import gradio as gr

def greet(name):
    return "Hello " + name + "!!"


# Model to use
net_path = 'fire.pth'

# CPU / GPU
device = 'cpu'

# Images will be downscaled to this size prior processing with the network
image_size = 1024

# Wrapper
def generate_matching_superfeatures(im1, im2, scale=6):
    
    # Possible Scales for multiscale inference
    scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25] 


# GRADIO APP
title = "Visualizing Super-features"
description = "TBD"
article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>"


iface = gr.Interface(
    fn=generate_matching_superfeatures,
    inputs=[
        gr.inputs.Image(shape=(240, 240), type="pil"),
        gr.inputs.Image(shape=(240, 240), type="pil"),
        gr.inputs.Slider(minimum=1, maximum=7, step=1, default=2, label="Scale")],
    outputs="plot",
    enable_queue=True,
    title=title,
    description=description,
    article=article,
    examples=[["chateau_1.png", "chateau_2.png", 6]],
)
iface.launch()