Update app.py

app.py

@@ -125,11 +125,12 @@ def decompose_cavity(pred, th2=0.7, amin=10):
 
     return cavities
 
-
-def default_threshold():
-    st.session_state.default = 0.0
-
-
+@st.cache
+def zip_predictions():
+    shutil.make_archive("predictions.zip", 'zip', "predictions")
+    with open('predictions.zip', 'rb') as f:
+        res = f.read()
+        return res
 
 bordersize = 0.6
 _, col, _ = st.columns([bordersize, 3, bordersize])
@@ -161,14 +162,12 @@ if uploaded_file is not None:
     col1.subheader("Input image")
     col3.subheader("Prediction")
     col5.subheader("Decomposed")
-
-    st.session_state.default = 0.0
     
     with col1:
         
         # st.markdown("""<style>[data-baseweb="select"] {margin-top: -26px;}</style>""", unsafe_allow_html=True)
         max_scale = int(data.shape[0] // 128)
-        scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden", on_change=default_threshold)
+        scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden")
         scale = int(scale.split("x")[0]) // 128
     
     with col3:
@@ -186,7 +185,7 @@ if uploaded_file is not None:
 
     with col4:
             st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
-            threshold = st.slider("", 0.0, 1.0, st.session_state.default, 0.05, label_visibility="hidden")
+            threshold = st.slider("", 0.0, 1.0, 0.0, 0.05, label_visibility="hidden")
     
     if detect or threshold:
         y_pred, wcs = cut_n_predict(data, wcs, scale)
@@ -214,17 +213,12 @@ if uploaded_file is not None:
                 ccd = CCDData(cav, unit="adu", wcs=wcs)
                 ccd.write(f"predictions/predicted_{i+1}.fits", overwrite=True)
                 image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
-    
-            shutil.make_archive("predictions.zip", 'zip', "predictions")
-
-            shutil.make_archive("predictions", 'zip', "predictions")
-        
+            
         try: _ = image_decomposed
         except: image_decomposed = np.zeros((128,128))
         plot_decomposed(image_decomposed)
     
-        with col6:
-            with open('predictions.zip', 'rb') as f:
-                res = f.read()
-            st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-            download = st.download_button(label="Download", data=res, file_name='prediction.fits', mime="application/octet-stream")
+    with col6:
+        res = zip_predictions()
+        st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+        download = st.download_button(label="Download", data=res, file_name='prediction.fits', mime="application/octet-stream")