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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()
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