import base64 import json from datetime import datetime import gradio as gr import torch import spaces from PIL import Image, ImageDraw from qwen_vl_utils import process_vision_info from transformers import Qwen2VLForConditionalGeneration, AutoProcessor import ast import os from datetime import datetime import numpy as np from huggingface_hub import hf_hub_download, list_repo_files # Define constants DESCRIPTION = "[ShowUI Demo](https://huggingface.co/showlab/ShowUI-2B)" _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." MIN_PIXELS = 256 * 28 * 28 MAX_PIXELS = 1344 * 28 * 28 # Specify the model repository and destination folder model_repo = "showlab/ShowUI-2B" destination_folder = "./showui-2b" # Ensure the destination folder exists os.makedirs(destination_folder, exist_ok=True) # List all files in the repository files = list_repo_files(repo_id=model_repo) # Download each file to the destination folder for file in files: file_path = hf_hub_download(repo_id=model_repo, filename=file, local_dir=destination_folder) print(f"Downloaded {file} to {file_path}") model = Qwen2VLForConditionalGeneration.from_pretrained( "./showui-2b", # "showlab/ShowUI-2B", torch_dtype=torch.bfloat16, device_map="cpu", ) # Load the processor processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS) # Helper functions def draw_point(image_input, point=None, radius=5): """Draw a point on the image.""" if isinstance(image_input, str): image = Image.open(image_input) else: image = Image.fromarray(np.uint8(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') return image def array_to_image_path(image_array): """Save the uploaded image and return its path.""" if image_array is None: raise ValueError("No image provided. Please upload an image before submitting.") img = Image.fromarray(np.uint8(image_array)) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"image_{timestamp}.png" img.save(filename) return os.path.abspath(filename) @spaces.GPU def run_showui(image, query): """Main function for inference.""" image_path = array_to_image_path(image) messages = [ { "role": "user", "content": [ {"type": "text", "text": _SYSTEM}, {"type": "image", "image": image_path, "min_pixels": MIN_PIXELS, "max_pixels": MAX_PIXELS}, {"type": "text", "text": query} ], } ] # Prepare inputs for the model global model model = model.to("cuda") 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") # Generate output 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] # Parse the output into coordinates click_xy = ast.literal_eval(output_text) # Draw the point on the image result_image = draw_point(image_path, click_xy, radius=10) return result_image, str(click_xy) # Function to record votes def record_vote(vote_type, image_path, query, action_generated): """Record a vote in a JSON file.""" vote_data = { "vote_type": vote_type, "image_path": image_path, "query": query, "action_generated": action_generated, "timestamp": datetime.now().isoformat() } with open("votes.json", "a") as f: f.write(json.dumps(vote_data) + "\n") return f"Your {vote_type} has been recorded. Thank you!" # Helper function to handle vote recording def handle_vote(vote_type, image_path, query, action_generated): """Handle vote recording by using the consistent image path.""" if image_path is None: return "No image uploaded. Please upload an image before voting." return record_vote(vote_type, image_path, query, action_generated) # Load logo and encode to Base64 with open("./assets/showui.png", "rb") as image_file: base64_image = base64.b64encode(image_file.read()).decode("utf-8") # Define layout and UI def build_demo(embed_mode, concurrency_count=1): with gr.Blocks(title="ShowUI Demo", theme=gr.themes.Default()) as demo: # State to store the consistent image path state_image_path = gr.State(value=None) if not embed_mode: gr.HTML( f"""
ShowUI

ShowUI is a lightweight vision-language-action model for GUI agents.

model arXiv GitHub
""" ) with gr.Row(): with gr.Column(scale=3): # Input components imagebox = gr.Image(type="numpy", label="Input Screenshot") textbox = gr.Textbox( show_label=True, placeholder="Enter a query (e.g., 'Click Nahant')", label="Query", ) submit_btn = gr.Button(value="Submit", variant="primary") # Placeholder examples gr.Examples( examples=[ ["./examples/safari_google.png", "Click on search bar."], ["./examples/apple_music.png", "Click on star."], ], inputs=[imagebox, textbox], examples_per_page=2 ) with gr.Column(scale=8): # Output components output_img = gr.Image(type="pil", label="Output Image") # Add a note below the image to explain the red point gr.Markdown( """ **Note:** The red point on the output image represents the predicted clickable coordinates. """ ) output_coords = gr.Textbox(label="Clickable Coordinates") # Buttons for voting, flagging, regenerating, and clearing with gr.Row(elem_id="action-buttons", equal_height=True): vote_btn = gr.Button(value="👍 Vote", variant="secondary") downvote_btn = gr.Button(value="👎 Downvote", variant="secondary") flag_btn = gr.Button(value="🚩 Flag", variant="secondary") regenerate_btn = gr.Button(value="🔄 Regenerate", variant="secondary") clear_btn = gr.Button(value="🗑️ Clear", interactive=True) # Combined Clear button # Define button actions def on_submit(image, query): """Handle the submit button click.""" if image is None: raise ValueError("No image provided. Please upload an image before submitting.") # Generate consistent image path and store it in the state image_path = array_to_image_path(image) return run_showui(image, query) + (image_path,) submit_btn.click( on_submit, [imagebox, textbox], [output_img, output_coords, state_image_path], ) clear_btn.click( lambda: (None, None, None, None, None), inputs=None, outputs=[imagebox, textbox, output_img, output_coords, state_image_path], # Clear all outputs queue=False ) regenerate_btn.click( lambda image, query, state_image_path: run_showui(image, query), [imagebox, textbox, state_image_path], [output_img, output_coords], ) # Record vote actions without feedback messages vote_btn.click( lambda image_path, query, action_generated: handle_vote( "upvote", image_path, query, action_generated ), inputs=[state_image_path, textbox, output_coords], outputs=[], queue=False ) downvote_btn.click( lambda image_path, query, action_generated: handle_vote( "downvote", image_path, query, action_generated ), inputs=[state_image_path, textbox, output_coords], outputs=[], queue=False ) flag_btn.click( lambda image_path, query, action_generated: handle_vote( "flag", image_path, query, action_generated ), inputs=[state_image_path, textbox, output_coords], outputs=[], queue=False ) return demo # Launch the app if __name__ == "__main__": demo = build_demo(embed_mode=False) demo.queue(api_open=False).launch( server_name="0.0.0.0", server_port=7860, share=True, debug=True )