# DAIOE Explorer (Shiny app).
#
# High-level flow:
# - Load pipeline outputs once at startup via `src.data_manager.load_data()` (disk-cached by default).
# - Treat everything below as filtering + presentation; no pipeline recomputation in reactives.
#
# Developer notes:
# - Add/adjust selectable options in `src/config.py`.
# - The sidebar contains all user-controlled inputs; `Reset filters` restores defaults.

from __future__ import annotations

from pathlib import Path
from typing import Dict, List, Tuple
from faicons import icon_svg
import pandas as pd
from shiny import reactive, render
import shiny.ui as classic_ui
from shiny.express import input, ui
from shinywidgets import render_widget

from src import config
from src.data_manager import load_data
from src.plotting import (
    build_bar_plot,
    build_trend_plot,
    format_metric_value,
    format_raw_value,
)

TAXONOMY_OPTIONS = config.TAXONOMY_OPTIONS
METRIC_OPTIONS: List[Tuple[str, str]] = config.METRIC_OPTIONS
WEIGHTING_OPTIONS: List[Tuple[str, str]] = config.WEIGHTING_OPTIONS

LEVEL_OPTIONS = config.LEVEL_OPTIONS
LEVEL_LABELS = {value: label for label, value in LEVEL_OPTIONS}
LEVEL_CHOICES = {str(value): label for label, value in LEVEL_OPTIONS}


DATA = load_data()

# Defaults are mostly sourced from `src/config.py`, except the global year range
# which is derived from whatever data is available at startup.

ALL_YEARS = sorted(
    {int(year) for frame in DATA.values() for year in frame["year"].unique()}
)
GLOBAL_YEAR_MIN = ALL_YEARS[0]
GLOBAL_YEAR_MAX = ALL_YEARS[-1]
DEFAULT_TAXONOMY = config.DEFAULT_TAXONOMY
DEFAULT_LEVEL = config.DEFAULT_LEVEL
DEFAULT_WEIGHTING = config.DEFAULT_WEIGHTING
DEFAULT_YEAR_RANGE = (GLOBAL_YEAR_MIN, GLOBAL_YEAR_MAX)
DEFAULT_SORT_DESC = config.DEFAULT_SORT_DESC
DEFAULT_LEVEL_CHOICE = str(DEFAULT_LEVEL)
DEFAULT_TOP_N = config.DEFAULT_TOP_N


css_file = Path(__file__).parent / "css" / "theme.scss"

ui.page_opts(
    fillable=False,
    fillable_mobile=True,
    full_width=True,
    id="page",
    lang="en",
)

ui.include_css(css_file)


def metric_mapping() -> Dict[str, str]:
    return {value: label for label, value in METRIC_OPTIONS}


def weighting_mapping() -> Dict[str, str]:
    return {value: label for label, value in WEIGHTING_OPTIONS}


def taxonomy_mapping() -> Dict[str, str]:
    return {value: label for label, value in TAXONOMY_OPTIONS}


def apply_search_filter(df: pd.DataFrame) -> pd.DataFrame:
    search_term = input.search().strip().lower()
    if not search_term:
        return df

    labels = df["label"].astype(str).str.lower()
    return df[labels.str.contains(search_term, na=False)]


@reactive.calc
def chart_title() -> str:
    # Shared title capturing the current metric/taxonomy/weighting/level plus the latest year.
    df = filtered_data()
    latest_year = int(df["year"].max()) if not df.empty else None

    metric_text = metric_label()
    weight_label = weighting_mapping().get(input.weighting(), input.weighting())
    taxonomy_label = taxonomy_mapping().get(input.taxonomy(), input.taxonomy())
    level_value = int(input.level())
    group_label = LEVEL_LABELS.get(level_value, f"Level {level_value}")

    base = f"{metric_text} ({weight_label}, {taxonomy_label}) — {group_label}"
    if latest_year is None:
        return base
    return f"{base} in {latest_year}"


# ---------------------------------------------------------------------------
# Sidebar UI
# ---------------------------------------------------------------------------

with ui.sidebar(open="open", class_="bg-light p-3", width=300, position="right"):
    ui.img(
        src="lab.svg",  # because it's in www/
        style="max-width: 200px; margin-bottom: 8px;",
    )
    ui.input_radio_buttons(
        "taxonomy",
        "Taxonomy",
        taxonomy_mapping(),
        selected=DEFAULT_TAXONOMY,
    )
    ui.input_select(
        "level",
        "Level",
        LEVEL_CHOICES,
        selected=DEFAULT_LEVEL_CHOICE,
    )
    ui.input_select(
        "metric",
        "Sub-index",
        metric_mapping(),
        selected=METRIC_OPTIONS[0][1],
    )
    ui.input_select(
        "weighting",
        "Weighting",
        weighting_mapping(),
        selected=DEFAULT_WEIGHTING,
    )

    ui.input_slider(
        "year_range",
        "Year range",
        min=GLOBAL_YEAR_MIN,
        max=GLOBAL_YEAR_MAX,
        value=DEFAULT_YEAR_RANGE,
        step=1,
        sep="",
    )

    ui.input_slider(
        "top_n",
        "Occupations to display (0 = all)",
        min=0,
        max=30,
        value=DEFAULT_TOP_N,
        step=1,
    )

    ui.input_switch("sort_desc", "Sort descending", value=DEFAULT_SORT_DESC)
    ui.input_text("search", "Search by occupation", placeholder="e.g. statistician")
    with ui.popover(id="help_popover"):
        ui.input_action_button(
            "show_help",
            "Quick Guide",
            class_="btn btn-outline-primary btn-sm w-100",
            icon=icon_svg("circle-info"),
        )
        ui.markdown(
            """
#### **Quick Guide**

- **Taxonomy**: SSYK 2012 = current; SSYK 1996 = historic.
- **Level**: 4-digit shows individual occupations; 1-digit shows broad groups.
- **Sub-index**: Pick the DAIOE metric to visualize; chart titles reflect your selection.
- **Weighting**: Employment-weighted highlights labour-market impact; Simple average treats each occupation equally.
- **Years**: Use the slider; charts always use the latest year within the range.
- **Top N / Search**: Limit to the N highest values (0 shows all) and filter by occupation name; toggle sort direction.
- **Reading charts**: Hover lines for per-year values; bars display raw + percentile labels; value boxes show the most/least exposed in the latest year.
            """
        )

    ui.hr()
    ui.input_action_button(
        "reset_filters",
        "Reset filters",
        class_="btn btn-primary w-100",
        icon=icon_svg("arrow-rotate-left"),
    )


# ---------------------------------------------------------------------------
# Reactive helpers
# ---------------------------------------------------------------------------


@reactive.effect
@reactive.event(input.reset_filters)
def _reset_filters() -> None:
    # Push defaults back into the UI (does not trigger a data reload).
    ui.update_radio_buttons("taxonomy", selected=DEFAULT_TAXONOMY)
    ui.update_select("level", selected=DEFAULT_LEVEL_CHOICE)
    ui.update_select("metric", selected=METRIC_OPTIONS[0][1])
    ui.update_select("weighting", selected=DEFAULT_WEIGHTING)
    ui.update_slider("year_range", value=DEFAULT_YEAR_RANGE)
    ui.update_slider("top_n", value=DEFAULT_TOP_N)
    ui.update_switch("sort_desc", value=DEFAULT_SORT_DESC)
    ui.update_text("search", value="")


@reactive.calc
def metric_name() -> str:
    return f"daioe_{input.metric()}"


@reactive.calc
def metric_label() -> str:
    return metric_mapping()[input.metric()]


@reactive.calc
def percentile_metric_name() -> str:
    return f"pct_rank_{input.metric()}"


@reactive.calc
def current_data() -> pd.DataFrame:
    # Structural filter: taxonomy + weighting + level (shared by all downstream reactives).
    taxonomy = input.taxonomy()
    if taxonomy not in DATA:
        return pd.DataFrame()

    df = DATA[taxonomy]

    level = int(input.level())
    weight = input.weighting()

    # Filter once here instead of repeatedly in downstream logic
    df = df[(df["weighting"] == weight) & (df["level"] == level)]

    return df


@reactive.calc
def filtered_data() -> pd.DataFrame:
    # Adds metric/year/search/top-N filtering on top of `current_data()`.
    df = current_data()
    if df.empty:
        return df

    metric_col = metric_name()

    # Keep only rows with valid metric values
    df = df.dropna(subset=[metric_col])

    # Year range filter
    year_min, year_max = input.year_range()
    df = df[(df["year"] >= year_min) & (df["year"] <= year_max)]

    # Search filter (occupation label in Swedish)
    df = apply_search_filter(df)

    if df.empty:
        return df

    # Top-N by latest year metric value
    latest_year = df["year"].max()
    latest_slice = df[df["year"] == latest_year].sort_values(
        metric_col,
        ascending=not input.sort_desc(),
    )

    top_n = input.top_n()
    if top_n > 0:
        latest_slice = latest_slice.head(top_n)

    keep_codes = latest_slice["code"].unique()
    df = df[df["code"].isin(keep_codes)]

    return df


@reactive.calc
def latest_order() -> List[str]:
    # Consistent label ordering based on the latest year and chosen sort direction.
    df = filtered_data()
    if df.empty:
        return []

    metric_col = metric_name()
    latest_year = df["year"].max()
    ascending = not input.sort_desc()

    latest_slice = df[df["year"] == latest_year].sort_values(
        metric_col, ascending=ascending
    )

    return latest_slice["label"].tolist()


# ---------------------------------------------------------------------------
# Extremes (value boxes)
# ---------------------------------------------------------------------------
@reactive.calc
def latest_extremes() -> Dict[str, Dict[str, float | str]]:
    # Use all occupations (ignore top_n) but respect other filters
    df = current_data()
    if df.empty:
        return {}

    metric_col = metric_name()  # raw DAIOE index
    percentile_col = percentile_metric_name()
    df = df.dropna(subset=[metric_col, percentile_col])

    # Apply year range filter (but not top_n)
    year_min, year_max = input.year_range()
    df = df[(df["year"] >= year_min) & (df["year"] <= year_max)]

    # Apply search filter (consistent with main plots)
    df = apply_search_filter(df)

    if df.empty:
        return {}

    latest_year = df["year"].max()
    latest_df = df[df["year"] == latest_year]

    sorted_df = latest_df.sort_values(metric_col, ascending=False)
    top_row = sorted_df.iloc[0]
    bottom_row = sorted_df.iloc[-1]

    return {
        "year": int(latest_year),
        "most": {
            "label": str(top_row["label"]),
            "value": float(top_row[metric_col]),
            "percentile": float(top_row[percentile_col]),
        },
        "least": {
            "label": str(bottom_row["label"]),
            "value": float(bottom_row[metric_col]),
            "percentile": float(bottom_row[percentile_col]),
        },
    }


# ---------------------------------------------------------------------------
# Top summary boxes
# ---------------------------------------------------------------------------
with ui.layout_columns(col_widths=[6, 6], gap="16px", class_="mb-3"):

    @render.ui
    def most_exposed_box():
        info = latest_extremes()
        if not info:
            return ui.value_box(
                "Most exposed occupation",
                "No data in range",
                "Adjust filters to see values",
            )

        most = info["most"]
        year = info["year"]
        return classic_ui.value_box(
            "Most exposed occupation\n",
            ui.h4(most["label"]),
            f"{metric_label()} raw: {format_raw_value(most['value'])} | \n"
            f"percentile: {format_metric_value(most['percentile'])} (year {year})",
            theme="blue",
            fill=True,
        )

    @render.ui
    def least_exposed_box():
        info = latest_extremes()
        if not info:
            return ui.value_box(
                "Least exposed occupation",
                "No data in range",
                "Adjust filters to see values",
            )

        least = info["least"]
        year = info["year"]
        return classic_ui.value_box(
            "Least exposed occupation\n",
            ui.h4(least["label"]),
            f"{metric_label()} raw: {format_raw_value(least['value'])} | \n"
            f"percentile: {format_metric_value(least['percentile'])} (year {year})",
            theme="bg-gradient-orange-red",
            full_screen=True,
            fill=True,
        )


# ---------------------------------------------------------------------------
# Main UI cards & plots
# ---------------------------------------------------------------------------
with ui.card(full_screen=True, fill=True, class_="mb-3"):
    ui.card_header("Trend by occupation")

    @render_widget
    def trend_plot():
        df = filtered_data()
        return build_trend_plot(
            df,
            metric_col=metric_name(),
            metric_label=metric_label(),
            title=chart_title(),
            order=latest_order(),
        )


with ui.card(full_screen=True, fill=True, class_="mb-3"):
    ui.card_header("Latest year comparison")

    @render_widget
    def bar_plot():
        df = filtered_data()
        return build_bar_plot(
            df,
            percentile_col=percentile_metric_name(),
            raw_col=metric_name(),
            metric_label=metric_label(),
            title=chart_title(),
            order=latest_order(),
        )


if __name__ == "__main__":
    ui.run()