from __future__ import annotations

import pandas as pd
import streamlit as st

from src.analytics import (
    MIN_CONFIG_N,
    RETRIEVAL_OK_THRESHOLD,
    demand_coverage,
    make_decision_brief,
    overview_metrics,
    retrieval_outcomes,
    risk_slices,
)
from src.app_state import DashboardContext, FilterState
from src.data import DataBundle, filter_eval, filter_retrieval_events, load_bundle, option_values
from src.formatting import fmt_int, fmt_latency_ms, fmt_money, fmt_pct
from src.models import APP_TITLE, AppSettings
from src.ui import badge, decision_strip, hero, inject_css, metric_card
from src.views import (
    ConfigComparisonViewMixin,
    ExportCenterViewMixin,
    OverviewViewMixin,
    PolicySimulatorViewMixin,
    QualityMapViewMixin,
    RetrievalLabViewMixin,
    RiskBoardViewMixin,
    TraceExplorerViewMixin,
)


@st.cache_data(show_spinner="Loading RAG QA logs and corpus tables...")
def cached_load_bundle(data_dir: str, docs_dir: str) -> DataBundle:
    return load_bundle(data_dir, docs_dir)


class CommandCenterApp(
    OverviewViewMixin,
    QualityMapViewMixin,
    RetrievalLabViewMixin,
    RiskBoardViewMixin,
    ConfigComparisonViewMixin,
    PolicySimulatorViewMixin,
    TraceExplorerViewMixin,
    ExportCenterViewMixin,
):
    """Thin Streamlit controller for the RAG QA Command Center.

    The controller owns application state and page orchestration. Individual tabs live
    in focused view mixins under ``src/views`` so UI concerns stay separated from
    data loading, analytics, chart construction, and formatting helpers.
    """

    def __init__(self, settings: AppSettings | None = None) -> None:
        self.settings = settings or AppSettings()
        self.bundle: DataBundle | None = None
        self.filter_state: FilterState | None = None
        self.context: DashboardContext | None = None

    def run(self) -> None:
        st.set_page_config(
            page_title=APP_TITLE,
            page_icon=self.settings.page_icon,
            layout="wide",
            initial_sidebar_state="expanded",
        )
        inject_css()

        try:
            self.bundle = cached_load_bundle(self.settings.data_dir, self.settings.docs_dir)
        except Exception as exc:  # pragma: no cover - rendered in Streamlit runtime
            st.error(str(exc))
            st.stop()

        self.filter_state = self._render_sidebar(self.bundle.eval_runs)
        self.context = self._build_context(self.bundle, self.filter_state)

        self._render_header(self.context, self.filter_state)

        if self.context.filtered_eval.empty:
            st.warning("No rows match the current filters. Relax the sidebar filters to continue.")
            st.stop()

        self._render_kpis(self.context.metrics)
        self._render_pages()

    def _render_sidebar(self, eval_df: pd.DataFrame) -> FilterState:
        st.sidebar.markdown("### Filters")
        preset = st.sidebar.selectbox(
            "Focus preset",
            ["All", "Error review", "Hallucination risk", "Retrieval failure", "High latency", "High cost"],
            index=0,
            help="Quickly narrow the evaluation rows to a common review queue.",
        )
        domains = st.sidebar.multiselect("Domain", option_values(eval_df, "domain"))
        difficulties = st.sidebar.multiselect("Difficulty", option_values(eval_df, "difficulty"))
        scenario_types = st.sidebar.multiselect("Scenario type", option_values(eval_df, "scenario_type"))
        retrievers = st.sidebar.multiselect("Retriever", option_values(eval_df, "retrieval_strategy"))
        generators = st.sidebar.multiselect("Generator", option_values(eval_df, "generator_model"))
        splits = st.sidebar.multiselect("Split", option_values(eval_df, "split"))
        min_slice_n = st.sidebar.slider(
            "Minimum rows per risk slice",
            min_value=10,
            max_value=200,
            value=30,
            step=5,
            help="Higher values reduce noisy small-sample slices.",
        )
        min_config_n = st.sidebar.slider(
            "Minimum rows per config",
            min_value=20,
            max_value=250,
            value=MIN_CONFIG_N,
            step=5,
            help="Higher values make configuration comparisons more reliable.",
        )
        return FilterState(
            preset=preset,
            domains=domains,
            difficulties=difficulties,
            scenario_types=scenario_types,
            retrievers=retrievers,
            generators=generators,
            splits=splits,
            min_slice_n=min_slice_n,
            min_config_n=min_config_n,
        )

    def _build_context(self, bundle: DataBundle, filters: FilterState) -> DashboardContext:
        filtered_eval = filter_eval(
            bundle.eval_runs,
            domains=filters.domains,
            difficulties=filters.difficulties,
            scenario_types=filters.scenario_types,
            retrievers=filters.retrievers,
            generators=filters.generators,
            splits=filters.splits,
        )
        filtered_eval = self._apply_preset(filtered_eval, filters.preset)
        filtered_retrieval = filter_retrieval_events(bundle.retrieval_events, filtered_eval)

        metrics = overview_metrics(filtered_eval, bundle.documents, bundle.chunks, filtered_retrieval)
        risk_table = risk_slices(filtered_eval, min_n=filters.min_slice_n)
        retrieval_table = retrieval_outcomes(filtered_eval)
        brief = make_decision_brief(
            filtered_eval,
            bundle.documents,
            bundle.chunks,
            filtered_retrieval,
            min_slice_n=filters.min_slice_n,
            min_config_n=filters.min_config_n,
            risk_table=risk_table,
            retrieval_table=retrieval_table,
        )
        return DashboardContext(
            filtered_eval=filtered_eval,
            filtered_retrieval=filtered_retrieval,
            metrics=metrics,
            brief=brief,
            risk_slices=risk_table,
            retrieval_outcomes=retrieval_table,
            demand_coverage=demand_coverage(filtered_eval, bundle.documents),
        )

    @staticmethod
    def _apply_preset(df: pd.DataFrame, preset: str) -> pd.DataFrame:
        out = df.copy()
        if preset == "Error review" and "is_correct" in out.columns:
            return out[pd.to_numeric(out["is_correct"], errors="coerce").fillna(0.0) < 0.5]
        if preset == "Hallucination risk" and "hallucination_flag" in out.columns:
            return out[pd.to_numeric(out["hallucination_flag"], errors="coerce").fillna(0.0) >= 0.5]
        if preset == "Retrieval failure" and "recall_at_10" in out.columns:
            return out[pd.to_numeric(out["recall_at_10"], errors="coerce").fillna(0.0) < RETRIEVAL_OK_THRESHOLD]
        if preset == "High latency" and "total_latency_ms" in out.columns:
            cutoff = pd.to_numeric(out["total_latency_ms"], errors="coerce").quantile(0.90)
            return out[pd.to_numeric(out["total_latency_ms"], errors="coerce") >= cutoff]
        if preset == "High cost" and "total_cost_usd" in out.columns:
            cutoff = pd.to_numeric(out["total_cost_usd"], errors="coerce").quantile(0.90)
            return out[pd.to_numeric(out["total_cost_usd"], errors="coerce") >= cutoff]
        return out

    def _render_header(self, context: DashboardContext, filters: FilterState) -> None:
        posture_kind = "good" if context.brief.posture == "Stable" else "bad" if context.brief.posture == "High Risk" else "warn"
        hero(
            APP_TITLE,
            "Inspect offline RAG QA evaluation logs across quality, retrieval behavior, hallucination exposure, latency, cost, review thresholds, and example-level evidence.",
            badges=[
                badge(f"Posture: {context.brief.posture}", posture_kind),
                badge(f"Preset: {filters.preset}", "info"),
                badge(f"Evaluations: {fmt_int(context.metrics['evaluations'])}", "info"),
                badge(f"Retrieval events: {fmt_int(context.metrics['retrieval_events'])}", "info"),
                badge("Offline evaluation dataset", "warn"),
            ],
        )
        decision_strip(
            [
                ("Quality posture", context.brief.posture, context.brief.posture_reason),
                ("Main driver", context.brief.main_driver, "Dominant non-healthy issue pattern under the active filters."),
                ("Highest-risk slice", context.brief.worst_slice, "Uses the current minimum risk-slice size."),
                ("Best config", context.brief.best_config, "Uses the current minimum config sample size."),
                ("Recommended action", context.brief.recommended_action, "Suggested next step from the filtered evidence."),
            ]
        )

    @staticmethod
    def _render_kpis(metrics: dict[str, float]) -> None:
        cols = st.columns(6)
        card_specs = [
            ("Answer correctness", fmt_pct(metrics["correct_rate"]), "Share of examples judged correct.", "stable" if metrics["correct_rate"] >= 0.75 else "watch"),
            ("Hallucination rate", fmt_pct(metrics["hallucination_rate"]), "Lower is better; elevated segments should be reviewed.", "risk" if metrics["hallucination_rate"] >= 0.18 else "stable"),
            ("Recall@10", fmt_pct(metrics["recall_at_10"]), "Retrieval coverage signal at top 10 chunks.", "stable" if metrics["recall_at_10"] >= 0.65 else "watch"),
            ("MRR@10", fmt_pct(metrics["mrr_at_10"]), "Rank-sensitive retrieval quality signal.", "stable" if metrics["mrr_at_10"] >= 0.45 else "watch"),
            ("P95 latency", fmt_latency_ms(metrics["p95_latency_ms"]), "Tail latency for selected evaluation rows.", "watch"),
            ("Avg cost", fmt_money(metrics["avg_cost_usd"]), "Average estimated cost per run.", "info"),
        ]
        for col, spec in zip(cols, card_specs, strict=False):
            with col:
                metric_card(*spec)

    def _render_pages(self) -> None:
        tabs = st.tabs(
            [
                "Overview",
                "Quality Map",
                "Retrieval Lab",
                "Risk Board",
                "Config Comparison",
                "Policy Simulator",
                "Trace Explorer",
                "Export Center",
            ]
        )
        with tabs[0]:
            self._page_overview()
        with tabs[1]:
            self._page_quality_map()
        with tabs[2]:
            self._page_retrieval_lab()
        with tabs[3]:
            self._page_risk_board()
        with tabs[4]:
            self._page_config_comparison()
        with tabs[5]:
            self._page_policy_simulator()
        with tabs[6]:
            self._page_trace_explorer()
        with tabs[7]:
            self._page_export_center()

    def _plot(self, fig, key: str) -> None:
        st.plotly_chart(
            fig,
            use_container_width=True,
            key=f"plot_{key}",
            config={"displayModeBar": False, "responsive": True},
        )

    @property
    def ctx(self) -> DashboardContext:
        if self.context is None:
            raise RuntimeError("Dashboard context has not been built.")
        return self.context

    @property
    def data(self) -> DataBundle:
        if self.bundle is None:
            raise RuntimeError("Data bundle has not been loaded.")
        return self.bundle

    @property
    def filters(self) -> FilterState:
        if self.filter_state is None:
            raise RuntimeError("Filter state has not been collected.")
        return self.filter_state