app.py

import os
import re
import json
import time
import unicodedata
import gc
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 & PROMPTS
# -----------------------------------------------------------------------------

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))

# --- 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_holo2_prompt(task, image):
    # Holo2 typically uses standard chat formatting
    return [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": task}
            ]
        }
    ]

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", 2),
        "min_pixels": getattr(ip, "min_pixels", 256 * 256),
        "max_pixels": getattr(ip, "max_pixels", 1280 * 1280),
    }

# -----------------------------------------------------------------------------
# 4. PARSING LOGIC
# -----------------------------------------------------------------------------

def parse_uitars_response(text: str) -> List[Dict]:
    """Parse UI-TARS specific output formats"""
    actions = []
    text = text.strip()
    
    m = re.search(r"Click\s*\(\s*(\d+)\s*,\s*(\d+)\s*\)", text, re.IGNORECASE)
    if m: actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})

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

    m = re.search(r"start_box=['\"]?\(\s*(\d+)\s*,\s*(\d+)\s*\)['\"]?", text, re.IGNORECASE)
    if m: actions.append({"type": "click", "x": int(m[0]), "y": int(m[1]), "text": ""})

    return actions

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 parse_holo2_response(generated_ids, processor, input_len) -> Tuple[str, str, List[Dict]]:
    """Parse Holo2 reasoning tokens and JSON content"""
    all_ids = generated_ids[0].tolist()
    
    # Token IDs for <|thought_start|> and <|thought_end|> (Qwen/Holo specific)
    THOUGHT_START = 151667
    THOUGHT_END = 151668
    
    thinking_content = ""
    content = ""
    
    try:
        if THOUGHT_START in all_ids:
            start_idx = all_ids.index(THOUGHT_START)
            try:
                end_idx = all_ids.index(THOUGHT_END)
            except ValueError:
                end_idx = len(all_ids)
                
            thinking_ids = all_ids[start_idx+1:end_idx]
            thinking_content = processor.decode(thinking_ids, skip_special_tokens=True).strip()
            
            # Content is everything after thought_end
            content_ids = all_ids[end_idx+1:]
            content = processor.decode(content_ids, skip_special_tokens=True).strip()
        else:
            # Fallback if no reasoning tokens found (just raw output)
            # Slice off input tokens first
            output_ids = all_ids[input_len:]
            content = processor.decode(output_ids, skip_special_tokens=True).strip()
    except Exception as e:
        print(f"Holo Parsing Error: {e}")
        content = processor.decode(all_ids[input_len:], skip_special_tokens=True).strip()

    # Parse JSON Content
    actions = []
    try:
        # Holo2 outputs strictly valid JSON usually
        # E.g. {"x": 500, "y": 300, "description": "search bar"}
        # Or {"action": "click", "point": [100, 200]}
        # Flattening to common format
        if "{" in content and "}" in content:
            # Find JSON block if surrounded by text
            json_str = re.search(r"(\{.*\})", content, re.DOTALL).group(1)
            data = json.loads(json_str)
            
            x, y = 0, 0
            if "x" in data and "y" in data:
                x, y = data["x"], data["y"]
            elif "point" in data:
                x, y = data["point"][0], data["point"][1]
            elif "coordinate" in data:
                x, y = data["coordinate"][0], data["coordinate"][1]
            
            if x or y:
                # Holo2 output is 0-1000 scale
                actions.append({
                    "type": "click",
                    "x": float(x),
                    "y": float(y), 
                    "text": data.get("description", "") or data.get("text", ""),
                    "scale_base": 1000 # Flag to indicate this needs normalization from 1000
                })
    except Exception as e:
        print(f"Holo JSON Parse Failed: {e}")

    return content, thinking_content, 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']
        
        # Holo2 Special Case (0-1000 scaling)
        if act.get('scale_base') == 1000:
            pixel_x = int((x / 1000) * width)
            pixel_y = int((y / 1000) * height)
        # Normalized (0-1)
        elif x <= 1.0 and y <= 1.0 and x > 0:
            pixel_x = int(x * width)
            pixel_y = int(y * height)
        # Absolute Pixels
        else:
            pixel_x = int(x)
            pixel_y = int(y)
            
        color = 'red' if 'click' in act['type'].lower() else 'blue'
        
        # Draw Visuals
        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 Cross
        draw.line([pixel_x - 10, pixel_y, pixel_x + 10, pixel_y], fill=color, width=2)
        draw.line([pixel_x, pixel_y - 10, pixel_x, pixel_y + 10], fill=color, width=2)
        
        # Label
        label = f"{act['type']}"
        if act['text']: label += f": {act['text']}"
        
        text_pos = (pixel_x + 20, pixel_y - 10)
        bbox = draw.textbbox(text_pos, label, font=font)
        draw.rectangle((bbox[0]-4, bbox[1]-2, bbox[2]+4, 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, None

    input_pil_image = array_to_image(input_numpy_image)
    orig_w, orig_h = input_pil_image.size
    actions = []
    raw_response = ""
    reasoning_text = None

    # --- UI-TARS Logic ---
    if model_choice == "UI-TARS-1.5-7B":
        if model_x is None: return "Error: UI-TARS model failed to load.", None, None
        print("Running UI-TARS...")
        
        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)
            
        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_response(raw_response)
        
        # Rescale
        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)

    # --- Holo2 Logic ---
    elif model_choice == "Holo2-8B":
        if model_h is None: return "Error: Holo2 model failed to load.", None, None
        print("Running Holo2...")
        
        messages = get_holo2_prompt(task, input_pil_image)
        text_prompt = processor_h.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        image_inputs, video_inputs = process_vision_info(messages)
        inputs = processor_h(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_h.generate(**inputs, max_new_tokens=512)
        
        # Parse Reasoning + Content
        input_len = len(inputs.input_ids[0])
        content, thinking, parsed_actions = parse_holo2_response(generated_ids, processor_h, input_len)
        
        raw_response = content
        reasoning_text = thinking
        actions = parsed_actions

    # --- Fara Logic ---
    else: 
        if model_v is None: return "Error: Fara model failed to load.", None, None
        print("Running Fara...")
        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: {raw_response}")
    if reasoning_text: print(f"Thinking: {reasoning_text}")

    # Visualize
    output_image = input_pil_image
    if actions:
        vis = create_localized_image(input_pil_image, actions)
        if vis: output_image = vis
            
    final_text_output = f"▶️ OUTPUT:\n{raw_response}"
    if reasoning_text:
        final_text_output = f"🧠 THINKING PROCESS:\n{reasoning_text}\n\n" + final_text_output

    return final_text_output, 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="Model Output & Reasoning", lines=12, 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()