import os
from tqdm.auto import tqdm

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

device = "cuda" if torch.cuda.is_available() else "cpu"

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

def preprocess_image(image_path):
    img = Image.open(image_path).convert('RGB')
    processed_img = transform(img)
    return processed_img

def create_resnet18_model():
    model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
    
    modules = list(model.children())[:-1]
    model = nn.Sequential(*modules)
    return model

def extract_features(model, processed_image):
    input = processed_image.unsqueeze(dim=0).to(device)
    model.eval()
    with torch.inference_mode():
        prediction = model(input)
    return prediction.squeeze().tolist()