import matplotlib.pyplot as plt
import os
from pathlib import Path

def plot_loss_curve(train_losses, val_accs=None, out_path=None, filename="loss_curve.png", prefix=""):
    """
    Plots the training loss and optionally validation accuracy over epochs.
    Saves the figure to out_path / filename.
    """
    if out_path is None:
        return
        
    out_path = Path(out_path)
    out_path.mkdir(parents=True, exist_ok=True)
    
    fig, ax1 = plt.subplots(figsize=(10, 5))
    
    epochs = range(len(train_losses))
    
    color = 'tab:red'
    ax1.set_xlabel('Epochs')
    ax1.set_ylabel('Training Loss', color=color)
    ax1.plot(epochs, train_losses, color=color, marker='o', label=f'{prefix} Train Loss')
    ax1.tick_params(axis='y', labelcolor=color)
    
    if val_accs and len(val_accs) == len(train_losses):
        ax2 = ax1.twinx()
        color = 'tab:blue'
        ax2.set_ylabel('Validation Acc', color=color)
        ax2.plot(epochs, val_accs, color=color, marker='x', label=f'{prefix} Val Acc')
        ax2.tick_params(axis='y', labelcolor=color)
        
    fig.tight_layout()
    plt.title(f'{prefix} Training Metrics')
    plt.grid(True)
    
    save_path = out_path / filename
    plt.savefig(save_path)
    plt.close()
    print(f"Saved {prefix} loss curve to {save_path}")