LLM 기반 접근성 인터페이스: Model-Based 접근법

# 논문 4.4절 기반 — GenAIEngine 프롬프트 템플릿 예시

prompt_template = """
[Instruction]: Simplify this medical note using Plain-Language guidelines (ISO 24495-1).
Add pictogram descriptions for key medical actions.
Structure the output as numbered steps.

[UserProfile]:
  cognitiveSupport: {cognitive_support}
  auditoryExclusion: {auditory_exclusion}
  contrastMode: high

[AdaptationRules]:
  - simplifyText()      # DAR-01: Plain Language
  - structureAsSteps()  # DAR-02: Step-wise
  - attachPictograms()  # DAR-03: Pictograms
  - disableAudio()      # DAR-04: Visual-only
  - applyHighContrast() # DAR-05: Contrast

[InputText]: {medical_note}

[OutputFormat]:
  plain_text: "..."
  pictogram_descriptions: ["...", "..."]
  steps: ["Step 1: ...", "Step 2: ..."]
"""

# 사용 예시
formatted_prompt = prompt_template.format(
    cognitive_support=True,
    auditory_exclusion=True,
    medical_note="Take Ibuprofen 400mg every 8 hours unless you experience gastric discomfort."
)

# Quality Gate — 출력 검증
def quality_gate(output: dict, original: str) -> bool:
    from textstat import flesch_reading_ease
    from sentence_transformers import SentenceTransformer, util

    # 가독성 체크 (Plain Language 기준: 60+ 권장)
    readability = flesch_reading_ease(output["plain_text"])
    if readability < 60:
        return False  # 재생성 트리거

    # 의미 충실도 체크 (코사인 유사도 0.8 이상)
    model = SentenceTransformer("all-MiniLM-L6-v2")
    similarity = util.cos_sim(
        model.encode(original),
        model.encode(output["plain_text"])
    ).item()
    return similarity >= 0.8