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

torch.hub.download_url_to_file(
    'https://cdn.theatlantic.com/thumbor/xoh2WVVSx4F2uboG9xbT5BDprtM=/0x0:4939x2778/960x540/media/img/mt/2023/11/LON68717_copy/original.jpg', 
    'one.jpg')
torch.hub.download_url_to_file(
    'https://i.ytimg.com/vi/lZQX2mmLo2s/maxresdefault.jpg', 
    'two.jpg')
torch.hub.download_url_to_file(
    'https://assets.bwbx.io/images/users/iqjWHBFdfxIU/ioQgA.854d7s/v1/-1x-1.jpg', 
    'three.jpg')
torch.hub.download_url_to_file(
    'https://cdn.apartmenttherapy.info/image/upload/f_jpg,q_auto:eco,c_fill,g_auto,w_1500,ar_1:1/at%2Fhouse%20tours%2Farchive%2FTour%20a%20Colorful%20Home%20in%20Montreal%2Ffada199d36b084830ef3563b555887f31851ca55', 
    'four.jpg')



def yoloV8_func(image: gr.Image = None,
                image_size: gr.Slider = 640,
                conf_threshold: gr.Slider = 0.4,
                iou_threshold: gr.Slider = 0.50):
    """
    This function performs YOLOv8 object detection on the given image.
    """
    # Load the YOLOv8 model from the 'best.pt' checkpoint
    model_path = "YOLO-best.pt"
    model = YOLO(model_path)

    # 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], rect_th = 4, text_th = 4)
    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 = "YOLOv8 Custom Object Detection by Uyen Nguyen"


examples = [['one.jpg', 900, 0.5, 0.8],
            ['two.jpg', 1152, 0.05, 0.05],
            ['three.jpg', 1024, 0.25, 0.25],
            ['four.jpg', 832, 0.3, 0.3]]

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, share=True)