Update app.py

app.py

@@ -175,13 +175,15 @@ with col:
         # st.markdown("Input images should be in units of counts, centred at the galaxy center, and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
         # st.markdown("If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
 
-        st.markdown("<div style='border-radius:5px;background-color:#F3F4F6;padding-top:8px;padding-bottom:8px;padding-left:14px;padding-right:14px'>\
+        st.markdown("<div style='border-radius:5px;background-color:#F3F4F6;padding-top:8px;padding-bottom:8px;padding-left:14px;padding-right:14px;line-height:140%;font-size:120%'>\
 Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect X-ray cavities from noisy Chandra images of early-type galaxies. <br>\
 To use this tool: upload your image, select the scale of interest, make a prediction, and decompose it into individual cavities! <br>\
 Input images should be in units of counts, centred at the galaxy center, and point sources should be filled with surrounding background \
 (<a href='https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html'>dmfilth</a>). <br><br>\
 If you use this tool for your research, please cite <a href='https://arxiv.org/abs/2304.05457'>Plšek et al. 2023</a>\
-</div>", unsafe_allow_html=True)
+</div><br>", unsafe_allow_html=True)
+
+        
 
 # _, col_1, col_2, col_3, _ = st.columns([bordersize, 2.0, 0.5, 0.5, bordersize])