Update app.py

app.py

@@ -245,7 +245,6 @@ def fetch_database_records(standard, version, selection, table_type):
         conn.text_factory = decode_sqlite_text 
 
         anchor_col_config, display_setting = get_table_config(table_type)
-        # 앵커가 쉼표로 여러 개일 경우 리스트로 분할
         anchor_cols_config = [x.strip() for x in anchor_col_config.split(',')]
 
         tables = pd.read_sql("SELECT name FROM sqlite_master WHERE type='table';", conn)['name'].tolist()
@@ -287,7 +286,6 @@ def fetch_database_records(standard, version, selection, table_type):
 
         df = df.drop(columns=[c for c in df.columns if c.lower() == "version"], errors="ignore")
         
-        # 대소문자 무시하고 실제 앵커 컬럼명 찾기 (다중 앵커 지원)
         real_anchors = []
         for ac in anchor_cols_config:
             real_ac = next((c for c in df.columns if c.lower() == ac.lower()), ac)
@@ -297,7 +295,6 @@ def fetch_database_records(standard, version, selection, table_type):
             cols_to_show = [c.strip() for c in display_setting.split(',')]
             actual_cols_to_show = [c for c in df.columns if next((True for req in cols_to_show if c.lower() == req.lower()), False)]
             
-            # 설정된 출력 열에 앵커들이 없으면 강제 삽입
             for ra in reversed(real_anchors):
                 if ra in df.columns and ra not in actual_cols_to_show:
                     actual_cols_to_show.insert(0, ra)
@@ -309,7 +306,7 @@ def fetch_database_records(standard, version, selection, table_type):
             df[col] = df[col].apply(convert_blob_to_html_img)
 
         conn.close()
-        return df, real_anchors # 단일 값이 아닌 앵커 '리스트' 반환
+        return df, real_anchors 
 
     except Exception as e:
         import traceback
@@ -322,7 +319,6 @@ 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 함수 (폭포수 방식: 상위 컬럼이 같을 때만 하위 컬럼 지움)
         def apply_visual_merge(df, cols):
             if not df.empty and len(cols) > 1:
                 is_dup = pd.Series([True] * len(df), index=df.index)
@@ -334,17 +330,13 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                         df.loc[is_dup, col] = ""
             return df
 
-        # ----------------------------------------
-        # 1. 단일 조회 
-        # ----------------------------------------
+        # 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
             return apply_visual_merge(df, df.columns)
 
-        # ----------------------------------------
-        # 2. 비교 조회 
-        # ----------------------------------------
+        # 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)
@@ -357,11 +349,9 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
             for ra in real_anchors_c:
                 if ra not in df_comp.columns: return pd.DataFrame({"Error": [f"비교 열 '{ra}'이 데이터에 없습니다."]})
 
-            # 다중 앵커를 하이픈(-)으로 연결해 단일 merge_key 생성 (예: "5.1-M6")
             df_base['merge_key'] = df_base[real_anchors_b].apply(lambda row: '-'.join(row.values.astype(str)), axis=1).str.replace(" ", "")
             df_comp['merge_key'] = df_comp[real_anchors_c].apply(lambda row: '-'.join(row.values.astype(str)), axis=1).str.replace(" ", "")
 
-            # 브릿지 매핑 복원
             conn_map = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
             query_fw = "SELECT Base_section, Comp_section FROM Mapping_table WHERE TRIM(Base_std)=? AND TRIM(Base_ver)=? AND TRIM(Comp_std)=? AND TRIM(Comp_ver)=?"
             df_fw = pd.read_sql(query_fw, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
@@ -412,7 +402,6 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
             merged['comp_idx'] = merged['comp_idx'].fillna(float('inf'))
             merged = merged.sort_values(['base_idx', 'comp_idx'])
 
-            # N열 동적 조립 및 Diff 처리
             result_rows = []
             b_cols_renamed = list(rename_b.values())
             c_cols_renamed = list(rename_c.values())
@@ -427,20 +416,22 @@ 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 ""
 
-                if table_type == "Main" and has_b and has_c:
-                    desc_b_col = next((c for c in b_cols_renamed if 'description' in c.lower()), None)
-                    desc_c_col = next((c for c in c_cols_renamed if 'description' in c.lower()), None)
-                    
-                    if desc_b_col and desc_c_col:
-                        b_val, c_val = row_dict[desc_b_col], row_dict[desc_c_col]
-                        if b_val and c_val and "<img" not in b_val and "<img" not in c_val and b_val != c_val:
-                            row_dict[desc_b_col], row_dict[desc_c_col] = generate_html_diff(b_val, c_val)
+                # [해결 1] Diff 적용: 원본 컬럼명에 'description'이 포함되어 있으면 모두 적용!
+                if has_b and has_c:
+                    for orig_col in rename_b.keys():
+                        if 'description' in orig_col.lower():
+                            b_col_name = rename_b[orig_col]
+                            c_col_name = rename_c.get(orig_col)
+                            
+                            if c_col_name and c_col_name in row_dict:
+                                b_val, c_val = row_dict[b_col_name], row_dict[c_col_name]
+                                if b_val and c_val and "<img" not in b_val and "<img" not in c_val and b_val != c_val:
+                                    row_dict[b_col_name], row_dict[c_col_name] = generate_html_diff(b_val, c_val)
 
                 result_rows.append(row_dict)
 
             final_df = pd.DataFrame(result_rows)
 
-            # 좌/우 각각 시각적 병합(Cascading Blanking) 적용
             final_df = apply_visual_merge(final_df, b_cols_renamed)
             final_df = apply_visual_merge(final_df, c_cols_renamed)
 
@@ -512,14 +503,41 @@ with gr.Blocks() as demo:
 css = """
 table { 
     table-layout: fixed !important; 
-    width: auto !important; 
-    min-width: 100%;
+    width: 100% !important; 
 }
 
+/* 7열 이상의 다중 열에 대한 기본 Fallback (가로 스크롤 허용) */
 th, td {
     min-width: 150px;
 }
 
+/* [해결 2] 작성하셨던 기존 2~6 컬럼 비율 완전 복구 (min-width 무시 설정 추가) */
+/* 2컬럼 */
+table th:nth-last-child(2):first-child, table td:nth-last-child(2):first-child { width: 15% !important; min-width: 0 !important; } 
+table th:nth-last-child(1), table td:nth-last-child(1) { width: 85% !important; min-width: 0 !important; }
+
+/* 4컬럼 */
+table th:nth-child(1):nth-last-child(4), table td:nth-child(1):nth-last-child(4) { width: 7% !important; min-width: 0 !important; } 
+table th:nth-child(2):nth-last-child(3), table td:nth-child(2):nth-last-child(3) { width: 43% !important; min-width: 0 !important; } 
+table th:nth-child(3):nth-last-child(2), table td:nth-child(3):nth-last-child(2) { width: 7% !important; min-width: 0 !important; } 
+table th:nth-child(4):nth-last-child(1), table td:nth-child(4):nth-last-child(1) { width: 43% !important; min-width: 0 !important; }
+
+/* 5컬럼 */
+table th:nth-child(1):nth-last-child(5), table td:nth-child(1):nth-last-child(5) { width: 15% !important; min-width: 0 !important; } 
+table th:nth-child(2):nth-last-child(4), table td:nth-child(2):nth-last-child(4) { width: 10% !important; min-width: 0 !important; } 
+table th:nth-child(3):nth-last-child(3), table td:nth-child(3):nth-last-child(3) { width: 30% !important; min-width: 0 !important; } 
+table th:nth-child(4):nth-last-child(2), table td:nth-child(4):nth-last-child(2) { width: 10% !important; min-width: 0 !important; } 
+table th:nth-child(5):nth-last-child(1), table td:nth-child(5):nth-last-child(1) { width: 35% !important; min-width: 0 !important; }
+
+/* 6컬럼 */
+table th:nth-child(1):nth-last-child(6), table td:nth-child(1):nth-last-child(6) { width: 8% !important; min-width: 0 !important; } 
+table th:nth-child(2):nth-last-child(5), table td:nth-child(2):nth-last-child(5) { width: 25% !important; min-width: 0 !important; } 
+table th:nth-child(3):nth-last-child(4), table td:nth-child(3):nth-last-child(4) { width: 8% !important; min-width: 0 !important; } 
+table th:nth-child(4):nth-last-child(3), table td:nth-child(4):nth-last-child(3) { width: 25% !important; min-width: 0 !important; } 
+table th:nth-child(5):nth-last-child(2), table td:nth-child(5):nth-last-child(2) { width: 8% !important; min-width: 0 !important; } 
+table th:nth-child(6):nth-last-child(1), table td:nth-child(6):nth-last-child(1) { width: 26% !important; min-width: 0 !important; }
+
+/* 기본 테이블 & 스크롤 설정 */
 thead th { 
     font-size: 18px !important;
     position: sticky;