app.py

import gradio as gr
from PIL import Image
import tensorflow as tf
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate
from keras.optimizers import Adam

def build_model(input_shape):
    size=128
    model = build_model(input_shape=(size, size, 1))
    model.load_weights('BreastCancerSegmentation.h5')
    return model

def preprocess_image(image, size: int=128):
    image = cv2.resize(image, (size,size))
    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  
    image = image/255.
    return image
  
def segment(image):
    image = preprocess_image(image, size=size)
    image = np.expand_dims(image, 0)
    output = model.predict(image, verbose=0)
    mask_image = output[0]
    mask_image = np.squeeze(mask_image, -1)
    mask_image *= 255
    mask_image = mask_image.astype(np.uint8)
    mask_image = Image.fromarray(mask_image).convert("L")
    return mask_image

if __name__ == "__main__":
  gr.Interface(
      fn=segment,
      inputs="image",
      outputs=gr.Image(type="pil", label="Breast Cancer Mask"),
      examples = [["/content/benign(10).png"], ["/content/benign(109).png"]],
      title = "Breast Cancer Ultrasound Image Segmentation",
      description = "Check out this exciting development in the field of breast cancer diagnosis and treatment! A demo of Breast Cancer Ultrasound Image Segmentation has been developed. Upload image file, or try out one of the examples below!"
      ).launch(share=True, debug=True)