from typing import Any, Dict

import cv2
import gradio as gr
import numpy as np
from gradio_image_annotation import image_annotator
from sam2 import load_model
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
from sam2.sam2_image_predictor import SAM2ImagePredictor

from src.plot_utils import export_mask


# @spaces.GPU()
def predict(model_choice, annotations: Dict[str, Any]):
    sam2_model = load_model(
        variant=model_choice,
        ckpt_path=f"assets/checkpoints/sam2_hiera_{model_choice}.pt",
        device="cpu",
    )
    if annotations["boxes"]:
        predictor = SAM2ImagePredictor(sam2_model)  # type:ignore
        predictor.set_image(annotations["image"])
        coordinates = []
        for i in range(len(annotations["boxes"])):
            coordinate = [
                int(annotations["boxes"][i]["xmin"]),
                int(annotations["boxes"][i]["ymin"]),
                int(annotations["boxes"][i]["xmax"]),
                int(annotations["boxes"][i]["ymax"]),
            ]
            coordinates.append(coordinate)

        masks, scores, _ = predictor.predict(
            point_coords=None,
            point_labels=None,
            box=np.array(coordinates),
            multimask_output=False,
        )

        if masks.shape[0] == 1:
            # handle single mask cases
            masks = np.expand_dims(masks, axis=0)

        return export_mask(masks)

    else:
        mask_generator = SAM2AutomaticMaskGenerator(sam2_model)  # type: ignore
        masks = mask_generator.generate(annotations["image"])
        return export_mask(masks, autogenerated=True)


with gr.Blocks(delete_cache=(30, 30)) as demo:
    gr.Markdown(
        """
        ## To read more about the Segment Anything Project please refer to the [Lightly AI blogpost](https://www.lightly.ai/post/segment-anything-model-and-friends)
        """
    )
    gr.Markdown(
        """
        # 1. Choose Model Checkpoint
        """
    )
    with gr.Row():
        model = gr.Dropdown(
            choices=["tiny", "small", "base_plus", "large"],
            value="tiny",
            label="Model Checkpoint",
            info="Which model checkpoint to load?",
        )

    gr.Markdown(
        """
        # 2. Upload your Image and draw bounding box(es)
        """
    )

    annotator = image_annotator(
        value={"image": cv2.imread("assets/example.png")},
        disable_edit_boxes=True,
        label="Draw a bounding box",
    )
    btn = gr.Button("Get Segmentation Mask(s)")
    btn.click(
        fn=predict, inputs=[model, annotator], outputs=[gr.Image(label="Mask(s)")]
    )

demo.launch()