app.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

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=["yolov8n", "yolov8s", "yolov8m"], label="Model Name", value="yolov8n"),
    ],
    outputs=gr.Image(type="pil", label="Result"),
    title="Ultralytics Gradio Application 🚀",
    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
    examples=[
        [ASSETS / "bus.jpg", 0.25, 0.45, "yolov8n.pt"],
        [ASSETS / "zidane.jpg", 0.25, 0.45, "yolov8n.pt"],
    ],
)
iface.launch(share=True)