Update app.py

app.py

@@ -10,14 +10,10 @@ import keras.backend as K
 from PIL import Image
 from matplotlib import cm
 
-#from tensorflow import keras
 
 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)
 
 # Download the model file
 def download_model():
@@ -33,7 +29,6 @@ model_file = download_model()
 def Combo_loss(y_true, y_pred, eps=1e-9, smooth=1):
     targets = tf.dtypes.cast(K.flatten(y_true), tf.float32)
     inputs = tf.dtypes.cast(K.flatten(y_pred), tf.float32)
-    
     intersection = K.sum(targets * inputs)
     dice = (2. * intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth)
     inputs = K.clip(inputs, eps, 1.0 - eps)
@@ -52,7 +47,7 @@ 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="pil", label="Upload an image")
 image_output = gr.outputs.Image(type="pil", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
@@ -84,6 +79,5 @@ gr.Interface(fn=gen_pred,
              outputs=image_output, 
              title=title, 
              examples=[["003e2c95d.jpg"], ["003b50a15.jpg"], ["003b48a9e.jpg"], ["0038cbe45.jpg"], ["00371aa92.jpg"]],
-             # css=css_code,
              description=description,
             enable_queue=True).launch()