Update app.py

app.py

@@ -245,11 +245,7 @@ def fetch_database_records(standard, version, selection):
         conn = sqlite3.connect(db_path)
         conn.text_factory = decode_sqlite_text 
 
-        # 1. 현재 어떤 종류의 테이블을 보려는지 판별 (TableA, TableB_C 또는 Main)
-        # selection이 "ALL"이거나 "2.12" 같은 형태면 "Main"으로 간주
         table_type = "Main" if selection == "ALL" or (selection and selection[0].isdigit() and "." in selection) else selection
-        
-        # 2. Table_Config에서 설정 가져오기 (가장 중요한 부분!)
         anchor_col, display_setting = get_table_config(table_type)
 
         tables = pd.read_sql("SELECT name FROM sqlite_master WHERE type='table';", conn)['name'].tolist()
@@ -269,12 +265,9 @@ def fetch_database_records(standard, version, selection):
                 target_table = t
                 break
 
-        # 3. 데이터 로드 로직
         if target_table and target_table != main_table:
-            # 부속 테이블(TableA 등) 처리
             df = pd.read_sql(f"SELECT * FROM [{target_table}]", conn)
         else:
-            # 메인 테이블 처리 (ALL 또는 카테고리 필터링)
             cursor = conn.cursor()
             cursor.execute(f"PRAGMA table_info([{main_table}])")
             cols_info = cursor.fetchall()
@@ -297,18 +290,14 @@ def fetch_database_records(standard, version, selection):
             
             df = pd.DataFrame(cursor.fetchall(), columns=cols)
 
-        # 4. [수정됨] 설정에 따른 컬럼 필터링 (하드코딩 제거)
         df = df.drop(columns=[c for c in df.columns if c.lower() == "version"], errors="ignore")
         
         if display_setting:
             cols_to_show = [c.strip() for c in display_setting.split(',')]
-            # Anchor(기준열)가 데이터에 있다면 반드시 포함
             if anchor_col in df.columns and anchor_col not in cols_to_show:
                 cols_to_show.insert(0, anchor_col)
-            # 존재하는 컬럼만 필터링
             df = df[[c for c in cols_to_show if c in df.columns]]
 
-        # 5. 이미지 렌더링 및 정리
         for col in df.columns:
             df[col] = df[col].apply(convert_blob_to_html_img)
 
@@ -317,142 +306,52 @@ def fetch_database_records(standard, version, selection):
 
     except Exception as e:
         import traceback
-        traceback.print_exc() # 에러 원인 추적을 위해 추가
+        traceback.print_exc()
         return pd.DataFrame({"Error": [str(e)]})
 
 def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, comp_cat):
-    # 1. 테이블 타입 판별 (선택된 카테고리 명칭 등으로 판별)
-    table_type = "Main" if base_cat == "ALL" or "." in base_cat else base_cat
-    anchor_col, _ = get_table_config(table_type)
-
-    # 2. 양측 데이터 로드 (이제 6열씩 가득 차서 옵니다)
-    df_base = fetch_database_records(base_std, base_ver, base_cat, table_type)
-    df_comp = fetch_database_records(comp_std, comp_ver, comp_cat, table_type)
-
-    # 3. 컬럼명에 버전 접미사 붙여서 12열 준비
-    df_base = df_base.add_suffix(f'_{base_ver}')
-    df_comp = df_comp.add_suffix(f'_{comp_ver}')
-
-    # 4. Anchor Column을 기준으로 JOIN (이게 6+6 핵심)
-    final_df = pd.merge(
-        df_base, df_comp, 
-        left_on=f"{anchor_col}_{base_ver}", 
-        right_on=f"{anchor_col}_{comp_ver}", 
-        how='outer'
-    )
-    
-    return final_df
+    try:
+        is_main = base_cat == "ALL" or (base_cat and base_cat[0].isdigit())
+        table_type = "Main" if is_main else base_cat
+        anchor_col, _ = get_table_config(table_type)
+
+        # 단일 조회 (비교법규 미선택)
+        if base_std and base_ver and base_cat and (not comp_std or not comp_ver or not comp_cat):
+            return fetch_database_records(base_std, base_ver, base_cat)
 
-        # 비교 조회 
+        # 비교 조회 (6+6 병합 로직)
         if all([base_std, base_ver, base_cat, comp_std, comp_ver, comp_cat]):
             df_base = fetch_database_records(base_std, base_ver, base_cat)
             df_comp = fetch_database_records(comp_std, comp_ver, comp_cat)
 
             if "Error" in df_base.columns: return df_base
             if "Error" in df_comp.columns: return df_comp
-            if 'section' not in df_base.columns or 'section' not in df_comp.columns:
-                return pd.DataFrame({"Error": ["해당 데이터에 'section' 컬럼이 존재하지 않습니다."]})
 
-            df_base['merge_key'] = df_base['section'].astype(str).str.replace(" ", "")
-            df_comp['merge_key'] = df_comp['section'].astype(str).str.replace(" ", "")
+            df_base = df_base.add_suffix(f'_{base_ver}')
+            df_comp = df_comp.add_suffix(f'_{comp_ver}')
 
-            df_b = df_base[['merge_key', 'section', 'description']].rename(columns={'section': 'sec_b', 'description': 'desc_b'})
-            df_c = df_comp[['merge_key', 'section', 'description']].rename(columns={'section': 'sec_c', 'description': 'desc_c'})
+            base_anchor = f"{anchor_col}_{base_ver}"
+            comp_anchor = f"{anchor_col}_{comp_ver}"
 
-            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()])
+            if base_anchor not in df_base.columns or comp_anchor not in df_comp.columns:
+                return pd.DataFrame({"Error": [f"기준 열 '{anchor_col}'을 찾을 수 없습니다. 설정을 확인하세요."]})
 
-            query_rv = "SELECT Comp_section AS Base_section, Base_section AS Comp_section FROM Mapping_table WHERE TRIM(Comp_std)=? AND TRIM(Comp_ver)=? AND TRIM(Base_std)=? AND TRIM(Base_ver)=?"
-            df_rv = pd.read_sql(query_rv, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
-            
-            conn_map.close()
-
-            df_mapping = pd.concat([df_fw, df_rv], ignore_index=True)
-
-            if not df_mapping.empty:
-                df_mapping['Base_section'] = df_mapping['Base_section'].astype(str).str.replace('\n', ',').str.replace('\r', '')
-                df_mapping['Comp_section'] = df_mapping['Comp_section'].astype(str).str.replace('\n', ',').str.replace('\r', '')
-                
-                df_mapping['Base_section'] = df_mapping['Base_section'].str.split(',')
-                df_mapping['Comp_section'] = df_mapping['Comp_section'].str.split(',')
-
-                df_mapping = df_mapping.explode('Base_section').explode('Comp_section')
-
-                df_mapping['Base_section'] = df_mapping['Base_section'].astype(str).str.strip().str.replace(" ", "")
-                df_mapping['Comp_section'] = df_mapping['Comp_section'].astype(str).str.strip().str.replace(" ", "")
-                
-                df_mapping = df_mapping[(df_mapping['Base_section'] != '') & (df_mapping['Base_section'] != 'nan')]
-                df_mapping = df_mapping[(df_mapping['Comp_section'] != '') & (df_mapping['Comp_section'] != 'nan')]
-                df_mapping = df_mapping.drop_duplicates()
-            else:
-                df_mapping = pd.DataFrame(columns=['Base_section', 'Comp_section'])
-
-            explicit_b = set(df_mapping['Base_section'].dropna())
-            explicit_c = set(df_mapping['Comp_section'].dropna())
-
-            unmapped_b = set(df_b['merge_key']) - explicit_b
-            unmapped_c = set(df_c['merge_key']) - explicit_c
-            implicit_keys = unmapped_b.intersection(unmapped_c)
-
-            df_implicit = pd.DataFrame({
-                'Base_section': list(implicit_keys),
-                'Comp_section': list(implicit_keys)
-            })
-
-            bridge = pd.concat([df_mapping, df_implicit], ignore_index=True)
-
-            df_b_clean = df_b.rename(columns={'merge_key': 'Base_section'})
-            df_c_clean = df_c.rename(columns={'merge_key': 'Comp_section'})
-
-            df_b_clean['base_idx'] = range(len(df_b_clean))
-            df_c_clean['comp_idx'] = range(len(df_c_clean))
-
-            merged = pd.merge(bridge, df_b_clean, on='Base_section', how='outer')
-            merged = pd.merge(merged, df_c_clean, on='Comp_section', how='outer')
-
-            merged['base_idx'] = merged['base_idx'].fillna(float('inf'))
-            merged['comp_idx'] = merged['comp_idx'].fillna(float('inf'))
-            
-            merged = merged.sort_values(['base_idx', 'comp_idx']).drop(columns=['base_idx', 'comp_idx'])
-
-            result_rows = []
-            for _, row in merged.iterrows():
-                sec_b = "" if pd.isna(row.get('sec_b')) else str(row['sec_b'])
-                sec_c = "" if pd.isna(row.get('sec_c')) else str(row['sec_c'])
-                desc_b = "" if pd.isna(row.get('desc_b')) else str(row['desc_b'])
-                desc_c = "" if pd.isna(row.get('desc_c')) else str(row['desc_c'])
-
-                if desc_b and not desc_c:
-                    result_rows.append({f"Section_{base_ver}": sec_b, f"Description_{base_ver}": desc_b, f"Section_{comp_ver}": "", f"Description_{comp_ver}": ""})
-                elif not desc_b and desc_c:
-                    result_rows.append({f"Section_{base_ver}": "", f"Description_{base_ver}": "", f"Section_{comp_ver}": sec_c, f"Description_{comp_ver}": desc_c})
-                elif desc_b and desc_c:
-                    if "<img" in desc_b or "<img" in desc_c or desc_b == desc_c:
-                        b_final, c_final = desc_b, desc_c
-                    else:
-                        b_final, c_final = generate_html_diff(desc_b, desc_c)
-                    
-                    result_rows.append({f"Section_{base_ver}": sec_b, f"Description_{base_ver}": b_final, f"Section_{comp_ver}": sec_c, f"Description_{comp_ver}": c_final})
-
-            final_df = pd.DataFrame(result_rows)
-
-            base_sec_col = f"Section_{base_ver}"
-            base_desc_col = f"Description_{base_ver}"
-
-            duplicate_mask = final_df.duplicated(subset=[base_sec_col], keep='first') & (final_df[base_sec_col] != "")
-
-            final_df.loc[duplicate_mask, base_sec_col] = ""
-            final_df.loc[duplicate_mask, base_desc_col] = ""
+            final_df = pd.merge(
+                df_base, 
+                df_comp, 
+                left_on=base_anchor, 
+                right_on=comp_anchor, 
+                how='outer'
+            )
             
             return final_df
 
-        return pd.DataFrame({"Info": ["조회 조건이 올바르지 않습니다."]})
+        return pd.DataFrame({"Info": ["조회 조건을 모두 선택해 주세요."] })
 
     except Exception as e:
+        import traceback
         traceback.print_exc()
-        return pd.DataFrame({"Error": [f"조회 중 오류 발생: {str(e)}"]})
+        return pd.DataFrame({"Error": [f"비교 조회 중 오류 발생: {str(e)}"]})
 
 
 # ==========================================
@@ -513,33 +412,14 @@ with gr.Blocks() as demo:
 css = """
 table { 
     table-layout: fixed !important; 
-    width: 100% !important; 
+    width: auto !important; 
+    min-width: 100%;
 }
 
-/* 2컬럼 */
-table th:nth-last-child(2):first-child, table td:nth-last-child(2):first-child { width: 15% !important; } 
-table th:nth-last-child(1), table td:nth-last-child(1) { width: 85% !important; }
-
-/* 4컬럼 */
-table th:nth-child(1):nth-last-child(4), table td:nth-child(1):nth-last-child(4) { width: 7% !important; } 
-table th:nth-child(2):nth-last-child(3), table td:nth-child(2):nth-last-child(3) { width: 43% !important; } 
-table th:nth-child(3):nth-last-child(2), table td:nth-child(3):nth-last-child(2) { width: 7% !important; } 
-table th:nth-child(4):nth-last-child(1), table td:nth-child(4):nth-last-child(1) { width: 43% !important; }
-
-/* 5컬럼 */
-table th:nth-child(1):nth-last-child(5), table td:nth-child(1):nth-last-child(5) { width: 15% !important; } 
-table th:nth-child(2):nth-last-child(4), table td:nth-child(2):nth-last-child(4) { width: 10% !important; } 
-table th:nth-child(3):nth-last-child(3), table td:nth-child(3):nth-last-child(3) { width: 30% !important; } 
-table th:nth-child(4):nth-last-child(2), table td:nth-child(4):nth-last-child(2) { width: 10% !important; } 
-table th:nth-child(5):nth-last-child(1), table td:nth-child(5):nth-last-child(1) { width: 35% !important; }
-
-/* 6컬럼 */
-table th:nth-child(1):nth-last-child(6), table td:nth-child(1):nth-last-child(6) { width: 8% !important; } 
-table th:nth-child(2):nth-last-child(5), table td:nth-child(2):nth-last-child(5) { width: 25% !important; } 
-table th:nth-child(3):nth-last-child(4), table td:nth-child(3):nth-last-child(4) { width: 8% !important; } 
-table th:nth-child(4):nth-last-child(3), table td:nth-child(4):nth-last-child(3) { width: 25% !important; } 
-table th:nth-child(5):nth-last-child(2), table td:nth-child(5):nth-last-child(2) { width: 8% !important; } 
-table th:nth-child(6):nth-last-child(1), table td:nth-child(6):nth-last-child(1) { width: 26% !important; }
+/* 테이블의 열 너비를 균등하게 분배 (가변 열 대비) */
+th, td {
+    min-width: 150px;
+}
 
 /* 기본 테이블 & 스크롤 설정 */
 thead th { 
@@ -552,6 +432,8 @@ thead th {
 .dataframe { 
     max-height: none !important; 
     overflow-y: visible !important; 
+    overflow-x: auto !important; /* 가로 스크롤 활성화 */
+    display: block;
 } 
 .dataframe > div { 
     max-height: none !important;