Update app.py

app.py

@@ -124,6 +124,11 @@ def fetch_database_records(std, ver, cat, table_type):
         
         real_anchors = [c for c in df.columns if any(a.lower() == c.lower() for a in anchors)]
         
+        # 💡 [핵심 로직] Status 열은 UI 상단에 표기되므로, 아래 표에서는 깔끔하게 삭제합니다!
+        status_cols = [c for c in df.columns if c.lower() == 'status']
+        if status_cols:
+            df = df.drop(columns=status_cols)
+        
         final_cols = []
         for d in displays:
             for c in df.columns:
@@ -432,84 +437,86 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                     s = s[:-2]
                 return s.replace(" ", "")
 
+            # 💡 [핵심 로직] 내부 처리용 임시 꼬리표를 달아 KeyError를 원천 차단합니다!
             internal_rename_b = {c: f"{c}_INTERNAL_BASE" for c in df_base.columns if c != 'merge_key'}
             internal_rename_c = {c: f"{c}_INTERNAL_COMP" for c in df_comp.columns if c != 'merge_key'}
             
             df_base['merge_key'] = df_base[real_anchors_b].apply(lambda row: '-'.join([clean_key_val(x) for x in row]), axis=1)
             df_comp['merge_key'] = df_comp[real_anchors_c].apply(lambda row: '-'.join([clean_key_val(x) for x in row]), axis=1)
 
-            is_same_std = (base_std.strip().upper() == comp_std.strip().upper() and type_b.strip().upper() == type_c.strip().upper())
+            # 💡 [핵심 로직] 매핑 장부(Mapping_registry)를 무조건 최우선으로 검사합니다.
+            conn_map = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
+            registry_query = """
+                SELECT Target_Table FROM Mapping_registry 
+                WHERE TRIM(Base_std)=? AND TRIM(Base_ver)=? AND TRIM(Comp_std)=? AND TRIM(Comp_ver)=?
+                LIMIT 1
+            """
+            reg_df = pd.read_sql(registry_query, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
+            
+            target_table_name = "Mapping_table"
+            if not reg_df.empty and pd.notna(reg_df.iloc[0]['Target_Table']):
+                val = str(reg_df.iloc[0]['Target_Table']).strip()
+                if val and val.lower() not in ["none", "nan"]:
+                    target_table_name = val
 
-            if is_same_std:
-                all_keys = list(set(df_base['merge_key']).union(set(df_comp['merge_key'])))
-                bridge = pd.DataFrame({'Base_section': all_keys, 'Comp_section': all_keys})
-            else:
-                conn_map = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
-                registry_query = """
-                    SELECT Target_Table FROM Mapping_registry 
-                    WHERE TRIM(Base_std)=? AND TRIM(Base_ver)=? AND TRIM(Comp_std)=? AND TRIM(Comp_ver)=?
-                    LIMIT 1
-                """
-                reg_df = pd.read_sql(registry_query, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
+            try:
+                q_fw = f"SELECT * FROM [{target_table_name}] WHERE TRIM(Base_std)=? AND TRIM(Base_ver)=? AND TRIM(Comp_std)=? AND TRIM(Comp_ver)=?"
+                df_fw = pd.read_sql(q_fw, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
                 
-                target_table_name = "Mapping_table"
-                if not reg_df.empty and pd.notna(reg_df.iloc[0]['Target_Table']):
-                    val = str(reg_df.iloc[0]['Target_Table']).strip()
-                    if val and val.lower() not in ["none", "nan"]:
-                        target_table_name = val
-
-                try:
-                    q_fw = f"SELECT * FROM [{target_table_name}] WHERE TRIM(Base_std)=? AND TRIM(Base_ver)=? AND TRIM(Comp_std)=? AND TRIM(Comp_ver)=?"
-                    df_fw = pd.read_sql(q_fw, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
-                    
-                    q_rv = f"SELECT * FROM [{target_table_name}] WHERE TRIM(Comp_std)=? AND TRIM(Comp_ver)=? AND TRIM(Base_std)=? AND TRIM(Base_ver)=?"
-                    df_rv = pd.read_sql(q_rv, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
-                except Exception as sql_e:
-                    conn_map.close()
-                    return pd.DataFrame({"Error": [f"매핑 '{target_table_name}'을 여는 데 실패했습니다. 테이블 이름을 확인하세요: {str(sql_e)}"]})
+                q_rv = f"SELECT * FROM [{target_table_name}] WHERE TRIM(Comp_std)=? AND TRIM(Comp_ver)=? AND TRIM(Base_std)=? AND TRIM(Base_ver)=?"
+                df_rv = pd.read_sql(q_rv, conn_map, params=[base_std.strip(), base_ver.strip(), comp_std.strip(), comp_ver.strip()])
+            except Exception as sql_e:
                 conn_map.close()
+                return pd.DataFrame({"Error": [f"매핑 장부 '{target_table_name}'을 여는 데 실패했습니다. 테이블 이름을 확인하��요: {str(sql_e)}"]})
+            conn_map.close()
 
-                cols_fw_lower = {c.lower(): c for c in df_fw.columns}
-                
-                if 'base_type' in cols_fw_lower and 'comp_type' in cols_fw_lower:
-                    b_col = cols_fw_lower['base_type']
-                    c_col = cols_fw_lower['comp_type']
-                    
-                    df_fw[b_col] = df_fw[b_col].fillna('Main').astype(str).str.strip().str.upper()
-                    df_fw[c_col] = df_fw[c_col].fillna('Main').astype(str).str.strip().str.upper()
-                    df_fw = df_fw[(df_fw[b_col] == type_b.strip().upper()) & (df_fw[c_col] == type_c.strip().upper())]
-                    
-                    if not df_rv.empty:
-                        df_rv[b_col] = df_rv[b_col].fillna('Main').astype(str).str.strip().str.upper()
-                        df_rv[c_col] = df_rv[c_col].fillna('Main').astype(str).str.strip().str.upper()
-                        df_rv = df_rv[(df_rv[c_col] == type_b.strip().upper()) & (df_rv[b_col] == type_c.strip().upper())]
-
-                b_sec = cols_fw_lower.get('base_section', 'Base_section')
-                c_sec = cols_fw_lower.get('comp_section', 'Comp_section')
+            cols_fw_lower = {c.lower(): c for c in df_fw.columns}
+            
+            if 'base_type' in cols_fw_lower and 'comp_type' in cols_fw_lower:
+                b_col = cols_fw_lower['base_type']
+                c_col = cols_fw_lower['comp_type']
                 
-                if b_sec not in df_fw.columns or c_sec not in df_fw.columns:
-                    return pd.DataFrame({"Error": [f"'{target_table_name}' 에 '{b_sec}' 또는 '{c_sec}' 열이 없습니다. 대소문자를 확인하세요."]})
+                df_fw[b_col] = df_fw[b_col].fillna('Main').astype(str).str.strip().str.upper()
+                df_fw[c_col] = df_fw[c_col].fillna('Main').astype(str).str.strip().str.upper()
+                df_fw = df_fw[(df_fw[b_col] == type_b.strip().upper()) & (df_fw[c_col] == type_c.strip().upper())]
                 
-                df_fw = df_fw[[b_sec, c_sec]].rename(columns={b_sec: 'Base_section', c_sec: 'Comp_section'})
                 if not df_rv.empty:
-                    df_rv = df_rv[[b_sec, c_sec]].rename(columns={b_sec: 'Comp_section', c_sec: 'Base_section'})
-                else:
-                    df_rv = pd.DataFrame(columns=['Base_section', 'Comp_section'])
+                    df_rv[b_col] = df_rv[b_col].fillna('Main').astype(str).str.strip().str.upper()
+                    df_rv[c_col] = df_rv[c_col].fillna('Main').astype(str).str.strip().str.upper()
+                    df_rv = df_rv[(df_rv[c_col] == type_b.strip().upper()) & (df_rv[b_col] == type_c.strip().upper())]
 
-                df_mapping = pd.concat([df_fw, df_rv], ignore_index=True)
+            b_sec = cols_fw_lower.get('base_section', 'Base_section')
+            c_sec = cols_fw_lower.get('comp_section', 'Comp_section')
+            
+            if b_sec not in df_fw.columns or c_sec not in df_fw.columns:
+                return pd.DataFrame({"Error": [f"'{target_table_name}' 장부에 '{b_sec}' 또는 '{c_sec}' 열이 없습니다. 대소문자를 확인하세요."]})
+            
+            df_fw = df_fw[[b_sec, c_sec]].rename(columns={b_sec: 'Base_section', c_sec: 'Comp_section'})
+            if not df_rv.empty:
+                df_rv = df_rv[[b_sec, c_sec]].rename(columns={b_sec: 'Comp_section', c_sec: 'Base_section'})
+            else:
+                df_rv = pd.DataFrame(columns=['Base_section', 'Comp_section'])
 
-                if not df_mapping.empty:
-                    df_mapping['Base_section'] = df_mapping['Base_section'].astype(str).str.replace('\n', ',').str.split(',')
-                    df_mapping['Comp_section'] = df_mapping['Comp_section'].astype(str).str.replace('\n', ',').str.split(',')
-                    df_mapping = df_mapping.explode('Base_section').explode('Comp_section')
-                    
-                    df_mapping['Base_section'] = df_mapping['Base_section'].apply(clean_key_val)
-                    df_mapping['Comp_section'] = df_mapping['Comp_section'].apply(clean_key_val)
-                    
-                    df_mapping = df_mapping[(df_mapping['Base_section'] != "") & (df_mapping['Comp_section'] != "")]
-                    bridge = df_mapping.dropna().drop_duplicates()
-                else:
-                    bridge = pd.DataFrame(columns=['Base_section', 'Comp_section'])
+            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.split(',')
+                df_mapping['Comp_section'] = df_mapping['Comp_section'].astype(str).str.replace('\n', ',').str.split(',')
+                df_mapping = df_mapping.explode('Base_section').explode('Comp_section')
+                
+                df_mapping['Base_section'] = df_mapping['Base_section'].apply(clean_key_val)
+                df_mapping['Comp_section'] = df_mapping['Comp_section'].apply(clean_key_val)
+                
+                df_mapping = df_mapping[(df_mapping['Base_section'] != "") & (df_mapping['Comp_section'] != "")]
+                bridge = df_mapping.dropna().drop_duplicates()
+            else:
+                bridge = pd.DataFrame(columns=['Base_section', 'Comp_section'])
+
+            # 💡 [핵심 로직] 매핑 장부가 완벽하게 비어있을 때만(즉, 아무것도 안 썼을 때만) 최후의 수단으로 1:1 병합
+            is_same_std = (base_std.strip().upper() == comp_std.strip().upper() and type_b.strip().upper() == type_c.strip().upper())
+            if bridge.empty and is_same_std:
+                all_keys = list(set(df_base['merge_key']).union(set(df_comp['merge_key'])))
+                bridge = pd.DataFrame({'Base_section': all_keys, 'Comp_section': all_keys})
 
             df_base = df_base.rename(columns=internal_rename_b)
             df_comp = df_comp.rename(columns=internal_rename_c)
@@ -556,6 +563,7 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
                 if final_df.empty:
                     return pd.DataFrame({"Info": ["💡 선택하신 조건 간에 변경된 내용이 없습니다. (100% 동일)"]})
             
+            # 💡 [핵심 로직] 화면 출력 직전에 지저분한 임시 꼬리표를 깔끔하게 떼어버립니다.
             final_rename_map = {}
             for col in final_df.columns:
                 if col.endswith("_INTERNAL_BASE"):
@@ -575,6 +583,8 @@ def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, com
 
         return pd.DataFrame({"Info": ["조건을 선택하세요."]})
     except Exception as e:
+        import traceback
+        traceback.print_exc()
         error_msg = str(e)
         if "database is locked" in error_msg.lower():
             return pd.DataFrame({"Error": ["🚨 DB가 잠겨있습니다! 켜놓으신 'DB Browser' 프로그램을 완전히 종료한 뒤 다시 조회해 주세요."]})