Update app.py

app.py

@@ -1,11 +1,94 @@
-import zipfile
 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
 
+# 1. 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)
 
-# Example usage
-zip_file_path = 'https://huggingface.co/spaces/Tanusree88/ViT-MRI-FineTuning/resolve/main/MS.zip'
-extract_to = 'https://huggingface.co/spaces/Tanusree88/ViT-MRI-FineTuning/resolve/main/extracttedfiles'
-extract_zip(zip_file_path, extract_to)
+# 2. Preprocess images
+def preprocess_image(image_path):
+    ext = os.path.splitext(image_path)[-1].lower()
+
+    if ext == '.nii' or ext == '.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
+
+# 3. Label images
+def prepare_dataset(extracted_folder):
+    image_paths = []
+    labels = []
+    for disease_folder in ['alzheimers', 'parkinsons', 'ms']:
+        folder_path = os.path.join(extracted_folder, disease_folder)
+        label = {'alzheimers': 0, 'parkinsons': 1, 'ms': 2}[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
+
+# 4. Custom Dataset
+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
+
+# 5. 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
+st.title("Fine-tune ViT on MRI Scans")
+
+if st.button("Start Training"):
+    extract_zip('your_zip_file.zip', 'extracted_folder/')
+    image_paths, labels = prepare_dataset('extracted_folder/')
+    dataset = CustomImageDataset(image_paths, labels)
+    train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
+    final_loss = fine_tune_model(train_loader)
+    st.write(f"Training Complete with Final Loss: {final_loss}")