app.py

import gradio


import matplotlib.pyplot as plt
from skimage import morphology
import numpy as np
import skimage


def inference(img):
  grayscale = skimage.color.rgb2gray(im)
  binarized = np.where(grayscale>0.1, 1, 0)
  processed = morphology.remove_small_objects(binarized.astype(bool), min_size=2, connectivity=2).astype(int)
  out = processed

  # black out pixels
  #mask_x, mask_y = np.where(processed == 0)
  #im[mask_x, mask_y, :3] = 0


  return out

# 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',
  outputs='image',
  title='Noise Removal w skimage', 
  description='Remove Noise with skimage.morphology!',
  examples=["detail_with_lines_and_noise.jpg", "lama.webp", "dT4KW.png"])  
  #examples=["detail_with_lines_and_noise.jpg", "lama.webp", "test_lines.jpg","llama.jpg", "dT4KW.png"])  

iface.launch()