Create app.py

app.py

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+import torchvision.utils as vutils
+import torchvision.transforms as T
+from PIL import Image
+import gradio as gr
+import torch
+
+def generate_images(num_images, z_dim=100):
+    # Generate batch of latent vectors
+    noise = torch.randn(num_images, z_dim, 1, 1)
+
+    # Generate fake image batch with G
+    generator.eval()  # Set the generator to evaluation mode
+    with torch.no_grad():
+        fake_images = generator(noise).detach().cpu()
+
+    # Plot the fake images
+    img = vutils.make_grid(fake_images, padding=2, normalize=True).permute(1, 2, 0)
+
+    img = img.permute(2,0,1)
+
+    transform = T.ToPILImage()
+
+    # convert the tensor to PIL image using above transform
+    img = transform(img)
+
+    return img
+
+
+batch_size = 16
+z = torch.randn(batch_size, 100, 1, 1)
+fake_images = generator(z)
+# Create a Gradio input component for a positive integer
+#inp = gr.Number(value=0, minimum=0, label="Enter a positive integer")
+
+# Create a Gradio output component for an image
+out = gr.Image(type="pil",label = "Generated Dogs")
+inp = gr.Number(value=0, minimum=0, label="Enter number of Dogs to Generate (positive integer)")
+
+demo = gr.Interface(fn=generate_images,inputs = inp, outputs=out, allow_flagging="never")
+
+# Launch the Gradio interface
+demo.launch(debug = True)