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import os
os.system("pip install tensorflow==2.3.0")
os.system("pip install tensorflow_hub")
os.system("pip install numpy==1.20.3")
import tensorflow as tf
# Load compressed models from tensorflow_hub
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
import numpy as np
import PIL.Image
import time
import functools
def tensor_to_image(tensor):
tensor = tensor*255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor)>3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
import tensorflow_hub as hub
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
import gradio as gr
def inference(content_image, style_image):
stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
img = tensor_to_image(stylized_image)
return img
title = "TTT"
gr.Interface(
inference,
gr.inputs.Image(type="pil", label="content_image"),
gr.inputs.Image(type="pil", label="style_image"),
gr.outputs.Image(type="pil", label="Output"),
title=title,
example=[],
description="",
enable_queue=True,
allow_flagging="auto"
).launch()
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