from typing import Optional
# import spaces

import gradio as gr
import numpy as np
import torch
from PIL import Image
import io


import base64, os
from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img
import torch
from PIL import Image

# yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt')
# caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence")

from ultralytics import YOLO
yolo_model = YOLO('weights/icon_detect/best.pt').to('cuda')
from transformers import AutoProcessor, AutoModelForCausalLM 
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("weights/icon_caption_florence", torch_dtype=torch.float16, trust_remote_code=True).to('cuda')
caption_model_processor = {'processor': processor, 'model': model}
print('finish loading model!!!')


MARKDOWN = """
# OmniParser for Pure Vision Based General GUI Agent 🔥
<div>
    <a href="https://arxiv.org/pdf/2408.00203">
        <img src="https://img.shields.io/badge/arXiv-2408.00203-b31b1b.svg" alt="Arxiv" style="display:inline-block;">
    </a>
</div>

OmniParser is a screen parsing tool to convert general GUI screen to structured elements. 

📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models](https://huggingface.co/microsoft/OmniParser)]
"""

# DEVICE = torch.device('cuda')

# @spaces.GPU
@torch.inference_mode()
# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
# @spaces.GPU(duration=65)
def process(
    image_input,
    box_threshold,
    iou_threshold
) -> Optional[Image.Image]:

    image_save_path = 'imgs/saved_image_demo.png'
    image_input.save(image_save_path)
    # import pdb; pdb.set_trace()
    image = Image.open(image_save_path)
    box_overlay_ratio = image.size[0] / 3200
    draw_bbox_config = {
        'text_scale': 0.8 * box_overlay_ratio,
        'text_thickness': max(int(2 * box_overlay_ratio), 1),
        'text_padding': max(int(3 * box_overlay_ratio), 1),
        'thickness': max(int(3 * box_overlay_ratio), 1),
    }

    ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=True)
    text, ocr_bbox = ocr_bbox_rslt
    # print('prompt:', prompt)
    dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold)
    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
    print('finish processing')
    parsed_content_list = '\n'.join(parsed_content_list)
    return image, str(parsed_content_list)



with gr.Blocks() as demo:
    gr.Markdown(MARKDOWN)
    with gr.Row():
        with gr.Column():
            image_input_component = gr.Image(
                type='pil', label='Upload image')
            # set the threshold for removing the bounding boxes with low confidence, default is 0.05
            box_threshold_component = gr.Slider(
                label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05)
            # set the threshold for removing the bounding boxes with large overlap, default is 0.1
            iou_threshold_component = gr.Slider(
                label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
            submit_button_component = gr.Button(
                value='Submit', variant='primary')
        with gr.Column():
            image_output_component = gr.Image(type='pil', label='Image Output')
            text_output_component = gr.Textbox(label='Parsed screen elements', placeholder='Text Output')

    submit_button_component.click(
        fn=process,
        inputs=[
            image_input_component,
            box_threshold_component,
            iou_threshold_component
        ],
        outputs=[image_output_component, text_output_component]
    )

# demo.launch(debug=False, show_error=True, share=True)
demo.launch(share=True, server_port=7861, server_name='0.0.0.0')
# demo.queue().launch(share=False)