Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 
 
 
-def inference(img,v1='121',v2='3'):
+def inference(img,v1,v2):
   #out = cv2.erode(img,(15,15))
   #out = cv2.dilate(out,(55,55))
   # https://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects  
@@ -34,12 +34,32 @@ def inference(img,v1='121',v2='3'):
 
   return out
 
+num_inference_steps_slider_component_v1 = gr.Slider(
+label="Number of inference steps",
+info="The number of denoising steps. More denoising steps "
+"usually lead to a higher quality image at the",
+minimum=1,
+maximum=500,
+step=1,
+value=121,
+)
+
+num_inference_steps_slider_component_v2 = gr.Slider(
+label="Number of inference steps",
+info="The number of denoising steps. More denoising steps "
+"usually lead to a higher quality image at the",
+minimum=1,
+maximum=100,
+step=1,
+value=3,
+)
+
 # For information on Interfaces, head to https://gradio.app/docs/
 # For user guides, head to https://gradio.app/guides/
 # For Spaces usage, head to https://huggingface.co/docs/hub/spaces
 iface = gradio.Interface(
   fn=inference,
-  inputs=['image','v1','v2'],
+  inputs=['image',num_inference_steps_slider_component_v1,num_inference_steps_slider_component_v2],
   outputs='image',
   title='Noise Removal', 
   description='Remove Noise with OpenCV and Adaptial Gaussian!',