Update app.py

app.py

@@ -177,9 +177,9 @@ with col:
 
         # #F3F4F6
         st.markdown("<div style='border-radius:5px;padding-top:8px;padding-bottom:8px;padding-left:14px;padding-right:14px;line-height:140%;font-size:120%;background-color:#FFFFFF;'>\
-Cavity Detection Tool <a href='https://github.com/tomasplsek/CADET'>(CADET)</a> is a machine learning pipeline trained to detect X-ray cavities from <em>Chandra</em> images of early-type galaxies, groups, and clusters.\
+Cavity Detection Tool <a href='https://github.com/tomasplsek/CADET'>(CADET)</a> is a machine learning pipeline trained to detect X-ray cavities from <em>Chandra</em> images of early-type galaxies, groups, and clusters. \
 If you use this tool in your research, please cite <a href='https://arxiv.org/abs/2304.05457'>Plšek et al. 2023</a>.\
-<br style='margin-bottom: 6px'>To use this tool:<br><b>1)</b> upload your FITS file<br><b>2)</b> select the scale of interest<br><b>3)</b> make a prediction<br><b>4)</b> decompose into individual cavities. \
+<br style='margin-bottom: 9px'>To use this tool:<br><b>1)</b> upload your FITS file<br><b>2)</b> select the scale of interest<br><b>3)</b> make a prediction<br><b>4)</b> decompose into individual cavities. \
 </div>", unsafe_allow_html=True)
 
 # Input images should be FITS files in units of counts, centred at the galaxy center, and point sources should be filled with surrounding background \
@@ -192,7 +192,8 @@ with col_1:
     uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'], on_change=reset_threshold)
 
 with col_2:
-    example = st.button("Examples")
+    st.markdown("<br style='margin:24px 0'>", unsafe_allow_html=True)
+    example = st.button("Example")
     
     # with col_2:
     #     st.markdown("### Examples")
@@ -247,7 +248,7 @@ with col5: decompose = st.button('Decompose', key="decompose")
 # Make two columns for plots
 _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
 
-if uploaded_file is not None:
+if data is not np.zeros((128,128)):
     # NORMALIZE IMAGE
     MIN = np.min(np.where(data == 0, 1, data))
     if MIN < 1: data = data / MIN