import gradio as gr
import PIL.Image as Image

from ultralytics import ASSETS, YOLO

model = YOLO("yolov8n.pt")


def predict_image(img, conf_threshold, iou_threshold):
    """Predicts objects in an image using a YOLOv8 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


with gr.Blocks() as demo:
  with gr.Row():
    with gr.Column():
      input_image = gr.Image(type="pil", label="Upload Image")
      conf = gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold")
      iou = gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
      with gr.Row():
        reset = gr.ClearButton([input_image])
        submit = gr.Button("Submit")
    with gr.Column():
      output_image = gr.Image(type="pil", label="Result")
      submit.click(fn=predict_image, inputs=[input_image, conf,iou], outputs=[output_image])
  examples = gr.Examples(([
          ['https://ultralytics.com/images/zidane.jpg', 0.25, 0.45],
          ['https://unsplash.com/photos/2pPw5Glro5I/download?ixid=M3wxMjA3fDB8MXxzZWFyY2h8Mnx8dXJsfGVufDB8fHx8MTcyMTgwNzkyMnww&force=true', 0.5, 0.3],
          ['https://unsplash.com/photos/5CUyfyde_io/download?ixid=M3wxMjA3fDB8MXxzZWFyY2h8OHx8dG9reW98ZW58MHx8fHwxNzIxODY4MzQzfDA&force=true', 0.3, 0.3]
      ]),[input_image,conf,iou]),

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