import torch
import timm
import ttach as tta
from PIL import Image
import os

from core.augmentations import get_eval_transforms

class ProductionAnalyzer:
    def __init__(self, model_path, num_classes=2):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.transform = get_eval_transforms()
        self.class_names = {0: "Legit", 1: "Phishing"}
        
        print("Loading Production Model...")
        base_model = timm.create_model('convnext_tiny', pretrained=False, num_classes=num_classes)
        
        # Load weights and strip the 'module.' prefix caused by AveragedModel saving
        state_dict = torch.load(model_path, map_location=self.device, weights_only=True)
        clean_state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
        
        base_model.load_state_dict(clean_state_dict)
        base_model.to(self.device)
        base_model.eval()
        
        # Wrap in TTA (Test Time Augmentation)
        self.model = tta.ClassificationTTAWrapper(
            base_model, 
            tta.aliases.five_crop_transform(224, 224)
        )

    def analyze_user_input(self, image_path):
        if not os.path.exists(image_path):
            return "Error: Image file not found."
            
        image = Image.open(image_path).convert("RGB")
        input_tensor = self.transform(image).unsqueeze(0).to(self.device)
        
        with torch.no_grad():
            logits = self.model(input_tensor)
            probabilities = torch.softmax(logits, dim=1)
            confidence, predicted_class = torch.max(probabilities, dim=1)
            
        class_id = predicted_class.item()
        return {
            "prediction": self.class_names[class_id],
            "class_id": class_id,
            "confidence_score": f"{confidence.item() * 100:.2f}%"
        }

if __name__ == "__main__":
    # Ensure production_convnext_ema.pth exists in the directory before running
    analyzer = ProductionAnalyzer(model_path="production_convnext_ema.pth")
    
    # Example usage:
    # result = analyzer.analyze_user_input("path_to_screenshot_to_test.jpg")
    # print(result)