Delete app.py

app.py

@@ -1,66 +0,0 @@
-from unet import UNet
-import streamlit as st
-import torch
-from torchvision import transforms
-import albumentations as A
-from albumentations.pytorch import ToTensorV2
-from PIL import Image
-import numpy as np
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-model = UNet(3, 1, [64, 128, 256, 512]).to(device)
-model.load_state_dict(torch.load("best_model.pth", map_location=torch.device(device)))
-# Set up transformations for the input image
-
-
-transform =  A.Compose([
-        A.Resize(224, 224, p=1.0),
-        ToTensorV2(),
-    ])
-# Streamlit app
-def main():
-    st.title("MRI segmenation App")
-
-    # Upload image through Streamlit
-    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-    if uploaded_image is not None:
-        # Display the uploaded and processed images side by side
-        col1, col2 = st.columns(2)  # Using beta_columns for side-by-side layout
-
-        # Display the uploaded image in the first column
-        col1.header("Original Image")
-        col1.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
-
-        # Process the image (replace this with your processing logic)
-        processed_image = generate_image(uploaded_image)
-
-        # Display the processed image in the second column
-        col2.header("Processed Image")
-        col2.image(processed_image, caption="Processed Image", use_column_width=True)
-
-# Function to generate an image using the PyTorch model
-def generate_image(uploaded_image):
-    # Load the uploaded image
-    input_image = Image.open(uploaded_image)
-
-    image = np.array(input_image).astype(np.float32) / 255.
-    # Apply transformations
-    input_tensor = transform(image=image)["image"].unsqueeze(0)
-
-    # Generate an image using the PyTorch model
-    with torch.no_grad():
-        input_tensor = input_tensor.type(torch.FloatTensor).to(device)
-        pred = model(input_tensor)
-        pred = torch.sigmoid(pred)
-        mask = (pred > 0.6).float()
-    mask = mask[0].permute(1, 2, 0)
-    image = input_tensor[0].permute(1, 2, 0)
-    
-    mask = image + mask*0.3
-    mask = mask.permute(2, 0, 1)
-    mask = transforms.ToPILImage()(mask)
-    return mask
-
-if __name__ == "__main__":
-    main()