app.py

import os
import zipfile
import numpy as np
import torch
from transformers import ViTForImageClassification, AdamW
import nibabel as nib
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import streamlit as st
import requests
import tempfile

# Function to download zip files from URL
def download_zip(url, download_path):
    response = requests.get(url)
    with open(download_path, 'wb') as file:
        file.write(response.content)

# Function to extract zip files
def extract_zip(zip_file, extract_to):
    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
        zip_ref.extractall(extract_to)

# Preprocess images
def preprocess_image(image_path):
    ext = os.path.splitext(image_path)[-1].lower()

    if ext in ['.nii', '.nii.gz']:
        nii_image = nib.load(image_path)
        image_data = nii_image.get_fdata()
        image_tensor = torch.tensor(image_data).float()
        if len(image_tensor.shape) == 3:
            image_tensor = image_tensor.unsqueeze(0)

    elif ext in ['.jpg', '.jpeg']:
        img = Image.open(image_path).convert('RGB').resize((224, 224))
        img_np = np.array(img)
        image_tensor = torch.tensor(img_np).permute(2, 0, 1).float()

    else:
        raise ValueError(f"Unsupported format: {ext}")

    image_tensor /= 255.0  # Normalize to [0, 1]
    return image_tensor

# Prepare dataset
def prepare_dataset(extracted_folder):
    image_paths = []
    labels = []
    
    # Define the paths for each disease dataset
    datasets = {
        'alzheimer_datasets': 0,
        'parkinson_datasets': 1,
        'MSjpg': 2
    }
    
    for disease_folder, label in datasets.items():
        folder_path = os.path.join(extracted_folder, 'neuroniiimages', disease_folder)
        for img_file in os.listdir(folder_path):
            if img_file.endswith(('.nii', '.jpg', '.jpeg')):
                image_paths.append(os.path.join(folder_path, img_file))
                labels.append(label)
    
    return image_paths, labels

# Custom Dataset class
class CustomImageDataset(Dataset):
    def __init__(self, image_paths, labels):
        self.image_paths = image_paths
        self.labels = labels

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

    def __getitem__(self, idx):
        image = preprocess_image(self.image_paths[idx])
        label = self.labels[idx]
        return image, label

# Training function
def fine_tune_model(train_loader):
    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k', num_labels=3)
    model.train()
    optimizer = AdamW(model.parameters(), lr=1e-4)
    criterion = torch.nn.CrossEntropyLoss()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    
    for epoch in range(10):
        running_loss = 0.0
        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(pixel_values=images).logits
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
    return running_loss / len(train_loader)

# Streamlit UI for Fine-tuning
st.title("Fine-tune ViT on MRI/CT Scans for MS & Neurodegenerative Diseases")

zip_url = "https://huggingface.co./spaces/Tanusree88/ViT-MRI-FineTuning/resolve/main/neuroniiimages.zip"

if st.button("Start Training"):
    extraction_dir = "extracted_files"
    os.makedirs(extraction_dir, exist_ok=True)

    # Download the zip file to a temporary file
    with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file:
        download_zip(zip_url, tmp_file.name)

        # Extract the zip file
        extract_zip(tmp_file.name, extraction_dir)

    # Prepare dataset
    image_paths, labels = prepare_dataset(extraction_dir)
    dataset = CustomImageDataset(image_paths, labels)
    
    if len(image_paths) == 0:
        st.error("No images found in the specified directory. Please check the folder structure.")
    else:
        train_loader = DataLoader(dataset, batch_size=32, shuffle=True)

        # Fine-tune the model
        final_loss = fine_tune_model(train_loader)
        st.write(f"Training Complete with Final Loss: {final_loss}")