main.py

import numpy
import keras
import gradio

# Building the neural network
model1 = keras.models.Sequential()
model1.add(keras.layers.InputLayer(input_shape=(101, 636, 1)))
model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same', strides=1))
model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same'))
model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same', strides=1))
model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same'))
model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same'))
model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same', strides=1))
model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same'))
model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=1))
model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same', strides=1))
model1.add(keras.layers.UpSampling2D((1, 1)))
model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
model1.add(keras.layers.UpSampling2D((1, 1)))
model1.add(keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same'))
model1.add(keras.layers.UpSampling2D((1, 1)))
model1.add(keras.layers.Conv2D(4, (7, 7), activation='tanh', padding='same'))
model1.add(keras.layers.UpSampling2D((1, 1)))
model1.add(keras.layers.Conv2D(3, (9, 9), activation='tanh', padding='same'))

#Loading the weights in the architecture (The file should be stored in the same directory as the code)
model1.load_weights('modelV13_500trained_1.h5')

def predict(mask):
  X = numpy.round((mask/255.0))[numpy.newaxis, :, :, numpy.newaxis]
  v = model1.predict(X)*255
  output = (v - v.min()) / (v.max() - v.min())
  print(output.shape)
  return output[0, :, :, 0], output[0, :, :, 1], output[0, :, :, 2]

demo = gradio.Interface(fn=predict, inputs=gradio.Image(image_mode="L", source="canvas", tool="sketch", values=numpy.zeros(636, 101), outputs=[gradio.Image(image_mode="L"), gradio.Image(image_mode="L"), gradio.Image(image_mode="L")])

demo.run()