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# 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}%")
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