import os
import sys
from pathlib import Path

# Add project root to sys.path
sys.path.append(str(Path(__file__).parent.parent))

import matplotlib  # noqa: E402

matplotlib.use("Agg")  # Use headless backend
import matplotlib.pyplot as plt  # noqa: E402
import torch  # noqa: E402
import torch.nn as nn  # noqa: E402
import yaml  # noqa: E402
from sklearn.metrics import (  # noqa: E402
    ConfusionMatrixDisplay,
    classification_report,
    confusion_matrix,
)


def load_config(config_path="config.yaml"):
    with open(config_path, "r") as f:
        return yaml.safe_load(f)


config = load_config()
CLASSES = config["classes"]


def get_device(config_device):
    if config_device == "auto":
        return "cuda" if torch.cuda.is_available() else "cpu"
    return config_device


DEVICE = get_device(config["device"])


def evaluate(model, data_loader, device=DEVICE, save_dir="models/plots"):
    """
    Evaluates a PyTorch model on a given DataLoader.

    Args:
        model: The PyTorch model to evaluate.
        data_loader: The DataLoader providing the evaluation data.
        device: The device to run evaluation on (e.g., 'cuda', 'cpu').
        save_dir: Directory to save plots.

    Returns:
        avg_loss (float): The average loss over the dataset.
        accuracy (float): The classification accuracy (0.0 to 1.0).
    """
    model.to(device)
    model.eval()
    criterion = nn.CrossEntropyLoss()

    total_loss = 0.0
    correct = 0
    total = 0

    all_preds = []
    all_labels = []

    with torch.no_grad():
        for images, labels in data_loader:
            images, labels = images.to(device), labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            total_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

            all_preds.extend(predicted.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())

    avg_loss = total_loss / len(data_loader)
    accuracy = correct / total

    print("\nEvaluation Results:")
    print(f"Average Loss: {avg_loss:.4f}")
    print(f"Accuracy: {accuracy:.4f}")

    # Classification Report
    print("\nClassification Report:")
    report = classification_report(
        all_labels, all_preds, target_names=CLASSES, labels=range(len(CLASSES)), zero_division=0
    )
    print(report)

    # Confusion Matrix
    cm = confusion_matrix(all_labels, all_preds, labels=range(len(CLASSES)))
    os.makedirs(save_dir, exist_ok=True)

    fig, ax = plt.subplots(figsize=(10, 8))
    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=CLASSES)
    disp.plot(cmap=plt.cm.Blues, ax=ax, xticks_rotation=45)
    plt.title("Confusion Matrix")
    plt.tight_layout()
    plt.savefig(f"{save_dir}/confusion_matrix.png")
    print(f"\nConfusion matrix saved to {save_dir}/confusion_matrix.png")

    return avg_loss, accuracy