import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from PIL import Image
import torch


class AnimalDataset(Dataset):
    def __init__(self, df, transform=None):
        self.paths = df["path"].values
        self.targets = df["target"].values
        self.encoded_target = df['encoded_target'].values
        self.transform = transform
        self.images = []
        for path in tqdm(self.paths):
            self.images.append(Image.open(path).convert("RGB").resize((224, 224)))
        
    def __len__(self):
        return len(self.paths)
    
    def __getitem__(self, idx):
        img = self.images[idx]
        if self.transform:
            img = self.transform(img)
        target = self.targets[idx]
        encoded_target = torch.tensor(self.encoded_target[idx]).type(torch.LongTensor)
        return img, encoded_target, target

train_transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Define the transformation pipeline
transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),  # Convert the images to PyTorch tensors
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])