app.py

import tensorflow as tf
from simple_unet_model import simple_unet_model
from tensorflow.keras.utils import normalize
import os
from PIL import Image, ImageOps
import numpy as np
import gradio as gr

#Loading Model
def get_model():
    return simple_unet_model(256, 256, 1)

model = get_model()
model.load_weights('mitochondria.hdf5')

def predict(input_image):
  img = Image.fromarray(input_image)
  gray_img = ImageOps.grayscale(img)
  resized_img = gray_img.resize((256,256))
  img = np.array(resized_img)
  img = np.expand_dims(img, axis = (0,3))
  img = normalize(img, axis=1)
  mask = model.predict(img)[0,:,:,0]
  return mask

def load_examples():
  files = os.listdir()
  img_list = []
  for file in files:
    if '.jpg' in file:
      img_list.append(file)
  return img_list

examples = load_examples()

demo = gr.Interface(fn=predict,
                    inputs="image",
                    outputs=gr.Image(shape=(256, 256)),
                    title = "Mitochondria Detection",
                    examples =examples )
demo.launch()