app.py

# masking_ver2.py
import re
import gradio as gr
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

def dummy(text):
    return text + " ✅"

with gr.Blocks() as demo:
    inp = gr.Textbox(label="입력")
    out = gr.Textbox(label="출력")
    btn = gr.Button("실행")
    btn.click(fn=dummy, inputs=inp, outputs=out)

def sanitize_sensitive_info(text, keyword_string, replace_word):
    # 📍 기관 키워드 치환
    keywords = [k.strip() for k in keyword_string.split(",") if k.strip()]
    for kw in keywords:
        pattern = rf"{re.escape(kw)}(?=\W|$)"
        text = re.sub(pattern, replace_word, text, flags=re.IGNORECASE)

    # 📍 기본 민감정보 마스킹 예시 (이메일 앞부분 마스킹)
    text = re.sub(r"\b[\w\.-]+@", "******@", text)

    return text



# =============================================
# Configurable Constants
# =============================================
TAG_PREFIX = "N"

NAME_ENTITY_EXCEPTIONS = set([
    '법적', '군의', '사회적', '심리적', '행정적', '의료적', '법률적',
    '개인정보', '본인', '해당', '현재', '아래', '위치', '소속',
    '상담', '그래도'
])

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

# 분리된 suffix 그룹
FAMILY_TITLES = ['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']

ACADEMIC_TITLES = ['학생', '초등학생', '중학생', '고등학생', '수험생', '학부모']

OCCUPATIONAL_TITLES = ['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', '보호자', '피해자', '당사자', '대상자', '주민', '어르신', '기사님']

COMMON_SUFFIXES = FAMILY_TITLES + ACADEMIC_TITLES + OCCUPATIONAL_TITLES

# =============================================
# Preload Model
# =============================================
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")

# =============================================
# Utility Functions
# =============================================
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 postprocess_sensitive_patterns(text):
    text = re.sub(r"\b[\w\.-]+@", r"******@", text)  # 이메일: 골뱅이 앞만 가리기

    def mask_sequence(match):
        parts = re.split(r'[.-]', match.group())
        masked = []
        for i, part in enumerate(parts):
            if part.isdigit():
                if i % 2 == 0:
                    masked.append(part)
                else:
                    masked.append('*' * len(part))
            else:
                masked.append(part)
        return '.'.join(masked) if '.' in match.group() else '-'.join(masked)

    text = re.sub(r"(?<![\\$\\\\])(?<!\d,)(?:\d{2,4}[.-]){1,3}\d{2,4}(?!\d)", mask_sequence, text)
    text = re.sub(r"(\d{1,3})동", r"***동", text)  # 동 정보
    text = re.sub(r"(\d{1,4})호", r"****호", text)  # 호수 정보
    return text

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

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

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

    for pattern in [attached, spaced]:
        for match in re.findall(pattern, text):
            name = match[0]
            if name not in names and name not in NAME_ENTITY_EXCEPTIONS:
                names.append(name)

    # 🧠 후처리: 이름+조사 붙은 경우로도 다시 추출
    for name in base_names:
        for suffix in COMMON_SUFFIXES:
            for josa in ["", "은", "는", "이", "가", "을", "를", "도", "과", "와", "께서", "에서", "으로"]:
                pattern = rf'{re.escape(name)}\s?{suffix}{josa}'
                if re.search(pattern, text):
                    if name not in names:
                        names.append(name)
    return names




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"{TAG_PREFIX}{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"{TAG_PREFIX}{counter:03d}"
            mapping[tag] = name
            masked = re.sub(pattern, tag, masked)
            counter += 1
    return masked, mapping

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

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

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

    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

def sanitize_sensitive_info(text, keyword_string, replace_word):
    text = postprocess_sensitive_patterns(text)  # 먼저 처리
    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)

    keywords = [k.strip() for k in keyword_string.split(",") if k.strip()] + 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{6})[-](\d)\d{6}", r"*******-\2*****", text)
    text = re.sub(r"([가-힣]+(대로|로|길))\s?(\d+)(호|번길|가)?", r"\1 ***", text)
    return text

# 🔹 마스킹 함수 (정리된 최종본)
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 NAME_ENTITY_EXCEPTIONS:
                names.append(name)
    return names


def refactored_mask_names(text, names):
    counter = 1
    mapping = {}
    used_names = set()
    masked = text

    for name in names:
        # 조사 구분 있는 경우
        for josa in ["은", "는", "이", "가", "을", "를", "께서", "도", "만", "의", "에서"]:
            pattern = rf'(?<![\w가-힣]){re.escape(name)}{josa}(?![\w가-힣])'
            if re.search(pattern, masked):
                tag = f"{TAG_PREFIX}{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"{TAG_PREFIX}{counter:03d}"
            mapping[tag] = name
            masked = re.sub(pattern, tag, masked)
            counter += 1
    return masked, mapping

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 sanitize_sensitive_info(text, keyword_string, replace_word):
    text = postprocess_sensitive_patterns(text)
    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)

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

    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)
    return text

def apply_masking(text, keyword_str, replace_word):
    keywords = [kw.strip() for kw in keyword_str.split(",") if kw.strip()]
    names = extract_names(text)
    masked_text, name_mapping = refactored_mask_names(text, names)
    sanitized_text = sanitize_sensitive_info(masked_text, keyword_str, replace_word)
    final_text = final_name_remask_exact_only(sanitized_text, name_mapping)
    mapping_table = "\n".join(f"{k} → {v}" for k, v in name_mapping.items())
    return final_text, mapping_table

# 📦 PART 4: 기관 키워드 치환기 + Gradio UI 실행기
import gradio as gr

# ✅ 마스킹 실행 함수는 기존에 작성된 apply_full_masking() 사용

with gr.Blocks() as demo:
    gr.Markdown("🧠 **v5.0 마스킹 통합 시스템** — 키워드, 이름, 개인정보, 학교 마스킹")

    input_text = gr.Textbox(lines=15, label="📄 원문 텍스트")
    keyword_input = gr.Textbox(lines=1, label="기관 키워드 (쉼표로 구분)", value="굿네이버스, 사회복지법인 굿네이버스")
    replace_input = gr.Textbox(lines=1, label="치환할 텍스트", value="우리기관")
    run_button = gr.Button("🚀 실행")

    masked_output = gr.Textbox(lines=15, label="🔐 마스킹 결과")
    mapping_output = gr.Textbox(lines=10, label="🏷️ 태그 매핑", interactive=False)

    run_button.click(
    fn=apply_masking,  # ← 이걸로 바꿔야 실제 정의된 함수와 연결됨
    inputs=[input_text, keyword_input, replace_input],
    outputs=[masked_output, mapping_output]
    )

# ✅ 반드시 필요! Gradio 실행
demo.launch(share=True, log=False)