import base64
import json
from datetime import datetime
import torch
import spaces
from PIL import Image, ImageDraw
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import ast
import os
from datetime import datetime
import numpy as np
from huggingface_hub import hf_hub_download, list_repo_files
import gradio as gr
import time
# Define constants
_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="cuda",
)
# Load the processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS)
model_moon = AutoModelForCausalLM.from_pretrained("vikhyatk/moondream2", revision="2025-01-09", trust_remote_code=True, device_map={"": "cuda"})
# 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)
def infer_moon(img, query):
start = time.time()
image = Image.fromarray(np.uint8(img))
points = model_moon.point(image, query)["points"]
converted_data = [round(points[0]["x"], 2), round(points[0]["y"], 2)]
end = time.time()
total_time = end - start
return converted_data, f"{round(total_time, 2)} seconds"
def infer_showui(image_path, query):
start = time.time()
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},
],
}
]
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)
end = time.time()
total_time = end - start
return click_xy, f"{round(total_time, 2)} seconds"
def run(image, query):
"""Main function for inference."""
image_path = array_to_image_path(image)
moon, time_taken_moon = infer_moon(image, query)
showui, time_taken_showui = infer_showui(image_path, query)
# Draw the point on the image
result_image = draw_point(image_path, showui, radius=10)
result_moon_image = draw_point(image_path, moon, radius=10)
return result_image, time_taken_showui, result_moon_image, time_taken_moon
def build_demo():
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)
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/app_store.png", "Download Kindle."],
["./examples/ios_setting.png", "Turn off Do not disturb."],
["./examples/image_13.png", "Tap on vehicle search."],
["./examples/map.png", "Boston."],
["./examples/wallet.png", "Scan a QR code."],
["./examples/word.png", "More shapes."],
["./examples/web_shopping.png", "Proceed to checkout."],
["./examples/web_forum.png", "Post my comment."],
["./examples/safari_google.png", "Click on search bar."],
],
inputs=[imagebox, textbox],
examples_per_page=3,
)
with gr.Column(scale=8):
# Output components
output_img1 = gr.Image(type="pil", label="Show UI Output")
output_time1 = gr.Text(label="showui inference time")
output_img2 = gr.Image(type="pil", label="Moon dream Output")
output_time2 = gr.Text(label="moondream inference time")
# Add a note below the images to explain the red point
gr.HTML(
"""
<p><strong>Note:</strong> The <span style="color: red;">red point</span> on the output images represents the predicted clickable coordinates.</p>
"""
)
# Buttons for voting, flagging, regenerating, and clearing
with gr.Row(elem_id="action-buttons", equal_height=True):
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(image, query) + (image_path,)
submit_btn.click(
on_submit,
[imagebox, textbox],
[output_img1, output_time1, output_img2, output_time2, state_image_path],
)
clear_btn.click(
lambda: (None, None, None, None, None),
inputs=None,
outputs=[imagebox, textbox, output_img1, output_img2, state_image_path], # Clear all outputs
queue=False,
)
regenerate_btn.click(
lambda image, query, state_image_path: run(image, query),
[imagebox, textbox, state_image_path],
[output_img1, output_time1, output_img2, output_time2],
)
return demo
if __name__ == "__main__":
demo = build_demo()
demo.queue(api_open=False).launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False, debug=True, share=True)