Update app.py

app.py

@@ -1,25 +1,53 @@
+import torch
+from torch import nn
+from huggingface_hub import hf_hub_download
+from torchvision.utils import save_image
 import gradio as gr
 
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        return num1 / num2
-
-def sampler(body, bottomwear, hair, topwear):
-    img_name = str(body) + str(bottomwear) + str(hair) + str(topwear)
-    return img_name
-
-
-demo = gr.Interface(
-    sampler,
-    ["number", gr.Radio(["body", "bottomwear", "hair", "topwear"]), "number"],
-    "number",
-    live=True,
-)
 
-demo.launch()
+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
+
+
+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 predict(seed, num_punks):
+    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'
+
+gr.Interface(
+    predict,
+    inputs=[
+        gr.Slider(0, 1000, label='Seed', default=42),
+        gr.Slider(4, 64, label='Number of Punks', step=1, default=10),
+    ],
+    outputs="image",
+    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],
+    live=True,
+).launch(cache_examples=True)