Update app.py

app.py

@@ -122,7 +122,7 @@ if uploaded_file is not None:
         # 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: 10px;}</style>""", unsafe_allow_html=True)
+        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)
@@ -136,8 +136,12 @@ if uploaded_file is not None:
     
     image = np.log10(data+1)
     plot_image(image, scale)
+
+    with col3:
+        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:
+    if detect or bool(threshold):
         data, wcs = cut(data, wcs, scale=scale)
         image = np.log10(data+1)
         
@@ -148,13 +152,8 @@ if uploaded_file is not None:
             pred = np.rot90(pred, -j)
             y_pred += pred / 4
 
-        with colB:
-            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 threshold > 0: detect = True
-            y_pred = np.where(y_pred > threshold, y_pred, 0)
-            # plot_prediction(y_pred)
-
+        y_pred = np.where(y_pred > threshold, y_pred, 0)
+        
         plot_prediction(y_pred)
 
     with colC: