"""
Model evaluation utilities for emotion recognition.
"""
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from typing import Dict, List, Optional, Tuple

from sklearn.metrics import (
    classification_report, confusion_matrix,
    accuracy_score, precision_recall_fscore_support,
    roc_curve, auc
)
from tensorflow.keras.models import Model

import sys
sys.path.append(str(Path(__file__).parent.parent.parent))
from src.config import EMOTION_CLASSES, NUM_CLASSES, MODELS_DIR


def evaluate_model(
    model: Model,
    test_generator,
    class_names: List[str] = EMOTION_CLASSES
) -> Dict:
    """
    Evaluate a trained model on test data.
    
    Args:
        model: Trained Keras model
        test_generator: Test data generator
        class_names: List of class names
        
    Returns:
        Dictionary with evaluation metrics
    """
    # Reset generator to start
    test_generator.reset()
    
    # Get predictions
    predictions = model.predict(test_generator, verbose=1)
    y_pred = np.argmax(predictions, axis=1)
    y_true = test_generator.classes
    
    # Calculate metrics
    accuracy = accuracy_score(y_true, y_pred)
    precision, recall, f1, support = precision_recall_fscore_support(
        y_true, y_pred, average=None
    )
    
    # Per-class metrics
    per_class_metrics = {}
    for i, class_name in enumerate(class_names):
        per_class_metrics[class_name] = {
            "precision": float(precision[i]),
            "recall": float(recall[i]),
            "f1_score": float(f1[i]),
            "support": int(support[i])
        }
    
    # Overall metrics
    precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(
        y_true, y_pred, average='macro'
    )
    precision_weighted, recall_weighted, f1_weighted, _ = precision_recall_fscore_support(
        y_true, y_pred, average='weighted'
    )
    
    results = {
        "accuracy": float(accuracy),
        "macro_precision": float(precision_macro),
        "macro_recall": float(recall_macro),
        "macro_f1": float(f1_macro),
        "weighted_precision": float(precision_weighted),
        "weighted_recall": float(recall_weighted),
        "weighted_f1": float(f1_weighted),
        "per_class": per_class_metrics,
        "predictions": y_pred.tolist(),
        "true_labels": y_true.tolist(),
        "probabilities": predictions.tolist()
    }
    
    return results


def generate_classification_report(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    class_names: List[str] = EMOTION_CLASSES,
    output_dict: bool = True
) -> Dict:
    """
    Generate a classification report.
    
    Args:
        y_true: True labels
        y_pred: Predicted labels
        class_names: List of class names
        output_dict: Whether to return as dictionary
        
    Returns:
        Classification report
    """
    report = classification_report(
        y_true, y_pred,
        target_names=class_names,
        output_dict=output_dict
    )
    
    if not output_dict:
        print(report)
    
    return report


def compute_confusion_matrix(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    normalize: bool = True
) -> np.ndarray:
    """
    Compute confusion matrix.
    
    Args:
        y_true: True labels
        y_pred: Predicted labels
        normalize: Whether to normalize the matrix
        
    Returns:
        Confusion matrix
    """
    cm = confusion_matrix(y_true, y_pred)
    
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    
    return cm


def plot_confusion_matrix(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    class_names: List[str] = EMOTION_CLASSES,
    normalize: bool = True,
    figsize: Tuple[int, int] = (12, 10),
    cmap: str = 'Blues',
    save_path: Optional[Path] = None,
    title: str = "Confusion Matrix"
) -> plt.Figure:
    """
    Plot confusion matrix as a heatmap.
    
    Args:
        y_true: True labels
        y_pred: Predicted labels
        class_names: List of class names
        normalize: Whether to normalize
        figsize: Figure size
        cmap: Colormap
        save_path: Optional path to save the figure
        title: Plot title
        
    Returns:
        Matplotlib figure
    """
    cm = compute_confusion_matrix(y_true, y_pred, normalize=normalize)
    
    fig, ax = plt.subplots(figsize=figsize)
    
    sns.heatmap(
        cm, annot=True, fmt='.2f' if normalize else 'd',
        cmap=cmap, ax=ax,
        xticklabels=class_names,
        yticklabels=class_names,
        square=True,
        cbar_kws={'shrink': 0.8}
    )
    
    ax.set_xlabel('Predicted Label', fontsize=12)
    ax.set_ylabel('True Label', fontsize=12)
    ax.set_title(title, fontsize=14)
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path, dpi=150, bbox_inches='tight')
        print(f"Confusion matrix saved to: {save_path}")
    
    return fig


def plot_training_history(
    history: Dict,
    metrics: List[str] = ['accuracy', 'loss'],
    figsize: Tuple[int, int] = (14, 5),
    save_path: Optional[Path] = None
) -> plt.Figure:
    """
    Plot training history curves.
    
    Args:
        history: Training history dictionary
        metrics: Metrics to plot
        figsize: Figure size
        save_path: Optional path to save the figure
        
    Returns:
        Matplotlib figure
    """
    num_metrics = len(metrics)
    fig, axes = plt.subplots(1, num_metrics, figsize=figsize)
    
    if num_metrics == 1:
        axes = [axes]
    
    for ax, metric in zip(axes, metrics):
        if metric in history:
            epochs = range(1, len(history[metric]) + 1)
            ax.plot(epochs, history[metric], 'b-', label=f'Training {metric.capitalize()}')
            
            val_metric = f'val_{metric}'
            if val_metric in history:
                ax.plot(epochs, history[val_metric], 'r-', label=f'Validation {metric.capitalize()}')
            
            ax.set_xlabel('Epoch')
            ax.set_ylabel(metric.capitalize())
            ax.set_title(f'{metric.capitalize()} over Epochs')
            ax.legend()
            ax.grid(True, alpha=0.3)
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path, dpi=150, bbox_inches='tight')
        print(f"Training history plot saved to: {save_path}")
    
    return fig


def plot_per_class_metrics(
    results: Dict,
    figsize: Tuple[int, int] = (14, 6),
    save_path: Optional[Path] = None
) -> plt.Figure:
    """
    Plot per-class precision, recall, and F1 scores.
    
    Args:
        results: Evaluation results dictionary
        figsize: Figure size
        save_path: Optional path to save
        
    Returns:
        Matplotlib figure
    """
    per_class = results['per_class']
    classes = list(per_class.keys())
    
    precision = [per_class[c]['precision'] for c in classes]
    recall = [per_class[c]['recall'] for c in classes]
    f1 = [per_class[c]['f1_score'] for c in classes]
    
    x = np.arange(len(classes))
    width = 0.25
    
    fig, ax = plt.subplots(figsize=figsize)
    
    bars1 = ax.bar(x - width, precision, width, label='Precision', color='#3498db')
    bars2 = ax.bar(x, recall, width, label='Recall', color='#2ecc71')
    bars3 = ax.bar(x + width, f1, width, label='F1-Score', color='#e74c3c')
    
    ax.set_xlabel('Emotion Class')
    ax.set_ylabel('Score')
    ax.set_title('Per-Class Performance Metrics')
    ax.set_xticks(x)
    ax.set_xticklabels(classes, rotation=45, ha='right')
    ax.legend()
    ax.set_ylim(0, 1.0)
    ax.grid(True, alpha=0.3, axis='y')
    
    # Add value labels
    for bars in [bars1, bars2, bars3]:
        for bar in bars:
            height = bar.get_height()
            ax.annotate(f'{height:.2f}',
                       xy=(bar.get_x() + bar.get_width() / 2, height),
                       xytext=(0, 3),
                       textcoords="offset points",
                       ha='center', va='bottom', fontsize=8)
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path, dpi=150, bbox_inches='tight')
        print(f"Per-class metrics plot saved to: {save_path}")
    
    return fig


def compute_roc_curves(
    y_true: np.ndarray,
    y_proba: np.ndarray,
    class_names: List[str] = EMOTION_CLASSES
) -> Dict:
    """
    Compute ROC curves for each class.
    
    Args:
        y_true: True labels (one-hot encoded)
        y_proba: Prediction probabilities
        class_names: List of class names
        
    Returns:
        Dictionary with ROC curve data
    """
    # Convert to one-hot if needed
    if len(y_true.shape) == 1:
        y_true_onehot = np.zeros((len(y_true), len(class_names)))
        y_true_onehot[np.arange(len(y_true)), y_true] = 1
        y_true = y_true_onehot
    
    roc_data = {}
    for i, class_name in enumerate(class_names):
        fpr, tpr, thresholds = roc_curve(y_true[:, i], y_proba[:, i])
        roc_auc = auc(fpr, tpr)
        
        roc_data[class_name] = {
            'fpr': fpr.tolist(),
            'tpr': tpr.tolist(),
            'auc': float(roc_auc)
        }
    
    return roc_data


def plot_roc_curves(
    roc_data: Dict,
    figsize: Tuple[int, int] = (10, 8),
    save_path: Optional[Path] = None
) -> plt.Figure:
    """
    Plot ROC curves for all classes.
    
    Args:
        roc_data: ROC curve data from compute_roc_curves
        figsize: Figure size
        save_path: Optional save path
        
    Returns:
        Matplotlib figure
    """
    fig, ax = plt.subplots(figsize=figsize)
    
    colors = plt.cm.Set2(np.linspace(0, 1, len(roc_data)))
    
    for (class_name, data), color in zip(roc_data.items(), colors):
        ax.plot(
            data['fpr'], data['tpr'],
            color=color, lw=2,
            label=f"{class_name} (AUC = {data['auc']:.2f})"
        )
    
    ax.plot([0, 1], [0, 1], 'k--', lw=2, label='Random')
    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.05])
    ax.set_xlabel('False Positive Rate')
    ax.set_ylabel('True Positive Rate')
    ax.set_title('ROC Curves by Emotion Class')
    ax.legend(loc='lower right')
    ax.grid(True, alpha=0.3)
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path, dpi=150, bbox_inches='tight')
        print(f"ROC curves saved to: {save_path}")
    
    return fig


def compare_models(
    model_results: Dict[str, Dict],
    save_path: Optional[Path] = None
) -> plt.Figure:
    """
    Compare multiple models.
    
    Args:
        model_results: Dictionary of model_name -> evaluation results
        save_path: Optional save path
        
    Returns:
        Matplotlib figure
    """
    models = list(model_results.keys())
    metrics = ['accuracy', 'macro_precision', 'macro_recall', 'macro_f1']
    
    fig, ax = plt.subplots(figsize=(12, 6))
    
    x = np.arange(len(models))
    width = 0.2
    
    for i, metric in enumerate(metrics):
        values = [model_results[m].get(metric, 0) for m in models]
        offset = (i - len(metrics)/2 + 0.5) * width
        bars = ax.bar(x + offset, values, width, label=metric.replace('_', ' ').title())
    
    ax.set_xlabel('Model')
    ax.set_ylabel('Score')
    ax.set_title('Model Comparison')
    ax.set_xticks(x)
    ax.set_xticklabels(models)
    ax.legend()
    ax.set_ylim(0, 1.0)
    ax.grid(True, alpha=0.3, axis='y')
    
    plt.tight_layout()
    
    if save_path:
        plt.savefig(save_path, dpi=150, bbox_inches='tight')
        print(f"Model comparison saved to: {save_path}")
    
    return fig


if __name__ == "__main__":
    # Example usage
    print("Evaluation module loaded successfully.")
    print(f"Emotion classes: {EMOTION_CLASSES}")