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import gradio as gr
import PIL.Image as Image
from ultralytics import ASSETS, YOLO
model = None
def predict_image(img, conf_threshold, iou_threshold, model_name):
"""Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
model = YOLO(model_name)
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
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"),
gr.Radio(choices=["yolo11n", "yolo11s", "yolo11n-seg", "yolo11s-seg", "yolo11n-pose", "yolo11s-pose"], label="Model Name", value="yolo11n"),
],
outputs=gr.Image(type="pil", label="Result"),
title="Ultralytics Gradio Application 🚀",
description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
examples=[
[ASSETS / "bus.jpg", 0.25, 0.45, "yolo11n.pt"],
[ASSETS / "zidane.jpg", 0.25, 0.45, "yolo11n.pt"],
],
)
iface.launch(share=True)
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