app.py

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

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

# Transformers & Qwen Utils
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

# Gradio Theme
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

# -----------------------------------------------------------------------------
# 1. THEME CONFIGURATION
# -----------------------------------------------------------------------------

colors.steel_blue = colors.Color(
    name="steel_blue",
    c50="#EBF3F8",
    c100="#D3E5F0",
    c200="#A8CCE1",
    c300="#7DB3D2",
    c400="#529AC3",
    c500="#4682B4",
    c600="#3E72A0",
    c700="#36638C",
    c800="#2E5378",
    c900="#264364",
    c950="#1E3450",
)

class SteelBlueTheme(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.steel_blue,
        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_800)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
        )

steel_blue_theme = SteelBlueTheme()
css = "#main-title h1 { font-size: 2.3em !important; } #out_img { height: 600px; object-fit: contain; }"

# -----------------------------------------------------------------------------
# 2. GLOBAL MODEL LOADING
# -----------------------------------------------------------------------------

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

# --- Load Fara-7B ---
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

# --- Load UI-TARS-1.5-7B ---
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

# --- Load Holo2-8B ---
print("🔄 Loading Holo2-8B...")
MODEL_ID_H = "Hcompany/Holo2-8B"
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.float16
    ).to(device).eval()
except Exception as e:
    print(f"Failed to load Holo2: {e}")
    model_h = None
    processor_h = None

print("✅ All Models Loaded Sequence Complete.")

# -----------------------------------------------------------------------------
# 3. UTILS: COMPATIBILITY & HELPERS (Specific for Holo2)
# -----------------------------------------------------------------------------

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)
    texts = []
    for m in messages:
        for c in m.get("content", []):
            if isinstance(c, dict) and c.get("type") == "text":
                texts.append(c.get("text", ""))
    return "\n".join(texts)

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

def get_image_proc_params(processor) -> Dict[str, int]:
    ip = getattr(processor, "image_processor", None)
    return {
        "patch_size": getattr(ip, "patch_size", 14),
        "merge_size": getattr(ip, "merge_size", 1),
        "min_pixels": getattr(ip, "min_pixels", 256 * 256),
        "max_pixels": getattr(ip, "max_pixels", 1280 * 1280),
    }

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 [out_ids for out_ids in generated_ids]
    return [out_ids[len(in_seq):] for in_seq, out_ids in zip(in_ids, generated_ids)]

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))

# -----------------------------------------------------------------------------
# 4. PROMPT BUILDERS
# -----------------------------------------------------------------------------

# --- Fara Prompt ---
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}"}]},
    ]

# --- UI-TARS Prompt ---
def get_uitars_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}"}
            ],
        }
    ]

# --- Holo2 Prompt ---
def get_holo_prompt(image, task) -> List[dict]:
    guidelines: str = (
        "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}"}
            ],
        }
    ]

# -----------------------------------------------------------------------------
# 5. PARSING LOGIC
# -----------------------------------------------------------------------------

def parse_uitars_holo_response(text: str) -> List[Dict]:
    """Parse UI-TARS and Holo2 output formats (usually Click(x,y))"""
    actions = []
    text = text.strip()
    
    # Matches: Click(123, 456)
    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=[x, y]
    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: start_box='(x, y)'
    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": ""})

    # Remove duplicates
    unique = []
    seen = set()
    for a in actions:
        k = (a['type'], a['x'], a['y'])
        if k not in seen:
            seen.add(k)
            unique.append(a)
    return unique

def parse_fara_response(response: str) -> List[Dict]:
    """Parse Fara <tool_call> JSON format"""
    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: pass
    return actions

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)
    width, height = img_copy.size
    
    try: font = ImageFont.load_default()
    except: font = None
    
    for act in actions:
        x = act['x']
        y = act['y']
        
        # Determine if we need to scale normalized coords (0-1) or use absolute
        if x <= 1.0 and y <= 1.0 and x > 0:
            pixel_x = int(x * width)
            pixel_y = int(y * height)
        else:
            pixel_x = int(x)
            pixel_y = int(y)
            
        color = 'red' if 'click' in act['type'].lower() else 'blue'
        
        # Draw Target
        r = 15
        draw.ellipse([pixel_x - r, pixel_y - r, pixel_x + r, pixel_y + r], outline=color, width=4)
        draw.ellipse([pixel_x - 3, pixel_y - 3, pixel_x + 3, pixel_y + 3], fill=color)
        
        # Draw Label
        label = f"{act['type']}"
        if act['text']: label += f": {act['text']}"
        
        text_pos = (pixel_x + 18, pixel_y - 12)
        bbox = draw.textbbox(text_pos, label, font=font)
        draw.rectangle((bbox[0]-2, bbox[1]-2, bbox[2]+2, bbox[3]+2), fill="black")
        draw.text(text_pos, label, fill="white", font=font)

    return img_copy

# -----------------------------------------------------------------------------
# 5. CORE LOGIC
# -----------------------------------------------------------------------------

@spaces.GPU(duration=120)
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

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

    # --- Holo2-8B Logic ---
    if model_choice == "Holo2-8B":
        if model_h is None: return "Error: Holo2 model failed to load.", None
        print("Using Holo2 Pipeline...")
        
        # 1. Resize
        ip_params = get_image_proc_params(processor_h)
        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)
        
        # 2. Prompt & Generate
        messages = get_holo_prompt(proc_image, task)
        text_prompt = apply_chat_template_compat(processor_h, messages)
        
        inputs = processor_h(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_h.generate(**inputs, max_new_tokens=128)
            
        generated_ids_trimmed = trim_generated(generated_ids, inputs)
        raw_response = batch_decode_compat(
            processor_h, generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
        )[0]
        
        # 3. Parse & Rescale
        actions = parse_uitars_holo_response(raw_response)
        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)

    # --- UI-TARS Logic ---
    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_uitars_prompt(task, proc_image)
        text_prompt = processor_x.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        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)
            
        # Manually decode if compat functions fail or use standard
        generated_ids = [out_ids[len(in_seq):] for in_seq, out_ids in zip(inputs.get("input_ids"), generated_ids)]
        raw_response = processor_x.batch_decode(generated_ids, skip_special_tokens=True)[0]
        
        actions = parse_uitars_holo_response(raw_response)
        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)

    # --- Fara Logic ---
    else: 
        if model_v is None: return "Error: Fara model failed to load.", 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 = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
        raw_response = processor_v.batch_decode(generated_ids, skip_special_tokens=True)[0]
        
        actions = parse_fara_response(raw_response)

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

    # Visualize
    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

# -----------------------------------------------------------------------------
# 6. UI SETUP
# -----------------------------------------------------------------------------

with gr.Blocks(theme=steel_blue_theme, css=css) as demo:
    gr.Markdown("# **CUA GUI Agent 🖥️**", elem_id="main-title")
    gr.Markdown("Upload a screenshot, select a model, and provide a task. The model will determine the precise UI coordinates and actions.")

    with gr.Row():
        with gr.Column(scale=2):
            input_image = gr.Image(label="Upload Screenshot", height=500)
            
            with gr.Row():
                model_choice = gr.Radio(
                    choices=["Fara-7B", "UI-TARS-1.5-7B", "Holo2-8B"],
                    label="Select Model",
                    value="Fara-7B",
                    interactive=True
                )
            
            task_input = gr.Textbox(
                label="Task Instruction",
                placeholder="e.g. Input the server address readyforquantum.com...",
                lines=2
            )
            submit_btn = gr.Button("Analyze UI & Generate Action", 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="Raw Model Output", lines=8, show_copy_button=True)

    submit_btn.click(
        fn=process_screenshot,
        inputs=[input_image, task_input, model_choice],
        outputs=[output_text, output_image]
    )
    
    gr.Examples(
        examples=[["./assets/google.png", "Search for 'Hugging Face'", "Fara-7B"]],
        inputs=[input_image, task_input, model_choice],
        label="Quick Examples"
    )

if __name__ == "__main__":
    demo.queue().launch()