Update app.py

app.py

@@ -33,7 +33,7 @@ with col:
     st.markdown("Input images should be centered at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
     
     # Create file uploader widget
-    uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
+    # uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
 
 # Define function to plot the uploaded image
 def plot_image(image, scale):
@@ -113,24 +113,28 @@ if uploaded_file is not None:
     _, col1, col2, col3, col4, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
     col1.subheader("Input image")
     col3.subheader("Prediction")
+    col5.subheader("Decomposed")
     
-    with col1:
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: -56px;}</style>""", unsafe_allow_html=True)
+    with col2:
+        # st.markdown("""<style>[data-baseweb="select"] {margin-top: -56px;}</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
     
-    with col3:
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-        detect = st.button('Detect cavities')
+    with col4:
+        # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+        detect = st.button('Detect')
 
-    with col5:
-        decompose = st.button('Docompose cavities')
+    with col6:
+        decompose = st.button('Docompose')
         
     # Make two columns for plots
     _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
 
+    with colB:
+        threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
+    
     image = np.log10(data+1)
     plot_image(image, scale)
     
@@ -145,9 +149,6 @@ if uploaded_file is not None:
             pred = np.rot90(pred, -j)
             y_pred += pred / 4
 
-        with colB:
-            threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
-
         # Thresholding
         y_pred = np.where(y_pred > threshold, y_pred, 0)