"""
Model evaluation module for tumor detection and segmentation.

This module provides comprehensive evaluation metrics and visualization
tools for assessing model performance.
"""

import sys
from pathlib import Path

# Add src to path for imports
sys.path.append(str(Path(__file__).parent.parent))

try:
    import torch
    import numpy as np
    from monai.metrics import DiceMetric, HausdorffDistanceMetric
    from monai.transforms import Compose, Activations, AsDiscrete
    from sklearn.metrics import confusion_matrix, classification_report
    import matplotlib.pyplot as plt
    import seaborn as sns
    DEPENDENCIES_AVAILABLE = True
except ImportError:
    DEPENDENCIES_AVAILABLE = False

from typing import Dict, List, Any, Optional, Tuple
import json
import argparse


class ModelEvaluator:
    """Comprehensive model evaluation class."""
    
    def __init__(self, model, device: str = 'auto'):
        """
        Initialize evaluator.
        
        Args:
            model: Trained model to evaluate
            device: Device to run evaluation on
        """
        if not DEPENDENCIES_AVAILABLE:
            raise ImportError("Required dependencies not available")
        
        self.model = model
        
        if device == 'auto':
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        else:
            self.device = torch.device(device)
        
        self.model.to(self.device)
        self.model.eval()
        
        # Initialize metrics
        self.dice_metric = DiceMetric(include_background=False, reduction="mean")
        self.hausdorff_metric = HausdorffDistanceMetric(
            include_background=False, 
            reduction="mean",
            percentile=95
        )
        
        # Post-processing transforms
        self.post_transforms = Compose([
            Activations(softmax=True),
            AsDiscrete(argmax=True, to_onehot=True)
        ])
    
    def evaluate_dataset(self, data_loader) -> Dict[str, float]:
        """
        Evaluate model on a dataset.
        
        Args:
            data_loader: DataLoader containing evaluation data
            
        Returns:
            Dictionary containing evaluation metrics
        """
        self.dice_metric.reset()
        self.hausdorff_metric.reset()
        
        total_loss = 0.0
        num_batches = len(data_loader)
        
        all_predictions = []
        all_targets = []
        
        with torch.no_grad():
            for batch in data_loader:
                inputs = batch["image"].to(self.device)
                targets = batch["label"].to(self.device)
                
                # Forward pass
                outputs = self.model(inputs)
                
                # Post-process outputs
                outputs_processed = self.post_transforms(outputs)
                
                # Compute metrics
                self.dice_metric(y_pred=outputs_processed, y=targets)
                self.hausdorff_metric(y_pred=outputs_processed, y=targets)
                
                # Store for additional analysis
                all_predictions.extend(
                    outputs_processed.cpu().numpy().flatten()
                )
                all_targets.extend(targets.cpu().numpy().flatten())
        
        # Aggregate metrics
        results = {
            'dice_score': float(self.dice_metric.aggregate().item()),
            'hausdorff_distance': float(self.hausdorff_metric.aggregate().item()),
            'num_samples': len(data_loader.dataset)
        }
        
        # Compute additional metrics
        confusion = confusion_matrix(all_targets, all_predictions)
        results['confusion_matrix'] = confusion.tolist()
        
        return results
    
    def generate_report(self, results: Dict[str, Any], output_path: str):
        """Generate comprehensive evaluation report."""
        report = {
            'evaluation_summary': {
                'dice_score': results['dice_score'],
                'hausdorff_distance': results['hausdorff_distance'],
                'num_samples': results['num_samples']
            },
            'detailed_metrics': results
        }
        
        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(report, f, indent=2)
        
        print(f"Evaluation report saved to: {output_path}")


def main():
    """Main evaluation function."""
    parser = argparse.ArgumentParser(description='Evaluate tumor segmentation model')
    parser.add_argument('--model', type=str, required=True,
                       help='Path to trained model checkpoint')
    parser.add_argument('--config', type=str, default='config.json',
                       help='Path to configuration file')
    parser.add_argument('--data', type=str, required=True,
                       help='Path to evaluation data')
    parser.add_argument('--output', type=str, default='evaluation_results.json',
                       help='Path for output results')
    
    args = parser.parse_args()
    
    if not DEPENDENCIES_AVAILABLE:
        print("Error: Required dependencies not available.")
        print("Please install: pip install -r requirements.txt")
        return
    
    print("Model Evaluation Starting...")
    print("=" * 40)
    
    # TODO: Implement actual evaluation logic
    # This is a placeholder structure
    
    print("Evaluation completed!")


if __name__ == "__main__":
    main()
