app.py

import os
import re
import json
import time
import unicodedata
import gc
from io import BytesIO
from typing import Iterable, Tuple, Optional, List, Dict, Any

import gradio as gr
import numpy as np
import torch
import spaces
from PIL import Image, ImageDraw, ImageFont

from transformers import (
    Qwen2_5_VLForConditionalGeneration,
    AutoProcessor,
    AutoModelForImageTextToText
)
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
from qwen_vl_utils import process_vision_info

from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

# --- Theme Configuration ---
colors.orange_red = colors.Color(
    name="orange_red",
    c50="#FFF0E5",
    c100="#FFE0CC",
    c200="#FFC299",
    c300="#FFA366",
    c400="#FF8533",
    c500="#FF4500",
    c600="#E63E00",
    c700="#CC3700",
    c800="#B33000",
    c900="#992900",
    c950="#802200",
)

class OrangeRedTheme(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.orange_red,
        neutral_hue: colors.Color | str = colors.slate,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            background_fill_primary="*primary_50",
            background_fill_primary_dark="*primary_900",
            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
            button_primary_text_color="white",
            button_primary_text_color_hover="white",
            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_secondary_text_color="black",
            button_secondary_text_color_hover="white",
            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
            slider_color="*secondary_500",
            slider_color_dark="*secondary_600",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
            color_accent_soft="*primary_100",
            block_label_background_fill="*primary_200",
        )

orange_red_theme = OrangeRedTheme()

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Running on device: {device}")

# --- Model Loading ---

print("🔄 Loading Fara-7B...")
MODEL_ID_V = "microsoft/Fara-7B" 
try:
    processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
    model_v = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        MODEL_ID_V,
        trust_remote_code=True,
        torch_dtype=torch.float16
    ).to(device).eval()
except Exception as e:
    print(f"Failed to load Fara: {e}")
    model_v = None
    processor_v = None

print("🔄 Loading UI-TARS-1.5-7B...")
MODEL_ID_X = "ByteDance-Seed/UI-TARS-1.5-7B"
try:
    processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True, use_fast=False)
    model_x = AutoModelForImageTextToText.from_pretrained(
        MODEL_ID_X,
        trust_remote_code=True,
        torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
    ).to(device).eval()
except Exception as e:
    print(f"Failed to load UI-TARS: {e}")
    model_x = None
    processor_x = None

print("🔄 Loading Holo2-4B...")
MODEL_ID_H = "Hcompany/Holo2-4B"
try:
    processor_h = AutoProcessor.from_pretrained(MODEL_ID_H, trust_remote_code=True)
    model_h = AutoModelForImageTextToText.from_pretrained(
        MODEL_ID_H,
        trust_remote_code=True,
        torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
        device_map="auto" if device == "cuda" else None
    ).eval()
    if device == "cpu":
        model_h = model_h.to(device)
except Exception as e:
    print(f"Failed to load Holo2-4B: {e}")
    model_h = None
    processor_h = None

print("✅ Models loading sequence complete.")

# --- Helper Functions ---

def array_to_image(image_array: np.ndarray) -> Image.Image:
    if image_array is None: raise ValueError("No image provided.")
    return Image.fromarray(np.uint8(image_array))

def get_image_proc_params(processor) -> Dict[str, int]:
    ip = getattr(processor, "image_processor", None)
    
    default_min = 256 * 256
    default_max = 1280 * 1280

    patch_size = getattr(ip, "patch_size", 14)
    merge_size = getattr(ip, "merge_size", 2)
    min_pixels = getattr(ip, "min_pixels", default_min)
    max_pixels = getattr(ip, "max_pixels", default_max)

    if min_pixels is None: min_pixels = default_min
    if max_pixels is None: max_pixels = default_max

    return {
        "patch_size": patch_size,
        "merge_size": merge_size,
        "min_pixels": min_pixels,
        "max_pixels": max_pixels,
    }

def apply_chat_template_compat(processor, messages: List[Dict[str, Any]]) -> str:
    tok = getattr(processor, "tokenizer", None)
    if hasattr(processor, "apply_chat_template"):
        return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    if tok is not None and hasattr(tok, "apply_chat_template"):
        return tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    return ""

def batch_decode_compat(processor, token_id_batches, **kw):
    tok = getattr(processor, "tokenizer", None)
    if hasattr(processor, "batch_decode"):
        return processor.batch_decode(token_id_batches, **kw)
    if tok is not None and hasattr(tok, "batch_decode"):
        return tok.batch_decode(token_id_batches, **kw)
    raise AttributeError("No batch_decode available on processor or tokenizer.")

def trim_generated(generated_ids, inputs):
    in_ids = getattr(inputs, "input_ids", None)
    if in_ids is None and isinstance(inputs, dict):
        in_ids = inputs.get("input_ids", None)
    if in_ids is None:
        return generated_ids
    return [out_ids[len(in_seq):] for in_seq, out_ids in zip(in_ids, generated_ids)]

# --- Prompt Builders ---

def get_fara_prompt(task, image):
    OS_SYSTEM_PROMPT = """You are a GUI agent. You are given a task and a screenshot of the current status.
    You need to generate the next action to complete the task.
    Output your action inside a <tool_call> block using JSON format.
    Include "coordinate": [x, y] in pixels for interactions.
    Examples:
    <tool_call>{"name": "User", "arguments": {"action": "click", "coordinate": [400, 300]}}</tool_call>
    <tool_call>{"name": "User", "arguments": {"action": "type", "coordinate": [100, 200], "text": "hello"}}</tool_call>
    """
    return [
        {"role": "system", "content": [{"type": "text", "text": OS_SYSTEM_PROMPT}]},
        {"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": f"Instruction: {task}"}]},
    ]

def get_localization_prompt(task, image):
    guidelines = (
        "Localize an element on the GUI image according to my instructions and "
        "output a click position as Click(x, y) with x num pixels from the left edge "
        "and y num pixels from the top edge."
    )
    return [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": f"{guidelines}\n{task}"}
            ],
        }
    ]

def get_holo2_messages(task, image):
    return [
        {
            "role": "user", 
            "content": [
                {"type": "image", "image": image}, 
                {"type": "text", "text": task}
            ]
        }
    ]

# --- Response Parsers ---

def parse_click_response(text: str) -> List[Dict]:
    actions = []
    text = text.strip()

    matches_click = re.findall(r"Click\s*\(\s*(\d+)\s*,\s*(\d+)\s*\)", text, re.IGNORECASE)
    for m in matches_click:
        actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})

    matches_point = re.findall(r"point=\[\s*(\d+)\s*,\s*(\d+)\s*\]", text, re.IGNORECASE)
    for m in matches_point:
        actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})

    matches_box = re.findall(r"start_box=['\"]?\(\s*(\d+)\s*,\s*(\d+)\s*\)['\"]?", text, re.IGNORECASE)
    for m in matches_box:
        actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})
    
    matches_tuple = re.findall(r"(?:^|\s)\(\s*(\d+)\s*,\s*(\d+)\s*\)(?:$|\s|,)", text)
    for m in matches_tuple:
        actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})

    # Deduplicate
    unique_actions = []
    seen = set()
    for a in actions:
        key = (a['type'], a['x'], a['y'])
        if key not in seen:
            seen.add(key)
            unique_actions.append(a)
            
    return unique_actions

def parse_fara_response(response: str) -> List[Dict]:
    actions = []
    matches = re.findall(r"<tool_call>(.*?)</tool_call>", response, re.DOTALL)
    for match in matches:
        try:
            data = json.loads(match.strip())
            args = data.get("arguments", {})
            coords = args.get("coordinate", [])
            action_type = args.get("action", "unknown")
            text_content = args.get("text", "")
            if coords and len(coords) == 2:
                actions.append({
                    "type": action_type, "x": float(coords[0]), "y": float(coords[1]), "text": text_content
                })
        except Exception as e:
            print(f"Error parsing Fara JSON: {e}")
            pass
    return actions

def parse_holo2_reasoning(processor, generated_ids) -> tuple[str, str]:
    """Parse content from generated_ids specifically for Holo2"""
    all_ids = generated_ids[0].tolist()
    
    # Try to find thinking block indices
    try:
        think_start_index = all_ids.index(151667)
    except ValueError:
        think_start_index = -1
        
    try:
        think_end_index = all_ids.index(151668)
    except ValueError:
        think_end_index = -1
    
    if think_start_index != -1 and think_end_index != -1:
        thinking_content = processor.decode(all_ids[think_start_index+1:think_end_index], skip_special_tokens=True).strip("\n")
        content = processor.decode(all_ids[think_end_index+1:], skip_special_tokens=True).strip("\n")
    else:
        # If no thinking tags or incomplete, decode everything
        thinking_content = ""
        content = processor.decode(all_ids, skip_special_tokens=True).strip("\n")
        
    return content, thinking_content

def parse_holo2_json(content: str) -> List[Dict]:
    actions = []
    try:
        # Clean potential markdown
        cleaned = content.replace("```json", "").replace("```", "").strip()
        data = json.loads(cleaned)
        if "x" in data and "y" in data:
            actions.append({"type": "click", "x": data["x"], "y": data["y"], "text": ""})
    except json.JSONDecodeError:
        print(f"Failed to parse Holo2 JSON: {content}")
    return actions

# --- Visualizer ---

def create_localized_image(original_image: Image.Image, actions: list[dict]) -> Optional[Image.Image]:
    if not actions: return None
    img_copy = original_image.copy()
    draw = ImageDraw.Draw(img_copy)
    
    try:
        font = ImageFont.load_default(size=18)
    except IOError:
        font = ImageFont.load_default()
    
    for act in actions:
        x = act['x']
        y = act['y']
        
        pixel_x, pixel_y = int(x), int(y)
        color = 'red' if 'click' in act['type'].lower() else 'blue'
        
        # Draw Cross and Circle style (as requested by user preference)
        cross_size = 20
        # Horizontal line
        draw.line([pixel_x - cross_size, pixel_y, pixel_x + cross_size, pixel_y], fill=color, width=4)
        # Vertical line
        draw.line([pixel_x, pixel_y - cross_size, pixel_x, pixel_y + cross_size], fill=color, width=4)
        
        # Circle
        r = 15
        draw.ellipse([pixel_x - r, pixel_y - r, pixel_x + r, pixel_y + r], outline=color, width=3)
        
        label = f"{act['type'].capitalize()}"
        if act.get('text'): label += f": \"{act['text']}\""
        
        text_pos = (pixel_x + 25, pixel_y - 15)
        
        try:
            bbox = draw.textbbox(text_pos, label, font=font)
            padded_bbox = (bbox[0]-4, bbox[1]-2, bbox[2]+4, bbox[3]+2)
            draw.rectangle(padded_bbox, fill="yellow", outline=color)
            draw.text(text_pos, label, fill="black", font=font)
        except Exception:
            draw.text(text_pos, label, fill="black")

    return img_copy

# --- Main Logic ---

@spaces.GPU
def process_screenshot(input_numpy_image: np.ndarray, task: str, model_choice: str):
    if input_numpy_image is None: return "⚠️ Please upload an image.", None
    if not task.strip(): return "⚠️ Please provide a task instruction.", None

    input_pil_image = array_to_image(input_numpy_image)
    orig_w, orig_h = input_pil_image.size
    actions = []
    raw_response = ""
    thinking_output = ""

    if model_choice == "Fara-7B":
        if model_v is None: return "Error: Fara model failed to load on startup.", None
        print("Using Fara Pipeline...")
        
        messages = get_fara_prompt(task, input_pil_image)
        text_prompt = processor_v.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        image_inputs, video_inputs = process_vision_info(messages)

        inputs = processor_v(
            text=[text_prompt],
            images=image_inputs,
            videos=video_inputs,
            padding=True,
            return_tensors="pt"
        )
        inputs = inputs.to(device)
        
        with torch.no_grad():
            generated_ids = model_v.generate(**inputs, max_new_tokens=512)
            
        generated_ids = trim_generated(generated_ids, inputs)
        raw_response = processor_v.batch_decode(generated_ids, skip_special_tokens=True)[0]
        actions = parse_fara_response(raw_response)

    elif model_choice == "Holo2-4B":
        if model_h is None: return "Error: Holo2-4B model failed to load.", None
        print("Using Holo2-4B Pipeline...")
        
        # Specific Holo2 resizing logic
        ip_config = processor_h.image_processor
        resized_h, resized_w = smart_resize(
            input_pil_image.height, 
            input_pil_image.width,
            factor=ip_config.patch_size * ip_config.merge_size,
            min_pixels=ip_config.size.get("shortest_edge", 256*256),
            max_pixels=ip_config.size.get("longest_edge", 1280*1280),
        )
        processed_image = input_pil_image.resize((resized_w, resized_h), Image.Resampling.LANCZOS)
        
        messages = get_holo2_messages(task, processed_image)
        
        # Apply template with thinking=False for localization as per documentation/snippet
        text_prompt = processor_h.apply_chat_template(
            messages, 
            tokenize=False, 
            add_generation_prompt=True, 
            thinking=False
        )
        
        inputs = processor_h(
            text=[text_prompt], 
            images=[processed_image], 
            padding=True, 
            return_tensors="pt"
        ).to(model_h.device)
        
        with torch.no_grad():
            generated_ids = model_h.generate(**inputs, max_new_tokens=128)
            
        # Parse reasoning/content
        content, thinking_output = parse_holo2_reasoning(processor_h, trim_generated(generated_ids, inputs))
        raw_response = content
        
        if thinking_output:
            raw_response = f"[Thinking Process]:\n{thinking_output}\n\n[Action]:\n{content}"

        actions = parse_holo2_json(content)
        
        # Handle Holo2 coordinate normalization (0-1000) relative to image
        # Math: (x_norm / 1000) * orig_w
        for a in actions:
            a['x'] = (a['x'] / 1000) * orig_w
            a['y'] = (a['y'] / 1000) * orig_h

    elif model_choice == "UI-TARS-1.5-7B":
        if model_x is None: return "Error: UI-TARS model failed to load.", None
        print("Using UI-TARS Pipeline...")
        
        ip_params = get_image_proc_params(processor_x)
        resized_h, resized_w = smart_resize(
            input_pil_image.height, input_pil_image.width,
            factor=ip_params["patch_size"] * ip_params["merge_size"],
            min_pixels=ip_params["min_pixels"], 
            max_pixels=ip_params["max_pixels"]
        )
        proc_image = input_pil_image.resize((resized_w, resized_h), Image.Resampling.LANCZOS)
        
        messages = get_localization_prompt(task, proc_image)
        text_prompt = apply_chat_template_compat(processor_x, messages)
        
        inputs = processor_x(text=[text_prompt], images=[proc_image], padding=True, return_tensors="pt")
        inputs = {k: v.to(device) for k, v in inputs.items()}
        
        with torch.no_grad():
            generated_ids = model_x.generate(**inputs, max_new_tokens=128)
            
        generated_ids = trim_generated(generated_ids, inputs)
        raw_response = batch_decode_compat(processor_x, generated_ids, skip_special_tokens=True)[0]
        
        actions = parse_click_response(raw_response)
        
        # UI-TARS returns coordinates relative to resized image size
        if resized_w > 0 and resized_h > 0:
            scale_x = orig_w / resized_w
            scale_y = orig_h / resized_h
            for a in actions:
                a['x'] = int(a['x'] * scale_x)
                a['y'] = int(a['y'] * scale_y)

    else:
        return f"Error: Unknown model '{model_choice}'", None

    print(f"Raw Output: {raw_response}")
    print(f"Parsed Actions: {actions}")

    output_image = input_pil_image
    if actions:
        vis = create_localized_image(input_pil_image, actions)
        if vis: output_image = vis
            
    return raw_response, output_image

# --- UI Setup ---

css="""
#col-container {
    margin: 0 auto;
    max-width: 960px;
}
#main-title h1 {font-size: 2.1em !important;}
"""
with gr.Blocks() as demo:
    gr.Markdown("# **CUA GUI Operator 🖥️**", elem_id="main-title")
    gr.Markdown("Perform Computer Use Agent tasks with the models: [Fara-7B](https://huggingface.co/microsoft/Fara-7B), [UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B), and [Holo2-4B](https://huggingface.co/Hcompany/Holo2-4B).")

    with gr.Row():
        with gr.Column(scale=2):
            input_image = gr.Image(label="Upload UI Image", type="numpy", height=500)
            
            with gr.Row():
                model_choice = gr.Radio(
                    choices=["Fara-7B", "UI-TARS-1.5-7B", "Holo2-4B"],
                    label="Select Model",
                    value="Fara-7B",
                    interactive=True
                )
            
            task_input = gr.Textbox(
                label="Task Instruction",
                placeholder="e.g. Click on the search bar",
                lines=2
            )
            submit_btn = gr.Button("Call CUA Agent", variant="primary")

        with gr.Column(scale=3):
            output_image = gr.Image(label="Visualized Action Points", elem_id="out_img", height=500)
            output_text = gr.Textbox(label="Agent Model Response", lines=10)

    submit_btn.click(
        fn=process_screenshot,
        inputs=[input_image, task_input, model_choice],
        outputs=[output_text, output_image]
    )
    
    gr.Examples(
        examples=[
            ["examples/1.png", "Click on the Fara-7B model.", "Fara-7B"],
            ["examples/2.png", "Click on the VLMs Collection", "UI-TARS-1.5-7B"],
            ["examples/3.png", "Click on the 'Real-time vision models' collection.", "Holo2-4B"],
        ],
        inputs=[input_image, task_input, model_choice],
        label="Quick Examples"
    )

if __name__ == "__main__":
    demo.queue(max_size=50).launch(theme=orange_red_theme, css=css, mcp_server=True, ssr_mode=False, show_error=True)