Update app.py

app.py

@@ -252,19 +252,6 @@ def generate_html_diff(base_text, comp_text):
 
     return " ".join(b_result), " ".join(c_result)
 
-def get_table_config(table_type):
-    try:
-        conn = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
-        query = "SELECT Anchor_Column, Display_Columns FROM Table_Config WHERE Table_Type=?"
-        df = pd.read_sql(query, conn, params=[table_type])
-        conn.close()
-        
-        if not df.empty:
-            return df.iloc[0]['Anchor_Column'], df.iloc[0]['Display_Columns']
-    except Exception:
-        pass
-    return "Section", "Description"
-
 def fetch_database_records(standard, version, selection, table_type):
     if not all([standard, version, selection]):
         return pd.DataFrame({"Info": ["선택 필요"]}), []
@@ -277,11 +264,7 @@ def fetch_database_records(standard, version, selection, table_type):
         conn = sqlite3.connect(db_path)
         conn.text_factory = decode_sqlite_text 
 
-        anchor_col_config, display_setting = get_table_config(table_type)
-        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()
-        
         valid_tables = [t for t in tables if t.lower() not in ['mapping_registry', 'mapping_table', 'table_config', 'sqlite_sequence']]
         
         main_table = f"{standard}_{version}"
@@ -325,6 +308,30 @@ 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")
         
+        # =========================================================
+        # 💡 [스마트 스캔 로직] DB 컬럼을 보고 맞는 Main 설정을 알아서 찾습니다.
+        # =========================================================
+        conn_map = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
+        config_df = pd.read_sql("SELECT Anchor_Column, Display_Columns FROM Table_Config WHERE Table_Type=?", conn_map, params=[table_type])
+        conn_map.close()
+
+        anchor_col_config = "Section"
+        display_setting = "Description"
+
+        if not config_df.empty:
+            df_cols_lower = [c.lower() for c in df.columns]
+            for _, row in config_df.iterrows():
+                # DB에 적힌 Anchor 열이 실제 데이터프레임에 존재하는지 확인
+                first_anchor = str(row['Anchor_Column']).split(',')[0].strip().lower()
+                if first_anchor in df_cols_lower:
+                    anchor_col_config = str(row['Anchor_Column'])
+                    raw_disp = row['Display_Columns']
+                    display_setting = str(raw_disp) if pd.notna(raw_disp) and str(raw_disp).strip() != "" else None
+                    break
+        # =========================================================
+
+        anchor_cols_config = [x.strip() for x in anchor_col_config.split(',')]
+
         real_anchors = []
         for ac in anchor_cols_config:
             real_ac = next((c for c in df.columns if c.lower() == ac.lower()), ac)
@@ -344,7 +351,6 @@ def fetch_database_records(standard, version, selection, table_type):
         for col in df.columns:
             df[col] = df[col].apply(convert_blob_to_html_img)
 
-        conn.close()
         return df, real_anchors 
 
     except Exception as e:
@@ -352,7 +358,6 @@ def fetch_database_records(standard, version, selection, table_type):
         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, diff_only):
     try:
         def get_type_by_cat(cat):
@@ -363,9 +368,6 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
         type_b = get_type_by_cat(base_cat)
         type_c = get_type_by_cat(comp_cat)
         
-        anchor_col_b, _ = get_table_config(type_b)
-        anchor_col_c, _ = get_table_config(type_c)
-
         def apply_visual_merge(df, cols):
             if not df.empty and len(cols) > 1:
                 is_dup = pd.Series([True] * len(df), index=df.index)