predictor.py

import torch
import timm
from PIL import Image
from torchvision import transforms

class AIDetector:
    def __init__(self, model_path, config_path):
        with open(config_path, 'r') as f:
            self.config = __import__('json').load(f)
        
        # Load Architecture
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = timm.create_model(self.config['model_name'], pretrained=False, num_classes=2)
        
        # Load Weights
        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
        self.model.to(self.device)
        self.model.eval()

        # Setup Transforms
        self.transform = transforms.Compose([
            transforms.Resize((self.config['img_size'], self.config['img_size'])),
            transforms.ToTensor(),
            transforms.Normalize(mean=self.config['mean'], std=self.config['std']),
        ])

    def predict(self, image_path):
        img = Image.open(image_path).convert('RGB')
        img_t = self.transform(img).unsqueeze(0).to(self.device)
        
        with torch.no_grad():
            outputs = self.model(img_t)
            probs = torch.nn.functional.softmax(outputs, dim=1)
            conf, pred = torch.max(probs, 1)
            
        label = self.config['labels'][str(pred.item())]
        return {"prediction": label, "confidence": conf.item()}

# Example usage:
# detector = AIDetector('convnext_ai_detector_final.pth', 'model_config.json')
# print(detector.predict('test_image.jpg'))