Update app.py

app.py

@@ -14,6 +14,9 @@ from astropy.nddata import Cutout2D, CCDData
 from astropy.convolution import Gaussian2DKernel as Gauss
 from astropy.convolution import convolve
 
+# Scikit-learn
+from sklearn.cluster import DBSCAN
+
 # Streamlit
 import streamlit as st
 st.set_option('deprecation.showPyplotGlobalUse', False)
@@ -121,6 +124,7 @@ if uploaded_file is not None:
         max_scale = int(data.shape[0] // 128)
         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
+        np.save("pred.npy", y_pred)
     
     with col3:
         detect = st.button('Detect')
@@ -151,18 +155,18 @@ if uploaded_file is not None:
 
         np.save("pred.npy", y_pred)
 
-    if detect or bool(threshold):
+    if bool(threshold):
         y_pred = np.load("pred.npy")
         
         pred = np.where(y_pred > threshold, y_pred, 0)
         
-        plot_prediction(pred)
+    plot_prediction(pred)
 
-    with colC:
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
+    # with colC:
+    #     st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
 
-        # if decompose:
-        #     cavs = decompose_cavity(y_pred, )
+    # if decompose:
+    #     cavs = decompose_cavity(y_pred)
         
     #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
     #     ccd.write("predicted.fits", overwrite=True)