import gradio as gr
from yolo import YOLO



yolo = YOLO()

def predict(image):
    r_image = yolo.detect_image(image)
    return r_image

title = "MASFNet: Multi-scale Adaptive Sampling Fusion Network for Object Detection in Adverse Weather "
description = ""
article = ""

def reset_interface():
    return gr.update(value=None), gr.update(visible=False)

example_images = [
    "img/1.png",
    "img/2.png",
    "img/3.png",
    "img/4.png",
    "img/5.png",
    "img/6.png",
    "img/7.png",
]

with gr.Blocks() as demo:
    gr.Markdown(f"### {title}")
    gr.Markdown(description)
    
    with gr.Row():
        with gr.Column():
            img_input = gr.Image(type="pil", label="Upload an Image")
            submit_btn = gr.Button("Submit")
        with gr.Column():
            output = gr.Image(type="pil", label="Prediction Result")
    
    submit_btn.click(fn=predict, inputs=img_input, outputs=output)
    demo.load(reset_interface, None, [output])
    gr.Examples(
        examples=example_images,
        inputs=img_input,
)


demo.launch()