updated app

addited the model path

app.py

@@ -1,107 +1,107 @@
-import torch
-import torch.nn as nn
-import torchvision.utils as vutils
-import gradio as gr
-import numpy as np
-
-
-# Define Generator architecture - must match the architecture used during training
-class Generator(nn.Module):
-    def __init__(self, ngpu=1, nz=100, ngf=64, nc=1):
-        super(Generator, self).__init__()
-        self.ngpu = ngpu
-        self.main = nn.Sequential(
-            # input is Z, going into a convolution
-            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
-            nn.BatchNorm2d(ngf * 8),
-            nn.ReLU(True),
-            # state size. (ngf*8) x 4 x 4
-            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(ngf * 4),
-            nn.ReLU(True),
-            # state size. (ngf*4) x 8 x 8
-            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(ngf * 2),
-            nn.ReLU(True),
-            # state size. (ngf*2) x 16 x 16
-            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(ngf),
-            nn.ReLU(True),
-            # state size. (ngf) x 32 x 32
-            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
-            nn.Tanh()
-            # state size. (nc) x 64 x 64
-        )
-
-    def forward(self, input):
-        return self.main(input)
-
-
-# Load the generator
-def load_model(model_path):
-    # Create the generator and load the saved weights
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    netG = Generator().to(device)
-    netG.load_state_dict(torch.load(model_path, map_location=device))
-    netG.eval()  # Set to evaluation mode
-    return netG, device
-
-
-# Generate images using the model
-def generate_images(num_images=16, seed=None, model_path="models/netG_epoch_21.pth"):
-    # Load the model
-    netG, device = load_model(model_path)
-
-    # Set random seed for reproducibility if provided
-    if seed is not None:
-        torch.manual_seed(seed)
-        np.random.seed(seed)
-
-    # Generate latent vectors
-    nz = 100  # Size of the latent vector (must match the model)
-    noise = torch.randn(num_images, nz, 1, 1, device=device)
-
-    # Generate fake images
-    with torch.no_grad():
-        fake_images = netG(noise).detach().cpu()
-
-    # Convert to grid for display
-    grid = vutils.make_grid(fake_images, padding=2, normalize=True, nrow=int(np.sqrt(num_images)))
-
-    # Convert from tensor to numpy array for Gradio
-    grid_np = grid.numpy().transpose((1, 2, 0))
-
-    # Convert from [-1, 1] to [0, 1] range for display
-    grid_np = (grid_np + 1) / 2.0
-
-    return grid_np
-
-
-# Create Gradio interface
-def create_gradio_app():
-    with gr.Blocks(title="DCGAN MNIST Generator") as app:
-        gr.Markdown("# DCGAN MNIST Generator")
-        gr.Markdown("Generate MNIST-like digits using a Deep Convolutional GAN")
-
-        with gr.Row():
-            with gr.Column():
-                num_images = gr.Slider(minimum=1, maximum=64, value=16, step=1, label="Number of Images")
-                seed = gr.Number(label="Random Seed (leave blank for random)", precision=0)
-                generate_button = gr.Button("Generate Images")
-
-            with gr.Column():
-                output_image = gr.Image(label="Generated Images")
-
-        generate_button.click(fn=generate_images, inputs=[num_images, seed], outputs=output_image)
-
-        gr.Markdown("## About")
-        gr.Markdown("This model was trained using PyTorch DCGAN implementation on the MNIST dataset. "
-                    "It generates new handwritten digit-like images from random noise.")
-
-    return app
-
-
-# Launch the app if the script is run directly
-if __name__ == "__main__":
-    app = create_gradio_app()
+import torch
+import torch.nn as nn
+import torchvision.utils as vutils
+import gradio as gr
+import numpy as np
+
+
+# Define Generator architecture - must match the architecture used during training
+class Generator(nn.Module):
+    def __init__(self, ngpu=1, nz=100, ngf=64, nc=1):
+        super(Generator, self).__init__()
+        self.ngpu = ngpu
+        self.main = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(ngf * 8),
+            nn.ReLU(True),
+            # state size. (ngf*8) x 4 x 4
+            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(ngf * 4),
+            nn.ReLU(True),
+            # state size. (ngf*4) x 8 x 8
+            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(ngf * 2),
+            nn.ReLU(True),
+            # state size. (ngf*2) x 16 x 16
+            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(ngf),
+            nn.ReLU(True),
+            # state size. (ngf) x 32 x 32
+            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. (nc) x 64 x 64
+        )
+
+    def forward(self, input):
+        return self.main(input)
+
+
+# Load the generator
+def load_model(model_path):
+    # Create the generator and load the saved weights
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    netG = Generator().to(device)
+    netG.load_state_dict(torch.load(model_path, map_location=device))
+    netG.eval()  # Set to evaluation mode
+    return netG, device
+
+
+# Generate images using the model
+def generate_images(num_images=16, seed=None, model_path="models/netG_epoch_29.pth"):
+    # Load the model
+    netG, device = load_model(model_path)
+
+    # Set random seed for reproducibility if provided
+    if seed is not None:
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+
+    # Generate latent vectors
+    nz = 100  # Size of the latent vector (must match the model)
+    noise = torch.randn(num_images, nz, 1, 1, device=device)
+
+    # Generate fake images
+    with torch.no_grad():
+        fake_images = netG(noise).detach().cpu()
+
+    # Convert to grid for display
+    grid = vutils.make_grid(fake_images, padding=2, normalize=True, nrow=int(np.sqrt(num_images)))
+
+    # Convert from tensor to numpy array for Gradio
+    grid_np = grid.numpy().transpose((1, 2, 0))
+
+    # Convert from [-1, 1] to [0, 1] range for display
+    grid_np = (grid_np + 1) / 2.0
+
+    return grid_np
+
+
+# Create Gradio interface
+def create_gradio_app():
+    with gr.Blocks(title="DCGAN MNIST Generator") as app:
+        gr.Markdown("# DCGAN MNIST Generator")
+        gr.Markdown("Generate MNIST-like digits using a Deep Convolutional GAN")
+
+        with gr.Row():
+            with gr.Column():
+                num_images = gr.Slider(minimum=1, maximum=64, value=16, step=1, label="Number of Images")
+                seed = gr.Number(label="Random Seed (leave blank for random)", precision=0)
+                generate_button = gr.Button("Generate Images")
+
+            with gr.Column():
+                output_image = gr.Image(label="Generated Images")
+
+        generate_button.click(fn=generate_images, inputs=[num_images, seed], outputs=output_image)
+
+        gr.Markdown("## About")
+        gr.Markdown("This model was trained using PyTorch DCGAN implementation on the MNIST dataset. "
+                    "It generates new handwritten digit-like images from random noise.")
+
+    return app
+
+
+# Launch the app if the script is run directly
+if __name__ == "__main__":
+    app = create_gradio_app()
     app.launch()