Create app.py

app.py

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+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()