"""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"""
{'정탐' if tp_pct > 8 else ''}
{'오탐' if fp_pct > 8 else ''}
{'미검증' if pending_pct > 8 else ''}
🔴 정탐 {tp_pct:.1f}%
🟢 오탐 {fp_pct:.1f}%
⚪ 미검증 {pending_pct:.1f}%
""", 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"""
{count}
{tier} ({pct:.0f}%)
""", 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''
brand_html += f'
{brand_name}
'
brand_html += f'
'
brand_html += f'📊 언급: {total_mentions}'
brand_html += f'✅ 긍정: {positive_rate:.1f}%'
brand_html += f'❌ 부정: {negative_rate:.1f}%'
brand_html += '