| |
| import streamlit as st |
| from PIL import Image |
| import torch |
| from utils.model_utils import load_model, predict |
| from utils.preprocessing import preprocess_image |
|
|
| st.set_page_config(page_title="X-ray Diagnosis Demo", layout="centered") |
| st.title("🩻 X-ray Multi-Label Diagnosis App (CheXNet)") |
|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model_path = "model/dannynet-55-best_model_20250422-211522.pth" |
| model = load_model(model_path, device) |
|
|
| uploaded_file = st.file_uploader("Upload a chest X-ray", type=["jpg", "jpeg", "png"]) |
|
|
| if uploaded_file: |
| image = Image.open(uploaded_file).convert("RGB") |
| st.image(image, caption="Uploaded X-ray", use_column_width=True) |
|
|
| img_tensor = preprocess_image(image) |
|
|
| probs = predict(model, img_tensor, device) |
|
|
| st.subheader("Predictions") |
| for disease, prob in probs.items(): |
| st.write(f"**{disease}**: {prob:.4f}") |
|
|
