app.py

from ultralyticsplus import YOLO, render_result

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

    
def PPE(image):
    # load model
    model = YOLO('keremberke/yolov8m-protective-equipment-detection')

    # set model parameters
    model.overrides['conf'] = 0.25  # NMS confidence threshold
    model.overrides['iou'] = 0.45  # NMS IoU threshold
    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
    model.overrides['max_det'] = 1000  # maximum number of detections per image



    # perform inference
    results = model.predict(image)

    # observe results
    print(results[0].boxes)
    render = render_result(model=model, image=image, result=results[0])
    render.show()


import gradio as gr

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


iface = gr.Interface(fn=PPE, inputs=image, outputs=PPE())
iface.launch()