import sys
import os
import time
import pandas as pd
import matplotlib.pyplot as plt

# Tambahkan root directory ke path agar dapat import dari src
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))

from src.data_loader import load_mnist_for_mlp
from src.models import create_mlp_model


def main():
    print("=" * 60)
    print("TUGAS 1: MEMUAT DATASET MNIST & EKSPERIMEN MLP")
    print("=" * 60)

    # 1. Load Data
    (x_train, y_train), (x_test, y_test) = load_mnist_for_mlp()
    print(f"Data training : {x_train.shape}")
    print(f"Data testing  : {x_test.shape}")

    # 2. Konfigurasi 3 Model MLP
    models_config = {
        "Model A - 1 Hidden Layer": [64],
        "Model B - 2 Hidden Layer": [128, 64],
        "Model C - 3 Hidden Layer": [256, 128, 64]
    }

    results = []
    EPOCHS = 10
    BATCH_SIZE = 128

    # 3. Training & Evaluasi
    for model_name, architecture in models_config.items():
        print("\n" + "=" * 60)
        print(model_name)
        print("=" * 60)

        model = create_mlp_model(architecture)
        model.summary()

        print("\nMemulai training...")
        start_time = time.time()

        model.fit(
            x_train,
            y_train,
            epochs=EPOCHS,
            batch_size=BATCH_SIZE,
            validation_split=0.1,
            verbose=1
        )

        total_time = time.time() - start_time
        average_time = total_time / EPOCHS

        test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)
        total_parameters = model.count_params()

        results.append({
            "model": model_name,
            "parameters": total_parameters,
            "accuracy": test_accuracy * 100,
            "time_per_epoch": average_time
        })

        print("\nHasil:")
        print(f"Test Accuracy     : {test_accuracy * 100:.2f}%")
        print(f"Jumlah Parameter  : {total_parameters:,}")
        print(f"Waktu/Epoch       : {average_time:.2f} detik")

    # 4. Ringkasan Perbandingan
    print("\n\n" + "=" * 80)
    print("HASIL PERBANDINGAN 3 MODEL MLP")
    print("=" * 80)
    print(f"{'Model':<30}{'Parameter':>15}{'Accuracy':>15}{'Time/Epoch':>15}")
    print("-" * 80)

    for res in results:
        print(
            f"{res['model']:<30}"
            f"{res['parameters']:>15,}"
            f"{res['accuracy']:>14.2f}%"
            f"{res['time_per_epoch']:>14.2f}s"
        )

    best_model = max(results, key=lambda x: x["accuracy"])
    print("\n" + "=" * 60)
    print("MODEL DENGAN TEST ACCURACY TERTINGGI")
    print("=" * 60)
    print(f"Model               : {best_model['model']}")
    print(f"Test Accuracy       : {best_model['accuracy']:.2f}%")
    print(f"Jumlah Parameter    : {best_model['parameters']:,}")
    print(f"Waktu Training/Epoch: {best_model['time_per_epoch']:.2f} detik")

    # 5. Simpan Hasil ke Folder results/01_mlp
    REPORTS_DIR = os.path.join("results", "01_mlp", "reports")
    FIGURES_DIR = os.path.join("results", "01_mlp", "figures")
    os.makedirs(REPORTS_DIR, exist_ok=True)
    os.makedirs(FIGURES_DIR, exist_ok=True)

    df_results = pd.DataFrame(results)
    csv_path = os.path.join(REPORTS_DIR, "perbandingan_mlp.csv")
    df_results.to_csv(csv_path, index=False)
    print(f"\n[OK] Ringkasan CSV disimpan ke : {csv_path}")

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
    names = ["Model A\n(1 Layer)", "Model B\n(2 Layer)", "Model C\n(3 Layer)"]
    accs = [r["accuracy"] for r in results]
    bars1 = ax1.bar(names, accs, color=["#4C72B0", "#55A868", "#C44E52"])
    ax1.set_title("Perbandingan Test Accuracy (%)", fontsize=12, fontweight="bold")
    ax1.set_ylabel("Accuracy (%)")
    ax1.set_ylim([max(0, min(accs) - 2), 100])
    for bar in bars1:
        yval = bar.get_height()
        ax1.text(bar.get_x() + bar.get_width()/2, yval + 0.2, f"{yval:.2f}%", ha='center', va='bottom')

    times = [r["time_per_epoch"] for r in results]
    bars2 = ax2.bar(names, times, color=["#4C72B0", "#55A868", "#C44E52"])
    ax2.set_title("Waktu Training per Epoch (detik)", fontsize=12, fontweight="bold")
    ax2.set_ylabel("Detik / Epoch")
    for bar in bars2:
        yval = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2, yval + 0.05, f"{yval:.2f}s", ha='center', va='bottom')

    plt.tight_layout()
    fig_path = os.path.join(FIGURES_DIR, "perbandingan_3_model_mlp.png")
    plt.savefig(fig_path, dpi=130)
    plt.close()
    print(f"[OK] Grafik perbandingan disimpan ke: {fig_path}")


if __name__ == "__main__":
    main()