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

from gradio.components import Slider, Image, Dropdown


def yolov8_inference(
    image: Image = None,
    model_path: Dropdown = None,
    image_size: Slider = 640,
    confidence_threshold: Slider = 0.25,
    iou_threshold: Slider = 0.45,
):
    model = YOLO(model_path)
    model.conf = confidence_threshold
    model.iou = iou_threshold
    results = model.predict(image, imgsz=image_size)
    object_prediction_list = []
    for _, image_results in enumerate(results):
        if len(image_results) != 0:
            image_predictions_in_xyxy_format = image_results.boxes.data
            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)
    
    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
    return output_image['image']

inputs = [
    "image",
    Dropdown(label="Model", choices=["yolo/runs/detect/train10/weights/best.pt"], value="yolo/runs/detect/train10/weights/best.pt", visible=False),
    Slider(minimum=320, maximum=1280, step=32, value=640, label="Image Size"),
    Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.45, label="Confidence Threshold"),
    Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.25, label="IOU Threshold"),
]

title = "Smartathon Pothole Challenge"

examples = [
    ["examples/0014.png", "yolo/runs/detect/train10/weights/best.pt", 640, 0.45, 0.25],
    ["examples/0055.png", "yolo/runs/detect/train10/weights/best.pt", 640, 0.45, 0.25],
    ["examples/0083.png", "yolo/runs/detect/train10/weights/best.pt", 640, 0.45, 0.25],
]

iface = gr.Interface(
    fn=yolov8_inference,
    inputs=inputs,
    outputs="image",
    title=title,
    examples=examples,
    theme="default",
)
iface.launch(debug=True)