Delete app.py

app.py

@@ -1,89 +0,0 @@
-import torch
-import streamlit as st
-import numpy as np
-import cv2
-from PIL import Image
-import torchvision.transforms as transforms
-
-# Load models
-@st.cache_resource
-def load_models():
-    # Load classification model
-    classification_model = torch.load("resnet152_final.pt", map_location=torch.device("cpu"))
-    classification_model.eval()
-    
-    # Load segmentation model
-    segmentation_model = torch.load("best_unet_model.pth", map_location=torch.device("cpu"))
-    segmentation_model.eval()
-    
-    return classification_model, segmentation_model
-
-classification_model, segmentation_model = load_models()
-
-# Define preprocessing function for classification
-def preprocess_image(image):
-    # Convert to grayscale & extract green channel
-    image = np.array(image)
-    green_channel = image[:, :, 1]
-
-    # Apply CLAHE
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    img_clahe = clahe.apply(green_channel)
-
-    # Convert to tensor
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.5], std=[0.5])
-    ])
-    
-    return transform(img_clahe).unsqueeze(0)
-
-# Define function for segmentation preprocessing (resize + normalize)
-def preprocess_segmentation(image):
-    transform = transforms.Compose([
-        transforms.Resize((512, 512)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.5], std=[0.5])
-    ])
-    
-    return transform(image).unsqueeze(0)
-
-# Streamlit UI
-st.title("Diabetic Retinopathy Detection, Classification & Segmentation")
-
-uploaded_file = st.file_uploader("Upload a Retinal Image", type=["jpg", "png", "jpeg"])
-if uploaded_file:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-    # Preprocess image for classification
-    input_tensor = preprocess_image(image)
-
-    # Run classification
-    with torch.no_grad():
-        output = classification_model(input_tensor)
-        predicted_class = torch.argmax(output).item()
-
-    # Display result
-    dr_stages = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]
-    st.write(f"**Diabetic Retinopathy Stage:** {dr_stages[predicted_class]}")
-
-    # If DR detected, proceed to segmentation
-    if predicted_class > 0:
-        st.write("Lesion segmentation in progress...")
-
-        # Preprocess for segmentation
-        segmentation_input = preprocess_segmentation(image)
-
-        # Run segmentation
-        with torch.no_grad():
-            segmentation_output = segmentation_model(segmentation_input)
-        
-        # Convert output to mask
-        segmentation_mask = segmentation_output.squeeze().cpu().numpy()
-        segmentation_mask = (segmentation_mask > 0.5).astype(np.uint8) * 255
-
-        # Display segmentation result
-        st.image(segmentation_mask, caption="Segmented Lesions", use_column_width=True)
-    else:
-        st.write("No DR detected. Segmentation not required.")