app.py

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

examples = [[Image.open(f'examples/{ex}'), 0.25, 0.45] for ex in os.listdir('examples')]

model = YOLO("license_plate_detector.pt")


def predict_image(img, conf_threshold, iou_threshold):
    """Predicts objects in an image using a YOLO11 model with adjustable confidence and IOU thresholds."""
    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
    )

    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])

    return im

title='License Plate Detector 🚗'
description='A license plate detector model fine-tuned from Ultralytics Yolov11'

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"),
    title=title,
    description=description,
    examples=examples,
    flagging_mode='never'
)

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