model.py

import torch
from torchvision import models, transforms
from PIL import Image
import json

# Load model
class CustomResNet:
    def __init__(self, model_path, num_classes):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = models.resnet152(pretrained=False)
        self.model.fc = torch.nn.Linear(self.model.fc.in_features, num_classes)
        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
        self.model.to(self.device)
        self.model.eval()

        # Preprocessing
        self.preprocess = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ])

    def predict(self, image_bytes):
        # Load and preprocess image
        image = Image.open(image_bytes).convert("RGB")
        tensor = self.preprocess(image).unsqueeze(0).to(self.device)

        # Make prediction
        with torch.no_grad():
            outputs = self.model(tensor)
            _, predicted = torch.max(outputs, 1)

        return predicted.item()


# API function
def load_model():
    with open("config.json", "r") as f:
        config = json.load(f)
    return CustomResNet("trained_model.pth", config["num_labels"])