Update app.py

app.py

@@ -15,9 +15,9 @@ import keras.backend as K
 resized_shape = (768, 768, 3)
 IMG_SCALING = (1, 1)
 
-def get_opencv_img_from_buffer(buffer, flags=cv2.IMREAD_COLOR):
-    bytes_as_np_array = np.frombuffer(buffer.read(), dtype=np.uint8)
-    return cv2.imdecode(bytes_as_np_array, flags)
+# def get_opencv_img_from_buffer(buffer, flags=cv2.IMREAD_COLOR):
+#     bytes_as_np_array = np.frombuffer(buffer.read(), dtype=np.uint8)
+#     return cv2.imdecode(bytes_as_np_array, flags)
 
 # Download the model file
 def download_model():
@@ -52,14 +52,14 @@ def dice_coef(y_true, y_pred, smooth=1):
 # Load the model
 seg_model = tf.keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
 
-inputs = gr.inputs.Image(type="pil", label="Upload an image", source="upload")
+inputs = gr.inputs.Image(type="numpy", label="Upload an image", source="upload")
 image_output = gr.outputs.Image(type="numpy", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
 def gen_pred(img=inputs, model=seg_model):
     # rgb_path = os.path.join(test_image_dir,img)
     # img = cv2.imread(rgb_path)
-    img = cv2.imread("./003e2c95d.jpg")
+    # img = cv2.imread("./003e2c95d.jpg")
     img = img[::IMG_SCALING[0], ::IMG_SCALING[1]]
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     img = img/255