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--- |
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tags: |
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- GUI agents |
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- vision-language-action model |
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- computer use |
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base_model: |
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- Qwen/Qwen2-VL-2B-Instruct |
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license: mit |
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--- |
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[Github](https://github.com/showlab/ShowUI/tree/main) | [arXiv](https://arxiv.org/abs/2411.17465) | [HF Paper](https://huggingface.co/papers/2411.17465) | [Spaces](https://huggingface.co/spaces/showlab/ShowUI) | [Datasets](https://huggingface.co/datasets/showlab/ShowUI-desktop-8K) | [Quick Start](https://huggingface.co/showlab/ShowUI-2B) |
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<img src="examples/showui.jpg" alt="ShowUI" width="640"> |
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ShowUI is a lightweight (2B) vision-language-action model designed for GUI agents. |
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## 🤗 Try our HF Space Demo |
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https://huggingface.co/spaces/showlab/ShowUI |
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## ⭐ Quick Start |
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1. Load model |
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```python |
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import ast |
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import torch |
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from PIL import Image, ImageDraw |
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from qwen_vl_utils import process_vision_info |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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def draw_point(image_input, point=None, radius=5): |
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if isinstance(image_input, str): |
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image = Image.open(BytesIO(requests.get(image_input).content)) if image_input.startswith('http') else Image.open(image_input) |
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else: |
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image = image_input |
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if point: |
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x, y = point[0] * image.width, point[1] * image.height |
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ImageDraw.Draw(image).ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') |
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display(image) |
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return |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"showlab/ShowUI-2B", |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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min_pixels = 256*28*28 |
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max_pixels = 1344*28*28 |
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processor = AutoProcessor.from_pretrained("showlab/ShowUI-2B", min_pixels=min_pixels, max_pixels=max_pixels) |
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``` |
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2. **UI Grounding** |
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```python |
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img_url = 'examples/web_dbd7514b-9ca3-40cd-b09a-990f7b955da1.png' |
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query = "Nahant" |
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_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." |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": _SYSTEM}, |
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{"type": "image", "image": img_url, "min_pixels": min_pixels, "max_pixels": max_pixels}, |
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{"type": "text", "text": query} |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True, |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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)[0] |
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click_xy = ast.literal_eval(output_text) |
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# [0.73, 0.21] |
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draw_point(img_url, click_xy, 10) |
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``` |
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This will visualize the grounding results like (where the red points are [x,y]) |
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3. **UI Navigation** |
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- Set up system prompt. |
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```python |
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_NAV_SYSTEM = """You are an assistant trained to navigate the {_APP} screen. |
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Given a task instruction, a screen observation, and an action history sequence, |
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output the next action and wait for the next observation. |
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Here is the action space: |
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{_ACTION_SPACE} |
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""" |
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_NAV_FORMAT = """ |
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Format the action as a dictionary with the following keys: |
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{'action': 'ACTION_TYPE', 'value': 'element', 'position': [x,y]} |
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If value or position is not applicable, set it as `None`. |
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Position might be [[x1,y1], [x2,y2]] if the action requires a start and end position. |
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Position represents the relative coordinates on the screenshot and should be scaled to a range of 0-1. |
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""" |
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action_map = { |
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'web': """ |
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1. `CLICK`: Click on an element, value is not applicable and the position [x,y] is required. |
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2. `INPUT`: Type a string into an element, value is a string to type and the position [x,y] is required. |
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3. `SELECT`: Select a value for an element, value is not applicable and the position [x,y] is required. |
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4. `HOVER`: Hover on an element, value is not applicable and the position [x,y] is required. |
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5. `ANSWER`: Answer the question, value is the answer and the position is not applicable. |
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6. `ENTER`: Enter operation, value and position are not applicable. |
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7. `SCROLL`: Scroll the screen, value is the direction to scroll and the position is not applicable. |
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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. |
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9. `COPY`: Copy the text, value is the text to copy and the position is not applicable. |
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""", |
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'phone': """ |
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1. `INPUT`: Type a string into an element, value is a string to type and the position [x,y] is required. |
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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. |
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3. `TAP`: Tap on an element, value is not applicable and the position [x,y] is required. |
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4. `ANSWER`: Answer the question, value is the status (e.g., 'task complete') and the position is not applicable. |
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5. `ENTER`: Enter operation, value and position are not applicable. |
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""" |
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} |
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``` |
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```python |
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img_url = 'examples/chrome.png' |
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split='web' |
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system_prompt = _NAV_SYSTEM.format(_APP=split, _ACTION_SPACE=action_map[split]) + _NAV_FORMAT |
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query = "Search the weather for the New York city." |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": system_prompt}, |
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{"type": "text", "text": f'Task: {query}'}, |
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# {"type": "text", "text": PAST_ACTION}, |
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{"type": "image", "image": img_url, "min_pixels": min_pixels, "max_pixels": max_pixels}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True, |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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)[0] |
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print(output_text) |
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# {'action': 'CLICK', 'value': None, 'position': [0.49, 0.42]}, |
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# {'action': 'INPUT', 'value': 'weather for New York city', 'position': [0.49, 0.42]}, |
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# {'action': 'ENTER', 'value': None, 'position': None} |
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``` |
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If you find our work helpful, please consider citing our paper. |
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``` |
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@misc{lin2024showui, |
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title={ShowUI: One Vision-Language-Action Model for GUI Visual Agent}, |
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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}, |
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year={2024}, |
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eprint={2411.17465}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2411.17465}, |
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} |
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``` |
