app.py

import os
import gradio as gr

# --- Patch gradio_client boolean-schema bug ---
import gradio_client.utils as gcu

orig_json_schema_to_python_type = gcu._json_schema_to_python_type

def _safe_json_schema_to_python_type(schema, defs):
    # Fix: handle boolean schema values for additionalProperties
    if isinstance(schema, bool):
        # True → any type allowed; False → never allowed
        return "Any" if schema else "Never"
    return orig_json_schema_to_python_type(schema, defs)

gcu._json_schema_to_python_type = _safe_json_schema_to_python_type
# ------------------------------------------------

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

# -------- Runtime / device --------
# Force CPU usage
device = "cpu"

# Hugging Face Spaces port
PORT = int(os.getenv("PORT", "7860"))

# -------- Model / Processor --------
# NOTE: device_map=None + .to(device) keeps everything on CPU
models = {
    "OS-Copilot/OS-Atlas-Base-7B": Qwen2VLForConditionalGeneration.from_pretrained(
        "OS-Copilot/OS-Atlas-Base-7B",
        dtype="auto",          # use 'dtype' (new) rather than deprecated 'torch_dtype'
        device_map=None
    ).to(device)
}

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

# -------- Helpers --------
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 or []:
        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):
    if not bounding_boxes:
        return []
    x_scale = original_width / scaled_width
    y_scale = original_height / scaled_height
    return [
        [xmin * x_scale, ymin * y_scale, xmax * x_scale, ymax * y_scale]
        for (xmin, ymin, xmax, ymax) in bounding_boxes
    ]

# -------- Inference --------
def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
    # Basic validation so the Space doesn't 500
    if image is None or (text_input is None or str(text_input).strip() == ""):
        return "", [], image

    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},
            ],
        }
    ]

    # Build inputs
    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",
    )

    # Move tensors to CPU explicitly
    inputs = {k: (v.to(device) if hasattr(v, "to") else v) for k, v in inputs.items()}

    # Generate
    with torch.no_grad():
        generated_ids = model.generate(**inputs, max_new_tokens=128)

    # Post-process
    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)]
    output_texts = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
    )
    text = output_texts[0] if output_texts else ""

    # Parse object_ref and bbox defensively
    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).strip() if object_match else ""
    box_content = box_match.group(1).strip() if box_match else ""

    boxes = []
    if box_content:
        try:
            # Expecting "(x1,y1),(x2,y2)" -> convert to [xmin, ymin, xmax, ymax]
            parts = [p.strip() for p in box_content.split("),(")]
            parts[0] = parts[0].lstrip("(")
            parts[-1] = parts[-1].rstrip(")")
            coords = [tuple(map(int, p.split(","))) for p in parts]
            if len(coords) >= 2:
                (x1, y1), (x2, y2) = coords[0], coords[1]
                boxes = [[x1, y1, x2, y2]]
        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

# -------- UI --------
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],
        # remove fn/outputs so examples only prefill inputs
    )

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

# ---- Make Gradio/Starlette error responses small & safe (no Content-Length drama) ----
from fastapi import Request
from starlette.responses import PlainTextResponse

app = demo.app  # FastAPI app behind Gradio Blocks

@app.exception_handler(Exception)
async def _catch_all_exceptions(request: Request, exc: Exception):
    # Return a very small body so Starlette/Uvicorn never miscounts bytes
    return PlainTextResponse("Internal Server Error", status_code=500)
# --------------------------------------------------------------------------------------


# -------- Launch (Spaces-friendly) --------
demo.queue().launch(
    server_name="0.0.0.0",
    server_port=PORT,
    show_error=False,   # avoid large HTML error bodies
    debug=False,        # avoid big pretty tracebacks (and Content-Length mismatch)
    show_api=False      # <— key: disables /api/info schema generation
    # api_open=False    # if your Gradio version expects the old name, use this instead of show_api
)