Update app.py

app.py

@@ -536,15 +536,19 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                 has_b = not pd.isna(row.get('base_idx')) and row.get('base_idx') != float('inf')
                 has_c = not pd.isna(row.get('comp_idx')) and row.get('comp_idx') != float('inf')
 
-                if mapped_only and not (has_b and has_c):
-                    continue
-                
                 for c in rename_b.values(): row_dict[c] = str(row[c]) if has_b and not pd.isna(row[c]) else ""
                 for c in rename_c.values(): row_dict[c] = str(row[c]) if has_c and not pd.isna(row[c]) else ""
-                
+
+                if mapped_only:
+                    b_has_val = any(str(row_dict[c]).strip() not in ["", "nan", "None", " "] for c in rename_b.values())
+                    c_has_val = any(str(row_dict[c]).strip() not in ["", "nan", "None", " "] for c in rename_c.values())
+                    
+                    if not (b_has_val and c_has_val):
+                        continue
+
                 if has_b and has_c:
                     for orig_col in rename_b.keys():
-                        if 'description' in orig_col.lower() and rename_c.get(orig_col) in row_dict:
+                        if ('description' in orig_col.lower() or '내용' in orig_col) and rename_c.get(orig_col) in row_dict:
                             b_v, c_v = row_dict[rename_b[orig_col]], row_dict[rename_c[orig_col]]
                             if b_v and c_v and "<img" not in b_v and "<img" not in c_v and b_v != c_v:
                                 row_dict[rename_b[orig_col]], row_dict[rename_c[orig_col]] = generate_html_diff(b_v, c_v)
@@ -552,6 +556,9 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
 
             final_df = combine_code_desc(pd.DataFrame(result_rows))
 
+            if final_df.empty:
+                return pd.DataFrame({"Info": ["💡 조건에 맞는 데이터가 없습니다."]})
+
             if diff_only:
                 mask = final_df.astype(str).apply(lambda col: col.str.contains('color:#ff4d4f|color:#2ecc71', case=False, regex=True)).any(axis=1)
                 final_df = final_df[mask]