Added app.py

app.py

@@ -0,0 +1,125 @@
+# Directory Structure Suggestion:
+# diabetic_retinopathy_app/
+# ├── Home.py (Landing Page)
+# ├── pages/
+# │   ├── 1_Upload_and_Predict.py
+# │   └── 2_Model_Evaluation.py
+# └── assets/
+#     └── banner.jpg
+
+# Home.py (Landing Page)
+import streamlit as st
+from PIL import Image
+
+def main():
+    st.set_page_config(page_title="DR Assistive Tool", layout="centered")
+    st.title("Welcome to the Diabetic Retinopathy Assistive Tool")
+
+    st.markdown("""
+    ### 🌟 Your AI-powered assistant for early detection of Diabetic Retinopathy.
+
+    #### Features:
+    - 🖼️ Upload a retinal image and receive a prediction of its DR stage.
+    - 📊 Evaluate model performance using real test datasets.
+
+    Select a page from the left sidebar to get started.
+    """)
+
+    # image = Image.open("assets/banner.jpg")  # Optional banner image
+    # st.image(image, use_column_width=True)
+
+if __name__ == '__main__':
+    main()
+
+
+# pages/1_Upload_and_Predict.py
+import streamlit as st
+import torch
+from torchvision import transforms, models
+from PIL import Image
+import numpy as np
+
+st.title("📷 Upload & Predict Diabetic Retinopathy")
+
+class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
+
+def load_model():
+    model = models.densenet121(pretrained=False)
+    num_ftrs = model.classifier.in_features
+    model.classifier = torch.nn.Linear(num_ftrs, len(class_names))
+    model.load_state_dict(torch.load("training/Pretrained_Densenet-121.pth", map_location='cpu'))
+    model.eval()
+    return model
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+def predict_image(model, image):
+    img_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        _, pred = torch.max(outputs, 1)
+        prob = torch.nn.functional.softmax(outputs, dim=1)[0][pred].item() * 100
+    return class_names[pred.item()], prob
+
+uploaded_file = st.file_uploader("Choose a retinal image", type=["jpg", "png"])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Retinal Image', use_column_width=True)
+
+    if st.button("🧠 Predict"):
+        with st.spinner('Analyzing image...'):
+            model = load_model()
+            pred_class, prob = predict_image(model, image)
+            st.success(f"Prediction: **{pred_class}** ({prob:.2f}% confidence)")
+
+
+# pages/2_Model_Evaluation.py
+import streamlit as st
+import torch
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms, models
+import torch.nn as nn
+from tqdm import tqdm
+
+st.title("📈 Model Evaluation on Test Dataset")
+
+@st.cache_data
+
+def load_test_data():
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    test_data = datasets.ImageFolder("test_dataset_path", transform=transform)
+    return DataLoader(test_data, batch_size=32, shuffle=False)
+
+def evaluate(model, loader):
+    model.eval()
+    correct, total, loss = 0, 0, 0.0
+    criterion = nn.CrossEntropyLoss()
+    with torch.no_grad():
+        for inputs, labels in loader:
+            outputs = model(inputs)
+            loss += criterion(outputs, labels).item()
+            _, pred = torch.max(outputs, 1)
+            correct += (pred == labels).sum().item()
+            total += labels.size(0)
+    return loss / len(loader), correct / total * 100
+
+if st.button("🧪 Evaluate Trained Model"):
+    test_loader = load_test_data()
+    model = models.densenet121(pretrained=False)
+    model.classifier = nn.Linear(model.classifier.in_features, 5)
+    model.load_state_dict(torch.load("dr_densenet121.pth", map_location='cpu'))
+    model.eval()
+
+    loss, acc = evaluate(model, test_loader)
+    st.write(f"**Test Loss:** {loss:.4f}")
+    st.write(f"**Test Accuracy:** {acc:.2f}%")