app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
import re

# 모델 초기화
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, grouped_entities=True)

def extract_names(text):
    results = ner_pipeline(text)
    names = []
    for entity in results:
        if entity["entity_group"] == "PS":
            name = entity["word"].replace("##", "").strip()
            if len(name) >= 2 and 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"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 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_school_names(text):
    school_patterns = [
        (r"(\b[가-힣]{2,20})(초등학교|중학교|고등학교)", True),
        (r"(\b[가-힣]{2,20})\s(초등학교|중학교|고등학교)", False),
    ]
    for pattern, attach in school_patterns:
        text = re.sub(pattern, lambda m: to_chosung(m.group(1)) + (" " if not attach else "") + m.group(2), text)
    return text


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()]
    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{2,6})[-]?(\d{2,6})[-]?(\d{2,6})", lambda m: f"{m.group(1)[:2]}{'*'*(len(m.group(1))-2)}{'*'*len(m.group(2))}{m.group(3)[-4:]}", text)
    text = re.sub(r"(\d{4})[- ]?(\d{4})[- ]?(\d{4})[- ]?(\d{4})", lambda m: f"{m.group(1)}-****-****-{m.group(4)}", text)
    text = re.sub(r"(\d{1,3})\.(\d{1,3})\.(\d{1,3})\.(\d{1,3})", lambda m: f"{m.group(1)}.{m.group(2)}.*.*", text)
    text = re.sub(r"([가-힣]{1,10})(은행|동|로|길)\s?([\d\-]{4,})", lambda m: m.group(1) + m.group(2) + " " + re.sub(r"\d", "*", m.group(3)), 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 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

def remask_with_mapping(text, mapping_string):
    mapping = {}
    for line in mapping_string.strip().split("\n"):
        if "→" in line:
            tag, name = line.split("→")
            mapping[tag.strip()] = name.strip()
    for tag, name in mapping.items():
        pattern = rf'(?<![\w가-힣]){re.escape(name)}(?![\w가-힣])'
        text = re.sub(pattern, tag, text)
    return text

with gr.Blocks() as demo:
    gr.Markdown("🛡️ 민감정보 마스킹 [땡땡이 마스킹] : 이름 + 민감정보 + 초/중/고 마스킹기 (초성 기반)")
    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()