app.py

import os
os.system("hub run stylepro_artistic")
import gradio as gr
import paddlehub as hub
import numpy as np
from PIL import Image
import cv2 

stylepro_artistic = hub.Module(name="stylepro_artistic")

def inference(content,style):
    result = stylepro_artistic.style_transfer(
    images=[{
        'content': cv2.imread(content.name),
        'styles': [cv2.imread(style.name)]
    }])
    return Image.fromarray(np.uint8(result[0]['data'])[:,:,::-1]).convert('RGB')
    
title = "OpenPose"
description = "Gradio demo for OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1812.08008' target='_blank'>OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields</a> | <a href='https://github.com/CMU-Perceptual-Computing-Lab/openpose' target='_blank'>Github Repo</a></p>"
examples=[['people.jpeg']]
iface = gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),enable_queue=True,title=title,article=article,description=description,examples=examples)
iface.launch()