import gradio as gr
from ultralyticsplus import YOLO, render_result
from ultralytics.yolo.utils.plotting import Annotator

def yolov8_inference(
    image: gr.Image = None,
    model_path = "eeshawn11/naruto_hand_seal_detection",
    conf_threshold: gr.Slider = 0.50,
    iou_threshold: gr.Slider = 0.45,
):
    """
    YOLOv8 inference function
    Args:
        image: Input image
        model_path: Path to the model
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """
    # model = YOLO(model_path)
    model = YOLO("ultralyticsplus/yolov8s")
    model.conf = conf_threshold
    model.iou = iou_threshold
    # results = model.predict(image, return_outputs=True)
    results = model.predict(image)
    # object_prediction_list = []
    # annotator = Annotator(image)
    # for _, image_results in enumerate(results):
    #     if len(image_results)!=0:
    #         image_predictions_in_xyxy_format = image_results['det']
    #         for pred in image_predictions_in_xyxy_format:
    #             x1, y1, x2, y2 = (
    #                 int(pred[0]),
    #                 int(pred[1]),
    #                 int(pred[2]),
    #                 int(pred[3]),
    #             )
    #             bbox = [x1, y1, x2, y2]
    #             score = pred[4]
    #             category_name = model.model.names[int(pred[5])]
    #             category_id = pred[5]
    #             annotator.box_label(bbox, f"{category_name} {score}")
                # object_prediction = ObjectPrediction(
                #     bbox=bbox,
                #     category_id=int(category_id),
                #     score=score,
                #     category_name=category_name,
                # )
                # object_prediction_list.append(object_prediction)

    # image = read_image(image)
    # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
    
    # return output_image['image']
    # return annotator.result()
    render = render_result(model=model, image=image, result=results[0])
    return render
        

inputs = [
    # gr.inputs.Image(type="filepath", label="Input Image"),
    gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.Image(type="filepath", label="Output Image")
title = "Naruto Hand Seal Detection with YOLOv8"

myapp = gr.Interface(
    fn=yolov8_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
)
myapp.queue()
myapp.launch()