Update app.py

app.py

@@ -1,9 +1,57 @@
-import gradio as gr
+from torch import nn
+
+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
+
+
+
+from huggingface_hub import hf_hub_download
+import torch
 
+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 greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-demo.launch()
+from torchvision.utils import save_image
+
+def predict(seed):
+    num_punks = 4
+    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'
+
+
+
+import gradio as gr
+
+gr.Interface(
+    predict,
+    inputs=[
+        gr.Slider(0, 1000, label='Seed', default=42),
+    ],
+    outputs="image",
+).launch()