Update app.py

app.py

@@ -9,6 +9,7 @@ from transformers import pipeline
 
 from PIL import Image
 from matplotlib import cm
+from tensorflow import keras
 
 
 resized_shape = (768, 768, 3)
@@ -45,7 +46,7 @@ def dice_coef(y_true, y_pred, smooth=1):
     return K.mean((2 * intersection + smooth) / (union + smooth), axis=0)
 
 # Load the model
-seg_model = tf.keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
+seg_model = 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")
 image_output = gr.outputs.Image(type="pil", label="Output Image")
@@ -57,9 +58,6 @@ def gen_pred(img, model=seg_model):
     # open_cv_image = np.array(pil_image)
     # img = open_cv_image[:, :, ::-1].copy() 
     # # img = cv2.imread("./003e2c95d.jpg")
-    img_byte_arr = io.BytesIO()
-    img.save(img_byte_arr, format='JPEG', subsampling=0, quality=100)
-    img = img_byte_arr.getvalue()
     img = img[::IMG_SCALING[0], ::IMG_SCALING[1]]
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     img = img/255