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

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

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}")

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 Holo1-3B...")
MODEL_ID_H = "Hcompany/Holo1-3B" 
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 Holo: {e}")
    model_h = None
    processor_h = None

print("✅ Models loading sequence complete.")

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

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)]

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

    print(f"Parsing click-style output: {text}")

    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": ""})

    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 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'
        
        r = 20
        line_width = 5
        
        draw.ellipse([pixel_x - r, pixel_y - r, pixel_x + r, pixel_y + r], outline=color, width=line_width)
        draw.ellipse([pixel_x - 4, pixel_y - 4, pixel_x + 4, pixel_y + 4], fill=color)
        
        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="black", outline=color)
            draw.text(text_pos, label, fill="white", font=font)
        except Exception as e:
            draw.text(text_pos, label, fill="white")

    return img_copy

@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 = ""

    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)

    else:
        if model_choice == "UI-TARS-1.5-7B":
            model, processor = model_x, processor_x
            if model is None: return "Error: UI-TARS model failed to load.", None
            print("Using UI-TARS Pipeline...")
        elif model_choice == "Holo1-3B":
            model, processor = model_h, processor_h
            if model is None: return "Error: Holo2-8B model failed to load.", None
            print("Using Holo1-3B Pipeline...")
        else:
            return f"Error: Unknown model '{model_choice}'", None

        ip_params = get_image_proc_params(processor)
        
        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, messages)
        
        inputs = processor(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.generate(**inputs, max_new_tokens=128)
            
        generated_ids = trim_generated(generated_ids, inputs)
        raw_response = batch_decode_compat(processor, generated_ids, skip_special_tokens=True)[0]
        
        actions = parse_click_response(raw_response)
        
        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)

    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

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 [Holo1](https://huggingface.co/Hcompany/Holo1-3B).")

    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", "Holo1-3B"],
                    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.", "Fara-7B"],
            ["examples/3.png", "Click on the 'Real-time vision models' collection.", "Holo1-3B"],
        ],
        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)