import gradio as gr
import torch
import torchvision
from torch import nn
from typing import List

def ifnone(a, b): # a fastai-specific (fastcore) function used below, redefined so it's independent
    "`b` if `a` is None else `a`"
    return b if a is None else a

def convT_norm_relu(ch_in:int, ch_out:int, norm_layer:nn.Module, ks:int=3, stride:int=2, bias:bool=True):
    return [nn.ConvTranspose2d(ch_in, ch_out, kernel_size=ks, stride=stride, padding=1, output_padding=1, bias=bias),
            norm_layer(ch_out), nn.ReLU(True)]

def pad_conv_norm_relu(ch_in:int, ch_out:int, pad_mode:str, norm_layer:nn.Module, ks:int=3, bias:bool=True, 
                       pad=1, stride:int=1, activ:bool=True, init=nn.init.kaiming_normal_, init_gain:int=0.02)->List[nn.Module]:
    layers = []
    if pad_mode == 'reflection': layers.append(nn.ReflectionPad2d(pad))
    elif pad_mode == 'border':   layers.append(nn.ReplicationPad2d(pad))
    p = pad if pad_mode == 'zeros' else 0
    conv = nn.Conv2d(ch_in, ch_out, kernel_size=ks, padding=p, stride=stride, bias=bias)
    if init:
        if init == nn.init.normal_:
            init(conv.weight, 0.0, init_gain)
        else:
            init(conv.weight)
        if hasattr(conv, 'bias') and hasattr(conv.bias, 'data'): conv.bias.data.fill_(0.)
    layers += [conv, norm_layer(ch_out)]
    if activ: layers.append(nn.ReLU(inplace=True))
    return layers

class ResnetBlock(nn.Module):
    "nn.Module for the ResNet Block"
    def __init__(self, dim:int, pad_mode:str='reflection', norm_layer:nn.Module=None, dropout:float=0., bias:bool=True):
        super().__init__()
        assert pad_mode in ['zeros', 'reflection', 'border'], f'padding {pad_mode} not implemented.'
        norm_layer = ifnone(norm_layer, nn.InstanceNorm2d)
        layers = pad_conv_norm_relu(dim, dim, pad_mode, norm_layer, bias=bias)
        if dropout != 0: layers.append(nn.Dropout(dropout))
        layers += pad_conv_norm_relu(dim, dim, pad_mode, norm_layer, bias=bias, activ=False)
        self.conv_block = nn.Sequential(*layers)

    def forward(self, x): return x + self.conv_block(x)
    

def resnet_generator(ch_in:int, ch_out:int, n_ftrs:int=64, norm_layer:nn.Module=None, 
                     dropout:float=0., n_blocks:int=9, pad_mode:str='reflection')->nn.Module:
    norm_layer = ifnone(norm_layer, nn.InstanceNorm2d)
    bias = (norm_layer == nn.InstanceNorm2d)
    layers = pad_conv_norm_relu(ch_in, n_ftrs, 'reflection', norm_layer, pad=3, ks=7, bias=bias)
    for i in range(2):
        layers += pad_conv_norm_relu(n_ftrs, n_ftrs *2, 'zeros', norm_layer, stride=2, bias=bias)
        n_ftrs *= 2
    layers += [ResnetBlock(n_ftrs, pad_mode, norm_layer, dropout, bias) for _ in range(n_blocks)]
    for i in range(2):
        layers += convT_norm_relu(n_ftrs, n_ftrs//2, norm_layer, bias=bias)
        n_ftrs //= 2
    layers += [nn.ReflectionPad2d(3), nn.Conv2d(n_ftrs, ch_out, kernel_size=7, padding=0), nn.Tanh()]
    return nn.Sequential(*layers)

model = resnet_generator(ch_in=3, ch_out=3, n_ftrs=64, norm_layer=None, dropout=0, n_blocks=9)
model.load_state_dict(torch.load('generator.pth',map_location=torch.device('cpu')))
model.eval()


totensor = torchvision.transforms.ToTensor()
normalize_fn = torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
topilimage = torchvision.transforms.ToPILImage()

def predict(input):
    im = normalize_fn(totensor(input))
    print(im.shape)
    preds = model(im.unsqueeze(0))/2 + 0.5
    print(preds.shape)
    return topilimage(preds.squeeze(0).detach())

gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), outputs="image", title='Horse-to-Zebra CycleGAN')
gr_interface.launch(inline=False,share=False)