Upload app.py

app.py

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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}")