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

model = Generator()
weights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')
model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) # Use 'cuda' if you have a GPU available

def predict(seed, num_punks):
    torch.manual_seed(seed)
    z = torch.randn(num_punks, 100, 1, 1)
    punks = model(z)
    save_image(punks, "punks.png", normalize=True)
    return 'punks.png'

gr.Interface(
    predict,
    inputs=[
        gr.Slider(0, 1000, label='Seed'),
        gr.Slider(4, 64, label='Number of Punks', step=1),
    ],
    outputs="image",
    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],
).launch()