Update app.py

app.py

@@ -319,6 +319,7 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
         is_main = base_cat == "ALL" or (base_cat and base_cat[0].isdigit() and "." in base_cat)
         table_type = "Main" if is_main else base_cat
 
+        # 시각적 중복 제거 (폭포수 방식)
         def apply_visual_merge(df, cols):
             if not df.empty and len(cols) > 1:
                 is_dup = pd.Series([True] * len(df), index=df.index)
@@ -330,13 +331,58 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                         df.loc[is_dup, col] = ""
             return df
 
-        # 단일 조회
+        # [핵심 추가] Code와 Description 컬럼을 시각적으로 위아래 병합
+        def combine_code_desc(df):
+            cols = list(df.columns)
+            new_cols = []
+            processed = set()
+
+            for col in cols:
+                if col in processed: continue
+                
+                # _Code 와 _Description 짝꿍 찾기
+                if "_Code" in col:
+                    desc_col = col.replace("_Code", "_Description")
+                    if desc_col in cols:
+                        new_col_name = col.replace("_Code", "")
+                        
+                        def combine_cells(row):
+                            c = str(row[col]).strip()
+                            d = str(row[desc_col]).strip()
+                            if c in ["nan", "None", ""]: return d
+                            if d in ["nan", "None", ""]: return f"<span style='font-weight:bold; color:#1a73e8;'>{c}</span>"
+                            
+                            # Code는 파란색 볼드체로, Description은 바로 아랫줄에 출력
+                            return f"<span style='font-weight:bold; color:#1a73e8; display:block; margin-bottom:4px;'>{c}</span>{d}"
+                            
+                        df[new_col_name] = df.apply(combine_cells, axis=1)
+                        new_cols.append(new_col_name)
+                        processed.add(col)
+                        processed.add(desc_col)
+                    else:
+                        new_cols.append(col)
+                elif "_Description" in col:
+                    code_col = col.replace("_Description", "_Code")
+                    if code_col not in cols:
+                        new_cols.append(col)
+                else:
+                    new_cols.append(col)
+                    
+            return df[new_cols]
+
+        # ----------------------------------------
+        # 1. 단일 조회
+        # ----------------------------------------
         if base_std and base_ver and base_cat and (not comp_std or not comp_ver or not comp_cat):
             df, _ = fetch_database_records(base_std, base_ver, base_cat, table_type)
             if "Error" in df.columns or "Info" in df.columns: return df
+            
+            df = combine_code_desc(df) # 결합 적용
             return apply_visual_merge(df, df.columns)
 
-        # 비교 조회 
+        # ----------------------------------------
+        # 2. 비교 조회 
+        # ----------------------------------------
         if all([base_std, base_ver, base_cat, comp_std, comp_ver, comp_cat]):
             df_base, real_anchors_b = fetch_database_records(base_std, base_ver, base_cat, table_type)
             df_comp, real_anchors_c = fetch_database_records(comp_std, comp_ver, comp_cat, table_type)
@@ -416,7 +462,7 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                 for c in c_cols_renamed:
                     row_dict[c] = str(row.get(c)) if has_c and not pd.isna(row.get(c)) else ""
 
-                # [해결 1] Diff 적용: 원본 컬럼명에 'description'이 포함되어 있으면 모두 적용!
+                # Diff 처리 (Description 짝꿍 찾아서 하이라이팅)
                 if has_b and has_c:
                     for orig_col in rename_b.keys():
                         if 'description' in orig_col.lower():
@@ -432,8 +478,15 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
 
             final_df = pd.DataFrame(result_rows)
 
-            final_df = apply_visual_merge(final_df, b_cols_renamed)
-            final_df = apply_visual_merge(final_df, c_cols_renamed)
+            # [핵심] Diff 처리된 데이터프레임의 Code와 Desc를 한 열로 병합!
+            final_df = combine_code_desc(final_df)
+
+            # 변경된 컬럼명 기준으로 좌/우 시각적 병합(Cascading Blanking) 따로 적용
+            b_cols_final = [c for c in final_df.columns if c.endswith(f"_{base_ver}")]
+            c_cols_final = [c for c in final_df.columns if c.endswith(f"_{comp_ver}")]
+
+            final_df = apply_visual_merge(final_df, b_cols_final)
+            final_df = apply_visual_merge(final_df, c_cols_final)
 
             return final_df