Update app.py

app.py

@@ -1,49 +1,20 @@
-import torch
-from torch import nn
-from huggingface_hub import hf_hub_download
-from torchvision.utils import save_image
 import gradio as gr
 
-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 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 forward(self, input):
-        output = self.network(input)
-        return output
+demo = gr.Interface(
+    calculator,
+    ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
+    "number",
+    live=True,
+)
 
-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]],
-).launch(cache_examples=True)
+demo.launch()