app.py

import torchvision.utils as vutils
import torchvision.transforms as T
from PIL import Image
import gradio as gr
import torch
from model import generator

def generate_images(num_images, z_dim=100):
    # Generate batch of latent vectors
    noise = torch.randn(num_images, z_dim, 1, 1)

    # Generate fake image batch with G
    generator.eval()  # Set the generator to evaluation mode
    with torch.no_grad():
        fake_images = generator(noise).detach().cpu()

    # Plot the fake images
    img = vutils.make_grid(fake_images, padding=2, normalize=True).permute(1, 2, 0)

    img = img.permute(2,0,1)

    transform = T.ToPILImage()

    # convert the tensor to PIL image using above transform
    img = transform(img)

    return img


batch_size = 16
z = torch.randn(batch_size, 100, 1, 1)
fake_images = generator(z)
# Create a Gradio input component for a positive integer
#inp = gr.Number(value=0, minimum=0, label="Enter a positive integer")

# Create a Gradio output component for an image
out = gr.Image(type="pil",label = "Generated Dogs")
inp = gr.Number(value=0, minimum=0, label="Enter number of Dogs to Generate (positive integer)")

demo = gr.Interface(fn=generate_images,inputs = inp, outputs=out, allow_flagging="never")

# Launch the Gradio interface
demo.launch(share=True)