utils.py

import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from torchvision.models import resnet18

class ResNet18Classifier(nn.Module):
    def __init__(self, num_classes=3):
        super().__init__()
        self.resnet = resnet18(weights=None)  # modern way
        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, num_classes)

    def forward(self, x):
        return self.resnet(x)

def load_model(model_path="model/best_classification_model.pth", num_classes=3):
    model = ResNet18Classifier(num_classes=num_classes)
    state_dict = torch.load(model_path, map_location='cpu')
    model.load_state_dict(state_dict)
    model.eval()
    return model

def predict_image(image_path, model, class_names):
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])
    image = Image.open(image_path).convert('RGB')
    image_tensor = transform(image).unsqueeze(0)

    with torch.no_grad():
        outputs = model(image_tensor)
        _, predicted = torch.max(outputs, 1)

    return class_names[predicted.item()]