app.py

import gradio as gr
import PIL.Image as Image
from ultralytics import ASSETS, YOLO

model = YOLO("yolo12x.pt")

def predict_image(img, conf_threshold, iou_threshold):
    """Predicts persons in an image and returns the image with detections and count."""
    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
        classes=[0]
    )

    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])
    
    person_count = len(results[0].boxes) if results[0].boxes is not None else 0

    return im, f"Number of persons detected: {person_count}"

iface = gr.Interface(
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
    ],
    outputs=[
        gr.Image(type="pil", label="Result"),
        gr.Textbox(label="Person Count")
    ],
    title="Image Person Detection",
    description="Upload images to detect persons and get a count",
)

if __name__ == "__main__":
    iface.launch()