"""Gera graficos do benchmark 1K (811 testes)."""

import json
import os
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np

plt.rcParams.update({
    "figure.facecolor": "#0d1117",
    "axes.facecolor": "#161b22",
    "axes.edgecolor": "#30363d",
    "axes.labelcolor": "#c9d1d9",
    "xtick.color": "#8b949e",
    "ytick.color": "#8b949e",
    "text.color": "#c9d1d9",
    "grid.color": "#21262d",
    "grid.linestyle": "--",
    "grid.alpha": 0.6,
    "font.family": "sans-serif",
    "font.size": 11,
})

OUTPUT_DIR = "training_plots"


def load_results(path="benchmark_1k_results.json"):
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def plot_accuracy_bars(data, save_dir):
    fig, axes = plt.subplots(1, 3, figsize=(16, 6))

    # 1. Por modalidade
    ax = axes[0]
    modalities = ["Texto\n(401)", "Imagem\n(250)", "Audio\n(160)", "COMPOSTO\n(811)"]
    accs = [
        data["text"]["_total"]["acc"],
        data["image"]["both_acc"],
        data["audio"]["acc"],
        data["composite_score"],
    ]
    colors = ["#58a6ff", "#7ee787", "#d2a8ff", "#ffa657"]
    bars = ax.bar(modalities, accs, color=colors, width=0.6, edgecolor="#30363d", linewidth=1.2)
    for bar, acc in zip(bars, accs):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
                f"{acc:.1f}%", ha="center", va="bottom", fontsize=12, fontweight="bold",
                color="#c9d1d9")
    ax.set_ylim(0, 110)
    ax.set_ylabel("Acuracia (%)")
    ax.set_title("Por Modalidade", fontsize=13, fontweight="bold", pad=10)
    ax.grid(axis="y", alpha=0.3)

    # 2. Texto por categoria
    ax = axes[1]
    cats = []
    cat_accs = []
    for cat, r in data["text"].items():
        if cat != "_total":
            label = cat.replace("(fora treino)", "\n(fora treino)")
            cats.append(label)
            cat_accs.append(r["acc"])
    bar_colors = ["#58a6ff"] * len(cats)
    bars2 = ax.barh(cats, cat_accs, color=bar_colors, height=0.5, edgecolor="#30363d", linewidth=1)
    for bar, acc in zip(bars2, cat_accs):
        ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2,
                f"{acc:.0f}%", ha="left", va="center", fontsize=11, fontweight="bold",
                color="#c9d1d9")
    ax.set_xlim(0, 115)
    ax.set_xlabel("Acuracia (%)")
    ax.set_title("Texto por Categoria", fontsize=13, fontweight="bold", pad=10)
    ax.grid(axis="x", alpha=0.3)

    # 3. Imagem & Audio detalhe
    ax = axes[2]
    labels = ["Forma", "Cor", "Forma+Cor", "Audio"]
    vals = [
        data["image"]["shape_acc"],
        data["image"]["color_acc"],
        data["image"]["both_acc"],
        data["audio"]["acc"],
    ]
    cols = ["#7ee787", "#7ee787", "#7ee787", "#d2a8ff"]
    bars3 = ax.bar(labels, vals, color=cols, width=0.5, edgecolor="#30363d", linewidth=1.2)
    for bar, acc in zip(bars3, vals):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
                f"{acc:.1f}%", ha="center", va="bottom", fontsize=12, fontweight="bold",
                color="#c9d1d9")
    ax.set_ylim(0, 110)
    ax.set_ylabel("Acuracia (%)")
    ax.set_title("Imagem & Audio", fontsize=13, fontweight="bold", pad=10)
    ax.grid(axis="y", alpha=0.3)

    fig.suptitle(
        f"Benchmark 1K  |  Score: {data['composite_score']:.1f}%  |  "
        f"{data['total_tests']} testes (texto + img + audio)",
        fontsize=14, fontweight="bold", y=0.98, color="#58a6ff"
    )
    plt.tight_layout(rect=[0, 0, 1, 0.94])
    path = os.path.join(save_dir, "benchmark.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    print(f"  Salvo: {path}")


def plot_radar(data, save_dir):
    cats_text = {k: v for k, v in data["text"].items() if k != "_total"}
    categories = list(cats_text.keys()) + ["Imagem", "Audio"]
    values = [v["acc"] for v in cats_text.values()] + [data["image"]["both_acc"], data["audio"]["acc"]]

    N = len(categories)
    angles = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
    values_closed = values + [values[0]]
    angles += [angles[0]]

    fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
    ax.set_facecolor("#161b22")
    fig.patch.set_facecolor("#0d1117")

    ax.plot(angles, values_closed, "o-", linewidth=2, color="#58a6ff", alpha=0.9)
    ax.fill(angles, values_closed, color="#58a6ff", alpha=0.15)

    short_names = []
    for c in categories:
        c2 = c.replace("(fora treino)", "").strip()
        if len(c2) > 14:
            c2 = c2[:13] + "."
        short_names.append(c2)

    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(short_names, fontsize=9, color="#c9d1d9")
    ax.set_ylim(0, 105)
    ax.set_yticks([20, 40, 60, 80, 100])
    ax.set_yticklabels(["20%", "40%", "60%", "80%", "100%"], fontsize=8, color="#8b949e")
    ax.spines["polar"].set_color("#30363d")
    ax.grid(color="#21262d", alpha=0.6)

    for angle, val in zip(angles[:-1], values):
        ax.text(angle, val + 5, f"{val:.0f}%", ha="center", va="bottom",
                fontsize=10, fontweight="bold", color="#ffa657")

    ax.set_title(f"Benchmark 1K - Score: {data['composite_score']:.1f}%",
                 fontsize=14, fontweight="bold", pad=20, color="#c9d1d9")

    path = os.path.join(save_dir, "benchmark_radar.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    print(f"  Salvo: {path}")


def main():
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    data = load_results()
    print(f"  Score: {data['composite_score']}% | {data['total_tests']} testes")
    plot_accuracy_bars(data, OUTPUT_DIR)
    plot_radar(data, OUTPUT_DIR)
    print("  Done!")


if __name__ == "__main__":
    main()