from __future__ import annotations

import io
from typing import Any

import matplotlib

matplotlib.use("Agg")
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


def category_scores_dataframe(by_category: dict[str, Any]) -> pd.DataFrame:
    rows = []
    for cat, block in sorted(by_category.items()):
        if not isinstance(block, dict):
            continue
        if block.get("n", 0) == 0 and block.get("note"):
            continue
        rows.append(
            {
                "category": cat,
                "n": block.get("n", 0),
                "pass_rate": block.get("pass_rate", 0.0),
                "mean_risk": block.get("mean_risk", 0.0),
                "mean_weighted_risk": block.get("mean_weighted_risk", 0.0),
                "boundary_rate": block.get("boundary_or_refusal_rate", 0.0),
                "critical_failures": block.get("critical_failures", 0),
            }
        )
    return pd.DataFrame(rows)


def metrics_summary_markdown(metrics: dict) -> str:
    o = metrics.get("overall", {})
    c = metrics.get("counts", {})
    comp = metrics.get("composite_indices", {})
    lines = [
        "### Run summary",
        f"- **Probes:** {c.get('probes_evaluated', 0)} (passed {c.get('passed', '—')}, failed {c.get('failed', '—')})",
        f"- **Pass rate:** {o.get('pass_rate', '—')}",
        f"- **Severity-weighted pass rate:** {o.get('severity_weighted_pass_rate', '—')}",
        f"- **Mean / median / P90 risk:** {o.get('mean_risk', '—')} / {o.get('median_risk', '—')} / {o.get('p90_risk', '—')}",
        f"- **Mean weighted risk:** {o.get('mean_weighted_risk', '—')}",
        f"- **High-stakes failure rate:** {o.get('high_stakes_failure_rate', '—')}",
        f"- **Boundary-language rate:** {o.get('boundary_language_rate', '—')}",
        f"- **Safe:unsafe signal ratio:** "
        f"{o.get('safe_to_unsafe_signal_ratio', '—') if o.get('safe_to_unsafe_signal_ratio') is not None else 'n/a (no unsafe hits)'} "
        f"(totals {o.get('safe_signal_total', '—')} / {o.get('unsafe_signal_total', '—')})",
        "",
        "### Composite indices",
        f"- **Resilience index** (higher is better): {comp.get('resilience_index', '—')}",
        f"- **Exposure index** (higher is worse): {comp.get('exposure_index', '—')}",
        f"- **Fragility spread** (risk std dev): {comp.get('fragility_spread', '—')}",
    ]
    return "\n".join(lines)


def severity_table_markdown(by_sev: dict[str, Any]) -> str:
    rows = []
    for tier, block in by_sev.items():
        n = block.get("n", 0)
        if n == 0:
            continue
        rows.append(
            f"| {tier} | {n} | {block.get('pass_count', 0)} | {block.get('fail_count', 0)} | {block.get('pass_rate', '—')} |"
        )
    if not rows:
        return "_No severity breakdown (empty run)._"
    header = "| Tier | n | Passed | Failed | Pass rate |\n| --- | ---: | ---: | ---: | --- |"
    return header + "\n" + "\n".join(rows)


def plot_category_risk_bars(by_category: dict[str, Any]) -> np.ndarray:
    df = category_scores_dataframe(by_category)
    fig, ax = plt.subplots(figsize=(8, 4))
    if df.empty:
        ax.text(0.5, 0.5, "No category data", ha="center", va="center")
    else:
        x = np.arange(len(df))
        ax.bar(x, df["mean_risk"], color="#c0392b", alpha=0.85, label="Mean risk")
        ax.bar(x, df["pass_rate"], color="#27ae60", alpha=0.35, label="Pass rate")
        ax.set_ylim(0, 1.05)
        ax.set_ylabel("Score (0–1)")
        ax.set_xticks(x, list(df["category"]), rotation=35, ha="right")
        ax.legend(loc="upper right")
        ax.set_title("Category mean risk vs pass rate (overlay)")
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=120)
    plt.close(fig)
    buf.seek(0)
    return mpimg.imread(buf)


def plot_composite_radar(metrics: dict) -> np.ndarray:
    """Radar-style polygon for category mean risk (6 axes)."""
    by_cat = metrics.get("by_category", {})
    labels: list[str] = []
    values: list[float] = []
    for cat in sorted(by_cat.keys()):
        block = by_cat[cat]
        if not isinstance(block, dict) or block.get("n", 0) == 0:
            continue
        labels.append(cat.replace("_", " "))
        values.append(float(block.get("mean_risk", 0.0)))
    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
    if len(values) < 3:
        fig.text(0.5, 0.5, "Need ≥3 categories\nwith probes", ha="center", va="center")
    else:
        angles = [n / len(values) * 2 * 3.14159 for n in range(len(values))]
        angles += angles[:1]
        vals = values + values[:1]
        ax.plot(angles, vals, color="#8e44ad", linewidth=2)
        ax.fill(angles, vals, color="#8e44ad", alpha=0.2)
        ax.set_xticks(angles[:-1])
        ax.set_xticklabels(labels, size=8)
        ax.set_ylim(0, 1)
        ax.set_title("Mean risk by category (radar)")
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=120)
    plt.close(fig)
    buf.seek(0)
    return mpimg.imread(buf)


_PALETTE_THREAT = ["#4C78A8", "#F58518", "#54A24B", "#E45756", "#72B7B2", "#B279A2"]


def observability_markdown(obs: dict[str, Any]) -> str:
    if not obs.get("eligible"):
        return f"### Observable geometry\n\n_{obs.get('message', 'Not eligible')}_"
    mi = obs.get("mutual_information") or {}
    return "\n".join(
        [
            "### Observable geometry (embed → cluster → MI)",
            f"- **Cases:** {obs.get('n_cases')} · **Distinct clusters:** {obs.get('n_clusters_used')}",
            f"- **MI(cluster, category):** `{mi.get('MI(cluster, category)', '—')}`",
            f"- **MI(cluster, severity):** `{mi.get('MI(cluster, severity)', '—')}`",
            f"- **MI(cluster, pass_fail):** `{mi.get('MI(cluster, pass_fail)', '—')}`",
            "",
            str(obs.get("interpretation", "")),
        ]
    )


def plot_mi_threat_bars(mi_scores: dict[str, float]) -> np.ndarray:
    labels = list(mi_scores.keys())
    values = list(mi_scores.values())
    fig, ax = plt.subplots(figsize=(7.5, 3.8))
    if not labels:
        ax.text(0.5, 0.5, "No MI scores", ha="center", va="center")
    else:
        max_val = max(values + [0.01])
        bars = ax.bar(labels, values, color=_PALETTE_THREAT[: len(labels)], width=0.55, zorder=2)
        ax.set_ylim(0, max_val * 1.35)
        ax.set_ylabel("Mutual information (nats)", fontsize=10)
        ax.set_title("Threat case clusters · mutual information", fontsize=11, pad=10)
        ax.grid(axis="y", linestyle="--", alpha=0.4, zorder=1)
        ax.tick_params(axis="x", labelsize=8)
        plt.setp(ax.xaxis.get_majorticklabels(), rotation=12, ha="right")
        for bar, value in zip(bars, values):
            ax.text(
                bar.get_x() + bar.get_width() / 2,
                bar.get_height() + max_val * 0.03,
                f"{value:.3f}",
                ha="center",
                va="bottom",
                fontsize=9,
                fontweight="bold",
            )
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=120)
    plt.close(fig)
    buf.seek(0)
    return mpimg.imread(buf)


def plot_threat_cluster_scatter(case_clusters: list[dict[str, Any]]) -> np.ndarray:
    fig, ax = plt.subplots(figsize=(7, 5))
    if not case_clusters:
        ax.text(0.5, 0.5, "No points", ha="center", va="center", transform=ax.transAxes)
    else:
        cats = [str(r.get("category", "")) for r in case_clusters]
        unique_cats = sorted(set(cats))
        color_map = {c: _PALETTE_THREAT[i % len(_PALETTE_THREAT)] for i, c in enumerate(unique_cats)}
        legend_handles: dict[str, Any] = {}
        for row in case_clusters:
            x = float(row.get("scatter_x", 0.0))
            y = float(row.get("scatter_y", 0.0))
            cid = int(row.get("cluster_id", 0))
            cat = str(row.get("category", ""))
            col = color_map.get(cat, "#888888")
            ax.scatter(x, y, c=col, s=72, alpha=0.85, edgecolors="white", linewidths=0.5, zorder=3)
            ax.text(x, y, str(cid), fontsize=7, ha="center", va="center", color="white", zorder=4)
            if cat not in legend_handles:
                legend_handles[cat] = plt.Line2D(
                    [0],
                    [0],
                    marker="o",
                    color="w",
                    markerfacecolor=col,
                    markersize=8,
                    label=cat,
                )
        ax.set_xlabel("SVD component 1", fontsize=9)
        ax.set_ylabel("SVD component 2", fontsize=9)
        ax.set_title("Threat scores in embedding space (colour = category, label = cluster)", fontsize=10)
        if legend_handles:
            ax.legend(handles=list(legend_handles.values()), fontsize=8, loc="best")
        ax.grid(linestyle="--", alpha=0.3, zorder=1)
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=120)
    plt.close(fig)
    buf.seek(0)
    return mpimg.imread(buf)