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app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+
+# Check for GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define the Generator architecture
+class Generator(nn.Module):
+    def __init__(self, latent_dim=100, img_channels=3, feature_dim=64):
+        super(Generator, self).__init__()
+        self.latent_dim = latent_dim
+        self.model = nn.Sequential(
+            nn.ConvTranspose2d(latent_dim, feature_dim * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(feature_dim * 8),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(feature_dim * 8, feature_dim * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(feature_dim * 4),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(feature_dim * 4, feature_dim * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(feature_dim * 2),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(feature_dim * 2, feature_dim, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(feature_dim),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(feature_dim, img_channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+    
+    def forward(self, z):
+        return self.model(z)
+    
+    def generate_latent_space(self, batch_size):
+        return torch.randn(batch_size, self.latent_dim, 1, 1, device=device)
+
+# Instantiate the generator and load pre-trained weights
+latent_dim = 100
+generator = Generator(latent_dim=latent_dim)
+# Make sure you have uploaded your pre-trained model file "generator.pth" to your Space
+generator.load_state_dict(torch.load("generator.pth", map_location=device))
+generator.to(device)
+generator.eval()
+
+# Function to generate a face image
+def generate_face():
+    with torch.no_grad():
+        # Generate a random latent vector and produce an image
+        z = generator.generate_latent_space(1)
+        generated_image = generator(z)
+        generated_image = generated_image.cpu().squeeze(0)
+        # Denormalize the image (from [-1, 1] to [0, 1])
+        generated_image = generated_image * 0.5 + 0.5
+        # Convert the tensor to a PIL Image
+        to_pil = transforms.ToPILImage()
+        image = to_pil(generated_image)
+    return image
+
+# Set up the Gradio interface
+demo = gr.Interface(
+    fn=generate_face,
+    inputs=[],  # No inputs – each button press generates a new image
+    outputs="image",
+    title="CelebA GAN Face Generator",
+    description="Generates a face image using a pre-trained GAN on the CelebA dataset.",
+)
+
+if __name__ == "__main__":
+    demo.launch()

generator.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3476e418d46809c315cca43789616e1a45ddef3351916fc5fa3f15cea259ead
+size 14322821

requirements.txt

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+torch
+torchvision
+gradio