app.py

# app_updated_with_filter_sets.py
import re
import gradio as gr
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

school_name_candidates = []

def mask_school_names(text):
    global school_name_candidates
    school_name_candidates = []

    def replacer(match):
        name = match.group(1)
        full = match.group(0)
        if 2 <= len(name) <= 20:
            school_name_candidates.append(name)
            return to_chosung(name) + match.group(2)
        else:
            return full

    text = re.sub(r"(\b[가-힣]{2,20})(초등학교|중학교|고등학교)", replacer, text)

    for name in school_name_candidates:
        pattern = rf"{re.escape(name)}\s?(초등학교|중학교|고등학교)"
        text = re.sub(pattern, to_chosung(name) + " " + r"\1", text)
    return text

model_name = "Leo97/KoELECTRA-small-v3-modu-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

# ✅ 예외 필터
NAME_ENTITY_EXCEPTIONS = set([
    '법적', '군의', '사회적', '심리적', '행정적', '의료적', '법률적',
    '개인정보', '본인', '해당', '현재', '아래', '위치', '소속'
])

REGEX_KEYWORDS_TO_MASK = set([
    '이메일', '전화번호', '연락처', '주소', '센터', '카드번호', '주민등록번호', 'IP', 'IP주소'
])

def extract_names(text):
    try:
        results = ner_pipeline(text)
    except Exception as e:
        print("NER 오류 발생:", e)
        return []

    names = []
    for entity in results:
        if entity.get("entity_group") == "PS":
            name = entity["word"].replace("##", "").strip()
            if len(name) >= 2 and name not in names and name not in NAME_ENTITY_EXCEPTIONS:
                names.append(name)

    COMMON_SUFFIXES = [
        '대표', '이사', '전무', '상무', '부장', '차장', '과장', '대리', '사원',
        '실장', '팀장', '소장', '국장', '본부장', '주임', '총무', '회장', '부회장', '사무장',
        '직원', '매니저', '지점장',
        '선생님', '선생', '교사', '교장', '교감', '부교장', '조교수', '교수', '연구원', '강사',
        '박사', '석사', '학사', '의사', '간호사', '간병인',
        '학생', '수험생', '초등학생', '중학생', '고등학생', '학부모',
        '어머니', '아버지', '엄마', '아빠', '형', '누나', '언니', '오빠', '동생',
        '아들', '딸', '할머니', '할아버지', '외할머니', '외할아버지',
        '이모', '고모', '삼촌', '숙모', '외삼촌', '고모부', '이모부', '조카', '사촌',
        '남편', '아내', '부인', '와이프', '신랑', '장모', '장인', '사위', '며느리',
        '올케', '형수', '제수씨', '매형', '처제', '시누이',
        '보호자', '피해자', '당사자', '대상자', '주민', '어르신', '기사님'
    ]

    KOREAN_JOSA = r'(이[가]|은|는|을|를|과|와|의|도|만|께서|에서|으로|에게|한테|보다|까지|부터)?'
    attached_pattern = r'([가-힣]{2,4})(' + '|'.join(COMMON_SUFFIXES) + r')' + KOREAN_JOSA
    spaced_pattern = r'([가-힣]{2,4})\s+(' + '|'.join(COMMON_SUFFIXES) + r')' + KOREAN_JOSA

    for pattern in [attached_pattern, spaced_pattern]:
        matches = re.findall(pattern, text)
        for match in matches:
            name = match[0]
            if name not in names and name not in NAME_ENTITY_EXCEPTIONS:
                names.append(name)
    return names

def to_chosung(text):
    CHOSUNG_LIST = [chr(i) for i in range(0x1100, 0x1113)]
    result = ""
    for ch in text:
        if '가' <= ch <= '힣':
            code = ord(ch) - ord('가')
            cho = code // 588
            result += CHOSUNG_LIST[cho]
        else:
            result += ch
    return result

def mask_department(text):
    text = re.sub(r"([가-힣]{2,20}학과)", lambda m: to_chosung(m.group(1)[:-2]) + "학과", text)
    return text

def sanitize_sensitive_info(text, keyword_string, replace_word):
    text = mask_school_names(text)
    text = mask_department(text)

    text = re.sub(r"(\d)학년(\s?(\d)반)?", lambda m: "*학년" + (" *반" if m.group(3) else ""), text)
    text = re.sub(r"(\d)학년\s?(\d)반", r"*학년 *반", text)

    keywords = [k.strip() for k in keyword_string.split(",") if k.strip()]
    keywords += list(REGEX_KEYWORDS_TO_MASK)
    for kw in keywords:
        pattern = rf"\b{re.escape(kw)}\b"
        text = re.sub(pattern, replace_word, text, flags=re.IGNORECASE)

    text = re.sub(r"(\d{3})-(\d{4})-(\d{4})", r"\1-****-\3", text)
    text = re.sub(r"(\d{4})년 (\d{1,2})월 (\d{1,2})일", r"19**년 \2월 *일", text)
    text = re.sub(r"(\d{1,3})번지", r"***번지", text)
    text = re.sub(r"(\d{1,3})동", r"***동", text)
    text = re.sub(r"(\d{1,4})호", r"****호", text)
    text = re.sub(r"[\w\.-]+@[\w\.-]+", r"******@****", text)
    text = re.sub(r"(\d{6})[-](\d)\d{6}", r"*******-\2*****", text)
    text = re.sub(r"([가-힣]+(대로|로|길))\s?(\d+)(호|번길|가)?", r"\1 ***", text)
    text = re.sub(r"(\d{4})[- ]?(\d{4})[- ]?(\d{4})[- ]?(\d{4})",
                  lambda m: f"{m.group(1)}-****-****-{m.group(4)}", text)
    return text

def final_name_remask_exact_only(text, mapping_dict):
    for tag, name in mapping_dict.items():
        pattern = rf'(?<![\w가-힣]){re.escape(name)}(?![\w가-힣])'
        text = re.sub(pattern, tag, text)
    return text

def refactored_mask_names(original_text, names, start_counter=100):
    korean_josa = ['이가','를','은','는','을','도','만','과','와','에게','에서','으로',
                   '까지','조차','마저','이며','이다','이나','이나마','밖에','이든','이라도',
                   '이','가','의']
    masked = original_text
    mapping = {}
    counter = start_counter
    used_names = set()
    for name in names:
        for josa in korean_josa:
            full = name + josa
            pattern = rf'(?<![\w가-힣]){re.escape(full)}(?![\w가-힣])'
            if re.search(pattern, masked):
                tag = f"N{counter:03d}"
                mapping[tag] = name
                masked = re.sub(pattern, tag + josa, masked)
                counter += 1
                used_names.add(name)
                break
    for name in names:
        if name in used_names:
            continue
        pattern = rf'(?<![\w가-힣]){re.escape(name)}(?![\w가-힣])'
        if re.search(pattern, masked):
            tag = f"N{counter:03d}"
            mapping[tag] = name
            masked = re.sub(pattern, tag, masked)
            counter += 1
    return masked, mapping

def apply_masking(text, keywords, replace_word):
    names = extract_names(text)
    masked, mapping = refactored_mask_names(text, names)
    sanitized = sanitize_sensitive_info(masked, keywords, replace_word)
    sanitized = final_name_remask_exact_only(sanitized, mapping)
    mapping_table = "\n".join([f"{k} → {v}" for k, v in mapping.items()])
    return sanitized, mapping_table

with gr.Blocks() as demo:
    gr.Markdown("""
🛡️ **민감정보 마스킹 [땡땡이 마스킹]**  
이름 + 민감정보 + 초/중/고 마스킹기 (초성 기반)  
⚠️ *완벽하지 않을 수 있습니다. 반드시 직접 최종 점검하세요.*
""")
    input_text = gr.Textbox(lines=15, label="📥 원본 텍스트 입력")
    keyword_input = gr.Textbox(lines=1, label="기관 키워드 (쉼표로 구분)", value="굿네이버스, good neighbors, gn, 사회복지법인 굿네이버스")
    replace_input = gr.Textbox(lines=1, label="치환할 텍스트", value="우리기관")
    run_button = gr.Button("🚀 마스킹 실행")
    masked_output = gr.Textbox(lines=15, label="🔐 마스킹된 텍스트")
    mapping_output = gr.Textbox(lines=10, label="🏷️ 이름 태그 매핑", interactive=False)

    run_button.click(fn=apply_masking, inputs=[input_text, keyword_input, replace_input], outputs=[masked_output, mapping_output])

demo.launch()