Update app.py

app.py

@@ -163,7 +163,7 @@ if uploaded_file is not None:
     # col6.subheader("")
     
     with col1:
-        # st.markdown("""<style>[data-baseweb="select"] {margin-top: -26px;}</style>""", unsafe_allow_html=True)
+        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")
         scale = int(scale.split("x")[0]) // 128
@@ -178,6 +178,16 @@ if uploaded_file is not None:
         
     # Decompose button
     with col5: decompose = st.button('Decompose')
+
+    with col6:
+        shutil.make_archive("predictions", 'zip', "predictions")
+        with open('predictions.zip', 'rb') as f:
+            res = f.read()
+        st.markdown("")
+        st.markdown("")
+        # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+        fname = uploaded_file.name.strip(".fits")    
+        download = st.download_button(label="Download", data=res, file_name=f'{fname}_{int(scale*128)}.zip', disable=True, mime="application/octet-stream")
         
     # Make two columns for plots
     _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
@@ -188,26 +198,13 @@ if uploaded_file is not None:
     if detect or threshold:
         y_pred, wcs = cut_n_predict(data, wcs, scale)
         
-        # try: _ = y_pred
-        # except: y_pred = np.zeros((128,128))
         y_pred_th = np.where(y_pred > threshold, y_pred, 0)
                 
         plot_prediction(y_pred_th)
         
-        if decompose:
+        if decompose or download:
+            download.disabled = False
             
             image_decomposed = decompose_cavity(y_pred_th)
     
-            # try: _ = image_decomposed
-            # except: image_decomposed = np.zeros((128,128))
-            plot_decomposed(image_decomposed)
-        
-            with col6:
-                shutil.make_archive("predictions", 'zip', "predictions")
-                with open('predictions.zip', 'rb') as f:
-                    res = f.read()
-                st.markdown("")
-                st.markdown("")
-                # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-                fname = uploaded_file.name.strip(".fits")    
-                download = st.download_button(label="Download", data=res, file_name=f'{fname}_{int(scale*128)}.zip', mime="application/octet-stream")
+            plot_decomposed(image_decomposed)