Update app.py

app.py

@@ -184,65 +184,63 @@ with col:
 if uploaded_file is not None:
     data, wcs = load_file(uploaded_file)
 
-if "data" not in locals():
-    data = np.zeros((128,128))
-
-# Make six columns for buttons
-_, col1, col2, col3, col4, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
-col1.subheader("Input image")
-col3.subheader("Prediction")
-col5.subheader("Decomposed")
-col6.subheader("")
-
-with col1:
-    st.markdown("""<style>[data-baseweb="select"] {margin-top: -46px;}</style>""", unsafe_allow_html=True)
-    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", on_change=change_scale)
-    scale = int(scale.split("x")[0]) // 128
-
-# Detect button
-with col3: detect = st.button('Detect', key="detect")
-
-# Threshold slider
-with col4:
-    st.markdown("")
-    # st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
-    threshold = st.slider("Threshold", 0.0, 1.0, 0.0, 0.05, key="threshold") #, label_visibility="hidden")
+if "data" in locals():
+    # Make six columns for buttons
+    _, col1, col2, col3, col4, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
+    col1.subheader("Input image")
+    col3.subheader("Prediction")
+    col5.subheader("Decomposed")
+    col6.subheader("")
     
-# Decompose button
-with col5: decompose = st.button('Decompose', key="decompose")
+    with col1:
+        st.markdown("""<style>[data-baseweb="select"] {margin-top: -46px;}</style>""", unsafe_allow_html=True)
+        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", on_change=change_scale)
+        scale = int(scale.split("x")[0]) // 128
     
-# Make two columns for plots
-_, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
-
-image = np.log10(data+1)
-plot_image(image, scale)
-
-if detect or threshold:
-# if st.session_state.get("detect", True):
-    y_pred, wcs = cut_n_predict(data, wcs, scale)
+    # Detect button
+    with col3: detect = st.button('Detect', key="detect")
     
-    y_pred_th = np.where(y_pred > threshold, y_pred, 0)
-            
-    plot_prediction(y_pred_th)
-
-    if decompose or st.session_state.get("download", False):            
-        image_decomposed = decompose_cavity(y_pred_th)
-
-        plot_decomposed(image_decomposed)
-
-        with col6:
-            st.markdown("<br style='margin:4px 0'>", unsafe_allow_html=True)
-            # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
-            fname = uploaded_file.name.strip(".fits")
+    # Threshold slider
+    with col4:
+        st.markdown("")
+        # st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
+        threshold = st.slider("Threshold", 0.0, 1.0, 0.0, 0.05, key="threshold") #, label_visibility="hidden")
+        
+    # Decompose button
+    with col5: decompose = st.button('Decompose', key="decompose")
+        
+    # Make two columns for plots
+    _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
     
-            # if st.session_state.get("download", False):
+    image = np.log10(data+1)
+    plot_image(image, scale)
+    
+    if detect or threshold:
+    # if st.session_state.get("detect", True):
+        y_pred, wcs = cut_n_predict(data, wcs, scale)
+        
+        y_pred_th = np.where(y_pred > threshold, y_pred, 0)
+                
+        plot_prediction(y_pred_th)
+    
+        if decompose or st.session_state.get("download", False):            
+            image_decomposed = decompose_cavity(y_pred_th)
     
-            shutil.make_archive("predictions", 'zip', "predictions")
-            with open('predictions.zip', 'rb') as f:
-                res = f.read()
-            
-            download = st.download_button(label="Download", data=res, key="download", 
-                                            file_name=f'{fname}_{int(scale*128)}.zip', 
-                                            # disabled=st.session_state.get("disabled", True), 
-                                            mime="application/octet-stream")
+            plot_decomposed(image_decomposed)
+    
+            with col6:
+                st.markdown("<br style='margin:4px 0'>", unsafe_allow_html=True)
+                # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
+                fname = uploaded_file.name.strip(".fits")
+        
+                # if st.session_state.get("download", False):
+        
+                shutil.make_archive("predictions", 'zip', "predictions")
+                with open('predictions.zip', 'rb') as f:
+                    res = f.read()
+                
+                download = st.download_button(label="Download", data=res, key="download", 
+                                                file_name=f'{fname}_{int(scale*128)}.zip', 
+                                                # disabled=st.session_state.get("disabled", True), 
+                                                mime="application/octet-stream")