app.py

import gradio as gr
import spaces
from PIL import Image
from ultralytics import YOLO

# Load Models
models = {
    "yolov10n": YOLO("jameslahm/yolov10n"),
    "yolov10s": YOLO("jameslahm/yolov10s"),
    "yolov10m": YOLO("jameslahm/yolov10m"),
    "yolov10b": YOLO("jameslahm/yolov10b"),
    "yolov10l": YOLO("jameslahm/yolov10l"),
    "yolov10x": YOLO("jameslahm/yolov10x"),
}


@spaces.GPU(duration=30)
def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold):
    model = models[model_id]
    results = model.predict(
        source=image,
        imgsz=image_size,
        conf=conf_threshold,
        iou=iou_threshold,
    )
    annotated_image = results[0].plot()
    return Image.fromarray(annotated_image[..., ::-1])


def app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                image = gr.Image(type="pil", label="Image")
                model_id = gr.Dropdown(
                    label="Model",
                    choices=[
                        "yolov10n",
                        "yolov10s",
                        "yolov10m",
                        "yolov10b",
                        "yolov10l",
                        "yolov10x",
                    ],
                    value="yolov10m",
                )
                image_size = gr.Slider(
                    label="Image Size",
                    minimum=320,
                    maximum=1280,
                    step=32,
                    value=640,
                )
                conf_threshold = gr.Slider(
                    label="Confidence Threshold",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.25,
                )
                iou_threshold = gr.Slider(
                    label="IoU Threshold",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.45,
                )
                yolov10_infer = gr.Button(value="Detect Objects")

            with gr.Column():
                output_image = gr.Image(type="pil", label="Annotated Image")

        gr.Examples(
            examples=[
                ["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
                ["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
                ["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
            ],
            fn=yolov10_inference,
            inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
            outputs=[output_image],
            cache_examples='lazy',
        )

        yolov10_infer.click(
            fn=yolov10_inference,
            inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
            outputs=[output_image],
        )


gradio_app = gr.Blocks()
with gradio_app:
    gr.HTML(
        """
    <h1 style='text-align: center'>
    YOLOv10: Real-Time End-to-End Object Detection
    </h1>
    """)
    gr.HTML(
        """
       <h3 style='text-align: center'>
       Follow me for more!
       <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
       </h3>
       """)
    with gr.Row():
        with gr.Column():
            image = gr.Image(type="pil", label="Image")
            model_id = gr.Dropdown(
                label="Model",
                choices=[
                    "yolov10n",
                    "yolov10s",
                    "yolov10m",
                    "yolov10b",
                    "yolov10l",
                    "yolov10x",
                ],
                value="yolov10m",
            )
            image_size = gr.Slider(
                label="Image Size",
                minimum=320,
                maximum=1280,
                step=32,
                value=640,
            )
            conf_threshold = gr.Slider(
                label="Confidence Threshold",
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                value=0.25,
            )
            iou_threshold = gr.Slider(
                label="IoU Threshold",
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                value=0.45,
            )
            yolov10_infer = gr.Button(value="Detect Objects")

        with gr.Column():
            output_image = gr.Image(type="pil", label="Annotated Image")

    gr.Examples(
        examples=[
            ["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
            ["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
            ["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
        ],
        fn=yolov10_inference,
        inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
        outputs=[output_image],
        cache_examples='lazy',
    )

    yolov10_infer.click(
        fn=yolov10_inference,
        inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
        outputs=[output_image],
    )

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