Update app.py

app.py

@@ -100,30 +100,30 @@ if image_file is not None:
 
       st.text("DONE ! ....")
       
-if image_file is not None:
-    img=load_image(image_file)
-      
-    st.text("Making A Prediction ....")
-    st.image(img,width=850)
-      
-    img=np.asarray(img)
-  
-    img_cv=convert_one_channel(img)
-    img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
-    img_cv=np.float32(img_cv/255)
-     
-    img_cv=np.reshape(img_cv,(1,512,512,1))
-    predict_img=model.predict(img_cv)
+#if image_file is not None:
+#    img=load_image(image_file)
+#      
+#    st.text("Making A Prediction ....")
+#    st.image(img,width=850)
+#      
+#    img=np.asarray(img)
+#  
+#    img_cv=convert_one_channel(img)
+#    img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
+#    img_cv=np.float32(img_cv/255)
+#     
+#    img_cv=np.reshape(img_cv,(1,512,512,1))
+#    predict_img=model.predict(img_cv)
 #    predict=predict_img[1,:,:,0]
-    plt.imsave("predict.png",predict_img)
-    
-    ## Plotting - Пример результата
-    img = cv2.imread(image_file)
-
-    predict1 = cv2.resize(predict_img, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
-
-    mask = np.uint8(predict1 * 255)
-    _, mask = cv2.threshold(mask, thresh=255/2, maxval=255, type=cv2.THRESH_BINARY)
-    cnts, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    img = cv2.drawContours(img, cnts, -1, (255, 0, 0), 2)
-    cv2_imshow(img)
+#    plt.imsave("predict.png",predict_img)
+#    
+#    ## Plotting - Пример результата
+#    img = cv2.imread(image_file)
+#
+#    predict1 = cv2.resize(predict_img, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+#
+#    mask = np.uint8(predict1 * 255)
+#    _, mask = cv2.threshold(mask, thresh=255/2, maxval=255, type=cv2.THRESH_BINARY)
+#    cnts, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+#    img = cv2.drawContours(img, cnts, -1, (255, 0, 0), 2)
+#    cv2_imshow(img)