|
|
|
|
|
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}") |
|
|
|
