features/sentiment/overview.py

"""감성분석 오버뷰 탭.

전체 감성 분석 + 브랜드 멘션 분석 + LLM 2차 검증 결과.
"""
import streamlit as st

from core.charts import create_brand_sentiment_chart
from core.supabase_client import get_polarity_stats, get_answers_by_polarity
from core.utils import truncate_text


def render(data: dict):
    """오버뷰 탭 렌더링."""
    # --- 전체 감성 분석 ---
    st.markdown("##### 📈 전체 감성 분석")
    st.caption("AI 답변의 전체 감성 분포를 확인합니다 (긍정/중립/부정)")

    polarity_in_house_only = st.checkbox(
        "🏠 자사 브랜드 언급 답변만 보기",
        value=False,
        key="sentiment:polarity_in_house_filter",
        help="체크 시 자사 브랜드가 언급된 답변만 표시합니다.",
    )

    try:
        polarity_stats = get_polarity_stats(data["campaign_id"], in_house_only=polarity_in_house_only)
        positive_count = polarity_stats.get("positive", 0)
        neutral_count = polarity_stats.get("neutral", 0)
        negative_count = polarity_stats.get("negative", 0)
        total_answers_pol = positive_count + neutral_count + negative_count

        pol_col1, pol_col2, pol_col3, pol_col4 = st.columns(4)
        with pol_col1:
            st.metric("전체 분석", f"{total_answers_pol:,}건")
        with pol_col2:
            pos_rate = (positive_count / total_answers_pol * 100) if total_answers_pol > 0 else 0
            st.metric("😊 긍정", f"{positive_count:,}건", f"{pos_rate:.1f}%")
        with pol_col3:
            neu_rate = (neutral_count / total_answers_pol * 100) if total_answers_pol > 0 else 0
            st.metric("😐 중립", f"{neutral_count:,}건", f"{neu_rate:.1f}%")
        with pol_col4:
            neg_rate = (negative_count / total_answers_pol * 100) if total_answers_pol > 0 else 0
            st.metric("😞 부정", f"{negative_count:,}건", f"{neg_rate:.1f}%")

        st.markdown("---")

        polarity_filter = st.selectbox(
            "감성 분류 선택",
            options=["positive", "neutral", "negative"],
            format_func=lambda x: {"positive": "😊 긍정", "neutral": "😐 중립", "negative": "😞 부정"}[x],
            key="sentiment:polarity_filter_tab6",
        )

        polarity_page = st.number_input("페이지", min_value=1, value=1, key="sentiment:polarity_page")
        polarity_items, polarity_total = get_answers_by_polarity(
            data["campaign_id"], polarity_filter, page=polarity_page, page_size=20,
            in_house_only=polarity_in_house_only,
        )

        st.markdown(f"**{polarity_total:,}건** 중 {len(polarity_items)}건 표시")

        for item in polarity_items:
            _render_polarity_item(item)

    except Exception as e:
        st.error(f"감성 데이터 로드 실패: {e}")
        st.info("Supabase 연결 설정을 확인하세요")

    # --- LLM 2차 검증 결과 ---
    _render_llm_verification_summary(data)

    # --- 브랜드 분석 ---
    st.markdown("---")
    st.markdown("##### 🏷️ 브랜드 멘션 분석")
    st.caption("자사 브랜드와 경쟁사 브랜드가 AI 답변에서 어떻게 언급되는지 분석합니다")

    brand_data = data["brand_data"] or {}
    in_house_summary = brand_data.get("in_house_summary", [])
    competitor_summary = brand_data.get("competitor_summary", [])
    total_answers = brand_data.get("total_answers", 0)

    st.markdown(f"**분석된 AI 답변**: {total_answers}건")
    st.markdown("---")

    brand_col1, brand_col2 = st.columns(2)

    with brand_col1:
        st.markdown("##### 🏠 자사 브랜드")
        if in_house_summary:
            for brand in in_house_summary[:5]:
                _render_brand_card(brand, "in_house")
        else:
            st.info("자사 브랜드 데이터가 없습니다")

    with brand_col2:
        st.markdown("##### 🏢 경쟁사 브랜드")
        if competitor_summary:
            for brand in competitor_summary[:5]:
                _render_brand_card(brand, "competitor")
        else:
            st.info("경쟁사 브랜드 데이터가 없습니다")

    if in_house_summary or competitor_summary:
        st.markdown("---")
        st.markdown("##### 📊 브랜드별 감성 비교")
        all_brands = in_house_summary + competitor_summary
        if all_brands:
            fig = create_brand_sentiment_chart(all_brands)
            st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})


def _render_llm_verification_summary(data: dict):
    """LLM 2차 검증 결과 요약 렌더링."""
    st.markdown("---")
    st.markdown("##### 🤖 LLM 2차 검증 결과")
    st.caption(
        "DeBERTa(1차 AI)가 부정 감지한 답변을 LLM(2차 AI)이 재검증한 결과입니다. "
        "🔴 정탐 = 실제 부정 확인 (리스크) | 🟢 오탐 = 부정 아님 확인 (안전)"
    )

    llm_stats = data.get("llm_verification_stats") or {}
    total_nudge = data.get("total_nudge", 0)
    total_verified = llm_stats.get("total_verified", 0)
    true_negatives = llm_stats.get("true_negatives", 0)
    false_positives = llm_stats.get("false_positives", 0)
    pending = total_nudge - total_verified

    if total_nudge == 0:
        st.info("부정 감지된 넛지 후보가 없습니다.")
        return

    # --- Metrics ---
    llm_col1, llm_col2, llm_col3, llm_col4 = st.columns(4)
    with llm_col1:
        st.metric("🔍 넛지 후보", f"{total_nudge:,}건")
    with llm_col2:
        verify_rate = (total_verified / total_nudge * 100) if total_nudge > 0 else 0
        st.metric("✅ 검증 완료", f"{total_verified:,}건", f"{verify_rate:.0f}%")
    with llm_col3:
        tp_rate = (true_negatives / total_verified * 100) if total_verified > 0 else 0
        st.metric("🎯 정탐", f"{true_negatives:,}건", f"{tp_rate:.1f}%")
    with llm_col4:
        fp_rate = (false_positives / total_verified * 100) if total_verified > 0 else 0
        st.metric("🚫 오탐", f"{false_positives:,}건", f"{fp_rate:.1f}%")

    # --- Visual bar ---
    if total_verified > 0:
        tp_pct = true_negatives / total_nudge * 100
        fp_pct = false_positives / total_nudge * 100
        pending_pct = pending / total_nudge * 100

        st.markdown(f"""
        <div style="display: flex; height: 28px; border-radius: 6px; overflow: hidden; margin: 8px 0;">
            <div style="width: {tp_pct}%; background: #EF4444; display: flex; align-items: center; justify-content: center; color: white; font-size: 12px; font-weight: bold;">
                {'정탐' if tp_pct > 8 else ''}
            </div>
            <div style="width: {fp_pct}%; background: #10B981; display: flex; align-items: center; justify-content: center; color: white; font-size: 12px; font-weight: bold;">
                {'오탐' if fp_pct > 8 else ''}
            </div>
            <div style="width: {pending_pct}%; background: #D1D5DB; display: flex; align-items: center; justify-content: center; color: #6B7280; font-size: 12px;">
                {'미검증' if pending_pct > 8 else ''}
            </div>
        </div>
        <div style="display: flex; gap: 16px; font-size: 12px; color: #6B7280; margin-bottom: 4px;">
            <span>🔴 정탐 {tp_pct:.1f}%</span>
            <span>🟢 오탐 {fp_pct:.1f}%</span>
            <span>⚪ 미검증 {pending_pct:.1f}%</span>
        </div>
        """, unsafe_allow_html=True)

    # --- Confirmed negative tier distribution ---
    candidates = data.get("candidates", [])
    confirmed = [c for c in candidates if c.get("llm_verified") and c.get("llm_is_negative")]

    if confirmed:
        tier_dist: dict[str, int] = {}
        for c in confirmed:
            tier = c.get("llm_adjusted_tier") or "UNKNOWN"
            tier_dist[tier] = tier_dist.get(tier, 0) + 1

        tier_colors = {"HIGH": "#EF4444", "MEDIUM": "#F59E0B", "LOW": "#3B82F6", "NONE": "#10B981", "UNKNOWN": "#9CA3AF"}

        st.markdown("**정탐 답변의 LLM 등급 분포**")
        tier_cols = st.columns(len(tier_dist))
        for i, (tier, count) in enumerate(sorted(tier_dist.items(), key=lambda x: -x[1])):
            color = tier_colors.get(tier, "#9CA3AF")
            pct = count / len(confirmed) * 100
            with tier_cols[i]:
                st.markdown(f"""
                <div style="text-align: center; padding: 8px; background: {color}15; border-radius: 8px; border: 1px solid {color}40;">
                    <div style="font-size: 20px; font-weight: bold; color: {color};">{count}</div>
                    <div style="font-size: 12px; color: #6B7280;">{tier} ({pct:.0f}%)</div>
                </div>
                """, unsafe_allow_html=True)


def _render_polarity_item(item: dict):
    """감성 항목 렌더링."""
    polarity_emoji = {"positive": "😊", "neutral": "😐", "negative": "😞"}.get(item.get('overall_polarity'), "❓")
    confidence = item.get('overall_confidence', 0) or 0

    with st.expander(f"{polarity_emoji} {truncate_text(item.get('question_content', 'N/A'), 80)}", expanded=False):
        st.markdown(f"**질문**: {item.get('question_content', 'N/A')}")
        st.markdown(f"**답변 미리보기**: {item.get('answer_preview', 'N/A')}")
        st.markdown("---")

        info_col1, info_col2, info_col3 = st.columns(3)
        with info_col1:
            st.markdown(f"**감성**: {item.get('overall_polarity', 'N/A')}")
            st.markdown(f"**신뢰도**: {confidence:.1%}")
        with info_col2:
            st.markdown(f"**플랫폼**: {item.get('platform', 'N/A')}")
            st.markdown(f"**CEJ**: {item.get('cej_depth1', 'N/A')} / {item.get('cej_depth2', 'N/A')}")
        with info_col3:
            st.markdown(f"**Tier**: {item.get('routing_tier', 'N/A')}")
            st.markdown(f"**감정**: {item.get('dominant_emotion', 'N/A')}")

        in_house = item.get('in_house_brands', []) or []
        mentioned = item.get('mentioned_brands', []) or []
        if in_house or mentioned:
            st.markdown(f"**자사 브랜드**: {', '.join(in_house) if in_house else 'N/A'}")
            st.markdown(f"**언급 브랜드**: {', '.join(mentioned) if mentioned else 'N/A'}")


def _render_brand_card(brand: dict, brand_type: str):
    """브랜드 카드 렌더링."""
    brand_name = brand.get("brand_name", "Unknown")
    total_mentions = brand.get("total_mentions", 0)
    positive_rate = brand.get("positive_rate", 0)
    negative_rate = brand.get("negative_rate", 0)

    bg_color = "#F0F9FF" if brand_type == "in_house" else "#FEF3C7"

    brand_html = f'<div style="background: {bg_color}; border-radius: 8px; padding: 12px; margin: 8px 0;">'
    brand_html += f'<div style="font-weight: bold; font-size: 16px; margin-bottom: 8px;">{brand_name}</div>'
    brand_html += f'<div style="display: flex; gap: 16px; font-size: 13px;">'
    brand_html += f'<span>📊 언급: <strong>{total_mentions}</strong></span>'
    brand_html += f'<span style="color: #10B981;">✅ 긍정: <strong>{positive_rate:.1f}%</strong></span>'
    brand_html += f'<span style="color: #EF4444;">❌ 부정: <strong>{negative_rate:.1f}%</strong></span>'
    brand_html += '</div></div>'
    st.markdown(brand_html, unsafe_allow_html=True)