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UNR / app.py
Update app.py
d18c7fa verified 6 months ago
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import gradio as gr
import sqlite3
import pandas as pd
import os
import re
import base64
import difflib

# ==========================================
# 1. Environment Setup & Data Helpers
# ==========================================
UPLOAD_DIR = "uploaded_dbs"
if not os.path.exists(UPLOAD_DIR):
    os.makedirs(UPLOAD_DIR)

db_registry = []

def convert_blob_to_html_img(blob_data):
    if blob_data is None or pd.isna(blob_data):
        return ""
    try:
        if isinstance(blob_data, (bytes, bytearray)):
            encoded = base64.b64encode(blob_data).decode('utf-8')
            return f'''
            <img src="data:image/png;base64,{encoded}"
                 style="width: 40%; 
                        max-height: 300px; 
                        object-fit: contain;
                        display: block;
                        margin: 10px 0;">
            '''
        return str(blob_data)
    except Exception:
        return str(blob_data)

def decode_sqlite_text(x):
    try:
        return x.decode('utf-8')
    except UnicodeDecodeError:
        return x

def fetch_available_standards():
    global db_registry
    db_registry = []
    for file_name in os.listdir(UPLOAD_DIR):
        if file_name.endswith(".db"):
            name = file_name.replace(".db", "")
            parts = name.split("_")
            if len(parts) >= 2:
                db_registry.append({
                    "path": os.path.join(UPLOAD_DIR, file_name),
                    "standard": parts[0],
                    "version": parts[1]
                })
    return sorted(list(set([d["standard"] for d in db_registry])))

def fetch_database_records(std, ver, cat, table_type):
    try:
        db_path = os.path.join(UPLOAD_DIR, f"{std}_{ver}.db")
        conn = sqlite3.connect(db_path)
        conn.text_factory = decode_sqlite_text
        
        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 = None
        if table_type and table_type.upper() != "MAIN":
            expected_name = f"{std}_{ver}_{table_type}"
            for t in valid_tables:
                if t.lower() == expected_name.lower() or t.lower() == table_type.lower():
                    main_table = t
                    break
                    
        if not main_table:
            main_table = f"{std}_{ver}"
            if main_table not in valid_tables:
                main_table = valid_tables[0] if valid_tables else None
                
        if not main_table:
            conn.close()
            return pd.DataFrame({"Error": ["데이터 테이블을 찾을 수 없습니다."]}), []
            
        cols = pd.read_sql(f"PRAGMA table_info([{main_table}])", conn)['name'].tolist()
        lower_cols = [c.lower() for c in cols]
        
        conn_map = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
        config_df = pd.read_sql("SELECT * FROM Table_Config WHERE TRIM(Table_Type)=?", conn_map, params=[table_type.strip()])
        conn_map.close()
        
        if config_df.empty:
            return pd.DataFrame({"Error": [f"Table_Config에서 '{table_type}' 설정을 찾을 수 없습니다."]}), []
            
        matched_config = None
        for _, row in config_df.iterrows():
            anchors_test = [x.strip().lower() for x in str(row['Anchor_Column']).split(',')]
            if any(a in lower_cols for a in anchors_test):
                matched_config = row
                break
                
        if matched_config is None:
            matched_config = config_df.iloc[0]
            
        anchors = [x.strip() for x in matched_config['Anchor_Column'].split(',')]
        displays = [x.strip() for x in matched_config['Display_Columns'].split(',')] if pd.notna(matched_config['Display_Columns']) else anchors
        
        query = f"SELECT * FROM [{main_table}]"
        conditions = []
        
        if cat and cat != "ALL":
            if "." in cat and 'chapter' in lower_cols and 'category' in lower_cols:
                ch, ca = cat.split(".", 1)
                ch_col = cols[lower_cols.index('chapter')]
                ca_col = cols[lower_cols.index('category')]
                conditions.append(f"[{ch_col}] = '{ch}' AND [{ca_col}] = '{ca}'")
            elif 'category' in lower_cols:
                ca_col = cols[lower_cols.index('category')]
                conditions.append(f"[{ca_col}] = '{cat}'")
                
        if conditions:
            query += " WHERE " + " AND ".join(conditions)
            
        df = pd.read_sql(query, conn)
        conn.close()
        
        real_anchors = [c for c in df.columns if any(a.lower() == c.lower() for a in anchors)]
        
        final_cols = []
        for d in displays:
            for c in df.columns:
                if d.lower() == c.lower():
                    if c not in final_cols:
                        final_cols.append(c)
                    break
                    
        for ra in real_anchors:
            if ra not in final_cols:
                final_cols.append(ra)
                
        if not final_cols:
            return df, real_anchors
            
        for c in final_cols:
            df[c] = df[c].apply(convert_blob_to_html_img)
            
        return df[final_cols], real_anchors
        
    except Exception as e:
        import traceback
        traceback.print_exc()
        return pd.DataFrame({"Error": [f"데이터 로드 오류: {str(e)}"]}), []

def generate_html_diff(text1, text2):
    try:
        s1, s2 = str(text1), str(text2)
        
        if len(s1) > 1000 or len(s2) > 1000:
            return s1, s2
            
        words1, words2 = s1.split(), s2.split()
        if not words1 or not words2: 
            return s1, s2
            
        common_words = set(words1) & set(words2)
        if len(common_words) / min(len(words1), len(words2)) < 0.05:
            return s1, s2

        matcher = difflib.SequenceMatcher(None, words1, words2)
        res1, res2 = [], []
        
        for tag, i1, i2, j1, j2 in matcher.get_opcodes():
            if tag == 'replace':
                res1.append(f"<span style='color:#ff4d4f; font-weight:bold;'>{' '.join(words1[i1:i2])}</span>")
                res2.append(f"<span style='color:#2ecc71; font-weight:bold;'>{' '.join(words2[j1:j2])}</span>")
            elif tag == 'delete':
                res1.append(f"<span style='color:#ff4d4f; font-weight:bold;'>{' '.join(words1[i1:i2])}</span>")
            elif tag == 'insert':
                res2.append(f"<span style='color:#2ecc71; font-weight:bold;'>{' '.join(words2[j1:j2])}</span>")
            elif tag == 'equal':
                res1.append(' '.join(words1[i1:i2]))
                res2.append(' '.join(words2[j1:j2]))
                
        return " ".join(res1), " ".join(res2)
    except Exception:
        return text1, text2

# ==========================================
# 2. UI Component Handlers
# ==========================================
def load_initial_standards():
    return gr.Dropdown(choices=fetch_available_standards())

def update_version_dropdown(standard):
    if not standard:
        return gr.Dropdown(choices=[])
    versions = []
    for file_name in os.listdir(UPLOAD_DIR):
        if file_name.startswith(standard + "_") and file_name.endswith(".db"):
            versions.append(file_name.replace(standard + "_", "").replace(".db", ""))
    return gr.Dropdown(choices=sorted(list(set(versions))))

def update_base_category_dropdown(standard, version):
    if not standard or not version:
        return gr.update(choices=[], value=None, interactive=False), gr.update(value="")

    choices = ["ALL"]
    status_value = ""
    db_path = os.path.join(UPLOAD_DIR, f"{standard}_{version}.db")
    
    if not os.path.exists(db_path): 
        return gr.update(choices=choices), gr.update(value="")

    try:
        conn = sqlite3.connect(db_path)
        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}"
        if main_table not in valid_tables:
            main_table = valid_tables[0] if valid_tables else None

        if main_table:
            try:
                cols_check = pd.read_sql(f"PRAGMA table_info([{main_table}])", conn)['name'].tolist()
                if "Status" in cols_check or "status" in cols_check:
                    status_df = pd.read_sql(f"SELECT Status FROM [{main_table}] WHERE Status IS NOT NULL AND Status != '' LIMIT 1", conn)
                    if not status_df.empty:
                        status_value = str(status_df.iloc[0]['Status'])
            except Exception:
                pass

            cols = pd.read_sql(f"PRAGMA table_info([{main_table}])", conn)['name'].tolist()
            lower_cols = [c.lower() for c in cols]
            if 'chapter' in lower_cols and 'category' in lower_cols:
                ch_col = cols[lower_cols.index('chapter')]
                ca_col = cols[lower_cols.index('category')]
                
                df = pd.read_sql(f"SELECT DISTINCT [{ch_col}], [{ca_col}] FROM [{main_table}]", conn)
                for _, row in df.iterrows():
                    ch = str(row[ch_col]).strip()
                    ca = str(row[ca_col]).strip()
                    if ch and ca and ch.lower() not in ['none', 'nan'] and ca.lower() not in ['none', 'nan']:
                        choices.append(f"{ch}.{ca}")

        pattern = re.compile(f"^{standard}[_\\s-]*{version}[_\\s-]*", re.IGNORECASE)
        for t in valid_tables:
            if t == main_table: continue
            short_name = pattern.sub("", t).strip(" _")
            if short_name and short_name not in choices:
                choices.append(short_name)
            elif t not in choices:
                choices.append(t)

        conn.close()
    except Exception: 
        pass
        
    return gr.update(choices=choices, value=None, interactive=True), gr.update(value=status_value)

def update_comp_standard_dropdown(base_std, base_ver):
    if not base_std or not base_ver:
        return gr.Dropdown(choices=[], value=None, interactive=False)

    try:
        conn = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
        query = "SELECT DISTINCT TRIM(Comp_std) AS Comp_std FROM Mapping_registry WHERE TRIM(Base_std)=TRIM(?) AND TRIM(Base_ver)=TRIM(?)"
        df = pd.read_sql(query, conn, params=[base_std, base_ver])
        conn.close()

        mapped_stds = sorted(df['Comp_std'].dropna().unique().tolist()) if not df.empty else []
        return gr.update(choices=mapped_stds, value=None, interactive=bool(mapped_stds))
    except Exception:
        return gr.update(choices=[], value=None, interactive=False)

def update_comp_version_dropdown(base_std, base_ver, comp_std):
    if not all([base_std, base_ver, comp_std]):
        return gr.update(choices=[], value=None, interactive=False)

    try:
        conn = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
        query = "SELECT DISTINCT TRIM(Comp_ver) AS Comp_ver FROM Mapping_registry WHERE TRIM(Base_std)=TRIM(?) AND TRIM(Base_ver)=TRIM(?) AND TRIM(Comp_std)=TRIM(?)"
        df = pd.read_sql(query, conn, params=[base_std, base_ver, comp_std])
        conn.close()

        mapped_vers = sorted(df['Comp_ver'].dropna().unique().tolist()) if not df.empty else []
        return gr.update(choices=mapped_vers, value=None, interactive=bool(mapped_vers))
    except Exception:
        return gr.update(choices=[], value=None, interactive=False)

def update_comp_category_dropdown(base_std, base_ver, base_cat, comp_std, comp_ver):
    if not all([base_std, base_ver, base_cat, comp_std, comp_ver]):
        return gr.update(choices=[], value=None, interactive=False), gr.update(value="")

    base_type = "Main" if not base_cat or base_cat == "ALL" or "." in base_cat else base_cat
    status_value = ""
    comp_db_path = os.path.join(UPLOAD_DIR, f"{comp_std}_{comp_ver}.db")
    c_main_table = None

    try:
        if os.path.exists(comp_db_path):
            c_conn = sqlite3.connect(comp_db_path)
            c_tables = pd.read_sql("SELECT name FROM sqlite_master WHERE type='table';", c_conn)['name'].tolist()
            c_valid_tables = [t for t in c_tables if t.lower() not in ['mapping_registry', 'mapping_table', 'table_config', 'sqlite_sequence']]
            c_main_table = f"{comp_std}_{comp_ver}"
            if c_main_table not in c_valid_tables:
                c_main_table = c_valid_tables[0] if c_valid_tables else None
            
            if c_main_table:
                try:
                    cols_check = pd.read_sql(f"PRAGMA table_info([{c_main_table}])", c_conn)['name'].tolist()
                    if "Status" in cols_check or "status" in cols_check:
                        status_df = pd.read_sql(f"SELECT Status FROM [{c_main_table}] WHERE Status IS NOT NULL AND Status != '' LIMIT 1", c_conn)
                        if not status_df.empty:
                            status_value = str(status_df.iloc[0]['Status'])
                except Exception:
                    pass
            c_conn.close()

        conn = sqlite3.connect(os.path.join(UPLOAD_DIR, "mapping.db"))
        query = """
        SELECT DISTINCT TRIM(Comp_Type) AS Comp_Type 
        FROM Mapping_registry 
        WHERE TRIM(Base_std)=TRIM(?) AND TRIM(Base_ver)=TRIM(?) AND TRIM(Base_Type)=TRIM(?) AND TRIM(Comp_std)=TRIM(?) AND TRIM(Comp_ver)=TRIM(?)
        """
        df = pd.read_sql(query, conn, params=[base_std, base_ver, base_type, comp_std, comp_ver])
        conn.close()

        allowed_types = df['Comp_Type'].dropna().tolist()
        if not allowed_types:
            return gr.update(choices=[], value=None), gr.update(value=status_value)

        final_choices = []
        if "Main" in allowed_types:
            final_choices.append("ALL")
            if os.path.exists(comp_db_path) and c_main_table:
                c_conn = sqlite3.connect(comp_db_path)
                cols = pd.read_sql(f"PRAGMA table_info([{c_main_table}])", c_conn)['name'].tolist()
                lower_cols = [c.lower() for c in cols]
                if 'chapter' in lower_cols and 'category' in lower_cols:
                    ch_col = cols[lower_cols.index('chapter')]
                    ca_col = cols[lower_cols.index('category')]
                    c_df = pd.read_sql(f"SELECT DISTINCT [{ch_col}], [{ca_col}] FROM [{c_main_table}]", c_conn)
                    for _, row in c_df.iterrows():
                        ch = str(row[ch_col]).strip()
                        ca = str(row[ca_col]).strip()
                        if ch and ca and ch.lower() not in ['none', 'nan'] and ca.lower() not in ['none', 'nan']:
                            final_choices.append(f"{ch}.{ca}")
                c_conn.close()
        
        for t in allowed_types:
            if t != "Main": final_choices.append(t)

        return gr.update(choices=final_choices, value=final_choices[0] if final_choices else None, interactive=True), gr.update(value=status_value)
        
    except Exception:
        return gr.update(choices=[], value=None), gr.update(value="")

def reset_base_selections():
    return gr.update(value=None), gr.update(choices=[], value=None), gr.update(choices=[], value=None), gr.update(value="")

def reset_comp_selections():
    return gr.update(value=None), gr.update(choices=[], value=None), gr.update(choices=[], value=None), gr.update(value="")

# ==========================================
# 3. Core Search Logic
# ==========================================
def execute_unified_search(base_std, base_ver, base_cat, comp_std, comp_ver, comp_cat, mapped_only, diff_only):
    try:
        def get_type_by_cat(cat):
            if not cat or cat == "ALL": return "Main"
            if "." in cat: return "Main"
            return cat

        type_b = get_type_by_cat(base_cat)
        type_c = get_type_by_cat(comp_cat)
        
        def apply_visual_merge(df, cols):
            if not df.empty and len(cols) > 1:
                is_dup = pd.Series([True] * len(df), index=df.index)
                for col in cols:
                    if col in df.columns:
                        curr = df[col].astype(str).str.strip()
                        match = (curr == curr.shift(1)) & (~curr.isin(["", "nan", "None", "&nbsp;"]))
                        is_dup = is_dup & match
                        df.loc[is_dup, col] = "&nbsp;"
            return df

        def combine_code_desc(df):
            cols = list(df.columns)
            new_cols = []
            processed = set()
            for col in cols:
                if col in processed: continue
                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, d = str(row[col]).strip(), str(row[desc_col]).strip()
                            if c in ["nan", "None", "", "&nbsp;"]: return d
                            if d in ["nan", "None", "", "&nbsp;"]: return f"<span style='font-weight:bold; color:#1a73e8;'>{c}</span>"
                            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.update([col, desc_col])
                    else: new_cols.append(col)
                elif "_Description" in col:
                    if col.replace("_Description", "_Code") not in cols: new_cols.append(col)
                else: new_cols.append(col)
            return df[new_cols]

        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, type_b)
            if "Error" in df.columns: return df
            return apply_visual_merge(df, df.columns)

        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, type_b)
            df_comp, real_anchors_c = fetch_database_records(comp_std, comp_ver, comp_cat, type_c)

            if "Error" in df_base.columns: return df_base
            if "Error" in df_comp.columns: return df_comp

            for ra in real_anchors_b:
                if ra not in df_base.columns: 
                    return pd.DataFrame({"Error": [f"기준 열(Anchor) '{ra}'이(가) 기준 데이터에 존재하지 않습니다. Table_Config를 확인하세요."]})
            for ra in real_anchors_c:
                if ra not in df_comp.columns: 
                    return pd.DataFrame({"Error": [f"비교 열(Anchor) '{ra}'이(가) 비교 데이터에 존재하지 않습니다. Table_Config를 확인하세요."]})

            def clean_key_val(v):
                s = str(v).strip()
                if s.endswith('.0') and s[:-2].isdigit(): 
                    s = s[:-2]
                return s.replace(" ", "")

            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)

            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 = None
            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

            # 💡 [핵심] 사용자님 시나리오 완벽 적용
            is_same_std = (base_std.strip().upper() == comp_std.strip().upper() and type_b.strip().upper() == type_c.strip().upper())
            df_mapping = pd.DataFrame()
            
            if target_table_name:
                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()])
                    
                    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')
                    
                    if b_sec not in df_fw.columns or c_sec not in df_fw.columns:
                        conn_map.close()
                        return pd.DataFrame({"Error": [f"'{target_table_name}' 에 '{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'])

                    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'])
                        
                except Exception as sql_e:
                    conn_map.close()
                    return pd.DataFrame({"Error": [f"매핑 테이블 '{target_table_name}'을 여는 데 실패했습니다. 테이블 이름을 확인하세요: {str(sql_e)}"]})
            else:
                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.close()
                    return pd.DataFrame({"Error": ["매핑 테이블이 존재하지 않습니다."]})
            
            conn_map.close()

            df_base = df_base.rename(columns=internal_rename_b)
            df_comp = df_comp.rename(columns=internal_rename_c)

            df_base['base_idx'], df_comp['comp_idx'] = range(len(df_base)), range(len(df_comp))
            merged = pd.merge(bridge, df_base, left_on='Base_section', right_on='merge_key', how='outer')
            merged = pd.merge(merged, df_comp, left_on='Comp_section', right_on='merge_key', 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'])

            result_rows = []
            for _, row in merged.iterrows():
                row_dict = {}
                has_b = not pd.isna(row.get('base_idx')) and row.get('base_idx') != float('inf')
                has_c = not pd.isna(row.get('comp_idx')) and row.get('comp_idx') != float('inf')
                
                for c in internal_rename_b.values(): row_dict[c] = str(row[c]) if has_b and not pd.isna(row[c]) else ""
                for c in internal_rename_c.values(): row_dict[c] = str(row[c]) if has_c and not pd.isna(row[c]) else ""
                
                if mapped_only:
                    b_has_val = any(str(row_dict[c]).strip() not in ["", "nan", "None", "&nbsp;"] for c in internal_rename_b.values())
                    c_has_val = any(str(row_dict[c]).strip() not in ["", "nan", "None", "&nbsp;"] for c in internal_rename_c.values())
                    if not (b_has_val and c_has_val):
                        continue
                
                if has_b and has_c:
                    for orig_col in internal_rename_b.keys():
                        if ('description' in orig_col.lower() or '내용' in orig_col) and internal_rename_c.get(orig_col) in row_dict:
                            b_v, c_v = row_dict[internal_rename_b[orig_col]], row_dict[internal_rename_c[orig_col]]
                            if b_v and c_v and "<img" not in b_v and "<img" not in c_v and b_v != c_v:
                                row_dict[internal_rename_b[orig_col]], row_dict[internal_rename_c[orig_col]] = generate_html_diff(b_v, c_v)
                result_rows.append(row_dict)

            final_df = combine_code_desc(pd.DataFrame(result_rows))

            if final_df.empty:
                return pd.DataFrame({"Info": ["💡 조건에 맞는 데이터가 없습니다."]})

            if diff_only:
                mask = final_df.astype(str).apply(lambda col: col.str.contains('color:#ff4d4f|color:#2ecc71', case=False, regex=True)).any(axis=1)
                final_df = final_df[mask]
                if final_df.empty:
                    return pd.DataFrame({"Info": ["💡 선택하신 조건 간에 변경된 내용이 없습니다."]})
            
            final_rename_map = {}
            for col in final_df.columns:
                if col.endswith("_INTERNAL_BASE"):
                    final_rename_map[col] = f"{col.replace('_INTERNAL_BASE', '')}_{base_ver}"
                elif col.endswith("_INTERNAL_COMP"):
                    final_rename_map[col] = f"{col.replace('_INTERNAL_COMP', '')}_{comp_ver}"
            
            final_df = final_df.rename(columns=final_rename_map)

            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

        return pd.DataFrame({"Info": ["조건을 선택하세요."]})
    except Exception as e:
        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."]})
        return pd.DataFrame({"Error": [f"시스템 오류 발생: {error_msg}"]})

# ==========================================
# 4. UI Layout & Event Binding
# ==========================================
with gr.Blocks() as demo:
    gr.Markdown("# 📜 Regulation Viewer")

    with gr.Row():
        with gr.Accordion("📌 기준 법규", open=True):
            with gr.Column():
                base_standard = gr.Dropdown(label="Standard")
                base_version = gr.Dropdown(label="Version")
                base_status = gr.Textbox(label="Status", interactive=False, lines=1)
                
                with gr.Row(elem_classes="reset-row"):
                    base_category = gr.Dropdown(label="Category", scale=4)
                    base_reset_btn = gr.Button("↺ 초기화", scale=1, elem_classes="reset-btn")
    
        with gr.Accordion("🔄 비교 법규", open=False):
            with gr.Column():
                comp_standard = gr.Dropdown(label="Standard", choices=[])
                comp_version = gr.Dropdown(label="Version", choices=[])
                comp_status = gr.Textbox(label="Status", interactive=False, lines=1)
                
                with gr.Row(elem_classes="reset-row"):
                    comp_category = gr.Dropdown(label="Category", choices=[], scale=4)
                    comp_reset_btn = gr.Button("↺ 초기화", scale=1, elem_classes="reset-btn")

    with gr.Row(elem_id="search_row"):
        search_btn = gr.Button("🔍 조회", variant="primary", scale=10)
        mapped_only_cb = gr.Checkbox(label="🔗 매핑된 항목만 보기", value=False, elem_id="mapped_cb_item", container=False, scale=1)
        diff_filter_cb = gr.Checkbox(label="💡 변경된 내용만 보기", value=False, elem_id="diff_cb_item", container=False, scale=1)

    output_df = gr.Dataframe(wrap=True, interactive=False, datatype="html", max_height=800)

    demo.load(fn=load_initial_standards, inputs=None, outputs=base_standard)

    base_standard.change(fn=update_version_dropdown, inputs=[base_standard], outputs=[base_version])
    base_version.change(fn=update_base_category_dropdown, inputs=[base_standard, base_version], outputs=[base_category, base_status])

    base_version.change(fn=update_comp_standard_dropdown, inputs=[base_standard, base_version], outputs=[comp_standard])
    comp_standard.change(fn=update_comp_version_dropdown, inputs=[base_standard, base_version, comp_standard], outputs=[comp_version])

    comp_change_triggers = [base_standard, base_version, base_category, comp_standard, comp_version]
    
    base_category.change(fn=update_comp_category_dropdown, inputs=comp_change_triggers, outputs=[comp_category, comp_status])
    comp_version.change(fn=update_comp_category_dropdown, inputs=comp_change_triggers, outputs=[comp_category, comp_status])

    search_btn.click(
        fn=execute_unified_search,
        inputs=[base_standard, base_version, base_category, comp_standard, comp_version, comp_category, mapped_only_cb, diff_filter_cb],
        outputs=[output_df]
    )
  
    mapped_only_cb.change(
        fn=execute_unified_search,
        inputs=[base_standard, base_version, base_category, comp_standard, comp_version, comp_category, mapped_only_cb, diff_filter_cb],
        outputs=[output_df]
    )

    diff_filter_cb.change(
        fn=execute_unified_search,
        inputs=[base_standard, base_version, base_category, comp_standard, comp_version, comp_category, mapped_only_cb, diff_filter_cb],
        outputs=[output_df]
    )

    base_reset_btn.click(fn=reset_base_selections, inputs=None, outputs=[base_standard, base_version, base_category, base_status])
    comp_reset_btn.click(fn=reset_comp_selections, inputs=None, outputs=[comp_standard, comp_version, comp_category, comp_status])

# ==========================================
# 5. Application Styling (CSS)
# ==========================================
css = """
.reset-row {
    align-items: flex-end !important; 
    margin-bottom: 5px !important; 
}
.reset-btn {
    margin-bottom: 10px !important; 
}
table { 
    table-layout: auto !important; 
    width: max-content !important; 
    min-width: 100% !important;
}
th, td {
    min-width: 150px; 
}
table:has(th:nth-last-child(2):first-child),
table:has(th:nth-last-child(3):first-child),
table:has(th:nth-last-child(4):first-child),
table:has(th:nth-last-child(5):first-child),
table:has(th:nth-last-child(6):first-child) {
    table-layout: fixed !important;
    width: 100% !important;
}
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; }
table th:nth-child(1):nth-last-child(3), table td:nth-child(1):nth-last-child(3) { width: 20% !important; min-width: 0 !important; } 
table th:nth-child(2):nth-last-child(2), table td:nth-child(2):nth-last-child(2) { width: 30% !important; min-width: 0 !important; } 
table th:nth-child(3):nth-last-child(1), table td:nth-child(3):nth-last-child(1) { width: 50% !important; min-width: 0 !important; }
table th:nth-child(1):nth-last-child(4), table td:nth-child(1):nth-last-child(4) { width: 10% !important; min-width: 0 !important; } 
table th:nth-child(2):nth-last-child(3), table td:nth-child(2):nth-last-child(3) { width: 40% !important; min-width: 0 !important; } 
table th:nth-child(3):nth-last-child(2), table td:nth-child(3):nth-last-child(2) { width: 10% !important; min-width: 0 !important; } 
table th:nth-child(4):nth-last-child(1), table td:nth-child(4):nth-last-child(1) { width: 40% !important; min-width: 0 !important; }
table th:nth-child(1):nth-last-child(5), table td:nth-child(1):nth-last-child(5) { width: 8% !important; min-width: 0 !important; } 
table th:nth-child(2):nth-last-child(4), table td:nth-child(2):nth-last-child(4) { width: 8% !important; min-width: 0 !important; } 
table th:nth-child(3):nth-last-child(3), table td:nth-child(3):nth-last-child(3) { width: 8% !important; min-width: 0 !important; } 
table th:nth-child(4):nth-last-child(2), table td:nth-child(4):nth-last-child(2) { width: 8% !important; min-width: 0 !important; } 
table th:nth-child(5):nth-last-child(1), table td:nth-child(5):nth-last-child(1) { width: 68% !important; min-width: 0 !important; }
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: 15% !important; min-width: 0 !important; } 
table th:nth-child(3):nth-last-child(4), table td:nth-child(3):nth-last-child(4) { width: 27% !important; min-width: 0 !important; } 
table th:nth-child(4):nth-last-child(3), table td:nth-child(4):nth-last-child(3) { width: 8% !important; min-width: 0 !important; } 
table th:nth-child(5):nth-last-child(2), table td:nth-child(5):nth-last-child(2) { width: 15% !important; min-width: 0 !important; } 
table th:nth-child(6):nth-last-child(1), table td:nth-child(6):nth-last-child(1) { width: 27% !important; min-width: 0 !important; }
thead th { 
    font-size: 18px !important;
    position: sticky;
    top: 0; 
    background: white; 
    z-index: 10;
} 
.dataframe { 
    max-height: none !important; 
    overflow-y: visible !important; 
    overflow-x: auto !important; 
    display: block;
} 
.dataframe > div { 
    max-height: none !important; 
    overflow: visible !important; 
} 
td { 
    font-size: 18px !important;
    white-space: pre-wrap !important;
    word-break: keep-all !important; 
    line-height: 1.6; 
    padding: 10px; 
    vertical-align: top !important;
    text-align: left !important;
} 
td img {
    display: block;
    max-width: none !important;
}
#search_row {
    align-items: center !important; 
    margin-bottom: 5px !important;
}
#diff_cb_item, #mapped_cb_item {
    margin-top: 0 !important;
    padding-left: 15px !important; 
    width: max-content !important;
    min-width: max-content !important;
    flex-grow: 0 !important;
}
"""

if __name__ == "__main__":
    demo.launch(
        theme=gr.themes.Soft(),
        share=True,
        css=css
    )