import torch
from torch import nn
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 path(action, body, hair, top, bottom):

    # body
    if body == "human": body = '0'
    elif body == "alien": body = '1'
    
    # hair

    name = action + str(body) + str(hair) + str(top) + str(bottom)
    return name


gr.Interface(
    path,
    inputs=[
        gr.Radio(choices=["shoot", "slash", "spellcard", "thrust", "walk"], value="shoot"),
        gr.Radio(choices=["human", "alien"], value="shoot"),
        gr.Radio(choices=["green", "yellow", "rose", "red", "wine"], value="green"),
        gr.Slider(0, 3, label='Top', step=1, default=0),
        gr.Slider(0, 4, label='Bottom', step=1, default=0)
    ],
    outputs="image",
    live=False,
).launch()