add application file

app.py

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+import os
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils import data
+from torchvision import transforms, utils
+from tqdm import tqdm
+torch.backends.cudnn.benchmark = True
+import copy
+from util import *
+from PIL import Image
+
+from model import *
+import moviepy.video.io.ImageSequenceClip
+import scipy
+import kornia.augmentation as K
+
+from base64 import b64encode
+import gradio as gr
+from torchvision import transforms
+
+# torch.hub.download_url_to_file('https://i.imgur.com/HiOTPNg.png', 'mona.png')
+# torch.hub.download_url_to_file('https://i.imgur.com/Cw8HcTN.png', 'painting.png')
+
+device = 'cpu'
+latent_dim = 8
+n_mlp = 5
+num_down = 3
+
+G_A2B = Generator(256, 4, latent_dim, n_mlp, channel_multiplier=1, lr_mlp=.01,n_res=1).to(device).eval()
+
+ensure_checkpoint_exists('GNR_checkpoint_full.pt')
+ckpt = torch.load('GNR_checkpoint_full.pt', map_location=device)
+
+G_A2B.load_state_dict(ckpt['G_A2B_ema'])
+
+# mean latent
+truncation = 1
+with torch.no_grad():
+    mean_style = G_A2B.mapping(torch.randn([1000, latent_dim]).to(device)).mean(0, keepdim=True)
+
+
+test_transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), inplace=True)
+])
+plt.rcParams['figure.dpi'] = 200
+
+# torch.manual_seed(84986)
+
+num_styles = 1
+style = torch.randn([num_styles, latent_dim]).to(device)
+
+
+def inference(input_im):
+    real_A = test_transform(input_im).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        A2B_content, _ = G_A2B.encode(real_A)
+        #fake_A2B = G_A2B.decode(A2B_content.repeat(num_styles,1,1,1), style)
+        fake_A2B = G_A2B.decode(A2B_content.repeat(num_styles,1,1,1), torch.randn([num_styles, latent_dim]).to(device))
+        std=(0.5, 0.5, 0.5)
+        mean=(0.5, 0.5, 0.5)
+        z = fake_A2B * torch.tensor(std).view(3, 1, 1)
+        z = z + torch.tensor(mean).view(3, 1, 1)
+        tensor_to_pil = transforms.ToPILImage(mode='RGB')(z.squeeze())
+    return tensor_to_pil
+
+title = "GANsNRoses"
+article = "<p style='text-align: center'>GANs N' Roses: Image to Iamge Translation | Obtained from :<a href='https://github.com/mchong6/GANsNRoses'>Github Repo</a></p>"
+demo = gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="pil"),
+    title=title,
+    # description=description,
+    article=article,
+    allow_flagging = "never",
+    )
+    
+demo.launch(share = True)