Create README.md

README.md

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+---
+tags:
+- GUI agents
+- vision-language-action model
+- computer use
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+license: mit
+---
+[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) 
+<img src="examples/showui.jpg" alt="ShowUI" width="640">
+
+ShowUI is a lightweight (2B) vision-language-action model designed for GUI agents.
+
+## 🤗 Try our HF Space Demo
+https://huggingface.co/spaces/showlab/ShowUI
+
+
+## ⭐ Quick Start
+
+1. Load model
+```python
+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)
+```
+
+2. **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])
+
+![download](https://github.com/user-attachments/assets/8fe2783d-05b6-44e6-a26c-8718d02b56cb)
+
+3. **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 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.
+"""
+}
+```
+
+```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}
+```
+
+![download](https://github.com/user-attachments/assets/624097ea-06f2-4c8f-83f6-b6b9ee439c0c)
+
+
+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}, 
+}
+```