import gradio as gr
# import torch
# from your_pix2pixhd_code import YourPix2PixHDModel, load_image, tensor2im # Adapt these imports

# # --- 1. Load your pix2pixHD model ---
# # You'll need to adapt this part to your specific model loading logic
# # This is a simplified example
# model = YourPix2PixHDModel()
# model.load_state_dict(torch.load('models/your_pix2pixhd_model.pth'))
# model.eval()

# --- 2. Define the prediction function ---
def predict(input_image):
    return 255 - input_image
    # # Pre-process the input image
    # processed_image = load_image(input_image)

    # # Run inference
    # with torch.no_grad():
    #     generated_image_tensor = model(processed_image)

    # # Post-process the output tensor to an image
    # output_image = tensor2im(generated_image_tensor)

    # return output_image

# --- 3. Create the Gradio Interface ---
title = "pix2pixHD Image-to-Image Translation"
description = "Upload an image to see the pix2pixHD model in action."
article = "<p style='text-align: center'>Model based on the <a href='https://github.com/NVIDIA/pix2pixHD' target='_blank'>pix2pixHD repository</a>.</p>"

gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy", label="Input Image"),
    outputs=gr.Image(type="numpy", label="Output Image"),
    title=title,
    description=description,
    article=article,
    examples=[["your_example_image.jpg"]] # Optional: add example images
).launch()