ShowUI is a lightweight vision-language-action model for GUI agents.
You can easily run this model on Windows and macOS using OOTB!
If you like our project, please give us a star ⭐ for the latest update.
- [2024.12.5] Major Update: ShowUI is integrated into OOTB for local run!
- [2024.12.1] We support iterative refinement to improve grounding accuracy. Try it at HF Spaces demo.
- [2024.11.27] We release the arXiv paper, HF Spaces demo and
ShowUI-desktop-8K. - [2024.11.16]
showlab/ShowUI-2Bis available at huggingface.
- Support UI-Graph Token selection for Efficient Inference.
- Release fine-tuned code and instructions.
- Install miniconda on your system through this link. (Python Version: >= 3.11).
Open the Conda Terminal. (After installation Of Miniconda, it will appear in the Start menu.) Run the following command on Conda Terminal.
git clone https://github.com/showlab/ShowUI.git
cd ShowUIpip install -r requirements.txtpython app.pyIf you successfully start the interface, you will see two URLs in the terminal:
* Running on local URL: http://127.0.0.1:7860
* Running on public URL: https://xxxxxxxxxxxxxxxx.gradio.live (Do not share this link with others, or they will be able to control your computer.)🔨 Note for macOS users: Please set the torch device to "mps" if you are using an Apple Silicon Mac.
import ast
import torch
from PIL import Image, ImageDraw
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
def draw_point(image_input, point=None, radius=5):
if isinstance(image_input, str):
image = Image.open(BytesIO(requests.get(image_input).content)) if image_input.startswith('http') else Image.open(image_input)
else:
image = image_input
if point:
x, y = point[0] * image.width, point[1] * image.height
ImageDraw.Draw(image).ellipse((x - radius, y - radius, x + radius, y + radius), fill='red')
display(image)
return
model = Qwen2VLForConditionalGeneration.from_pretrained(
"showlab/ShowUI-2B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
min_pixels = 256*28*28
max_pixels = 1344*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)img_url = 'examples/web_dbd7514b-9ca3-40cd-b09a-990f7b955da1.png'
query = "Nahant"
_SYSTEM = "Based on the screenshot of the page, I give a text description and you give its corresponding location. The coordinate represents a clickable location [x, y] for an element, which is a relative coordinate on the screenshot, scaled from 0 to 1."
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": _SYSTEM},
{"type": "image", "image": img_url, "min_pixels": min_pixels, "max_pixels": max_pixels},
{"type": "text", "text": query}
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
click_xy = ast.literal_eval(output_text)
# [0.73, 0.21]
draw_point(img_url, click_xy, 10)This will visualize the grounding results like (where the red points are [x,y])
- Set up system prompt.
_NAV_SYSTEM = """You are an assistant trained to navigate the {_APP} screen.
Given a task instruction, a screen observation, and an action history sequence,
output the next action and wait for the next observation.
Here is the action space:
{_ACTION_SPACE}
"""
_NAV_FORMAT = """
Format the action as a dictionary with the following keys:
{'action': 'ACTION_TYPE', 'value': 'element', 'position': [x,y]}
If value or position is not applicable, set it as `None`.
Position might be [[x1,y1], [x2,y2]] if the action requires a start and end position.
Position represents the relative coordinates on the screenshot and should be scaled to a range of 0-1.
"""
action_map = {
'web': """
1. `CLICK`: Click on an element, value is not applicable and the position [x,y] is required.
2. `INPUT`: Type a string into an element, value is a string to type and the position [x,y] is required.
3. `SELECT`: Select a value for an element, value is not applicable and the position [x,y] is required.
4. `HOVER`: Hover on an element, value is not applicable and the position [x,y] is required.
5. `ANSWER`: Answer the question, value is the answer and the position is not applicable.
6. `ENTER`: Enter operation, value and position are not applicable.
7. `SCROLL`: Scroll the screen, value is the direction to scroll and the position is not applicable.
8. `SELECT_TEXT`: Select some text content, value is not applicable and position [[x1,y1], [x2,y2]] is the start and end position of the select operation.
9. `COPY`: Copy the text, value is the text to copy and the position is not applicable.
""",
'phone': """
1. `INPUT`: Type a string into an element, value is not applicable and the position [x,y] is required.
2. `SWIPE`: Swipe the screen, value is not applicable and the position [[x1,y1], [x2,y2]] is the start and end position of the swipe operation.
3. `TAP`: Tap on an element, value is not applicable and the position [x,y] is required.
4. `ANSWER`: Answer the question, value is the status (e.g., 'task complete') and the position is not applicable.
5. `ENTER`: Enter operation, value and position are not applicable.
"""
}img_url = 'examples/chrome.png'
split='web'
system_prompt = _NAV_SYSTEM.format(_APP=split, _ACTION_SPACE=action_map[split])
query = "Search the weather for the New York city."
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": system_prompt},
{"type": "text", "text": f'Task: {query}'},
# {"type": "text", "text": PAST_ACTION},
{"type": "image", "image": img_url, "min_pixels": min_pixels, "max_pixels": max_pixels},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
print(output_text)
# {'action': 'CLICK', 'value': None, 'position': [0.49, 0.42]},
# {'action': 'INPUT', 'value': 'weather for New York city', 'position': [0.49, 0.42]},
# {'action': 'ENTER', 'value': None, 'position': None}If you find our work helpful, please consider citing our paper.
@misc{lin2024showui,
title={ShowUI: One Vision-Language-Action Model for GUI Visual Agent},
author={Kevin Qinghong Lin and Linjie Li and Difei Gao and Zhengyuan Yang and Shiwei Wu and Zechen Bai and Weixian Lei and Lijuan Wang and Mike Zheng Shou},
year={2024},
eprint={2411.17465},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.17465},
}