Update app.py

app.py

@@ -117,15 +117,12 @@ if uploaded_file is not None:
     col5.subheader("Decomposed")
     
     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 = int(st.selectbox('Scale:',[i+1 for i in range(max_scale)], label_visibility="hidden"))
         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
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: 30px;}</style>""", unsafe_allow_html=True)
     
     with col3:
-        # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
         detect = st.button('Detect')
 
     with col5:
@@ -137,11 +134,11 @@ if uploaded_file is not None:
     image = np.log10(data+1)
     plot_image(image, scale)
 
-    with col3:
+    with col4:
         st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
         threshold = st.slider("", 0.0, 1.0, 0.0, 0.05, label_visibility="hidden")
     
-    if detect or bool(threshold):
+    if detect:
         data, wcs = cut(data, wcs, scale=scale)
         image = np.log10(data+1)
         
@@ -152,9 +149,11 @@ if uploaded_file is not None:
             pred = np.rot90(pred, -j)
             y_pred += pred / 4
 
-        y_pred = np.where(y_pred > threshold, y_pred, 0)
+     if detect or bool(threshold):
+        
+        pred = np.where(y_pred > threshold, y_pred, 0)
         
-        plot_prediction(y_pred)
+        plot_prediction(pred)
 
     with colC:
         st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)