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ef78361 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | """Dashboard card components."""
import html
import streamlit as st
from core.charts import CONFIDENCE_TIER_COLORS
from core.styles import TIER_BORDER_COLORS
from core.utils import get_confidence_tier, truncate_text, format_brands_list
def render_nudge_card(
item: dict,
tier: str,
emoji: str,
tier_desc: str,
confidence: float,
) -> None:
"""Render nudge candidate card with summary info.
Args:
item: Nudge candidate data dict
tier: Confidence tier (HIGH/MEDIUM/LOW)
emoji: Tier emoji
tier_desc: Tier description
confidence: Confidence score (0-1)
"""
cej_stage = item.get("cej_depth2") or item.get("cej_depth1") or "N/A"
platform = item.get("platform", "N/A")
in_house = item.get("in_house_brands", [])
mentioned = item.get("mentioned_brands", [])
question = item.get("question_content", "")
answer = item.get("answer_preview", "")
tier_color = CONFIDENCE_TIER_COLORS.get(tier, "#6B7280")
border_color = TIER_BORDER_COLORS.get(tier, "#6B7280")
question_display = html.escape(truncate_text(question, 200))
answer_short = html.escape(truncate_text(answer, 150))
in_house_display = html.escape(format_brands_list(in_house))
mentioned_display = html.escape(format_brands_list(mentioned))
header_html = f"""
<div style="border: 2px solid {border_color}; border-radius: 12px; padding: 16px; margin: 12px 0; background: #FAFAFA;">
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 12px;">
<span style="background: #E0E7FF; padding: 4px 12px; border-radius: 20px; font-size: 13px;">
📍 CEJ: <strong>{cej_stage}</strong>
</span>
<span style="background: {tier_color}; color: white; padding: 4px 12px; border-radius: 20px; font-size: 13px;" title="{tier_desc}">
{emoji} 답변 전체: {tier} ({confidence:.0%})
</span>
</div>
<div style="background: #F0F9FF; border-left: 4px solid #3B82F6; padding: 10px; margin-bottom: 10px; border-radius: 0 8px 8px 0;">
<div style="font-size: 11px; color: #3B82F6; margin-bottom: 2px;">💬 질문</div>
<div style="font-size: 14px;">{question_display}</div>
</div>
<div style="font-size: 13px; color: #6B7280; margin-bottom: 8px;">{answer_short}...</div>
<div style="display: flex; gap: 12px; flex-wrap: wrap; font-size: 12px; color: #6B7280;">
<span>🏷️ <strong>{in_house_display}</strong></span>
<span>📢 {mentioned_display}</span>
<span>🖥️ {platform}</span>
</div>
</div>
"""
st.markdown(header_html, unsafe_allow_html=True)
def render_brand_card(
brand_name: str,
brand_type: str,
sentiment_data: dict,
) -> None:
"""Render brand mention card.
Args:
brand_name: Brand name
brand_type: 'in_house' or 'competitor'
sentiment_data: Dict with sentiment, confidence, count
"""
sentiment = sentiment_data.get("sentiment", "neutral")
confidence = sentiment_data.get("confidence", 0)
mention_count = sentiment_data.get("count", 0)
type_badge = "🏠 자사" if brand_type == "in_house" else "🏢 경쟁사"
type_bg = "#DBEAFE" if brand_type == "in_house" else "#FEE2E2"
sentiment_colors = {
"positive": "#10B981",
"negative": "#EF4444",
"neutral": "#6B7280",
}
sent_color = sentiment_colors.get(sentiment, "#6B7280")
sentiment_ko = {"positive": "긍정", "negative": "부정", "neutral": "중립"}.get(sentiment, sentiment)
card_html = f"""
<div style="border: 1px solid #E5E7EB; border-radius: 8px; padding: 12px; margin: 8px 0; background: white;">
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 8px;">
<span style="font-weight: 600; font-size: 15px;">{html.escape(brand_name)}</span>
<span style="background: {type_bg}; padding: 2px 8px; border-radius: 12px; font-size: 11px;">{type_badge}</span>
</div>
<div style="display: flex; gap: 12px; font-size: 12px;">
<span style="background: {sent_color}; color: white; padding: 2px 8px; border-radius: 4px;">{sentiment_ko}</span>
<span>신뢰도: {confidence:.0%}</span>
<span>언급: {mention_count}회</span>
</div>
</div>
"""
st.markdown(card_html, unsafe_allow_html=True)
def render_verification_item(item: dict, is_false_positive: bool = True) -> None:
"""Render LLM verification item info (used inside expander).
Args:
item: Verification result dict
is_false_positive: True for FP, False for TN
"""
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"**플랫폼**: {item.get('platform', 'N/A')}")
st.markdown(f"**CEJ**: {item.get('cej_depth1', 'N/A')} / {item.get('cej_depth2', 'N/A')}")
with info_col2:
st.markdown(f"**1차 판정**: {item.get('routing_tier', 'N/A')}")
st.markdown(f"**1차 감성**: {item.get('overall_polarity', 'N/A')}")
with info_col3:
llm_conf = item.get('llm_confidence', 0) or 0
st.markdown(f"**LLM 신뢰도**: {llm_conf:.1%}")
st.markdown(f"**LLM 조정 Tier**: {item.get('llm_adjusted_tier', 'N/A')}")
# LLM reasoning
if item.get('llm_reasoning'):
st.markdown("**LLM 판단 근거**:")
if is_false_positive:
st.info(item.get('llm_reasoning'))
else:
st.warning(item.get('llm_reasoning'))
# Evidence spans
if item.get('llm_evidence_spans'):
label = "**근거 문장**:" if is_false_positive else "**부정 근거 문장**:"
st.markdown(label)
for span in (item.get('llm_evidence_spans') or []):
st.markdown(f"- _{span}_")
# Brands
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_polarity_item(item: dict) -> None:
"""Render polarity (sentiment) item info (used inside expander).
Args:
item: Sentiment summary dict
"""
confidence = item.get('overall_confidence', 0) or 0
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:
tier = item.get('routing_tier', 'N/A')
st.markdown(f"**라우팅 Tier**: {tier}")
emotion = item.get('dominant_emotion', 'N/A')
st.markdown(f"**감정**: {emotion}")
# Brands
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'}")
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