Create app.py

app.py

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+!pip install gradio
+import gradio as gr
+import torch
+from torchvision.utils import make_grid
+from torchvision.transforms.functional import to_pil_image
+
+latent_dim = 100
+n_classes = 10
+img_size = 32
+channels = 1
+
+model = Generator()
+model.load_state_dict(torch.load("generator1.pth", map_location=torch.device('cpu')))
+model.eval()
+
+
+def generate_image(class_idx):
+    with torch.no_grad():
+
+        # Generate random noise vector of latent_dim size
+        noise = torch.randn(1, latent_dim)
+        label = torch.tensor([int(class_idx)])
+        gen_img = model(noise, label).squeeze(0)
+    return to_pil_image(make_grid(gen_img, normalize=True))
+
+
+# Create Gradio Interface
+noise_input = gr.inputs.Slider(minimum=-1.0, maximum=1.0, default=0, step=0.1, label="Noise")
+class_input = gr.inputs.Dropdown([str(i) for i in range(n_classes)], label="Class")
+output_image = gr.outputs.Image('pil')
+
+gr.Interface(
+    fn=generate_image,
+    inputs=[class_input],
+    outputs=output_image,
+    title="MNIST Generator",
+    description="Generate images of handwritten digits from the MNIST dataset using a GAN.",
+    theme="default",
+    layout="vertical",
+    live=True
+).launch(debug=True)