Delete app.py

app.py

@@ -1,65 +0,0 @@
-import gradio as gr
-import numpy as np
-from torchvision.utils import save_image
-import torch.nn as nn
-import torch
-import torchvision.datasets as datasets
-import torchvision.transforms as T
-from torch.utils.data import DataLoader, ConcatDataset
-import torchvision.utils as vutils
-import random
-
-LATENT_VECTOR_DIM = 16 # latent vector dimension
-
-class Generator_128(nn.Module):
-    def __init__(self, GPU_COUNT):
-        super(Generator_128, self).__init__()
-        self.GPU_COUNT = GPU_COUNT
-        self.main = nn.Sequential(
-            # LATENT_VECTOR_DIM x 1 x 1
-            nn.ConvTranspose2d(LATENT_VECTOR_DIM, 1024, 4, 1, 0, bias=False),
-            nn.BatchNorm2d(1024),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(512),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(256),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(128),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(128,64, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(64),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(64,3, 4, 2, 1, bias=False),
-            nn.Tanh()
-            # 128 x 128 x 3
-        )
-    def forward(self, input):
-        return self.main(input)
-
-
-
-trained_gen = Generator_128(0)
-trained_gen.load_state_dict(torch.load("generator_epoch_1000.h5",map_location=torch.device('cpu')))
-
-
-def predict(seed, pokemon_count):
-    torch.manual_seed(seed)
-    z = torch.randn(pokemon_count, LATENT_VECTOR_DIM, 1, 1)
-    punks = trained_gen(z)
-    save_image(punks, "pokemon.png", normalize=True)
-    return 'pokemon.png'
-
-gr.Interface(
-    predict,
-    inputs=[
-        gr.Slider(0, 1000, label='Seed', default=42),
-        gr.Slider(1, 8, label='Number of pokemon', step=1, default=10),
-    ],
-    outputs="image",
-).launch()
-
-
-