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# app.py
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}")
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