"""Page 4: Current Admin Headcount (from scraper)."""

import duckdb
from dash import html, dcc, dash_table
import plotly.express as px
import plotly.graph_objects as go

from admin_analytics.dashboard.queries import (
    query_admin_headcount,
    query_headcount_summary,
)

_NO_DATA = html.Div(
    "No headcount data loaded. Run: admin-analytics ingest scrape",
    style={"textAlign": "center", "padding": "40px", "color": "#888"},
)


def _kpi_card(title: str, value: str) -> html.Div:
    return html.Div(
        [
            html.H4(title, style={"margin": "0", "color": "#666", "fontSize": "14px"}),
            html.H2(value, style={"margin": "5px 0", "color": "#00539F"}),
        ],
        style={
            "flex": "1",
            "padding": "20px",
            "backgroundColor": "#f8f9fa",
            "borderRadius": "8px",
            "textAlign": "center",
            "margin": "0 8px",
        },
    )


def layout(conn: duckdb.DuckDBPyConnection):
    detail_df = query_admin_headcount(conn)
    if detail_df.height == 0:
        return _NO_DATA

    summary_df = query_headcount_summary(conn)
    detail_pd = detail_df.to_pandas()
    summary_pd = summary_df.to_pandas()

    total = len(detail_pd)
    overhead_count = int(detail_pd["is_overhead"].sum()) if "is_overhead" in detail_pd.columns else 0
    overhead_pct = round(overhead_count * 100 / total, 1) if total > 0 else 0

    # KPI cards
    kpi_row = html.Div(
        [
            _kpi_card("Total Staff Scraped", str(total)),
            _kpi_card("Overhead Staff", str(overhead_count)),
            _kpi_card("Overhead %", f"{overhead_pct}%"),
        ],
        style={"display": "flex", "marginBottom": "24px"},
    )

    # Staff by unit bar chart
    unit_counts = summary_pd.groupby("unit")["count"].sum().reset_index().sort_values("count")
    unit_fig = px.bar(
        unit_counts, x="count", y="unit", orientation="h",
        title="Staff Count by Unit",
        labels={"count": "Staff", "unit": ""},
        color_discrete_sequence=["#00539F"],
    )
    unit_fig.update_layout(template="plotly_white", height=max(300, len(unit_counts) * 30 + 100))

    # Overhead pie
    oh_data = detail_pd["is_overhead"].value_counts()
    oh_labels = {True: "Overhead", False: "Non-Overhead"}
    pie_fig = px.pie(
        names=[oh_labels.get(k, "Debatable") for k in oh_data.index],
        values=oh_data.values,
        title="Overhead vs Non-Overhead",
        color_discrete_sequence=["#E07A5F", "#7FB069", "#999"],
    )
    pie_fig.update_layout(template="plotly_white", height=350)

    # Category distribution per unit
    cat_fig = px.bar(
        summary_pd, x="count", y="unit", color="category", orientation="h",
        title="Category Distribution by Unit",
        labels={"count": "Staff", "unit": "", "category": "Category"},
    )
    cat_fig.update_layout(template="plotly_white", height=max(300, len(unit_counts) * 30 + 100))

    # Detail table
    table = dash_table.DataTable(
        columns=[
            {"name": "Unit", "id": "unit"},
            {"name": "Name", "id": "person_name"},
            {"name": "Title", "id": "title"},
            {"name": "Category", "id": "category"},
            {"name": "Overhead", "id": "is_overhead"},
        ],
        data=detail_pd.to_dict("records"),
        page_size=20,
        sort_action="native",
        filter_action="native",
        style_table={"overflowX": "auto"},
        style_cell={"textAlign": "left", "padding": "8px", "fontSize": "13px"},
        style_header={"fontWeight": "bold", "backgroundColor": "#f0f0f0"},
    )

    return html.Div([
        kpi_row,
        html.Div(
            [
                html.Div(dcc.Graph(figure=unit_fig), style={"flex": "1"}),
                html.Div(dcc.Graph(figure=pie_fig), style={"flex": "1"}),
            ],
            style={"display": "flex", "gap": "16px"},
        ),
        dcc.Graph(figure=cat_fig),
    ])