Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+import numpy as np
+
+class Generator(nn.Module):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self.main = nn.Sequential(
+            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(64 * 8),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 4),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64 * 2),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+
+            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
+            nn.Tanh()
+        )
+
+    def forward(self, input):
+        return self.main(input)
+
+netG = Generator()
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+netG.load_state_dict(torch.load("dcgan.pth", map_location=device))
+netG.eval()
+
+def generate_image():
+    with torch.no_grad():
+        noise = torch.randn(1, 100, 1, 1)
+        fake_image = netG(noise)
+
+    generated_image = fake_image.squeeze().cpu().numpy()
+    generated_image = np.transpose(generated_image, (1, 2, 0))
+    generated_image = (generated_image + 1) / 2.0 
+    generated_image = (generated_image * 255).astype(np.uint8)
+
+    return generated_image
+
+title = "DCGAN Image Generator 🖌️🎨"
+description = "Generate non-existing images using DCGAN."
+    
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=None,
+    outputs="image",
+    title=title,
+    description=description,
+    theme="soft"
+)
+
+iface.launch()
+