app.py

import gradio as gr
import os 
import sys
os.system("mkdir pose2pose")
os.system("cd pose2pose")
os.system("mkdir -p datasets/DeepFashion")
os.system("mkdir -p output/DeepFashion/ckpt/pretrained")
os.system("git clone https://github.com/prasunroy/pose-transfer.git")
os.system("cd pose-transfer")
os.system("pip install -r requirements.txt")
sys.path.append("pose-transfer")
import torch 
from api import Pose2Pose
from PIL import Image
import canvas
p2p = Pose2Pose(pretrained=True)
gr.Interface("spaces/Omnibus/model-mover")

def infer(con_im,ref_im):
    condition = Image.open(con_im)
    reference = Image.open(ref_im)
    generated = p2p.transfer_as(condition, reference)
    #generated.show()
    return generated
with gr.Blocks() as app:
    gr.Markdown("<center><h1 style='font-size:xxx-large;'>Pose Transfer Demo</h1><br><h3>repo: <a href='https://github.com/prasunroy/pose-transfer'>https://github.com/prasunroy/pose-transfer</a></h3>")
    with gr.Row():
        pose_maker=gr.HTML("<pose-maker/>")
        condition_im = gr.Image(label='Style Image', type='filepath')
        reference_im = gr.Image(label='Pose Image', type='filepath')
    btn=gr.Button()
    gr.Interface("spaces/Omnibus/model-mover").load()

    output_im = gr.Image()
    btn.click(infer,[condition_im,reference_im], output_im)
app.launch()