"""
Unified Inference Engine for MedAdapt-SAM Dashboard
"""

import torch
import torch.nn as nn
import numpy as np
from typing import Dict, Any

class MedAdaptEngine:
    """Unified engine to handle different model types for the dashboard"""
    
    def __init__(self, checkpoints_path: str = "checkpoints/"):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.checkpoints_path = checkpoints_path
        self.models = {}
        
    def load_unet(self):
        from models.unet import UNet
        model = UNet(in_channels=3, out_channels=3)
        # Load weights if exist
        return model.to(self.device).eval()
        
    def load_sam_adapter(self):
        from models.sam_adapter import SAMAdapter
        model = SAMAdapter(model_type='vit_b')
        # Load weights if exist
        return model.to(self.device).eval()

    def predict(self, image_np: np.ndarray, model_type: str) -> Dict[str, np.ndarray]:
        """Generic prediction wrapper"""
        # 1. Preprocess
        img_tensor = torch.from_numpy(image_np).permute(2, 0, 1).float().divide(255.0).unsqueeze(0).to(self.device)
        
        # 2. Logic based on model_type
        # (This is where the actual multi-phase model logic connects)
        
        # Placeholder for real output
        # In production: with torch.no_grad(): out = self.models[model_type](img_tensor)
        
        # Returning dummy masks for structural validation
        h, w = image_np.shape[:2]
        return {
            'ET': (np.random.rand(h, w) > 0.95).astype(np.float32),
            'TC': (np.random.rand(h, w) > 0.90).astype(np.float32),
            'WT': (np.random.rand(h, w) > 0.85).astype(np.float32)
        }