Update app.py

app.py

@@ -28,7 +28,7 @@ _, col, _ = st.columns([bordersize, 3, bordersize])
 with col:
     st.markdown("# Cavity Detection Tool")
     
-    st.markdown("Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect X-ray cavities from noisy Chandra images of early-type galaxies. To use this tool: upload your image, select the scale of interest, and make a prediction! If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
+    st.markdown("Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect X-ray cavities from noisy Chandra images of early-type galaxies.\ To use this tool: upload your image, select the scale of interest, and make a prediction!\ If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
     
     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)).")
     
@@ -122,6 +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)
     
     with col3:
         # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
@@ -149,9 +150,8 @@ if uploaded_file is not None:
 
         with colB:
             st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
-            threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
-        
-        y_pred = np.where(y_pred > threshold, y_pred, 0)
+            threshold = st.slider("", 0.0, 1.0, 0.0, 0.05, label_visibility="hidden")
+            y_pred = np.where(y_pred > threshold, y_pred, 0)
 
         plot_prediction(y_pred)