features/action_items/trend_charts.py

"""Trigger metric trend charts — Plotly mini charts for action items dashboard."""
from __future__ import annotations

from collections import defaultdict
from datetime import date, timedelta

import plotly.graph_objects as go
import streamlit as st

CHART_HEIGHT = 260
MINI_LAYOUT = dict(
    margin=dict(t=30, b=40, l=50, r=20),
    height=CHART_HEIGHT,
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    xaxis=dict(tickformat="%m/%d"),
)


def _get_client():
    from core.supabase_client import get_supabase_client
    return get_supabase_client()


@st.cache_data(ttl=120)
def _fetch_visibility_data(campaign_id: int, start_date: str, end_date: str) -> list[dict]:
    """Cached fetch for visibility trend data."""
    client = _get_client()
    try:
        result = (
            client.table("report_visibility_daily")
            .select("task_date, brand_name, brand_type, visibility_pct")
            .eq("campaign_id", campaign_id)
            .gte("task_date", start_date)
            .lte("task_date", end_date)
            .order("task_date")
            .execute()
        )
        return result.data or []
    except Exception:
        return []


@st.cache_data(ttl=120)
def _fetch_citation_type_data(campaign_id: int, start_date: str, end_date: str) -> list[dict]:
    """Cached fetch for citation type trend data."""
    client = _get_client()
    try:
        result = (
            client.table("report_source_daily")
            .select("task_date, source_host_type, citation_count")
            .eq("campaign_id", campaign_id)
            .eq("agg_level", "host")
            .gte("task_date", start_date)
            .lte("task_date", end_date)
            .order("task_date")
            .execute()
        )
        return result.data or []
    except Exception:
        return []


@st.cache_data(ttl=120)
def _fetch_negative_rate_data(campaign_id: int, start_date: str, end_date: str) -> list[dict]:
    """Cached fetch for negative sentiment rate data."""
    client = _get_client()
    try:
        result = client.rpc("get_nudge_export_data", {
            "p_campaign_id": campaign_id,
            "p_in_house_only": False,
            "p_date_from": f"{start_date}T00:00:00+00:00",
            "p_date_to": f"{_next_day(end_date)}T00:00:00+00:00",
            "p_limit": 10000,
        }).execute()
        data = result.data or {}
        return data.get("rows", []) if isinstance(data, dict) else []
    except Exception:
        return []


@st.cache_data(ttl=120)
def _fetch_action_items_history(campaign_id: int) -> list[dict]:
    """Cached fetch for action items history data."""
    client = _get_client()
    try:
        result = (
            client.table("action_items")
            .select("created_at, completed_at, status")
            .eq("campaign_id", campaign_id)
            .order("created_at")
            .limit(5000)
            .execute()
        )
        return result.data or []
    except Exception:
        return []


# ============================================================================
# Chart 1: Visibility trend (own brand vs competitor average)
# ============================================================================

def render_visibility_trend(campaign_id: int, start_date: str, end_date: str):
    """Show daily own-brand vs competitor average visibility."""
    rows = _fetch_visibility_data(campaign_id, start_date, end_date)

    if not rows:
        st.info("가시성 데이터가 없습니다.")
        return

    # Group by date: own avg, competitor avg
    own_by_date: dict[str, list[float]] = defaultdict(list)
    comp_by_date: dict[str, list[float]] = defaultdict(list)

    for r in rows:
        d = r["task_date"]
        pct = r.get("visibility_pct", 0)
        if r.get("brand_type") == "PRIMARY":
            own_by_date[d].append(pct)
        else:
            comp_by_date[d].append(pct)

    dates = sorted(set(list(own_by_date.keys()) + list(comp_by_date.keys())))
    own_avgs = [
        sum(own_by_date[d]) / len(own_by_date[d]) if own_by_date.get(d) else None
        for d in dates
    ]
    comp_avgs = [
        sum(comp_by_date[d]) / len(comp_by_date[d]) if comp_by_date.get(d) else None
        for d in dates
    ]

    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=dates, y=own_avgs,
        mode="lines+markers", name="자사",
        line=dict(color="#3B82F6", width=2),
        marker=dict(size=5),
    ))
    fig.add_trace(go.Scatter(
        x=dates, y=comp_avgs,
        mode="lines+markers", name="경쟁사 평균",
        line=dict(color="#EF4444", width=2, dash="dash"),
        marker=dict(size=5),
    ))
    fig.update_layout(
        title=dict(text="자사 vs 경쟁사 가시성", font=dict(size=13)),
        yaxis_title="Visibility %",
        **MINI_LAYOUT,
    )
    st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})


# ============================================================================
# Chart 2: Citation type trend (OFFICIAL % + top channel concentration)
# ============================================================================

def render_citation_type_trend(campaign_id: int, start_date: str, end_date: str):
    """Show daily OFFICIAL citation % and top channel concentration."""
    rows = _fetch_citation_type_data(campaign_id, start_date, end_date)

    if not rows:
        st.info("인용 데이터가 없습니다.")
        return

    # Group by date
    by_date: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
    for r in rows:
        d = r["task_date"]
        ht = r.get("source_host_type") or "UNKNOWN"
        by_date[d][ht] += r.get("citation_count", 0)

    dates = sorted(by_date.keys())
    official_pcts = []
    top_channel_pcts = []

    for d in dates:
        type_counts = by_date[d]
        total = sum(type_counts.values())
        if total > 0:
            official_pcts.append(round(type_counts.get("OFFICIAL", 0) / total * 100, 1))
            max_count = max(type_counts.values())
            top_channel_pcts.append(round(max_count / total * 100, 1))
        else:
            official_pcts.append(0)
            top_channel_pcts.append(0)

    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=dates, y=official_pcts,
        mode="lines+markers", name="OFFICIAL %",
        line=dict(color="#7C3AED", width=2),
        marker=dict(size=5),
    ))
    fig.add_trace(go.Scatter(
        x=dates, y=top_channel_pcts,
        mode="lines+markers", name="Top 채널 %",
        line=dict(color="#F59E0B", width=2, dash="dot"),
        marker=dict(size=5),
    ))
    # Threshold lines
    fig.add_hline(y=10, line_dash="dash", line_color="#EF4444", opacity=0.5,
                  annotation_text="OFFICIAL 10%", annotation_position="bottom right")
    fig.add_hline(y=50, line_dash="dash", line_color="#F97316", opacity=0.5,
                  annotation_text="집중 50%", annotation_position="top right")
    fig.update_layout(
        title=dict(text="인용 유형 추이", font=dict(size=13)),
        yaxis_title="%",
        **MINI_LAYOUT,
    )
    st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})


# ============================================================================
# Chart 3: Negative sentiment rate trend
# ============================================================================

def render_negative_rate_trend(campaign_id: int, start_date: str, end_date: str):
    """Show daily in-house brand negative sentiment rate."""
    rows = _fetch_negative_rate_data(campaign_id, start_date, end_date)

    if not rows:
        st.info("감성 분석 데이터가 없습니다.")
        return

    if len(rows) >= 10000:
        st.warning("감성 데이터가 10,000건을 초과하여 일부만 표시됩니다. 기간을 좁혀보세요.")

    # Group by date
    total_by_date: dict[str, int] = defaultdict(int)
    neg_by_date: dict[str, int] = defaultdict(int)

    for r in rows:
        d = (r.get("analyzed_at") or "")[:10]
        if not d:
            continue
        total_by_date[d] += 1
        if r.get("overall_polarity") == "negative":
            neg_by_date[d] += 1

    dates = sorted(total_by_date.keys())
    neg_rates = [
        round(neg_by_date.get(d, 0) / total_by_date[d] * 100, 1) if total_by_date[d] > 0 else 0
        for d in dates
    ]

    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=dates, y=neg_rates,
        mode="lines+markers", name="부정 비율",
        line=dict(color="#EF4444", width=2),
        marker=dict(size=5),
        fill="tozeroy",
        fillcolor="rgba(239,68,68,0.1)",
    ))
    fig.add_hline(y=30, line_dash="dash", line_color="#F97316", opacity=0.5,
                  annotation_text="경고 30%", annotation_position="top right")
    fig.update_layout(
        title=dict(text="부정 감성 비율 추이", font=dict(size=13)),
        yaxis_title="부정 %",
        **MINI_LAYOUT,
    )
    st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})


# ============================================================================
# Chart 4: Action items history (created vs completed per week)
# ============================================================================

def render_action_items_history(campaign_id: int):
    """Show weekly created vs completed action items."""
    rows = _fetch_action_items_history(campaign_id)

    if not rows:
        st.info("액션아이템 이력이 없습니다.")
        return

    # Group by ISO week
    created_by_week: dict[str, int] = defaultdict(int)
    completed_by_week: dict[str, int] = defaultdict(int)

    for r in rows:
        c_date = (r.get("created_at") or "")[:10]
        if c_date:
            week = _iso_week_label(c_date)
            created_by_week[week] += 1

        if r.get("status") == "completed" and r.get("completed_at"):
            d_date = r["completed_at"][:10]
            week = _iso_week_label(d_date)
            completed_by_week[week] += 1

    weeks = sorted(set(list(created_by_week.keys()) + list(completed_by_week.keys())))
    created_vals = [created_by_week.get(w, 0) for w in weeks]
    completed_vals = [completed_by_week.get(w, 0) for w in weeks]

    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=weeks, y=created_vals, name="생성",
        marker_color="#6366F1",
    ))
    fig.add_trace(go.Bar(
        x=weeks, y=completed_vals, name="완료",
        marker_color="#10B981",
    ))
    fig.update_layout(
        title=dict(text="주별 액션아이템 생성/완료", font=dict(size=13)),
        barmode="group",
        yaxis_title="건수",
        **MINI_LAYOUT,
    )
    st.plotly_chart(fig, use_container_width=True, config={"displayModeBar": False})


# ============================================================================
# Helpers
# ============================================================================

def _next_day(date_str: str) -> str:
    """Return next day as ISO string (for half-open TIMESTAMPTZ filter)."""
    d = date.fromisoformat(date_str)
    return (d + timedelta(days=1)).isoformat()


def _iso_week_label(date_str: str) -> str:
    """Convert date string to 'MM/DD' label of the week's Monday."""
    d = date.fromisoformat(date_str)
    monday = d - timedelta(days=d.weekday())
    return monday.strftime("%m/%d")