core/charts.py

"""Chart components for Gen3 Nudge Detection Dashboard."""

import plotly.graph_objects as go
import pandas as pd


# =============================================================================
# Color Constants
# =============================================================================

CONFIDENCE_TIER_COLORS = {
    "HIGH": "#EF4444",    # Red
    "MEDIUM": "#F59E0B",  # Amber
    "LOW": "#10B981",     # Green
}

PLATFORM_COLORS = {
    "CHATGPT": "#10A37F",
    "GOOGLE_AI": "#4285F4",
    "GOOGLE_OVERVIEW": "#34A853",
    "PERPLEXITY": "#6366F1",
    "GEMINI": "#8B5CF6",
    "BING": "#00A4EF",
    "CLAUDE": "#D97706",
}

POLARITY_COLORS = {
    "positive": "#10B981",
    "negative": "#EF4444",
    "neutral": "#6B7280",
}

# English to Korean emotion mapping
EMOTION_KO = {
    "admiration": "감탄",
    "amusement": "재미",
    "anger": "분노",
    "annoyance": "짜증",
    "approval": "인정",
    "caring": "배려",
    "confusion": "혼란",
    "curiosity": "호기심",
    "desire": "욕구",
    "disappointment": "실망",
    "disapproval": "반대",
    "disgust": "혐오",
    "embarrassment": "당혹",
    "excitement": "흥분",
    "fear": "두려움",
    "gratitude": "감사",
    "grief": "슬픔",
    "joy": "기쁨",
    "love": "사랑",
    "nervousness": "불안",
    "optimism": "낙관",
    "pride": "자부심",
    "realization": "깨달음",
    "relief": "안도",
    "remorse": "후회",
    "sadness": "슬픔",
    "surprise": "놀라움",
    "neutral": "중립",
    "trust": "신뢰",
    "anticipation": "기대",
    "interest": "관심",
    "satisfaction": "만족",
    "frustration": "좌절",
    "hope": "희망",
    "worry": "걱정",
}

# CEJ Korean labels
CEJ_LABELS = {
    "VERIFICATION": "검증 질문",
    "INFORMATION_DISCOVERY": "정보 탐색",
    "HOW_TO": "사용법",
    "WHERE_TO_BUY": "구매처",
    "RECOMMENDATION": "추천 요청",
    "SIDE_EFFECT": "부작용",
    "MARKET_TRENDS": "시장 동향",
    "RESULT_EFFECTIVENESS": "효과/결과",
    "COMPARISON": "비교",
    "INGREDIENT": "성분",
    "AWARENESS_COMPARISON": "인지/비교",
    "PURCHASE": "구매",
    "POST_PURCHASE": "구매 후",
}


# =============================================================================
# Gen3 Nudge Charts
# =============================================================================

def create_confidence_tier_pie_chart(tier_stats: dict) -> go.Figure:
    """Create pie chart for confidence tier distribution (HIGH/MEDIUM/LOW)."""
    if not tier_stats:
        return go.Figure()

    labels = list(tier_stats.keys())
    values = list(tier_stats.values())
    colors = [CONFIDENCE_TIER_COLORS.get(k, "#6B7280") for k in labels]

    label_map = {
        "HIGH": "🔴 HIGH",
        "MEDIUM": "🟡 MEDIUM",
        "LOW": "🟢 LOW",
    }
    display_labels = [label_map.get(k, k) for k in labels]

    fig = go.Figure(data=[go.Pie(
        labels=display_labels,
        values=values,
        marker=dict(colors=colors),
        hole=0.4,
        textinfo="percent+value",
        textposition="outside",
    )])

    fig.update_layout(
        title="",
        showlegend=True,
        legend=dict(orientation="h", yanchor="bottom", y=-0.2, xanchor="center", x=0.5),
        margin=dict(t=20, b=60, l=20, r=20),
        height=280,
    )

    return fig


def create_platform_bar_chart(platform_stats: dict) -> go.Figure:
    """Create horizontal bar chart for platform distribution."""
    if not platform_stats:
        return go.Figure()

    sorted_items = sorted(platform_stats.items(), key=lambda x: x[1], reverse=True)
    platforms = [item[0] for item in sorted_items]
    counts = [item[1] for item in sorted_items]
    colors = [PLATFORM_COLORS.get(p, "#6B7280") for p in platforms]

    fig = go.Figure(data=[
        go.Bar(
            y=platforms,
            x=counts,
            orientation="h",
            marker_color=colors,
            text=[f"{c}건" for c in counts],
            textposition="auto",
        )
    ])

    fig.update_layout(
        title="",
        xaxis_title="넛지 후보 수",
        yaxis=dict(autorange="reversed"),
        margin=dict(t=20, b=40, l=100, r=20),
        height=max(200, len(platforms) * 35),
    )

    return fig


def create_nudge_by_cej_bar_chart(cej_counts: dict) -> go.Figure:
    """Create horizontal bar chart for nudge distribution by CEJ stage."""
    if not cej_counts:
        return go.Figure()

    sorted_items = sorted(cej_counts.items(), key=lambda x: x[1], reverse=True)
    cej_stages = [item[0] for item in sorted_items]
    counts = [item[1] for item in sorted_items]

    display_labels = [CEJ_LABELS.get(cej, cej) for cej in cej_stages]

    fig = go.Figure(data=[
        go.Bar(
            y=display_labels,
            x=counts,
            orientation="h",
            marker_color="#6366F1",
            text=[f"{c}건" for c in counts],
            textposition="auto",
        )
    ])

    fig.update_layout(
        title="",
        xaxis_title="넛지 후보 수",
        yaxis=dict(autorange="reversed"),
        margin=dict(t=20, b=40, l=100, r=20),
        height=max(200, len(cej_stages) * 35),
    )

    return fig


def create_brand_sentiment_chart(brands: list[dict]) -> go.Figure:
    """Create horizontal stacked bar chart for brand sentiment comparison."""
    if not brands:
        return go.Figure()

    # Sort by total mentions
    sorted_brands = sorted(brands, key=lambda x: x.get("total_mentions", 0), reverse=True)[:10]

    brand_names = []
    for b in sorted_brands:
        name = b.get("brand_name", "Unknown")
        brand_type = b.get("brand_type", "")
        prefix = "🏠 " if brand_type == "IN_HOUSE" else "🏢 "
        brand_names.append(f"{prefix}{name}")

    positive_rates = [b.get("positive_rate", 0) for b in sorted_brands]
    negative_rates = [b.get("negative_rate", 0) for b in sorted_brands]
    neutral_rates = [100 - p - n for p, n in zip(positive_rates, negative_rates)]

    fig = go.Figure()

    fig.add_trace(go.Bar(
        y=brand_names,
        x=positive_rates,
        name="긍정",
        orientation="h",
        marker_color="#10B981",
        text=[f"{v:.1f}%" for v in positive_rates],
        textposition="inside",
    ))

    fig.add_trace(go.Bar(
        y=brand_names,
        x=neutral_rates,
        name="중립",
        orientation="h",
        marker_color="#E5E7EB",
        text=[f"{v:.1f}%" for v in neutral_rates],
        textposition="inside",
    ))

    fig.add_trace(go.Bar(
        y=brand_names,
        x=negative_rates,
        name="부정",
        orientation="h",
        marker_color="#EF4444",
        text=[f"{v:.1f}%" for v in negative_rates],
        textposition="inside",
    ))

    fig.update_layout(
        title="",
        barmode="stack",
        xaxis_title="비율 (%)",
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
        margin=dict(t=50, b=50, l=150, r=20),
        height=max(300, len(sorted_brands) * 40),
        yaxis=dict(autorange="reversed"),
    )

    return fig


def create_domain_bar_chart(domain_counts: dict) -> go.Figure:
    """Create horizontal bar chart for citation domain distribution."""
    if not domain_counts:
        return go.Figure()

    # Take top 15 domains
    sorted_items = list(domain_counts.items())[:15]
    domains = [item[0] for item in sorted_items]
    counts = [item[1] for item in sorted_items]

    # Truncate long domain names
    display_domains = [d[:40] + "..." if len(d) > 40 else d for d in domains]

    fig = go.Figure(data=[
        go.Bar(
            y=display_domains,
            x=counts,
            orientation="h",
            marker_color="#3B82F6",
            text=[f"{c}회" for c in counts],
            textposition="auto",
        )
    ])

    fig.update_layout(
        title="",
        xaxis_title="인용 횟수",
        yaxis=dict(autorange="reversed"),
        margin=dict(t=20, b=40, l=200, r=20),
        height=max(300, len(sorted_items) * 30),
    )

    return fig


def create_bit_quadrant_chart(bit_stats: dict) -> go.Figure:
    """Create bar chart for BIT quadrant distribution."""
    if not bit_stats:
        return go.Figure()

    BIT_LABELS = {
        "neutral": "중립",
        "product_satisfaction": "제품 만족",
        "unmet_expectations": "기대 미충족",
        "brand_trust": "브랜드 신뢰",
    }

    sorted_items = sorted(bit_stats.items(), key=lambda x: x[1], reverse=True)
    quadrants = [BIT_LABELS.get(item[0], item[0]) for item in sorted_items]
    counts = [item[1] for item in sorted_items]

    colors = ["#6366F1", "#8B5CF6", "#A855F7", "#D946EF"]

    fig = go.Figure(data=[
        go.Bar(
            x=quadrants,
            y=counts,
            marker_color=colors[:len(quadrants)],
            text=[f"{c}건" for c in counts],
            textposition="outside",
        )
    ])

    fig.update_layout(
        title="",
        yaxis_title="넛지 후보 수",
        margin=dict(t=20, b=50, l=50, r=20),
        height=280,
    )

    return fig


def create_emotion_distribution_chart(emotion_stats: dict) -> go.Figure:
    """Create bar chart for emotion distribution in nudge candidates."""
    if not emotion_stats:
        return go.Figure()

    sorted_items = sorted(emotion_stats.items(), key=lambda x: x[1], reverse=True)[:8]
    emotions = [EMOTION_KO.get(item[0], item[0]) for item in sorted_items]
    counts = [item[1] for item in sorted_items]

    colors = ["#6366F1", "#8B5CF6", "#A855F7", "#D946EF", "#EC4899", "#F43F5E", "#F97316", "#FBBF24"]

    fig = go.Figure(data=[
        go.Bar(
            x=emotions,
            y=counts,
            marker_color=colors[:len(emotions)],
            text=[f"{c}건" for c in counts],
            textposition="outside",
        )
    ])

    fig.update_layout(
        title="",
        yaxis_title="넛지 후보 수",
        xaxis_tickangle=-30,
        margin=dict(t=20, b=80, l=50, r=20),
        height=280,
    )

    return fig