"""ENGIN industrial diagnostic console built on the tested application core."""

from __future__ import annotations

import html
import logging
from dataclasses import dataclass
from pathlib import Path

import pandas as pd
import streamlit as st

from engin.charts import cylinder_spectrum, deviation_chart, engine_heatmap
from engin.config import (
    LABEL_COLORS,
    LABEL_DISPLAY,
    SEVERITY_DISPLAY,
    AppConfig,
)
from engin.errors import UserFacingError
from engin.explainability import (
    analyze_engine,
    explain_cylinder,
    rank_cylinders,
    summarize_engine,
)
from engin.io import PandasCsvReader
from engin.model import PrecomputedPredictionModel, SklearnPredictionModel
from engin.service import DiagnosisResult, DiagnosticService
from engin.validation import SpectrumFrameValidator

LOGGER = logging.getLogger("engin.app")
BASE_DIR = Path(__file__).resolve().parent
MODEL_VERSION = "engin-2026.08.25-1"
MODEL_ARTIFACT_DIR = BASE_DIR / "artifacts" / MODEL_VERSION
VIEW_OVERVIEW = "Przegląd"
VIEW_DETAIL = "Szczegóły cylindra"
NO_COMPARISON = 0
PLOTLY_CONFIG = {"displayModeBar": False, "displaylogo": False}
DIAGNOSTIC_COLUMN_DISPLAY = {
    "engine_id": "Silnik",
    "cylinder": "Cylinder",
    "label": "Diagnoza",
    "severity": "Nasilenie",
    "raw_model_label": "Surowa diagnoza modelu",
    "decision_source": "Źródło decyzji",
    "label_confidence": "Wynik diagnozy",
    "raw_model_confidence": "Surowy wynik diagnozy",
    "label_margin": "Margines decyzji",
    "severity_confidence": "Wynik oceny nasilenia",
    "anomaly_score_mean_abs_mv": "Średnie odchylenie bezwzględne [mV]",
    "anomaly_ratio_to_engine_median": "Odchylenie względem mediany silnika",
    "ood_absolute_threshold_mv": "Bezwzględny próg anomalii [mV]",
    "ood_ratio_threshold": "Względny próg anomalii",
    "top_anomalous_frequencies_khz": "Anomalne częstotliwości [kHz]",
    "missing_spectral_cells": "Brakujące pomiary widma",
    "model_version": "Wersja modelu",
    "model_artifact_sha256": "Suma SHA-256 artefaktu",
}


@dataclass(frozen=True)
class AppDependencies:
    service: DiagnosticService
    demo_payload: bytes
    live_inference: bool = True
    startup_warning: str | None = None
    model_version: str = "unknown"


def _reader() -> PandasCsvReader:
    return PandasCsvReader(AppConfig().max_upload_bytes)


@st.cache_resource(show_spinner="Ładowanie modelu diagnostycznego…")
def build_dependencies() -> AppDependencies:
    reader = _reader()
    validator = SpectrumFrameValidator()
    demo_path = BASE_DIR / "test.csv"
    demo_payload = demo_path.read_bytes()
    try:
        model = SklearnPredictionModel.from_artifact(
            MODEL_ARTIFACT_DIR,
            expected_model_version=MODEL_VERSION,
        )
        return AppDependencies(
            service=DiagnosticService(reader=reader, validator=validator, model=model),
            demo_payload=demo_payload,
            model_version=MODEL_VERSION,
        )
    except Exception:
        LOGGER.exception("Live model initialization failed; enabling demo fallback")
        submission = pd.read_csv(BASE_DIR / "predictions.csv")
        diagnostics = pd.read_csv(BASE_DIR / "prediction_diagnostics.csv")
        fallback = PrecomputedPredictionModel(submission, diagnostics)
        return AppDependencies(
            service=DiagnosticService(reader=reader, validator=validator, model=fallback),
            demo_payload=demo_payload,
            live_inference=False,
            startup_warning=(
                "Artefakt modelu nie został załadowany. Aplikacja działa w bezpiecznym "
                "trybie demonstracyjnym na zapisanych predykcjach."
            ),
            model_version="tryb-demonstracyjny",
        )


@st.cache_data(show_spinner="Analiza widm i klasyfikacja cylindrów…")
def diagnose_payload(_service: DiagnosticService, payload: bytes) -> DiagnosisResult:
    return _service.diagnose_bytes(payload)


def _load_css() -> None:
    css_path = BASE_DIR / "assets" / "app.css"
    st.markdown(f"<style>{css_path.read_text(encoding='utf-8')}</style>", unsafe_allow_html=True)


def _render_error(error: UserFacingError) -> None:
    st.error(f"**{error.title}**\n\n{error.message}")
    if error.hint:
        st.info(error.hint)
    st.caption(f"Kod błędu: `{error.code}`")


def _diagnostics_for_display(frame: pd.DataFrame) -> pd.DataFrame:
    display = frame.copy()
    for column in ("label", "raw_model_label"):
        if column in display:
            display[column] = display[column].map(LABEL_DISPLAY).fillna(display[column])
    if "severity" in display:
        display["severity"] = (
            display["severity"].map(SEVERITY_DISPLAY).fillna(display["severity"])
        )
    if "decision_source" in display:
        display["decision_source"] = display["decision_source"].replace(
            {
                "classifier": "Klasyfikator spektralny",
                "ood_override": "Reguła anomalii",
            }
        )
    if "model_version" in display:
        display["model_version"] = display["model_version"].replace(
            {"demo-precomputed": "tryb demonstracyjny"}
        )
    return display.rename(columns=DIAGNOSTIC_COLUMN_DISPLAY)


def _render_header() -> None:
    st.markdown(
        """
        <div class="product-header">
          <div class="eyebrow">AESTEEL · DIAGNOSTYKA WTRYSKU DIESEL</div>
          <h1>Konsola diagnostyczna ENGIN</h1>
          <p>Diagnoza cylindra, nasilenie i następny krok.</p>
        </div>
        """,
        unsafe_allow_html=True,
    )


def _render_summary(summary) -> None:
    st.markdown(
        f"""
        <div class="status-strip status-{html.escape(summary.status_tone)}">
          <div><span>STATUS SILNIKA</span><strong>{html.escape(summary.status)}</strong></div>
          <div><span>NAJWYŻSZE NASILENIE</span><strong>{html.escape(summary.highest_severity_display)}</strong></div>
          <div><span>WYMAGA UWAGI</span><strong>{summary.attention} / {summary.cylinders}</strong></div>
          <div><span>PIERWSZY DO KONTROLI</span><strong>C{summary.top_cylinder:02d}</strong></div>
        </div>
        """,
        unsafe_allow_html=True,
    )


def _render_engine_overview(analysis) -> None:
    left, right = st.columns(2, gap="large", vertical_alignment="top")
    with left:
        st.markdown(
            '<div class="overview-panel-title">Mapa odchyleń</div>',
            unsafe_allow_html=True,
        )
        st.plotly_chart(
            engine_heatmap(analysis),
            width="stretch",
            key=f"heatmap_{analysis.engine_id}",
            config=PLOTLY_CONFIG,
        )
    with right:
        st.markdown(
            '<div class="overview-panel-title">Priorytet kontroli</div>',
            unsafe_allow_html=True,
        )
        ranking = rank_cylinders(analysis).head(6).copy()
        ranking["Cylinder"] = ranking["cylinder"].map(lambda value: f"C{int(value):02d}")
        ranking["Diagnoza"] = ranking["label_display"]
        ranking["Nasilenie"] = ranking["severity_display"]
        ranking["Priorytet"] = ranking["priority_display"]
        st.dataframe(
            ranking[["Cylinder", "Diagnoza", "Nasilenie", "Priorytet"]],
            hide_index=True,
            width="stretch",
            height=360,
        )


def _render_cylinder_detail(
    analysis,
    cylinder: int,
    comparison_cylinders: list[int],
) -> None:
    explanation = explain_cylinder(analysis, cylinder)
    color = LABEL_COLORS[explanation.label]
    priority = rank_cylinders(analysis).loc[
        lambda frame: frame["cylinder"].eq(cylinder), "priority_display"
    ].iloc[0]
    top_bands = ", ".join(f"{value} kHz" for value in explanation.top_frequencies)
    st.markdown(
        f"""
        <div class="diagnosis-card" style="--diagnosis-color:{color}">
          <div>
            <span class="diagnosis-kicker">CYLINDER {explanation.cylinder:02d}</span>
            <h2>{html.escape(explanation.label_display)}</h2>
            <p>{html.escape(explanation.severity_display)}</p>
          </div>
          <div class="diagnosis-metrics">
            <div><span>Priorytet</span><strong>{html.escape(priority)}</strong></div>
            <div><span>Średnie odchylenie</span><strong>{explanation.anomaly_score:.1f} mV</strong></div>
            <div><span>Główne pasma</span><strong>{html.escape(top_bands)}</strong></div>
          </div>
        </div>
        """,
        unsafe_allow_html=True,
    )
    st.markdown("#### Widma porównawcze")
    st.plotly_chart(
        cylinder_spectrum(analysis, cylinder, comparison_cylinders),
        width="stretch",
        key=(
            f"spectrum_{analysis.engine_id}_"
            + "_".join(str(value) for value in comparison_cylinders)
        ),
        config=PLOTLY_CONFIG,
    )

    st.markdown("#### Odchylenie cylindra głównego")
    st.plotly_chart(
        deviation_chart(analysis, cylinder),
        width="stretch",
        key=f"deviation_{analysis.engine_id}_{cylinder}",
        config=PLOTLY_CONFIG,
    )

    why, next_step = st.columns(2, gap="large")
    with why:
        st.markdown("#### Uzasadnienie")
        st.write(explanation.reason)
    with next_step:
        st.markdown("#### Następny krok")
        st.write(explanation.recommendation)


def _store_selected_cylinder(state_key: str, widget_key: str) -> None:
    st.session_state[state_key] = int(st.session_state[widget_key])


def _comparison_label(value: int) -> str:
    if value == NO_COMPARISON:
        return "Bez porównania"
    return f"Cylinder {value:02d}"


def _render_cylinder_selectors(
    analysis,
    available: list[int],
    selected_state_key: str,
) -> tuple[int, list[int]]:
    selector_key = f"cylinder_selector_{analysis.engine_id}"
    selected = int(st.session_state[selected_state_key])
    if selector_key not in st.session_state or st.session_state[selector_key] not in available:
        st.session_state[selector_key] = selected

    with st.container(border=True):
        st.markdown("### Wybór cylindra")
        columns = st.columns([1.35, 1.0, 1.0, 1.0], gap="medium")
        with columns[0]:
            primary = int(
                st.selectbox(
                    "Cylinder do analizy",
                    available,
                    format_func=lambda value: f"Cylinder {value:02d}",
                    key=selector_key,
                    on_change=_store_selected_cylinder,
                    args=(selected_state_key, selector_key),
                )
            )

        comparisons: list[int] = []
        for slot, column in enumerate(columns[1:], start=1):
            comparison_key = f"comparison_slot_{analysis.engine_id}_{slot}"
            options = [
                NO_COMPARISON,
                *(
                    cylinder
                    for cylinder in available
                    if cylinder != primary and cylinder not in comparisons
                ),
            ]
            if (
                comparison_key not in st.session_state
                or st.session_state[comparison_key] not in options
            ):
                st.session_state[comparison_key] = NO_COMPARISON
            with column:
                comparison = int(
                    st.selectbox(
                        f"Porównanie {slot}",
                        options,
                        format_func=_comparison_label,
                        key=comparison_key,
                    )
                )
            if comparison != NO_COMPARISON:
                comparisons.append(comparison)

    st.session_state[selected_state_key] = primary
    return primary, comparisons


def _render_technical(result: DiagnosisResult) -> None:
    st.markdown("### Walidacja i model")
    c1, c2, c3, c4 = st.columns(4)
    c1.metric("Makro F1 (grupowe)", "0.981")
    c2.metric("Trafność nasilenia", "0.930")
    c3.metric("Punkty walidacyjne", "33.65 / 40")
    c4.metric("Testy", "38 / 38")
    st.caption(
        "Walidacja grupowa według silników · średnia z 5 uruchomień · "
        "makro F1 przy 5% braków: 0.983 · wnioskowanie lokalne na CPU."
    )
    st.markdown("#### Dane diagnostyczne")
    st.dataframe(
        _diagnostics_for_display(result.diagnostics),
        width="stretch",
        hide_index=True,
    )


def render_app(dependencies: AppDependencies | None = None) -> None:
    st.set_page_config(
        page_title="Konsola diagnostyczna ENGIN",
        page_icon="⚙️",
        layout="wide",
        initial_sidebar_state="expanded",
    )
    _load_css()
    deps = dependencies or build_dependencies()
    _render_header()

    with st.sidebar:
        st.markdown("## Centrum diagnostyczne")
        source = st.radio(
            "Źródło danych",
            ["Dane demonstracyjne", "Wgraj plik CSV"],
            captions=["50 silników testowych", "Własne kompletne silniki 8/12/16"],
        )
        payload: bytes | None
        if source == "Dane demonstracyjne":
            payload = deps.demo_payload
            st.success("Dane demonstracyjne załadowane")
        else:
            uploaded = st.file_uploader("Plik pomiarowy CSV", type=["csv"])
            payload = uploaded.getvalue() if uploaded is not None else None
            if payload is None:
                st.info("Wgraj CSV, aby rozpocząć diagnozę.")
                st.caption("Wymagane: kompletne silniki oraz mV_0...mV_20.")

    if deps.startup_warning:
        st.warning(deps.startup_warning)
    if payload is None:
        st.markdown("### Oczekiwanie na dane")
        st.write("Po wgraniu pliku aplikacja zweryfikuje strukturę przed uruchomieniem modelu.")
        return

    try:
        result = diagnose_payload(deps.service, payload)
    except UserFacingError as exc:
        _render_error(exc)
        return
    except Exception:
        LOGGER.exception("Unexpected application failure")
        st.error("**Nieoczekiwany błąd aplikacji**\n\nDiagnoza została bezpiecznie przerwana; dane nie zostały zmodyfikowane.")
        st.info("Uruchom aplikację ponownie lub użyj zestawu demonstracyjnego.")
        return

    for warning in result.warnings:
        st.warning(warning)

    with st.sidebar:
        engine_id = st.selectbox("Aktywny silnik", result.engine_ids)
        st.download_button(
            "Pobierz wyniki (CSV)",
            data=result.submission.to_csv(index=False).encode("utf-8"),
            file_name="predictions.csv",
            mime="text/csv",
            width="stretch",
        )
        st.download_button(
            "Pobierz dane diagnostyczne (CSV)",
            data=result.diagnostics.to_csv(index=False).encode("utf-8"),
            file_name="prediction_diagnostics.csv",
            mime="text/csv",
            width="stretch",
        )
    analysis = analyze_engine(result, str(engine_id))
    summary = summarize_engine(analysis)
    _render_summary(summary)

    session_key = f"active_cylinder_{analysis.engine_id}"
    available = analysis.measurements["cylinder"].astype(int).tolist()
    if session_key not in st.session_state or st.session_state[session_key] not in available:
        st.session_state[session_key] = summary.top_cylinder

    view_key = f"active_view_{analysis.engine_id}"
    if view_key not in st.session_state:
        st.session_state[view_key] = VIEW_OVERVIEW
    view = st.segmented_control(
        "Widok",
        [VIEW_OVERVIEW, VIEW_DETAIL],
        required=True,
        key=view_key,
        label_visibility="collapsed",
        width="stretch",
    )

    if view == VIEW_OVERVIEW:
        _render_engine_overview(analysis)
    elif view == VIEW_DETAIL:
        selected_from_box, additional_cylinders = _render_cylinder_selectors(
            analysis,
            available,
            session_key,
        )
        comparison_cylinders = [
            int(selected_from_box),
            *(int(value) for value in additional_cylinders),
        ]
        _render_cylinder_detail(
            analysis,
            int(selected_from_box),
            comparison_cylinders,
        )

    with st.expander("Informacje techniczne", expanded=False):
        _render_technical(result)


if __name__ == "__main__":
    render_app()