app.py

import numpy as np
import torch
import torchvision.models as models
from torchvision import transforms
from PIL import Image
import gradio as gr

# Load a pre-trained GAN model (e.g., DCGAN)
# You can replace this with any other GAN or VAE model
generator = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub', 'DCGAN', pretrained=True, useGPU=False)

def generate_image(input_image):
    # Convert the input image to a PIL Image
    input_image = Image.fromarray(input_image.astype('uint8'), 'RGB')
    
    # Preprocess the image (resize, normalize, etc.)
    preprocess = transforms.Compose([
        transforms.Resize((64, 64)),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
    
    input_tensor = preprocess(input_image).unsqueeze(0)
    
    # Generate an image using the GAN model
    with torch.no_grad():
        generated_tensor = generator(input_tensor)
    
    # Convert the generated tensor back to an image
    generated_image = transforms.ToPILImage()(generated_tensor.squeeze(0))
    
    return generated_image
    
def draw_line(image):
    # Convert the image to a numpy array
    image_np = np.array(image)
    
    # Generate a similar image using the GAN model
    generated_image = generate_image(image_np)
    
    return generated_image

# Create the Gradio interface
iface = gr.Interface(
    fn=draw_line,
    inputs="sketchpad",
    outputs="image",
    live=True,
    title="Draw a Line and Generate a Similar Image",
    description="Draw a line on the sketchpad, and the app will generate a similar image using a GAN model."
)

# Launch the app
iface.launch()