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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()
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