import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result


# torch.hub.download_url_to_file(
#     'https://en.wikipedia.org/wiki/Human_eye#/media/File:Human_eye,_anterior_view.jpg', 'one.jpg')
# torch.hub.download_url_to_file(
#     'https://glaucoma.org/wp-content/uploads/2024/01/amazing-eye-close-up_900-585x540.jpg', 'two.jpg')
# torch.hub.download_url_to_file(
#     'https://www.nvisioncenters.com/wp-content/uploads//woman-blue-eye-close-up-352x235.jpg', 'three.jpg')


def yoloV8_func(image: gr.Image = None,
                image_size: int = 640,
                conf_threshold: float = 0.70,
                iou_threshold: float = 0.50):
    """This function performs YOLOv8 object detection on the given image.

    Args:
        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
        image_size (int, optional): Desired image size for the model. Defaults to 640.
        conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.7.
        iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
    """
    # Load the YOLOv8 model from the 'best.pt' checkpoint
    #model_path = "EyesCareEyeDetectModel.pt"
    model = YOLO("yolov8n.pt")

    # Perform object detection on the input image using the YOLOv8 model
    results = model.predict(image,
                            conf=conf_threshold,
                            iou=iou_threshold,
                            imgsz=image_size)

    # Print the detected objects' information (class, coordinates, and probability)
    box = results[0].boxes
    print("Object type:", box.cls)
    print("Coordinates:", box.xyxy)
    print("Probability:", box.conf)

    # Render the output image with bounding boxes around detected objects
    render = render_result(model=model, image=image, result=results[0])
    return render


inputs = [
    gr.Image(type="filepath", label="Input Image"),
    gr.Slider(minimum=320, maximum=1280, value=640,
              step=32, label="Image Size"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.25,
              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 = "YOLOv9 101: Custom Object Detection on Eye"

examples = [['one.jpg', 640, 0.5, 0.7],
            ['two.jpg', 640, 0.5, 0.6],
            ['three.jpg', 640, 0.5, 0.8]]

yolo_app = gr.Interface(
    fn=yoloV8_func,
    inputs=inputs,
    outputs=outputs,
    title=title,
    examples=examples,
    cache_examples=True,
)

# Launch the Gradio interface in debug mode with queue enabled
yolo_app.launch(debug=True, enable_queue=True)