import os
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import models
import torch.nn as nn
from tqdm import tqdm

 

 
def load_model(model_path="models/style_model.pth", class_names=[]):
    import torch
    from torchvision import models
    model = models.resnet18(pretrained=False)
    model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
    model.eval()
    return model

def predict_style(image_path, model, class_names):
    from PIL import Image
    from torchvision import transforms
    import torch

    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor()
    ])
    image = Image.open(image_path).convert("RGB")
    image = transform(image).unsqueeze(0)

    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output, 1)
    
    return class_names[predicted.item()]