import os, sys
import numpy as np
import torch
from torchvision import transforms
import matplotlib.pyplot as plt
from PIL import Image

# loading models ---- create model repo
from huggingface_hub import hf_hub_url
default_modnet_path = hf_hub_url('Pie31415/rome','modnet_photographic_portrait_matting.ckpt')
default_model_path = hf_hub_url('Pie31415/rome','models/rome.pth')

# parser configurations
parser = argparse.ArgumentParser(conflict_handler='resolve')
parser.add_argument('--save_dir', default='.', type=str)
parser.add_argument('--save_render', default='True', type=args_utils.str2bool, choices=[True, False])
parser.add_argument('--model_checkpoint', default=default_model_path, type=str)
parser.add_argument('--modnet_path', default=default_modnet_path, type=str)
parser.add_argument('--random_seed', default=0, type=int)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--verbose', default='False', type=args_utils.str2bool, choices=[True, False])
args, _ = parser.parse_known_args()

parser = importlib.import_module(f'src.rome').ROME.add_argparse_args(parser)
args = parser.parse_args()
args.deca_path = 'DECA'

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

from infer import Infer

infer = Infer(args)
infer = infer.to(device)

def predict(source_img, driver_img):
    out = infer.evaluate(source_img, driver_img, crop_center=False)
    res = tensor2image(torch.cat([out['source_information']['data_dict']['source_img'][0].cpu(),
                              out['source_information']['data_dict']['target_img'][0].cpu(),
                        out['render_masked'].cpu(), out['pred_target_shape_img'][0].cpu()], dim=2))
    return res[..., ::-1]


import gradio as gr
gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil"),
        gr.Image(type="pil")
    ],
    outputs=gr.Image(),
    examples=[]).launch()