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("showlab/ShowUI-2B", min_pixels=min_pixels, max_pixels=max_pixels)

UI Grounding

python

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])

UI Navigation

Set up system prompt.

python

_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 a string to type 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.
"""
}

python

img_url = 'examples/chrome.png'
split='web'
system_prompt = _NAV_SYSTEM.format(_APP=split, _ACTION_SPACE=action_map[split]) + _NAV_FORMAT
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}, 
}