Update app.py

app.py

@@ -158,18 +158,20 @@ if uploaded_file is not None:
     col1.subheader("Input image")
     col3.subheader("Prediction")
     col5.subheader("Decomposed")
+
+    st.session_state.default = 0.0
     
     with col1:
         # st.markdown("""<style>[data-baseweb="select"] {margin-top: -26px;}</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")
+        scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden", on_change=lambda _: st.session_state.default = 0.0)
         scale = int(scale.split("x")[0]) // 128
     
     with col3:
         detect = st.button('Detect')
 
     with col5:
-        decompose = st.button('Docompose')
+        decompose = st.button('Decompose')
         
     # Make two columns for plots
     _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
@@ -180,7 +182,7 @@ if uploaded_file is not None:
 
     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")
+            threshold = st.slider("", 0.0, 1.0, st.session_state.default, 0.05, label_visibility="hidden")
     
     if detect or threshold:
         y_pred, wcs = cut_n_predict(data, wcs, scale)
@@ -189,7 +191,7 @@ if uploaded_file is not None:
     
         # try: y_pred = np.load("pred.npy")
         # except: y_pred = np.zeros((128,128))
-        try: y_pred
+        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)
@@ -214,7 +216,7 @@ if uploaded_file is not None:
     
         # try: image_decomposed = np.load("decomposed.npy")
         # except: image_decomposed = np.zeros((128,128))
-        try: image_decomposed
+        try: _ = image_decomposed
         except: image_decomposed = np.zeros((128,128))
         plot_decomposed(image_decomposed)