app.py

import gradio as gr
import numpy as np
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.models import load_model
from PIL import Image
import matplotlib.pyplot as plt

i1 = gr.inputs.Image(shape=(256, 256))
#i2 = gr.inputs.Slider(minimum=2, maximum=4, step=0.1, default=None, label="Scale for intensity - the more value the less the intensity in the pixels")
o1 = gr.outputs.Image(type='numpy')
o2 = gr.outputs.Image(type='numpy')
gen_model = load_model('256_model_250ep.h5')

def colorify(pixels):
  
  pixels = (pixels - 127.5) / 127.5
  pixels = np.expand_dims(pixels, 0)
  gen_image = gen_model.predict(pixels)
  gen_image = (gen_image + 1) / 2
  
  return Image.fromarray((gen_image[0] * 255.0).astype(np.uint8))
  
title = "Colorify"
description = "Recolor your images using this lite version of PIX2PIX GAN , model is trained on 700 randomly collected images from the internet with 256*256 pixels. Due to the above constraint please note that the resolution of your images will decrease" 
examples=[['example1.png'],['example2.jpg']]
article = "<p style='text-align: center'>"

gr.Interface(fn=colorify, inputs=i1, outputs=o1, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()