app.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import os
import pickle
import sys

import gradio as gr
import numpy as np
import torch
import torch_utils
import torch.nn as nn
from huggingface_hub import hf_hub_download
from Time_TravelRephotography.utils import torch_helpers as th
from transformers import pipeline
sys.path.insert(0, 'StyleGAN-Human')

TITLE = 'Time-TravelRephotography'
DESCRIPTION = '''This is an unofficial demo for https://github.com/Time-Travel-Rephotography.
'''
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.stylegan-human" alt="visitor badge"/></center>'

TOKEN = "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"


pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

def load_model(file_name: str, path:str,device: torch.device) -> nn.Module:
    path = hf_hub_download(f'{path}',
                           f'{file_name}',
                           use_auth_token=TOKEN)
    with open(path, 'rb') as f:
        model = torch.load(f)
    model.eval()
    model.to(device)
    with torch.inference_mode():
        z = torch.zeros((1, model.z_dim)).to(device)
        label = torch.zeros([1, model.c_dim], device=device)
        model(z, label, force_fp32=True)
    return model
   
def predict(text):
  return pipe(text)[0]["translation_text"]
 
def main():
    load_model("stylegan2-ffhq-config-f","feng2022/Time-TravelRephotography_stylegan2-ffhq-config-f")
    iface = gr.Interface(
      fn=predict, 
      inputs='text',
      outputs='text',
      examples=[["Time-TravelRephotography"]]
    )
    
    iface.launch()
    
if __name__ == '__main__':
    main()