import torch
from torch import nn
from huggingface_hub import hf_hub_download
from torchvision.utils import save_image
import gradio as gr


class Generator(nn.Module):
    # Refer to the link below for explanations about nc, nz, and ngf
    # https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html#inputs
    def __init__(self, nc=4, nz=100, ngf=64):
        super(Generator, self).__init__()
        self.network = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh(),
        )

    def forward(self, input):
        output = self.network(input)
        return output


def predict(body, hair, top, bottom):
    name = str(body) + str(hair) + str(top) + str(bottom)
    return name

gr.Interface(
    predict,
    inputs=[
        gr.Slider(0, 1, label='Body', step=1, default=0),
        gr.Slider(0, 5, label='Hair', step=1, default=0),
        gr.Slider(0, 3, label='Top', step=1, default=0),
        gr.Slider(0, 4, label='Bottom', step=1, default=0),
    ],
    outputs="name",
    live=True,
).launch(cache_examples=True)