Update app.py

app.py

@@ -135,24 +135,24 @@ if uploaded_file is not None:
     image = np.log10(data+1)
     plot_image(image, scale)
 
-    with colB:
-        threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
+    # with colB:
+    #     threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
     
-    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 > threshold, y_pred, 0)
+    #     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 > threshold, y_pred, 0)
                 
-        plot_prediction(y_pred)
+    #     plot_prediction(y_pred)
 
         # if decompose:
         #     cavs = decompose_cavity(y_pred, )