app.py

import os
import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import base64
from PIL import Image, ImageDraw
from io import BytesIO
import re


# ---- HF Spaces: ensure we read the platform port ----
PORT = int(os.getenv("PORT", "7860"))

models = {
    "OS-Copilot/OS-Atlas-Base-7B": Qwen2VLForConditionalGeneration.from_pretrained(
        "OS-Copilot/OS-Atlas-Base-7B",
        torch_dtype="auto",
        device_map="auto",
    ),
}

processors = {
    "OS-Copilot/OS-Atlas-Base-7B": AutoProcessor.from_pretrained("OS-Copilot/OS-Atlas-Base-7B")
}


def image_to_base64(image: Image.Image) -> str:
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")


def draw_bounding_boxes(image: Image.Image, bounding_boxes, outline_color="red", line_width=2):
    draw = ImageDraw.Draw(image)
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
    return image


def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
    x_scale = original_width / scaled_width
    y_scale = original_height / scaled_height
    rescaled_boxes = []
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        rescaled_boxes.append([xmin * x_scale, ymin * y_scale, xmax * x_scale, ymax * y_scale])
    return rescaled_boxes


@spaces.GPU
def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
    model = models[model_id].eval()
    processor = processors[model_id]

    prompt = f'In this UI screenshot, what is the position of the element corresponding to the command "{text_input}" (with bbox)?'
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
                {"type": "text", "text": prompt},
            ],
        }
    ]

    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",
    ).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=False, clean_up_tokenization_spaces=False
    )
    text = output_text[0]

    # ---- simple, defensive parsing so the Space doesn't 500 if pattern not found ----
    object_ref_pattern = r"<\|object_ref_start\|>(.*?)<\|object_ref_end\|>"
    box_pattern = r"<\|box_start\|>(.*?)<\|box_end\|>"

    object_match = re.search(object_ref_pattern, text or "")
    box_match = re.search(box_pattern, text or "")

    object_ref = object_match.group(1) if object_match else ""
    box_content = box_match.group(1) if box_match else ""

    boxes = []
    if box_content:
        try:
            parsed = [tuple(map(int, pair.strip("()").split(","))) for pair in box_content.split("),(")]
            # expecting two points -> convert to [xmin, ymin, xmax, ymax]
            if len(parsed) >= 2:
                boxes = [[parsed[0][0], parsed[0][1], parsed[1][0], parsed[1][1]]]
        except Exception:
            boxes = []

    scaled_boxes = rescale_bounding_boxes(boxes, image.width, image.height) if boxes else []
    annotated = draw_bounding_boxes(image.copy(), scaled_boxes) if scaled_boxes else image

    return object_ref, scaled_boxes, annotated


css = """
  #output {
    height: 500px;
    overflow: auto;
    border: 1px solid #ccc;
  }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents")

    with gr.Row():
        with gr.Column():
            input_img = gr.Image(label="Input Image", type="pil")
            model_selector = gr.Dropdown(
                choices=list(models.keys()),
                label="Model",
                value="OS-Copilot/OS-Atlas-Base-7B"
            )
            text_input = gr.Textbox(label="User Prompt")
            submit_btn = gr.Button(value="Submit")
        with gr.Column():
            model_output_text = gr.Textbox(label="Model Output Text")
            model_output_box = gr.Textbox(label="Model Output Box")
            annotated_image = gr.Image(label="Annotated Image")

    gr.Examples(
        examples=[
            ["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "select search textfield"],
            ["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "switch to discussions"],
        ],
        inputs=[input_img, text_input],
        outputs=[model_output_text, model_output_box, annotated_image],
        fn=run_example,
        cache_examples=True,
        label="Try examples",
    )

    submit_btn.click(
        run_example,
        [input_img, text_input, model_selector],
        [model_output_text, model_output_box, annotated_image],
    )

# ---- HF Spaces: bind to all interfaces + use provided port; disable API schema to avoid json-schema bug ----
demo.queue(api_open=False).launch(server_name="0.0.0.0", server_port=PORT, show_error=True, debug=True)