if 'employee_name' in merged_df.columns and 'employee_name' in template_df.columns:
             merged_df = merged_df.merge(template_df, on='employee_name', how='left', suffixes=('', '_template'))
         else: