Update app.py

app.py

@@ -159,12 +159,13 @@ if uploaded_file is not None:
     
     image = np.log10(data+1)
     plot_image(image, scale)
-
-    with col4:
-        st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
-        threshold = st.slider("", 0.0, 1.0, 0.0, 0.05, label_visibility="hidden")
     
-    if detect or threshold:
+    if detect:
+
+        with col4:
+            st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
+            threshold = st.slider("", 0.0, 1.0, 0.0, 0.05, label_visibility="hidden")
+        
         data, wcs = cut(data, wcs, scale=scale)
         image = np.log10(data+1)
         
@@ -176,43 +177,43 @@ if uploaded_file is not None:
             y_pred += pred / 4
 
         # np.save("pred.npy", y_pred)
-
-    # try: y_pred = np.load("pred.npy")
-    # except: y_pred = np.zeros((128,128))
-    try: y_pred
-    except: y_pred = np.zeros((128,128))
-    y_pred_th = np.where(y_pred > threshold, y_pred, 0)
-    # np.save("thresh.npy", y_pred)
-        
-    plot_prediction(y_pred_th)
-
-    if decompose:
-        # y_pred = np.load("thresh.npy")
-        
-        cavs = decompose_cavity(y_pred_th)
-
-        # ccd = CCDData(y_pred, unit="adu", wcs=wcs)
-        # ccd.write(f"predictions/predicted.fits", overwrite=True)
-        image_decomposed = np.zeros((128,128))
-        for i, cav in enumerate(cavs):
-            # ccd = CCDData(cav, unit="adu", wcs=wcs)
-            # ccd.write(f"predictions/predicted_{i+1}.fits", overwrite=True)
-            image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
-
-        # shutil.make_archive("predictions.zip", 'zip', "predictions")
-        # np.save("decomposed.npy", image_decomposed)
-
-    # try: image_decomposed = np.load("decomposed.npy")
-    # except: image_decomposed = np.zeros((128,128))
-    try: image_decomposed
-    except: image_decomposed = np.zeros((128,128))
-    plot_decomposed(image_decomposed)
-
-    # shutil.make_archive("predictions", 'zip', "predictions")
-
-    # with col6:
-    #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
-    #     # with open('predictions.zip', 'rb') as f:
-    #     #     res = f.read()
-    #     st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-    #     download = st.download_button(label="Download", data=ccd, file_name='prediction.fits', mime="application/octet-stream")
+    
+        # try: y_pred = np.load("pred.npy")
+        # except: y_pred = np.zeros((128,128))
+        try: y_pred
+        except: y_pred = np.zeros((128,128))
+        y_pred_th = np.where(y_pred > threshold, y_pred, 0)
+        # np.save("thresh.npy", y_pred)
+            
+        plot_prediction(y_pred_th)
+    
+        if decompose:
+            # y_pred = np.load("thresh.npy")
+            
+            cavs = decompose_cavity(y_pred_th)
+    
+            # ccd = CCDData(y_pred, unit="adu", wcs=wcs)
+            # ccd.write(f"predictions/predicted.fits", overwrite=True)
+            image_decomposed = np.zeros((128,128))
+            for i, cav in enumerate(cavs):
+                # ccd = CCDData(cav, unit="adu", wcs=wcs)
+                # ccd.write(f"predictions/predicted_{i+1}.fits", overwrite=True)
+                image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
+    
+            # shutil.make_archive("predictions.zip", 'zip', "predictions")
+            # np.save("decomposed.npy", image_decomposed)
+    
+        # try: image_decomposed = np.load("decomposed.npy")
+        # except: image_decomposed = np.zeros((128,128))
+        try: image_decomposed
+        except: image_decomposed = np.zeros((128,128))
+        plot_decomposed(image_decomposed)
+    
+        # shutil.make_archive("predictions", 'zip', "predictions")
+    
+        # with col6:
+        #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
+        #     # with open('predictions.zip', 'rb') as f:
+        #     #     res = f.read()
+        #     st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+        #     download = st.download_button(label="Download", data=ccd, file_name='prediction.fits', mime="application/octet-stream")