### Example loading of the dataset

import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
import zipfile
import os
import pandas as pd
from torchvision.io import read_image

class CustomImageDataset(Dataset):
    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
        self.img_labels = pd.read_csv(annotations_file)
        self.img_dir = img_dir
        self.transform = transform
        self.target_transform = target_transform

    def __len__(self):
        return len(self.img_labels)

    def __getitem__(self, idx):
        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, -1])
        image = read_image(img_path)
        label = self.img_labels.iloc[idx, 2]
        if self.transform:
            image = self.transform(image)
        if self.target_transform:
            label = self.target_transform(label)
        return image, label

with zipfile.ZipFile("150_Dataset(1).zip", 'r') as zip_ref:
    zip_ref.extractall(".")

train_dataset = CustomImageDataset(annotations_file="./images/train/train.csv",
                                   img_dir="./images/train")
                                 
train_dataloader = DataLoader(train_dataset, batch_size=12, shuffle=True)

train_features, train_labels = next(iter(train_dataloader))
print(f"Feature batch shape: {train_features.size()}")
print(f"Labels batch shape: {len(train_labels)}")
img = train_features[0].squeeze()
label = train_labels[0]
plt.imshow(img, cmap="gray")
plt.show()
print(f"Label: {label}")