app.py

from typing import Optional
import gradio as gr
import numpy as np
import torch
from PIL import Image
import io
import base64
import os

from util.utils import (
    check_ocr_box,
    get_yolo_model,
    get_caption_model_processor,
    get_som_labeled_img
)
from huggingface_hub import snapshot_download

# Define repository and local directory
repo_id = "microsoft/OmniParser-v2.0"
local_dir = "weights"
snapshot_download(repo_id=repo_id, local_dir=local_dir)

print(f"Repository downloaded to: {local_dir}")

# Force CPU usage
DEVICE = torch.device('cpu')

# Load models to CPU
yolo_model = get_yolo_model(model_path='weights/icon_detect/model.pt')
caption_model_processor = get_caption_model_processor(
    model_name="florence2",
    model_name_or_path="weights/icon_caption"
)

MARKDOWN = """
# OmniParser V2 for Pure Vision Based General GUI Agent 🔥 (Only CPU Test)
<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. 
"""

@torch.inference_mode()
def process(
    image_input,
    box_threshold,
    iou_threshold,
    use_paddleocr,
    imgsz
) -> Optional[Image.Image]:

    box_overlay_ratio = image_input.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_input,
        display_img=False,
        output_bb_format='xyxy',
        goal_filtering=None,
        easyocr_args={'paragraph': False, 'text_threshold': 0.9},
        use_paddleocr=use_paddleocr
    )
    text, ocr_bbox = ocr_bbox_rslt

    dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(
        image_input,
        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,
        imgsz=imgsz
    )

    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
    print('finish processing')

    parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i, v in enumerate(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')
            box_threshold_component = gr.Slider(
                label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05)
            iou_threshold_component = gr.Slider(
                label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
            use_paddleocr_component = gr.Checkbox(
                label='Use PaddleOCR', value=True)
            imgsz_component = gr.Slider(
                label='Icon Detect Image Size', minimum=640, maximum=1920, step=32, value=640)
            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,
            use_paddleocr_component,
            imgsz_component
        ],
        outputs=[image_output_component, text_output_component]
    )

demo.queue().launch(share=False)