features/sentiment/summary.py

"""감성분석 Feature 요약 카드.

기존 sections/executive_summary.py + quick_overview.py + KPI 통합.
"""
import streamlit as st

from core.charts import (
    create_confidence_tier_pie_chart,
    create_nudge_by_cej_bar_chart,
    create_platform_bar_chart,
)


def get_risk_grade(high_nudges: int) -> tuple[str, str, str]:
    """Get risk grade based on HIGH tier nudge count."""
    if high_nudges == 0:
        return "A", "grade-a", "우수 (부정 언급 없음)"
    elif high_nudges <= 10:
        return "B", "grade-b", "양호"
    elif high_nudges <= 30:
        return "C", "grade-c", "주의 필요"
    else:
        return "D", "grade-d", "즉시 대응"


def render_summary(data: dict):
    """감성분석 요약 카드 렌더링."""
    total_nudge = data.get("total_nudge", 0)
    high_count = data.get("high_count", 0)
    medium_count = data.get("medium_count", 0)
    risk_score = data.get("risk_score", 0.0)
    tier_stats = data.get("tier_stats") or {}
    platform_stats = data.get("platform_stats") or {}
    cej_stats = data.get("cej_stats") or {}
    candidates = data.get("candidates") or []
    campaign_overview = data.get("campaign_overview") or {}

    # --- Sentiment Summary Card ---
    grade, grade_class, grade_desc = get_risk_grade(high_count)
    if total_nudge == 0:
        nudge_insight = "부정 언급 없음"
    elif high_count == 0:
        nudge_insight = f"잠재 리스크 {total_nudge}건 (확신도 낮음)"
    else:
        nudge_insight = f"HIGH {high_count}건 / 총 {total_nudge}건"

    col1, col2, col3, col4 = st.columns(4)
    with col1:
        st.markdown(f"""
        <div style="background: linear-gradient(135deg, #EEF2FF 0%, #E0E7FF 100%);
                    padding: 16px; border-radius: 12px; min-height: 120px;">
            <div style="font-size: 13px; color: #6B7280;">건강 등급</div>
            <div style="font-size: 32px; font-weight: bold; color: #4338CA;">{grade}</div>
            <div style="font-size: 12px; color: #4B5563;">{grade_desc}</div>
        </div>
        """, unsafe_allow_html=True)
    with col2:
        st.metric("🔴 HIGH", f"{high_count}건", help="≥85% 확신도 - 즉시 대응 권장")
    with col3:
        st.metric("리스크 점수", f"{risk_score:.1f}", help="가중 평균 점수")
    with col4:
        citation_total = sum(c.get("citation_count", 0) or 0 for c in candidates)
        st.metric("총 인용 소스", f"{citation_total}개")

    # --- Pipeline Overview (collapsible) ---
    with st.expander("📊 데이터 파이프라인 상세", expanded=False):
        _render_pipeline_overview(campaign_overview)

    # --- Quick Charts ---
    st.markdown("---")
    chart_col1, chart_col2, chart_col3, chart_col4 = st.columns(4)

    with chart_col1:
        st.markdown("##### Confidence Tier 분포")
        if tier_stats and sum(tier_stats.values()) > 0:
            fig = create_confidence_tier_pie_chart(tier_stats)
            st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})
        else:
            st.info("부정 언급이 없습니다")

    with chart_col2:
        st.markdown("##### 플랫폼별 분포")
        if platform_stats and sum(platform_stats.values()) > 0:
            fig = create_platform_bar_chart(platform_stats)
            st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})
        else:
            st.info("플랫폼 데이터가 없습니다")

    with chart_col3:
        st.markdown("##### CEJ 단계별 분포")
        if cej_stats and sum(cej_stats.values()) > 0:
            fig = create_nudge_by_cej_bar_chart(cej_stats)
            st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})
        else:
            st.info("CEJ 데이터가 없습니다")

    with chart_col4:
        _render_llm_verification_summary(campaign_overview)


def _render_pipeline_overview(campaign_overview: dict):
    """데이터 파이프라인 현황."""
    overview_total = campaign_overview.get("total_answers", 0)
    overview_ih_neg = campaign_overview.get("in_house_negative_count", 0)
    overview_llm_done = campaign_overview.get("llm_verified_in_house", 0)
    overview_llm_pending = campaign_overview.get("llm_pending", 0)
    overview_llm_confirmed = campaign_overview.get("llm_confirmed_negative", 0)

    pipe1, pipe2, pipe3, pipe4, pipe5 = st.columns(5)

    with pipe1:
        st.metric(
            label="전체 AI 답변",
            value=f"{overview_total:,}건",
            help="감성 분석이 완료된 전체 AI 답변 수",
        )
    with pipe2:
        ih_rate = (overview_ih_neg / overview_total * 100) if overview_total > 0 else 0
        st.metric(
            label="1차 부정 감지 (DeBERTa)",
            value=f"{overview_ih_neg:,}건",
            delta=f"{ih_rate:.1f}%",
            delta_color="inverse",
            help="자사 브랜드에 대한 부정 감성이 감지된 답변 (ABSA 기반)",
        )
    with pipe3:
        verify_rate = (overview_llm_done / overview_ih_neg * 100) if overview_ih_neg > 0 else 0
        st.metric(
            label="2차 검증 완료 (LLM)",
            value=f"{overview_llm_done:,}건",
            delta=f"{verify_rate:.0f}% 완료",
            delta_color="normal" if verify_rate >= 90 else "off",
            help="LLM 2차 검증이 완료된 건수",
        )
    with pipe4:
        st.metric(
            label="2차 검증 대기",
            value=f"{overview_llm_pending:,}건",
            help="아직 LLM 2차 검증이 안 된 건수",
        )
    with pipe5:
        confirm_rate = (overview_llm_confirmed / overview_llm_done * 100) if overview_llm_done > 0 else 0
        st.metric(
            label="최종 정탐",
            value=f"{overview_llm_confirmed:,}건",
            delta=f"정탐률 {confirm_rate:.1f}%",
            help="1차 + 2차 검증 모두에서 부정으로 확정된 건수",
        )


def _render_llm_verification_summary(campaign_overview: dict):
    """LLM 2차 검증 요약."""
    st.markdown("##### 🤖 LLM 2차 검증")

    overview_llm_done = campaign_overview.get("llm_verified_in_house", 0)
    overview_llm_pending = campaign_overview.get("llm_pending", 0)
    overview_llm_confirmed = campaign_overview.get("llm_confirmed_negative", 0)

    if overview_llm_done > 0:
        fp_count = overview_llm_done - overview_llm_confirmed
        fp_rate = (fp_count / overview_llm_done * 100) if overview_llm_done > 0 else 0
        st.metric(
            label="검증 완료",
            value=f"{overview_llm_done}건",
            delta=f"오탐 {fp_count}건 ({fp_rate:.0f}%)",
            delta_color="inverse",
        )
        st.caption(f"✅ 정탐: {overview_llm_confirmed}건 | ❌ 오탐: {fp_count}건")
        if overview_llm_pending > 0:
            st.caption(f"⏳ 대기: {overview_llm_pending}건")
    elif overview_llm_pending > 0:
        st.info(f"⏳ {overview_llm_pending}건 검증 대기 중")
    else:
        st.info("검증 데이터 없음")