Create app.py

app.py

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+import torch
+import gradio as gr
+import matplotlib.pyplot as plt
+import torchvision
+use_gpu = True if torch.cuda.is_available() else False
+
+model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=use_gpu)
+
+def dcgan(num_images):
+  noise, _ = model.buildNoiseData(num_images)
+  with torch.no_grad():
+      generated_images = model.test(noise)
+  plt.imshow(torchvision.utils.make_grid(generated_images).permute(1, 2, 0).cpu().numpy())
+  plt.axis("off")
+  return plt
+
+
+inputs = gr.inputs.Number(label="number of images")
+outputs = gr.outputs.Image(label="Output Image")
+
+title = "DCGAN"
+description = "demo for DCGAN. To use it, simply add the number of images to generate or click on the examples. Read more below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1511.06434'>Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a> | <a href='https://github.com/facebookresearch/pytorch_GAN_zoo/blob/master/models/DCGAN.py'>Github Repo</a></p>"
+examples = [
+            [1], 
+            [2],
+            [3],
+            [4], 
+            [64]
+]
+
+
+gr.Interface(dcgan, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False, examples=examples).launch(debug=True)