Add application file

app.py

@@ -0,0 +1,65 @@
+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()
+
+
+