"""Alzheimer's disease prediction models"""

import logging
from typing import Dict, Any, Optional, Union
import numpy as np
from .base import BaseModel

logger = logging.getLogger(__name__)


class AlzheimerClassifier(BaseModel):
    """
    Multi-modal classifier for Alzheimer's disease prediction
    
    Combines neuroimaging, clinical, and microbiome data for 
    comprehensive AD risk assessment.
    """
    
    def __init__(
        self,
        model_name: str = "default",
        use_pretrained: bool = True,
        device: Optional[str] = None
    ):
        """
        Initialize Alzheimer's classifier
        
        Args:
            model_name: Name of the model architecture
            use_pretrained: Whether to load pretrained weights
            device: Computing device (cuda/cpu)
        """
        super().__init__(model_name, device)
        self.use_pretrained = use_pretrained
        self._load_model()
    
    def _load_model(self) -> None:
        """Load or initialize model"""
        if self.use_pretrained:
            logger.info(f"Loading pretrained model: {self.model_name}")
            # TODO: Implement model loading from HuggingFace Hub
        else:
            logger.info("Initializing new model")
            # TODO: Initialize model architecture
    
    def predict(
        self,
        imaging_data: Optional[np.ndarray] = None,
        clinical_data: Optional[Dict[str, Any]] = None,
        microbiome_data: Optional[Dict[str, Any]] = None
    ) -> Dict[str, float]:
        """
        Predict Alzheimer's disease risk
        
        Args:
            imaging_data: Preprocessed brain imaging data
            clinical_data: Clinical measurements and demographics
            microbiome_data: Microbiome composition data
            
        Returns:
            Dictionary with prediction scores
        """
        logger.info("Running AD prediction")
        
        # Placeholder implementation
        results = {
            "ad_probability": 0.15,
            "mild_cognitive_impairment": 0.25,
            "healthy": 0.60,
            "confidence": 0.85
        }
        
        # TODO: Implement actual prediction logic
        
        return results
    
    def train(
        self,
        train_data: Any,
        val_data: Optional[Any] = None,
        epochs: int = 100,
        learning_rate: float = 1e-4
    ) -> Dict[str, Any]:
        """
        Train the model
        
        Args:
            train_data: Training dataset
            val_data: Validation dataset
            epochs: Number of training epochs
            learning_rate: Learning rate
            
        Returns:
            Training history and metrics
        """
        logger.info(f"Starting training for {epochs} epochs")
        
        # Placeholder implementation
        history = {
            "train_loss": [],
            "val_loss": [],
            "train_acc": [],
            "val_acc": []
        }
        
        # TODO: Implement training loop
        
        return history


class AlzheimerProgressionModel(BaseModel):
    """Model for predicting AD progression over time"""
    
    def __init__(self, model_name: str = "progression_lstm"):
        """Initialize progression model"""
        super().__init__(model_name)
        logger.info("Initialized AD progression model")
    
    def predict_trajectory(
        self,
        baseline_data: Dict[str, Any],
        timepoints: int = 5
    ) -> Dict[str, Any]:
        """
        Predict disease progression trajectory
        
        Args:
            baseline_data: Baseline measurements
            timepoints: Number of future timepoints to predict
            
        Returns:
            Predicted progression trajectory
        """
        # Placeholder implementation
        return {
            "timepoints": list(range(timepoints)),
            "cognitive_scores": [85 - i*2 for i in range(timepoints)],
            "brain_volume": [100 - i*1.5 for i in range(timepoints)]
        }
