Update app.py

app.py

@@ -53,14 +53,14 @@ def dice_coef(y_true, y_pred, smooth=1):
 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")
-image_output = gr.outputs.Image(type="numpy", label="Output Image")
+image_output = gr.outputs.Image(type="pil", 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")
-    pil_image = PIL.Image.open('./003b50a15.jpg')
+    pil_image = Image.open('./003b50a15.jpg')
     img = cv2.cvtColor(numpy.array(pil_image), cv2.COLOR_RGB2BGR)
     img = img[::IMG_SCALING[0], ::IMG_SCALING[1]]
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
@@ -79,7 +79,7 @@ description = "Upload an image and get prediction mask"
 
 gr.Interface(fn=gen_pred, 
              inputs=inputs, 
-             outputs='text', 
+             outputs=image_output, 
              title=title, 
              examples=[["003e2c95d.jpg"], ["003b50a15.jpg"], ["003b48a9e.jpg"], ["0038cbe45.jpg"], ["00371aa92.jpg"]],
              # css=css_code,