app.py

import gradio as gr
import torch
from PIL import Image
from torchvision import transforms

# Load pre-trained U-Net model
model = torch.hub.load('nvidia/DeepLearningExamples:torchhub', 'unet', pretrained=True)

# Define a function to segment an image
def segment_image(image):
    # Preprocess image
    image = Image.fromarray(image)
    image = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])(image)
    
    # Run segmentation model
    output = model(image.unsqueeze(0))
    output = torch.argmax(output, dim=1)
    
    # Postprocess output
    output = output.squeeze(0).cpu().numpy()
    output = Image.fromarray(output.astype('uint8'))
    
    return output

# Create Gradio app
demo = gr.Interface(
    fn=segment_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="Segment Anything",
    description="Segment any image using a pre-trained U-Net model"
)

# Launch Gradio app
demo.launch()