import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO

# Images
#torch.hub.download_url_to_file('https://ibb.co/zrFWRhJ][img]https://i.ibb.co/37v5Nyz/01-missing-hole-02-jpg-rf-3bd05d8784b12c66da03831d326edd2b.jpg', 'hole.jpg')
#torch.hub.download_url_to_file('https://ibb.co/qgPMXvN][img]https://i.ibb.co/rp1GTVx/01-mouse-bite-04-jpg-rf-8b3b06d80ac553a92606bdac3e394bb9.jpg', 'mouse.jpg')
#torch.hub.download_url_to_file('https://ibb.co/0Q35SLN][img]https://i.ibb.co/DLndyFX/12-spur-05-jpg-rf-39e4c0bbc17325d83c938d49e5efdf47.jpg', 'spur.jpg')

def yolov8_inference(
    image: gr.inputs.Image = None,
    model_path: gr.inputs.Dropdown = None,
    image_size: gr.inputs.Slider = 640,
    conf_threshold: gr.inputs.Slider = 0.25,
    iou_threshold: gr.inputs.Slider = 0.45,
):
    """
    YOLOv8 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """
    model = YOLO(model_path)
    model.conf = conf_threshold
    model.iou = iou_threshold
    results = model.predict(image, imgsz=image_size, return_outputs=True)
    object_prediction_list = []
    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]
                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']
        

inputs = [
    gr.inputs.Image(type="filepath", label="Input Image"),
    gr.inputs.Dropdown(["ethanrom/pcb_def"], 
                       default="ethanrom/pcb_def", label="Model"),
    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YOLOv8 PCB defect detection"

#examples = [['highway.jpg', 'ethanrom/pcb_def', 640, 0.25, 0.45]]
demo_app = gr.Interface(
    fn=yolov8_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    #examples=examples,
    #cache_examples=True,
    theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)