gradio_demo.py

from typing import Optional, Text, Tuple
import gradio as gr
import torch
from PIL import Image
import io
import base64
import json
import numpy as np

from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        if isinstance(obj, np.float32):
            return float(obj)
        return json.JSONEncoder.default(self, obj)

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")
platform = 'pc'
if platform == 'pc':
    draw_bbox_config = {
        'text_scale': 0.8,
        'text_thickness': 2,
        'text_padding': 2,
        'thickness': 2,
    }
elif platform == 'web':
    draw_bbox_config = {
        'text_scale': 0.8,
        'text_thickness': 2,
        'text_padding': 3,
        'thickness': 3,
    }
elif platform == 'mobile':
    draw_bbox_config = {
        'text_scale': 0.8,
        'text_thickness': 2,
        'text_padding': 3,
        'thickness': 3,
    }



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. 
"""

DEVICE = torch.device('cuda')

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

    image_save_path = 'imgs/saved_image_demo.png'
    image_input.save(image_save_path)

    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})
    text, ocr_bbox = ocr_bbox_rslt

    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)

    # Convert base64 string to PIL Image
    image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))

    print('finish processing')

    # Combine text and bounding boxes into JSON-friendly format
    result = {
        "label_coordinates": label_coordinates,
        "parsed_content_list": parsed_content_list,
    }
    
    # Convert to JSON string format for return using the custom encoder
    result_json = json.dumps(result, indent=4, cls=NumpyEncoder)
    
    return image, result_json


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')