from PIL import Image
import torch
import requests
import gradio as gr

model2 = torch.hub.load(
    "AK391/animegan2-pytorch:main",
    "generator",
    pretrained=True,
    device="cpu",
    progress=False
)
model1 = torch.hub.load("AK391/animegan2-pytorch:main",
                        "generator", pretrained="face_paint_512_v1",  device="cpu")
face2paint = torch.hub.load(
    'AK391/animegan2-pytorch:main', 'face2paint',
    size=512, device="cpu", side_by_side=False
)

def inference(img, ver):
    if ver == 'version 2 (🔺 robustness,🔻 stylization)':
        out = face2paint(model2, img)
    else:
        out = face2paint(model1, img)
    return out

title = "Face Trans For You"
description = "Online Demo for AnimeGanv2 Face Portrait v2. To use it, simply upload your image, or click one of the examples to load them. Please use a cropped portrait picture for best results similar to the examples below."
# article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a> | <a href='https://github.com/Kazuhito00/AnimeGANv2-ONNX-Sample' target='_blank'>Github Repo ONNX</a></p><p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/></p>"
# article = "<p style='text-align: center'>samples: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/></p>"
article = "<p style='text-align: center'>❤ from Bruce</p>"

img_musk = Image.open(requests.get("https://z3.ax1x.com/2021/11/22/IxVvz4.png", stream=True).raw).convert("RGB")
examples = [[img_musk, 'version 2 (🔺 robustness,🔻 stylization)'],
            ['IU.png', 'version 1 (🔺 stylization, 🔻 robustness)']]
gr.Interface(inference, [gr.inputs.Image(type="pil"), gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)', 'version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')
                         ], gr.outputs.Image(type="pil"), title=title, description=description, article=article, enable_queue=True, examples=examples, allow_flagging=False).launch()