Update app.py

app.py

@@ -138,21 +138,24 @@ if uploaded_file is not None:
         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
+
+        with colB:
+            threshold = st.slider("", (0, 1, 0.5, 0.05), label_visibility="hidden")
+
+        # Thresholding
+        y_pred = np.where(y_pred > threshold, y_pred, 0)
+                
+        plot_prediction(y_pred, decompose)
+
+        # if decompose:
+        #     cavs = decompose_cavity(y_pred, )
         
-    #     plot_prediction(y_pred, decompose)
-
     #     ccd = CCDData(y_pred, unit="adu", wcs=wcs)
     #     ccd.write("predicted.fits", overwrite=True)
     #     with open('predicted.fits', 'rb') as f: