app.py

import tempfile
import time
from collections.abc import Sequence
from typing import Any, cast
import os
from huggingface_hub import login, hf_hub_download

import gradio as gr
import numpy as np
import pillow_heif
import spaces
import torch
from gradio_image_annotation import image_annotator
from gradio_imageslider import ImageSlider
from PIL import Image
from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
from refiners.fluxion.utils import no_grad
from refiners.solutions import BoxSegmenter
from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
from diffusers import FluxPipeline
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import gc

from PIL import Image, ImageDraw, ImageFont
from PIL import Image
from gradio_client import Client, handle_file
import uuid


def clear_memory():
    """메모리 정리 함수"""
    gc.collect()
    try:
        if torch.cuda.is_available():
            with torch.cuda.device(0):  # 명시적으로 device 0 사용
                torch.cuda.empty_cache()
    except:
        pass

# GPU 설정
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # 명시적으로 cuda:0 지정

# GPU 설정을 try-except로 감싸기
if torch.cuda.is_available():
    try:
        with torch.cuda.device(0):
            torch.cuda.empty_cache()
            torch.backends.cudnn.benchmark = True
            torch.backends.cuda.matmul.allow_tf32 = True
    except:
        print("Warning: Could not configure CUDA settings")

# 번역 모델 초기화
model_name = "Helsinki-NLP/opus-mt-ko-en"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to('cpu')
translator = pipeline("translation", model=model, tokenizer=tokenizer, device=-1)

def translate_to_english(text: str) -> str:
    """한글 텍스트를 영어로 번역"""
    try:
        if any(ord('가') <= ord(char) <= ord('힣') for char in text):
            translated = translator(text, max_length=128)[0]['translation_text']
            print(f"Translated '{text}' to '{translated}'")
            return translated
        return text
    except Exception as e:
        print(f"Translation error: {str(e)}")
        return text

BoundingBox = tuple[int, int, int, int]

pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

# HF 토큰 설정
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("Please set the HF_TOKEN environment variable")

try:
    login(token=HF_TOKEN)
except Exception as e:
    raise ValueError(f"Failed to login to Hugging Face: {str(e)}")

# 모델 초기화
segmenter = BoxSegmenter(device="cpu")
segmenter.device = device
segmenter.model = segmenter.model.to(device=segmenter.device)

gd_model_path = "IDEA-Research/grounding-dino-base"
gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
gd_model = gd_model.to(device=device)
assert isinstance(gd_model, GroundingDinoForObjectDetection)

# FLUX 파이프라인 초기화
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.float16,
    use_auth_token=HF_TOKEN
)
pipe.enable_attention_slicing(slice_size="auto")

# LoRA 가중치 로드
pipe.load_lora_weights(
    hf_hub_download(
        "ByteDance/Hyper-SD",
        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
        use_auth_token=HF_TOKEN
    )
)
pipe.fuse_lora(lora_scale=0.125)

# GPU 설정을 try-except로 감싸기
try:
    if torch.cuda.is_available():
        pipe = pipe.to("cuda:0")  # 명시적으로 cuda:0 지정
except Exception as e:
    print(f"Warning: Could not move pipeline to CUDA: {str(e)}")

client = Client("NabeelShar/BiRefNet_for_text_writing")

class timer:
    def __init__(self, method_name="timed process"):
        self.method = method_name
    def __enter__(self):
        self.start = time.time()
        print(f"{self.method} starts")
    def __exit__(self, exc_type, exc_val, exc_tb):
        end = time.time()
        print(f"{self.method} took {str(round(end - self.start, 2))}s")

def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
    if not bboxes:
        return None
    for bbox in bboxes:
        assert len(bbox) == 4
        assert all(isinstance(x, int) for x in bbox)
    return (
        min(bbox[0] for bbox in bboxes),
        min(bbox[1] for bbox in bboxes),
        max(bbox[2] for bbox in bboxes),
        max(bbox[3] for bbox in bboxes),
    )

def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)

def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
    with no_grad():
        outputs = gd_model(**inputs)
    width, height = img.size
    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
        outputs,
        inputs["input_ids"],
        target_sizes=[(height, width)],
    )[0]
    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
    return bbox_union(bboxes.numpy().tolist())

def apply_mask(img: Image.Image, mask_img: Image.Image, defringe: bool = True) -> Image.Image:
    assert img.size == mask_img.size
    img = img.convert("RGB")
    mask_img = mask_img.convert("L")
    if defringe:
        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
        img = Image.fromarray((foreground * 255).astype("uint8"))
    result = Image.new("RGBA", img.size)
    result.paste(img, (0, 0), mask_img)
    return result


def adjust_size_to_multiple_of_8(width: int, height: int) -> tuple[int, int]:
    """이미지 크기를 8의 배수로 조정하는 함수"""
    new_width = ((width + 7) // 8) * 8
    new_height = ((height + 7) // 8) * 8
    return new_width, new_height

def calculate_dimensions(aspect_ratio: str, base_size: int = 512) -> tuple[int, int]:
    """선택된 비율에 따라 이미지 크기 계산"""
    if aspect_ratio == "1:1":
        return base_size, base_size
    elif aspect_ratio == "16:9":
        return base_size * 16 // 9, base_size
    elif aspect_ratio == "9:16":
        return base_size, base_size * 16 // 9
    elif aspect_ratio == "4:3":
        return base_size * 4 // 3, base_size
    return base_size, base_size

@spaces.GPU(duration=20)  # 40초에서 20초로 감소
def generate_background(prompt: str, aspect_ratio: str) -> Image.Image:
    try:
        width, height = calculate_dimensions(aspect_ratio)
        width, height = adjust_size_to_multiple_of_8(width, height)
        
        max_size = 768
        if width > max_size or height > max_size:
            ratio = max_size / max(width, height)
            width = int(width * ratio)
            height = int(height * ratio)
            width, height = adjust_size_to_multiple_of_8(width, height)

        with timer("Background generation"):
            try:
                with torch.inference_mode():
                    image = pipe(
                        prompt=prompt,
                        width=width,
                        height=height,
                        num_inference_steps=8,
                        guidance_scale=4.0
                    ).images[0]
            except Exception as e:
                print(f"Pipeline error: {str(e)}")
                return Image.new('RGB', (width, height), 'white')
                
        return image
    except Exception as e:
        print(f"Background generation error: {str(e)}")
        return Image.new('RGB', (512, 512), 'white')

def create_position_grid():
    return """
    <div class="position-grid" style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; width: 150px; margin: auto;">
        <button class="position-btn" data-pos="top-left">↖</button>
        <button class="position-btn" data-pos="top-center">↑</button>
        <button class="position-btn" data-pos="top-right">↗</button>
        <button class="position-btn" data-pos="middle-left">←</button>
        <button class="position-btn" data-pos="middle-center">•</button>
        <button class="position-btn" data-pos="middle-right">→</button>
        <button class="position-btn" data-pos="bottom-left">↙</button>
        <button class="position-btn" data-pos="bottom-center" data-default="true">↓</button>
        <button class="position-btn" data-pos="bottom-right">↘</button>
    </div>
    """

def calculate_object_position(position: str, bg_size: tuple[int, int], obj_size: tuple[int, int]) -> tuple[int, int]:
    """오브젝트의 위치 계산"""
    bg_width, bg_height = bg_size
    obj_width, obj_height = obj_size
    
    positions = {
        "top-left": (0, 0),
        "top-center": ((bg_width - obj_width) // 2, 0),
        "top-right": (bg_width - obj_width, 0),
        "middle-left": (0, (bg_height - obj_height) // 2),
        "middle-center": ((bg_width - obj_width) // 2, (bg_height - obj_height) // 2),
        "middle-right": (bg_width - obj_width, (bg_height - obj_height) // 2),
        "bottom-left": (0, bg_height - obj_height),
        "bottom-center": ((bg_width - obj_width) // 2, bg_height - obj_height),
        "bottom-right": (bg_width - obj_width, bg_height - obj_height)
    }
    
    return positions.get(position, positions["bottom-center"])

def resize_object(image: Image.Image, scale_percent: float) -> Image.Image:
    """오브젝트 크기 조정"""
    width = int(image.width * scale_percent / 100)
    height = int(image.height * scale_percent / 100)
    return image.resize((width, height), Image.Resampling.LANCZOS)

def combine_with_background(foreground: Image.Image, background: Image.Image, 
                          position: str = "bottom-center", scale_percent: float = 100) -> Image.Image:
    """전경과 배경 합성 함수"""
    # 배경 이미지 준비
    result = background.convert('RGBA')
    
    # 오브젝트 크기 조정
    scaled_foreground = resize_object(foreground, scale_percent)
    
    # 오브젝트 위치 계산
    x, y = calculate_object_position(position, result.size, scaled_foreground.size)
    
    # 합성
    result.paste(scaled_foreground, (x, y), scaled_foreground)
    return result

@spaces.GPU(duration=30)  # 120초에서 30초로 감소
def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
    time_log: list[str] = []
    try:
        if isinstance(prompt, str):
            t0 = time.time()
            bbox = gd_detect(img, prompt)
            time_log.append(f"detect: {time.time() - t0}")
            if not bbox:
                print(time_log[0])
                raise gr.Error("No object detected")
        else:
            bbox = prompt
        t0 = time.time()
        mask = segmenter(img, bbox)
        time_log.append(f"segment: {time.time() - t0}")
        return mask, bbox, time_log
    except Exception as e:
        print(f"GPU process error: {str(e)}")
        raise

def _process(img: Image.Image, prompt: str | BoundingBox | None, bg_prompt: str | None = None, aspect_ratio: str = "1:1") -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
    try:
        # 입력 이미지 크기 제한
        max_size = 1024
        if img.width > max_size or img.height > max_size:
            ratio = max_size / max(img.width, img.height)
            new_size = (int(img.width * ratio), int(img.height * ratio))
            img = img.resize(new_size, Image.LANCZOS)

        # CUDA 메모리 관리 수정
        try:
            if torch.cuda.is_available():
                current_device = torch.cuda.current_device()
                with torch.cuda.device(current_device):
                    torch.cuda.empty_cache()
        except Exception as e:
            print(f"CUDA memory management failed: {e}")

        with torch.cuda.amp.autocast(enabled=torch.cuda.is_available()):
            mask, bbox, time_log = _gpu_process(img, prompt)
            masked_alpha = apply_mask(img, mask, defringe=True)

        if bg_prompt:
            background = generate_background(bg_prompt, aspect_ratio)
            combined = background
        else:
            combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)

        clear_memory()

        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp:
            combined.save(temp.name)
            return (img, combined, masked_alpha), gr.DownloadButton(value=temp.name, interactive=True)
    except Exception as e:
        clear_memory()
        print(f"Processing error: {str(e)}")
        raise gr.Error(f"Processing failed: {str(e)}")

def on_change_bbox(prompts: dict[str, Any] | None):
    return gr.update(interactive=prompts is not None)


def on_change_prompt(img: Image.Image | None, prompt: str | None, bg_prompt: str | None = None):
    return gr.update(interactive=bool(img and prompt))


        
def process_prompt(img: Image.Image, prompt: str, bg_prompt: str | None = None, 
                  aspect_ratio: str = "1:1", position: str = "bottom-center", 
                  scale_percent: float = 100) -> tuple[Image.Image, Image.Image]:
    try:
        if img is None or prompt.strip() == "":
            raise gr.Error("Please provide both image and prompt")
        
        print(f"Processing with position: {position}, scale: {scale_percent}")
        
        try:
            prompt = translate_to_english(prompt)
            if bg_prompt:
                bg_prompt = translate_to_english(bg_prompt)
        except Exception as e:
            print(f"Translation error (continuing with original text): {str(e)}")
        
        results, _ = _process(img, prompt, bg_prompt, aspect_ratio)
        
        if bg_prompt:
            try:
                combined = combine_with_background(
                    foreground=results[2],
                    background=results[1],
                    position=position,
                    scale_percent=scale_percent
                )
                print(f"Combined image created with position: {position}")
                return combined, results[2]
            except Exception as e:
                print(f"Combination error: {str(e)}")
                return results[1], results[2]
        
        return results[1], results[2]
    except Exception as e:
        print(f"Error in process_prompt: {str(e)}")
        raise gr.Error(str(e))
    finally:
        clear_memory()
        
def process_bbox(img: Image.Image, box_input: str) -> tuple[Image.Image, Image.Image]:
    try:
        if img is None or box_input.strip() == "":
            raise gr.Error("Please provide both image and bounding box coordinates")
        
        try:
            coords = eval(box_input)
            if not isinstance(coords, list) or len(coords) != 4:
                raise ValueError("Invalid box format")
            bbox = tuple(int(x) for x in coords)
        except:
            raise gr.Error("Invalid box format. Please provide [xmin, ymin, xmax, ymax]")
        
        # Process the image
        results, _ = _process(img, bbox)
        
        # 합성된 이미지와 추출된 이미지만 반환
        return results[1], results[2]
    except Exception as e:
        raise gr.Error(str(e))

# Event handler functions 수정
def update_process_button(img, prompt):
    return gr.update(
        interactive=bool(img and prompt),
        variant="primary" if bool(img and prompt) else "secondary"
    )

def update_box_button(img, box_input):
    try:
        if img and box_input:
            coords = eval(box_input)
            if isinstance(coords, list) and len(coords) == 4:
                return gr.update(interactive=True, variant="primary")
        return gr.update(interactive=False, variant="secondary")
    except:
        return gr.update(interactive=False, variant="secondary")


# CSS 정의
css = """
footer {display: none}
.main-title {
    text-align: center;
    margin: 2em 0;
    padding: 1em;
    background: #f7f7f7;
    border-radius: 10px;
}
.main-title h1 {
    color: #2196F3;
    font-size: 2.5em;
    margin-bottom: 0.5em;
}
.main-title p {
    color: #666;
    font-size: 1.2em;
}
.container {
    max-width: 1200px;
    margin: auto;
    padding: 20px;
}
.tabs {
    margin-top: 1em;
}
.input-group {
    background: white;
    padding: 1em;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.output-group {
    background: white;
    padding: 1em;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
button.primary {
    background: #2196F3;
    border: none;
    color: white;
    padding: 0.5em 1em;
    border-radius: 4px;
    cursor: pointer;
    transition: background 0.3s ease;
}
button.primary:hover {
    background: #1976D2;
}
.position-btn {
    transition: all 0.3s ease;
}
.position-btn:hover {
    background-color: #e3f2fd;
}
.position-btn.selected {
    background-color: #2196F3;
    color: white;
}
"""



def add_text_with_stroke(draw, text, x, y, font, text_color, stroke_width):
    """Helper function to draw text with stroke"""
    # Draw the stroke/outline
    for adj_x in range(-stroke_width, stroke_width + 1):
        for adj_y in range(-stroke_width, stroke_width + 1):
            draw.text((x + adj_x, y + adj_y), text, font=font, fill=text_color)

def remove_background(image):
    # Save the image to a specific location
    filename = f"image_{uuid.uuid4()}.png"  # Generates a universally unique identifier (UUID) for the filename
    image.save(filename)
    # Call gradio client for background removal
    result = client.predict(images=handle_file(filename), api_name="/image")
    return Image.open(result[0])

def superimpose(image_with_text, overlay_image):
    # Open image as RGBA to handle transparency
    overlay_image = overlay_image.convert("RGBA")
    # Paste overlay on the background
    image_with_text.paste(overlay_image, (0, 0), overlay_image)
    # Save the final image
    # image_with_text.save("output_image.png")
    return image_with_text

def add_text_to_image(
    input_image,
    text,
    font_size,
    color,
    opacity,
    x_position,
    y_position,
    thickness,
    text_position_type
):
    """
    Add text to an image with customizable properties
    """
    try:
        if input_image is None:
            return None
            
        # PIL Image 객체로 변환
        if not isinstance(input_image, Image.Image):
            if isinstance(input_image, np.ndarray):
                image = Image.fromarray(input_image)
            else:
                raise ValueError("Unsupported image type")
        else:
            image = input_image.copy()

        # 이미지를 RGBA 모드로 변환
        if image.mode != 'RGBA':
            image = image.convert('RGBA')

        # Text Behind Image 처리
        if text_position_type == "Text Behind Image":
            # 원본 이미지의 배경 제거
            overlay_image = remove_background(image)

        # 텍스트 오버레이 생성
        txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
        draw = ImageDraw.Draw(txt_overlay)
        
        # 폰트 설정
        try:
            font = ImageFont.truetype("DejaVuSans.ttf", int(font_size))
        except:
            try:
                font = ImageFont.truetype("arial.ttf", int(font_size))
            except:
                print("Using default font")
                font = ImageFont.load_default()
        
        # 색상 설정
        color_map = {
            'White': (255, 255, 255),
            'Black': (0, 0, 0),
            'Red': (255, 0, 0),
            'Green': (0, 255, 0),
            'Blue': (0, 0, 255),
            'Yellow': (255, 255, 0),
            'Purple': (128, 0, 128)
        }
        rgb_color = color_map.get(color, (255, 255, 255))
        
        # 텍스트 크기 계산
        text_bbox = draw.textbbox((0, 0), text, font=font)
        text_width = text_bbox[2] - text_bbox[0]
        text_height = text_bbox[3] - text_bbox[1]

        # 위치 계산
        actual_x = int((image.width - text_width) * (x_position / 100))
        actual_y = int((image.height - text_height) * (y_position / 100))

        # 텍스트 색상 설정
        text_color = (*rgb_color, int(opacity))
        
        # 텍스트 그리기
        add_text_with_stroke(
            draw, 
            text, 
            actual_x, 
            actual_y, 
            font, 
            text_color, 
            int(thickness)
        )

        if text_position_type == "Text Behind Image":
            # 텍스트를 먼저 그리고 그 위에 이미지 오버레이
            output_image = Image.alpha_composite(image, txt_overlay)
            output_image = superimpose(output_image, overlay_image)
        else:
            # 기존 방식대로 텍스트를 이미지 위에 그리기
            output_image = Image.alpha_composite(image, txt_overlay)
        
        # RGB로 변환
        output_image = output_image.convert('RGB')
        
        return output_image

    except Exception as e:
        print(f"Error in add_text_to_image: {str(e)}")
        return input_image
    
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    gr.HTML("""
        <div class="main-title">
            <h1>🎨GiniGen Canvas</h1>
            <p>AI Integrated Image Creator: Extract objects, generate backgrounds, and adjust ratios and positions to create complete images with AI.</p>
        </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            input_image = gr.Image(
                type="pil",
                label="Upload Image",
                interactive=True
            )
            text_prompt = gr.Textbox(
                label="Object to Extract",
                placeholder="Enter what you want to extract...",
                interactive=True
            )
            with gr.Row():
                bg_prompt = gr.Textbox(
                    label="Background Prompt (optional)",
                    placeholder="Describe the background...",
                    interactive=True,
                    scale=3
                )
                aspect_ratio = gr.Dropdown(
                    choices=["1:1", "16:9", "9:16", "4:3"],
                    value="1:1",
                    label="Aspect Ratio",
                    interactive=True,
                    visible=True,
                    scale=1
                )

            with gr.Row(visible=False) as object_controls:
                with gr.Column(scale=1):
                    with gr.Row():
                        position = gr.State(value="bottom-center")
                        btn_top_left = gr.Button("↖")
                        btn_top_center = gr.Button("↑")
                        btn_top_right = gr.Button("↗")
                    with gr.Row():
                        btn_middle_left = gr.Button("←")
                        btn_middle_center = gr.Button("•")
                        btn_middle_right = gr.Button("→")
                    with gr.Row():
                        btn_bottom_left = gr.Button("↙")
                        btn_bottom_center = gr.Button("↓")
                        btn_bottom_right = gr.Button("↘")
                with gr.Column(scale=1):
                    scale_slider = gr.Slider(
                        minimum=10,
                        maximum=200,
                        value=50,
                        step=5,
                        label="Object Size (%)"
                    )

            process_btn = gr.Button(
                "Process",
                variant="primary",
                interactive=False
            )

        with gr.Column(scale=1):
            with gr.Tab("Result"):
                combined_image = gr.Image(
                    label="Combined Result",
                    show_download_button=True,
                    type="pil",
                    height=512
                )
                
                # 텍스트 삽입 컨트롤을 더 명확하게 구분
                with gr.Group():
                    gr.Markdown("### Add Text to Image")
                    with gr.Row():
                        text_input = gr.Textbox(
                            label="Text Content",
                            placeholder="Enter text to add to image..."
                        )
                        text_position_type = gr.Radio(
                            choices=["Text Over Image", "Text Behind Image"],
                            value="Text Over Image",
                            label="Text Position Type",
                            interactive=True
                        )
                    
                    with gr.Row():
                        with gr.Column(scale=1):
                            font_size = gr.Slider(
                                minimum=10,
                                maximum=200,
                                value=40,
                                step=5,
                                label="Font Size"
                            )
                            color_dropdown = gr.Dropdown(
                                choices=["White", "Black", "Red", "Green", "Blue", "Yellow", "Purple"],
                                value="White",
                                label="Text Color"
                            )
                            thickness = gr.Slider(
                                minimum=0,
                                maximum=10,
                                value=1,
                                step=1,
                                label="Text Thickness"
                            )
                        with gr.Column(scale=1):
                            opacity_slider = gr.Slider(
                                minimum=0,
                                maximum=255,
                                value=255,
                                step=1,
                                label="Opacity"
                            )
                            x_position = gr.Slider(
                                minimum=0,
                                maximum=100,
                                value=50,
                                step=1,
                                label="X Position (%)"
                            )
                            y_position = gr.Slider(
                                minimum=0,
                                maximum=100,
                                value=50,
                                step=1,
                                label="Y Position (%)"
                            )
                    add_text_btn = gr.Button("Apply Text", variant="primary")

                with gr.Row():
                    extracted_image = gr.Image(
                        label="Extracted Object",
                        show_download_button=True,
                        type="pil",
                        height=256
                    )

    # 각 버튼에 대한 클릭 이벤트 처리
    def update_position(new_position):
        return new_position

    btn_top_left.click(fn=lambda: update_position("top-left"), outputs=position)
    btn_top_center.click(fn=lambda: update_position("top-center"), outputs=position)
    btn_top_right.click(fn=lambda: update_position("top-right"), outputs=position)
    btn_middle_left.click(fn=lambda: update_position("middle-left"), outputs=position)
    btn_middle_center.click(fn=lambda: update_position("middle-center"), outputs=position)
    btn_middle_right.click(fn=lambda: update_position("middle-right"), outputs=position)
    btn_bottom_left.click(fn=lambda: update_position("bottom-left"), outputs=position)
    btn_bottom_center.click(fn=lambda: update_position("bottom-center"), outputs=position)
    btn_bottom_right.click(fn=lambda: update_position("bottom-right"), outputs=position)

    # Event bindings
    input_image.change(
        fn=update_process_button,
        inputs=[input_image, text_prompt],
        outputs=process_btn,
        queue=False
    )
    
    text_prompt.change(
        fn=update_process_button,
        inputs=[input_image, text_prompt],
        outputs=process_btn,
        queue=False
    )

    def update_controls(bg_prompt):
        """배경 프롬프트 입력 여부에 따라 컨트롤 표시 업데이트"""
        is_visible = bool(bg_prompt)
        return [
            gr.update(visible=is_visible),  # aspect_ratio
            gr.update(visible=is_visible),  # object_controls
        ]

    bg_prompt.change(
        fn=update_controls,
        inputs=bg_prompt,
        outputs=[aspect_ratio, object_controls],
        queue=False
    )

    process_btn.click(
        fn=process_prompt,
        inputs=[
            input_image,
            text_prompt,
            bg_prompt,
            aspect_ratio,
            position,
            scale_slider
        ],
        outputs=[combined_image, extracted_image],
        queue=True
    )

    # 텍스트 추가 버튼 이벤트 연결
    add_text_btn.click(
        fn=add_text_to_image,
        inputs=[
            combined_image,
            text_input,
            font_size,
            color_dropdown,
            opacity_slider,
            x_position,
            y_position,
            thickness,
            text_position_type
        ],
        outputs=combined_image
    )

demo.queue(max_size=5)  # 큐 크기 제한
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    max_threads=2  # 스레드 수 제한
)