import gradio as gr
import keras
from keras.models import load_model
from tensorflow_addons.layers import InstanceNormalization
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

cust = {'InstanceNormalization': InstanceNormalization}
model=load_model('g-cycleGAN-photo2monet-500images-epoch10_30_30_30_30_30_1000images_30_30_30.h5',cust)
path  = [['ex1.jpg'], ['ex2.jpg'], ['ex4.jpg'],['ex6.jpg'],['ex7.jpg'],['ex8.jpg'],['ex9.jpg'],['ex10.jpg'],['ex12.jpg'],['ex13.jpg']]

# preprocess
AUTOTUNE = tf.data.AUTOTUNE
BUFFER_SIZE = 400
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256

def resize(image,height,width):
  resized_image = tf.image.resize(image,[height,width],method = tf.image.ResizeMethod.NEAREST_NEIGHBOR)
  return resized_image

def normalize(input_image):
  input_image = (input_image/127.5) - 1
  return input_image

def load(img_file):
  img = tf.io.read_file(img_file)
  img = tf.io.decode_jpeg(img)
  real_image = tf.cast(img,tf.float32)

  return real_image

def load_image_test(image_file):
  re = load(image_file)
  re = resize(re,IMG_HEIGHT,IMG_WIDTH)
  re = normalize(re)
  return re


def show_preds_image(image_path):
    A = load_image_test(image_path)
    A = np.expand_dims(A,axis=0)
    B = model(A)
    B = B[0]
    B = B * 0.5 + 0.5
    B = B.numpy()
    return B
 
inputs_image = [
    gr.components.Image(shape=(256,256),type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(shape=(256,256),type="numpy", label="Output Image").style(width=256, height=256),
]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="photo2monet",
    examples=path,
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image],
    tab_names=['Image inference']
).queue().launch()