app.py

import gradio as gr

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

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()


# # 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()