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README.md

@@ -1,10 +1,17 @@
----
-title: Pneumonia Detection App
-emoji: 🫁
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-app_file: streamlit_app.py
-pinned: false
-sdk_version: 1.51.0
----
+# Pneumonia Detection — Streamlit App
+
+## Place model
+Copy your model file into this folder as `Model2_exact_serialized.keras` (or edit `MODEL_FILENAME` inside streamlit_app.py).
+
+## Run locally
+1. pip install -r requirements.txt
+2. streamlit run streamlit_app.py
+3. Open http://localhost:8501
+
+## Docker
+docker build -t pneumonia-streamlit .
+docker run -p 8501:8501 pneumonia-streamlit
+
+## Notes
+- DICOMs may contain PHI. Do not store/share patient-identifying DICOM metadata.
+- If your DICOMs are compressed, the pylibjpeg plugins in requirements help decode them.

streamlit_app.py

@@ -1,4 +1,3 @@
-%%writefile pneumonia_app/streamlit_app.py
 # streamlit_app.py
 import io
 import os
@@ -128,7 +127,7 @@ if uploaded is not None:
         st.error(f"Failed to process file: {e}")
         st.stop()
 
-    st.image(rgb, caption="Input (resized)", use_container_width=True)
+    st.image(rgb, caption="Input (resized)", use_column_width=False)
 
     # load model (cached)
     model = load_predict_model(MODEL_FILENAME)
@@ -144,7 +143,7 @@ if uploaded is not None:
     if ENABLE_GRADCAM:
         try:
             cam = make_gradcam_image(rgb, model)
-            st.image(cam, caption="Grad-CAM overlay", use_container_width=True)
+            st.image(cam, caption="Grad-CAM overlay", use_column_width=False)
         except Exception as e:
             st.warning(f"Grad-CAM failed: {e}")