app.py

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)