data_loader.py

# data_loader.py
import os
import pandas as pd
import numpy as np
import torch  # Add this import
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from sklearn.model_selection import train_test_split
import torchvision.transforms as transforms

class XRayDataset(Dataset):
    def __init__(self, dataframe, image_dir, transform=None):
        self.dataframe = dataframe
        self.image_dir = image_dir
        self.transform = transform
        self.classes = [
            'pneumonia',
            'atelectasis',
            'pleural effusion',
            'consolidation',
            'cardiomegaly',
            'edema',
            'emphysema',
            'tuberculosis'
        ]

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

    def __getitem__(self, idx):
        img_name = self.dataframe.iloc[idx]['image_name'] + '.png'
        img_path = os.path.join(self.image_dir, img_name)
        image = Image.open(img_path).convert('RGB')
        labels = self.dataframe.iloc[idx][self.classes].values.astype(np.float32)

        if self.transform:
            image = self.transform(image)

        return image, torch.tensor(labels)

# Define transforms
data_transforms = {
    'train': transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.RandomHorizontalFlip(),
        transforms.RandomRotation(10),
        transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}


def prepare_loaders(batch_size=16, test_size=0.2):
    df = pd.read_csv('data/xray_illness_classification.csv')
    train_df, val_df = train_test_split(df, test_size=test_size, random_state=42)

    train_dataset = XRayDataset(train_df, 'data/xray_images', data_transforms['train'])
    val_dataset = XRayDataset(val_df, 'data/xray_images', data_transforms['val'])

    dataloaders = {
        'train': DataLoader(train_dataset, batch_size=batch_size, shuffle=True),
        'val': DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
    }

    return dataloaders