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, render_result


def yolov8_inference(
    image,
    model_path,
    image_size,
    conf_threshold,
    iou_threshold,
):
    """
    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(f'kadirnar/{model_path}-v8.0')
    # set model parameters
    model.overrides['conf'] = conf_threshold  # NMS confidence threshold
    model.overrides['iou'] = iou_threshold  # NMS IoU threshold
    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
    model.overrides['max_det'] = 1000  # maximum number of detections per image
    results = model.predict(image, imgsz=image_size)
    render = render_result(model=model, image=image, result=results[0])
    return render
        

inputs = [
    gr.Image(type="filepath", label="Input Image"),
    gr.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"], 
                       value="yolov8m", label="Model"),
    gr.Slider(minimum=320, maximum=1280, value=640, step=320, 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 = "State-of-the-Art YOLO Models for Object detection"

examples = [['demo_01.jpg', 'yolov8n', 640, 0.25, 0.45], ['demo_02.jpg', 'yolov8l', 640, 0.25, 0.45], ['demo_03.jpg', 'yolov8x', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
    fn=yolov8_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    examples=examples,
    cache_examples=True,
)
demo_app.launch(debug=True)