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from typing import Optional
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/omniparser/icon_caption_blip2/best.pt')
caption_model_processor = get_caption_model_processor(model_name_or_path="weights/omniparser/icon_caption_blip2", device='cuda')
platform = 'pc'
if platform == 'pc':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 2,
'thickness': 2,
}
BOX_TRESHOLD = 0.05
elif platform == 'web':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
BOX_TRESHOLD = 0.05
elif platform == 'mobile':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
BOX_TRESHOLD = 0.05
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. **Trained models will be released soon**
"""
DEVICE = torch.device('cuda')
# @spaces.GPU
# @torch.inference_mode()
# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def process(
image_input,
prompt: str = None
) -> Optional[Image.Image]:
image_save_path = 'imgs/saved_image_demo.png'
image_input.save(image_save_path)
# import pdb; pdb.set_trace()
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
print('prompt:', prompt)
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = BOX_TRESHOLD, 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=0.3,prompt=prompt)
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')
prompt_input_component = gr.Textbox(label='Prompt', placeholder='')
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,
prompt_input_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') |