Update app.py

app.py

@@ -117,8 +117,8 @@ if uploaded_file is not None:
         st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
         max_scale = int(data.shape[0] // 128)
         # scale = int(st.selectbox('Scale:',[i+1 for i in range(max_scale)], label_visibility="hidden"))
-        scale = int(st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden"))
-        scale = scale.split("x")[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
     
     with col2:
         detect = st.button('Detect cavities')
@@ -126,30 +126,30 @@ if uploaded_file is not None:
     with col3:
         decompose = st.button('Docompose cavities')
         
-    # # Make two columns for plots
-    # colA, colB = st.columns(2)
+    # Make two columns for plots
+    _, colA, colB, colC, _ = st.columns([1,1,1,1,1])
 
-    # image = np.log10(data+1)
-    # plot_image(image, scale)
+    image = np.log10(data+1)
+    plot_image(image, scale)
     
-    # if detect:
-    #     data, wcs = cut(data, wcs, scale=scale)
-    #     image = np.log10(data+1)
+    if detect:
+        data, wcs = cut(data, wcs, scale=scale)
+        image = np.log10(data+1)
         
-    #     y_pred = 0
-    #     for j in [0,1,2,3]:
-    #         rotated = np.rot90(image, j)
-    #         pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
-    #         pred = np.rot90(pred, -j)
-    #         y_pred += pred / 4
-
-    #     # Thresholding
-    #     y_pred = np.where(y_pred > 0.4, y_pred, 0)
-
-    #     # if decompose:
-    #     #     cavs = decompose_cavity(y_pred, )
+        y_pred = 0
+        for j in [0,1,2,3]:
+            rotated = np.rot90(image, j)
+            pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
+            pred = np.rot90(pred, -j)
+            y_pred += pred / 4
+
+        # Thresholding
+        y_pred = np.where(y_pred > 0.4, y_pred, 0)
+
+        # if decompose:
+        #     cavs = decompose_cavity(y_pred, )
         
-    #     plot_prediction(y_pred, decompose)
+        plot_prediction(y_pred, decompose)
 
     #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
     #     ccd.write("predicted.fits", overwrite=True)