app.py

import gradio as gr
import numpy as np
from ultralytics import YOLO

model = YOLO("yolov8n.pt")

def detect_persons(image):
    image_np = np.array(image)
    results = model(image_np)

    persons = []

    for r in results:
        for box, cls, conf in zip(r.boxes.xyxy, r.boxes.cls, r.boxes.conf):

            # class 0 in COCO = person
            if int(cls) != 0:
                continue

            if conf < 0.4:
                continue

            xmin, ymin, xmax, ymax = box.cpu().numpy()
            cx = (xmin + xmax) / 2
            cy = (ymin + ymax) / 2

            persons.append({
                "cx": float(cx),
                "cy": float(cy),
                "confidence": float(conf),
                "box": {
                    "xmin": float(xmin),
                    "ymin": float(ymin),
                    "xmax": float(xmax),
                    "ymax": float(ymax)
                }
            })

    return persons

iface = gr.Interface(
    fn=detect_persons,
    inputs=gr.Image(type="pil"),
    outputs="json"
)

iface.launch()