app.py

import os
import gradio as gr
import numpy as np
import spaces
import torch
import random
import io
import base64
import json
from PIL import Image
from gradio_client import Client
from huggingface_hub import InferenceClient
from deep_translator import GoogleTranslator
from transformers import pipeline
from diffusers import Flux2Pipeline, Flux2Transformer2DModel
from datetime import date

# ==========================================
# 1. تنظیمات و پیکربندی سیستم (Configuration)
# ==========================================

# رنگ‌ها و تنظیمات ظاهری
USAGE_LIMIT = 5
DATA_FILE = "usage_data.json"
PREMIUM_PAGE_ID = '1149636'
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
device = "cuda" if torch.cuda.is_available() else "cpu"

# بارگذاری مدل تشخیص محتوای نامناسب (Safety Checker)
print("Loading Safety Checker...")
safety_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=-1)

# کلاینت‌های هوش مصنوعی
hf_client = InferenceClient(api_key=os.environ.get("HF_TOKEN"))
VLM_MODEL = "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT"

# پرامپت‌های سیستمی برای بهبود متن
SYSTEM_PROMPT_TEXT_ONLY = """You are an expert prompt engineer for FLUX.2. Rewrite user prompts to be more descriptive while strictly preserving their core subject and intent. Add concrete visual specifics."""
SYSTEM_PROMPT_WITH_IMAGES = """You are FLUX.2 image-editing expert. Convert editing requests into one concise instruction (50-80 words)."""

# لیست کلمات ممنوعه (Strict Mode)
BANNED_WORDS = [
    "nsfw", "nude", "naked", "sex", "porn", "erotic", "xxx", "18+", "adult",
    "explicit", "uncensored", "sexual", "lewd", "sensual", "lust", "horny",
    "breast", "breasts", "nipple", "nipples", "vagina", "pussy", "cunt",
    "penis", "dick", "cock", "genital", "genitals", "groin", "pubic",
    "ass", "butt", "buttocks", "anus", "anal", "rectum",
    "intercourse", "masturbation", "orgasm", "blowjob", "bj", "cum", "sperm",
    "ejaculation", "penetration", "fucking", "sucking", "licking",
    "lingerie", "bikini", "swimwear", "underwear", "panties", "bra", "thong",
    "topless", "bottomless", "undressed", "unclothed", "skimpy", "transparent",
    "fetish", "bdsm", "bondage", "latex", "hentai", "ecchi", "ahegao",
    "gore", "bloody", "blood", "kill", "murder", "dead", "torture", "abuse"
]

# ==========================================
# 2. بارگذاری مدل FLUX.2
# ==========================================
print("Loading FLUX.2 Pipeline...")
repo_id = "black-forest-labs/FLUX.2-dev"

dit = Flux2Transformer2DModel.from_pretrained(
    repo_id,
    subfolder="transformer",
    torch_dtype=torch.bfloat16
)

pipe = Flux2Pipeline.from_pretrained(
    repo_id,
    text_encoder=None,
    transformer=dit,
    torch_dtype=torch.bfloat16
)
pipe.to(device)

# بهینه‌سازی ZeroGPU
spaces.aoti_blocks_load(pipe.transformer, "zerogpu-aoti/FLUX.2", variant="fa3")

# ==========================================
# 3. توابع کمکی (Helpers)
# ==========================================

def load_usage_data():
    if os.path.exists(DATA_FILE):
        try:
            with open(DATA_FILE, 'r') as f:
                return json.load(f)
        except:
            return {}
    return {}

def save_usage_data(data):
    try:
        with open(DATA_FILE, 'w') as f:
            json.dump(data, f)
    except Exception as e:
        print(f"Error saving data: {e}")

usage_data_cache = load_usage_data()

def is_image_nsfw(image):
    if image is None: return False
    try:
        # اگر ورودی لیست گالری باشد، اولین تصویر را چک کن
        img_to_check = image
        if isinstance(image, list):
             # هندل کردن فرمت گالری گرادیو
             if len(image) > 0:
                img_to_check = image[0][0] if isinstance(image[0], tuple) else image[0]
             else:
                return False

        results = safety_classifier(img_to_check)
        for result in results:
            if result['label'] == 'nsfw' and result['score'] > 0.75:
                return True
        return False
    except Exception as e:
        print(f"Safety check error: {e}")
        return False

def check_text_safety(text):
    if not text: return True
    text_lower = text.lower()
    padded_text = f" {text_lower} "
    for char in [".", ",", "!", "?", "-", "_", "(", ")", "[", "]", "{", "}"]:
        padded_text = padded_text.replace(char, " ")

    for word in BANNED_WORDS:
        if f" {word} " in padded_text:
            return False
    return True

def translate_prompt(text):
    if not text: return ""
    try:
        translated = GoogleTranslator(source='auto', target='en').translate(text)
        return translated
    except Exception as e:
        print(f"Translation Error: {e}")
        return text

def get_error_html(message):
    return f"""<div style="background-color: #fee2e2; border: 1px solid #ef4444; color: #b91c1c; padding: 12px; border-radius: 8px; text-align: center; margin-bottom: 10px; font-weight: bold; display: flex; align-items: center; justify-content: center; gap: 8px;"><span style="font-size: 1.2em;">⛔</span>{message}</div>"""

def get_success_html(message):
    return f"""<div style="background-color: #dcfce7; border: 1px solid #22c55e; color: #15803d; padding: 12px; border-radius: 8px; text-align: center; margin-bottom: 10px; font-weight: bold; display: flex; align-items: center; justify-content: center; gap: 8px;"><span style="font-size: 1.2em;">✅</span>{message}</div>"""

def get_quota_exceeded_html():
    return """<div style="background: linear-gradient(135deg, #fffbeb 0%, #fef3c7 100%); border: 2px solid #f59e0b; padding: 20px; border-radius: 16px; text-align: center; box-shadow: 0 4px 15px rgba(245, 158, 11, 0.1);"><div style="font-size: 3rem; margin-bottom: 10px;">💎</div><h3 style="color: #92400e; margin: 0 0 10px 0; font-weight: 800;">اعتبار رایگان امروز تمام شد</h3><p style="color: #b45309; margin: 0; font-size: 0.95em;">شما از ۵ تصویر رایگان امروز استفاده کرده‌اید.<br>برای ساخت تصاویر نامحدود و حرفه‌ای، لطفا نسخه خود را ارتقا دهید.</p></div>"""

def get_user_record(fingerprint):
    global usage_data_cache
    if not fingerprint: return None
    usage_data_cache = load_usage_data()
    today_str = date.today().isoformat()
    user_record = usage_data_cache.get(fingerprint)
    if not user_record or user_record.get("last_reset") != today_str:
        return {"count": 0, "last_reset": today_str}
    return user_record

def consume_quota(fingerprint):
    global usage_data_cache
    today_str = date.today().isoformat()
    usage_data_cache = load_usage_data()
    user_record = usage_data_cache.get(fingerprint)
    if not user_record or user_record.get("last_reset") != today_str:
        user_record = {"count": 0, "last_reset": today_str}
    user_record["count"] += 1
    usage_data_cache[fingerprint] = user_record
    save_usage_data(usage_data_cache)
    return user_record["count"]

def check_initial_quota(fingerprint, subscription_status):
    if not fingerprint: return gr.update(visible=True), gr.update(visible=False), None
    if subscription_status == 'paid': return gr.update(visible=True), gr.update(visible=False), None
    user_record = get_user_record(fingerprint)
    current_usage = user_record["count"] if user_record else 0
    if current_usage >= USAGE_LIMIT:
        return gr.update(visible=False), gr.update(visible=True), get_quota_exceeded_html()
    else:
        return gr.update(visible=True), gr.update(visible=False), None

def image_to_data_uri(img):
    buffered = io.BytesIO()
    img.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return f"data:image/png;base64,{img_str}"

def remote_text_encoder(prompts):
    client = Client("multimodalart/mistral-text-encoder")
    result = client.predict(prompt=prompts, api_name="/encode_text")
    prompt_embeds = torch.load(result[0])
    return prompt_embeds

def upsample_prompt_logic(prompt, image_list):
    try:
        if image_list and len(image_list) > 0:
            system_content = SYSTEM_PROMPT_WITH_IMAGES
            user_content = [{"type": "text", "text": prompt}]
            for img in image_list:
                data_uri = image_to_data_uri(img)
                user_content.append({"type": "image_url", "image_url": {"url": data_uri}})
            messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}]
        else:
            system_content = SYSTEM_PROMPT_TEXT_ONLY
            messages = [{"role": "system", "content": system_content}, {"role": "user", "content": prompt}]

        completion = hf_client.chat.completions.create(model=VLM_MODEL, messages=messages, max_tokens=1024)
        return completion.choices[0].message.content
    except Exception as e:
        print(f"Upsampling failed: {e}")
        return prompt

def get_duration(prompt_embeds, image_list, width, height, num_inference_steps, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
    num_images = 0 if image_list is None else len(image_list)
    step_duration = 1 + 0.8 * num_images
    return max(65, num_inference_steps * step_duration + 10)

# ==========================================
# 4. تابع اصلی GPU (Inference)
# ==========================================

@spaces.GPU(duration=get_duration)
def generate_image(prompt_embeds, image_list, width, height, num_inference_steps, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
    prompt_embeds = prompt_embeds.to(device)
    generator = torch.Generator(device=device).manual_seed(seed)

    pipe_kwargs = {
        "prompt_embeds": prompt_embeds,
        "image": image_list,
        "num_inference_steps": num_inference_steps,
        "guidance_scale": guidance_scale,
        "generator": generator,
        "width": width,
        "height": height,
    }

    if progress: progress(0, desc="Starting generation...")
    image = pipe(**pipe_kwargs).images[0]
    return image

def infer(
    prompt, input_images, seed, randomize_seed, width, height,
    num_inference_steps, guidance_scale, prompt_upsampling,
    fingerprint, subscription_status,
    progress=gr.Progress(track_tqdm=True)
):
    # 1. بررسی اعتبار قبل از شروع
    if subscription_status != 'paid':
        user_record = get_user_record(fingerprint)
        if user_record and user_record["count"] >= USAGE_LIMIT:
             return None, seed, get_quota_exceeded_html(), gr.update(visible=False), gr.update(visible=True)

    # 2. بررسی‌های ایمنی (Safety Checks)
    # الف) بررسی تصویر ورودی
    image_list = None
    if input_images is not None and len(input_images) > 0:
        image_list = [item[0] for item in input_images]
        if is_image_nsfw(image_list):
             return None, seed, get_error_html("تصویر ورودی دارای محتوای نامناسب است."), gr.update(visible=True), gr.update(visible=False)

    # ب) ترجمه و بررسی متن
    progress(0.1, desc="Translating...")
    english_prompt = translate_prompt(prompt)
    if not check_text_safety(english_prompt):
        return None, seed, get_error_html("متن درخواست شامل کلمات غیرمجاز است."), gr.update(visible=True), gr.update(visible=False)

    # 3. کسر اعتبار (اگر کاربر رایگان است)
    if subscription_status != 'paid':
        consume_quota(fingerprint)

    # 4. آماده‌سازی تنظیمات
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    try:
        # Upsampling Prompt (Optional)
        final_prompt = english_prompt
        if prompt_upsampling:
            progress(0.2, desc="Enhancing prompt...")
            final_prompt = upsample_prompt_logic(english_prompt, image_list)

        # Text Encoding (CPU/Network)
        progress(0.3, desc="Encoding...")
        prompt_embeds = remote_text_encoder(final_prompt)

        # Generation (GPU)
        progress(0.4, desc="Generating...")
        result_image = generate_image(
            prompt_embeds, image_list, width, height,
            num_inference_steps, guidance_scale, seed, progress
        )

        # 5. بررسی تصویر خروجی
        if is_image_nsfw(result_image):
            return None, seed, get_error_html("تصویر تولید شده حاوی محتوای نامناسب بود."), gr.update(visible=True), gr.update(visible=False)

        # 6. محاسبه اعتبار باقی‌مانده
        user_record = get_user_record(fingerprint)
        remaining = USAGE_LIMIT - user_record["count"] if user_record else 0
        success_msg = f"تصویر با موفقیت ساخته شد."
        if subscription_status != 'paid':
             success_msg += f" (اعتبار باقی‌مانده امروز: {remaining})"

        btn_run_update = gr.update(visible=True)
        btn_upg_update = gr.update(visible=False)

        if subscription_status != 'paid' and remaining <= 0:
             btn_run_update = gr.update(visible=False)
             btn_upg_update = gr.update(visible=True)

        return result_image, seed, get_success_html(success_msg), btn_run_update, btn_upg_update

    except Exception as e:
        error_str = str(e)
        if "quota" in error_str.lower() or "exceeded" in error_str.lower():
             raise e # Raise to be caught by JS
        return None, seed, get_error_html(f"خطا در پردازش: {error_str}"), gr.update(visible=True), gr.update(visible=False)


def update_dimensions_from_image(image_list):
    if image_list is None or len(image_list) == 0:
        return 1024, 1024
    img = image_list[0][0]
    img_width, img_height = img.size
    aspect_ratio = img_width / img_height
    if aspect_ratio >= 1:
        new_width = 1024
        new_height = int(1024 / aspect_ratio)
    else:
        new_height = 1024
        new_width = int(1024 * aspect_ratio)
    new_width = round(new_width / 8) * 8
    new_height = round(new_height / 8) * 8
    return max(256, min(1024, new_width)), max(256, min(1024, new_height))

# ==========================================
# 5. جاوااسکریپت و CSS (UI/UX)
# ==========================================

js_download_func = """
async (image) => {
    if (!image) { alert("لطفاً ابتدا تصویر را تولید کنید."); return; }
    let fileUrl = image.url;
    if (fileUrl && !fileUrl.startsWith('http')) { fileUrl = window.location.origin + fileUrl; }
    else if (!fileUrl && image.path) { fileUrl = window.location.origin + "/file=" + image.path; }
    window.parent.postMessage({ type: 'DOWNLOAD_REQUEST', url: fileUrl }, '*');
}
"""

js_upgrade_func = """() => { window.parent.postMessage({ type: 'NAVIGATE_TO_PREMIUM' }, '*'); }"""

js_global_content = """
<script>
document.addEventListener('DOMContentLoaded', () => {
    async function getBrowserFingerprint() {
        const components = [navigator.userAgent, navigator.language, screen.colorDepth, screen.width + 'x' + screen.height, new Date().getTimezoneOffset()];
        try {
            const canvas = document.createElement('canvas');
            const ctx = canvas.getContext('2d');
            ctx.textBaseline = "top"; ctx.font = "14px 'Arial'"; ctx.textBaseline = "alphabetic";
            ctx.fillStyle = "#f60"; ctx.fillRect(125, 1, 62, 20);
            ctx.fillStyle = "#069"; ctx.fillText("Alpha_Flux_FP_v1", 2, 15);
            components.push(canvas.toDataURL());
        } catch (e) { components.push("canvas-err"); }
        const str = components.join('~~~');
        let hash = 0;
        for (let i = 0; i < str.length; i++) { hash = ((hash << 5) - hash) + str.charCodeAt(i); hash |= 0; }
        return 'fp_' + Math.abs(hash).toString(16);
    }

    function isUserPaid(userObject) {
        const PREMIUM_PAGE_ID = '1149636';
        if (userObject && userObject.isLogin && userObject.accessible_pages) {
            if (Array.isArray(userObject.accessible_pages)) return userObject.accessible_pages.some(page => String(page) === String(PREMIUM_PAGE_ID));
        }
        return false;
    }

    function updateHiddenInputs(fingerprint, status) {
        const fpInput = document.querySelector('#fingerprint_storage textarea');
        const stInput = document.querySelector('#status_storage textarea');
        if(fpInput && fingerprint && fpInput.value !== fingerprint) { fpInput.value = fingerprint; fpInput.dispatchEvent(new Event('input', { bubbles: true })); }
        if(stInput && status && stInput.value !== status) { stInput.value = status; stInput.dispatchEvent(new Event('input', { bubbles: true })); }
    }

    function updateSubscriptionBadge(status) {
        const badge = document.getElementById('user-sub-badge');
        if (!badge) return;
        if (status === 'paid') {
            badge.innerHTML = '✨ اشتراک: <span style="color: #FFD700; font-weight: bold;">نامحدود (PRO)</span>';
            badge.style.background = 'linear-gradient(45deg, #1e3a8a, #3b82f6)';
        } else {
            badge.innerHTML = '👤 اشتراک: <span style="color: #fff; font-weight: bold;">رایگان (۵ اعتبار روزانه)</span>';
            badge.style.background = 'linear-gradient(45deg, #4b5563, #6b7280)';
        }
        badge.style.display = 'inline-block';
    }

    async function initUserIdentity() {
        window.userFingerprint = await getBrowserFingerprint();
        window.userStatus = 'free';
        window.parent.postMessage({ type: 'REQUEST_USER_STATUS' }, '*');
        updateSubscriptionBadge('free');
        updateHiddenInputs(window.userFingerprint, window.userStatus);
        setInterval(() => { if(window.userFingerprint) updateHiddenInputs(window.userFingerprint, window.userStatus || 'free'); }, 1500);
    }

    window.addEventListener('message', (event) => {
        if (event.data && event.data.type === 'USER_STATUS_RESPONSE') {
            try {
                const userObject = typeof event.data.payload === 'string' ? JSON.parse(event.data.payload) : event.data.payload;
                const status = isUserPaid(userObject) ? 'paid' : 'free';
                window.userStatus = status;
                updateSubscriptionBadge(status);
                updateHiddenInputs(window.userFingerprint, status);
            } catch (e) { console.error(e); }
        }
    });

    initUserIdentity();

    // GPU Quota Modal
    window.retryGeneration = function() { document.getElementById('custom-quota-modal')?.remove(); document.getElementById('run-btn')?.click(); };
    window.closeErrorModal = function() { document.getElementById('custom-quota-modal')?.remove(); };

    const showQuotaModal = () => {
        if (document.getElementById('custom-quota-modal')) return;
        const modalHtml = `
            <div id="custom-quota-modal" style="position: fixed; top: 0; left: 0; width: 100%; height: 100%; background: rgba(0,0,0,0.6); backdrop-filter: blur(5px); z-index: 99999; display: flex; align-items: center; justify-content: center; font-family: 'Vazirmatn', sans-serif;">
                <div class="ip-reset-guide-container">
                    <div class="guide-header">
                        <h2>یک قدم تا ساخت تصاویر جدید</h2>
                    </div>
                    <div class="guide-content">
                        <p>برای ادامه ساخت تصویر، لطفاً طبق آموزش زیر IP خود را تغییر دهید (اینترنت را خاموش/روشن کنید یا VPN را قطع کنید) و سپس دکمه تلاش مجدد را بزنید.</p>
                        <div class="video-button-container">
                            <button onclick="parent.postMessage({ type: 'NAVIGATE_TO_URL', url: '#/nav/online/news/getSingle/1149635' }, '*')" class="elegant-video-button">
                                <span>دیدن ویدیو آموزشی</span>
                            </button>
                        </div>
                    </div>
                    <div class="guide-actions">
                        <button class="action-button back-button" onclick="window.closeErrorModal()">بازگشت</button>
                        <button class="action-button retry-button" onclick="window.retryGeneration()">تلاش مجدد</button>
                    </div>
                </div>
            </div>`;
        document.body.insertAdjacentHTML('beforeend', modalHtml);
        setTimeout(window.closeErrorModal, 15000);
    };

    setInterval(() => {
        const potentialErrors = document.querySelectorAll('.toast-body, .error, .toast-wrap');
        potentialErrors.forEach(el => {
            const text = el.innerText || "";
            if (text.toLowerCase().includes('quota') || text.toLowerCase().includes('exceeded')) {
                 showQuotaModal();
                 el.style.display = 'none';
                 const parent = el.closest('.toast-wrap');
                 if(parent) parent.style.display = 'none';
            }
        });
    }, 100);

    const forceLight = () => {
        document.body.classList.remove('dark');
        document.body.style.backgroundColor = '#f5f7fa';
        document.body.style.color = '#333333';
    };
    forceLight(); setInterval(forceLight, 1000);
});
</script>
"""

css_code = """
<style>
@import url('https://fonts.googleapis.com/css2?family=Vazirmatn:wght@300;400;500;700&display=swap');
:root, .dark, body, .gradio-container {
    --body-background-fill: #f5f7fa !important;
    --body-text-color: #1f2937 !important;
    font-family: 'Vazirmatn', sans-serif !important;
}
.ip-reset-guide-container { text-align: right; direction: rtl; background: white; padding: 20px; border-radius: 16px; width: 90%; max-width: 420px; box-shadow: 0 20px 25px -5px rgba(0,0,0,0.1); }
.elegant-video-button { background: #fff; color: #667eea; border: 1px solid #e2e8f0; padding: 10px 20px; border-radius: 50px; cursor: pointer; font-weight: bold; margin-top: 10px; }
.guide-actions { display: flex; gap: 10px; margin-top: 20px; }
.action-button { flex: 1; padding: 10px; border-radius: 12px; border: none; cursor: pointer; font-weight: bold; }
.retry-button { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; }
.back-button { background: white; border: 1px solid #e2e8f0; }

#col-container { max-width: 1200px; margin: 0 auto; direction: rtl; text-align: right; padding: 30px; background: white; border-radius: 24px; box-shadow: 0 10px 40px -10px rgba(0,0,0,0.08); }
#badge-container { text-align: center; margin-bottom: 20px; height: 30px; }
#user-sub-badge { padding: 6px 16px; border-radius: 20px; font-size: 0.9em; color: white; display: none; }
.primary-btn { background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important; color: white !important; font-size: 1.1em !important; border-radius: 14px !important; margin-top: 15px; border: none !important; }
.upgrade-btn { background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%) !important; color: white !important; font-size: 1.1em !important; border-radius: 14px !important; margin-top: 15px; animation: pulse 2s infinite; border: none !important; }
@keyframes pulse { 0% { transform: scale(1); } 70% { transform: scale(1.02); } 100% { transform: scale(1); } }
footer { display: none !important; }
#fingerprint_storage, #status_storage { display: none !important; }
</style>
"""

# ==========================================
# 6. ساخت رابط کاربری (Gradio Blocks)
# ==========================================

# ******** این خط اصلاح شده است ********
with gr.Blocks() as demo:
    gr.HTML(js_global_content + css_code)

    fingerprint_box = gr.Textbox(elem_id="fingerprint_storage", visible=True)
    status_box_input = gr.Textbox(elem_id="status_storage", visible=True)

    with gr.Column(elem_id="col-container"):
        gr.Markdown("# **ساخت تصویر با FLUX.2 (پیشرفته)**", elem_id="main-title")
        gr.Markdown("با استفاده از مدل قدرتمند FLUX.2 متن فارسی خود را به تصاویر شگفت‌انگیز تبدیل کنید.", elem_id="main-description")
        gr.HTML('<div id="badge-container"><span id="user-sub-badge"></span></div>')

        with gr.Row():
            with gr.Column():
                with gr.Row():
                    prompt = gr.Text(
                        label="توصیف تصویر (به فارسی)",
                        show_label=True,
                        max_lines=3,
                        placeholder="یک منظره زیبا از...",
                        rtl=True
                    )

                with gr.Accordion("بارگذاری تصویر (اختیاری برای ویرایش/ایده)", open=False):
                    input_images = gr.Gallery(
                        label="تصاویر ورودی",
                        type="pil",
                        columns=3,
                        rows=1,
                        height=200
                    )

                status_box = gr.HTML(label="وضعیت")

                run_button = gr.Button("✨ ساخت تصویر", variant="primary", elem_classes="primary-btn", elem_id="run-btn", visible=True)
                upgrade_button = gr.Button("💎 خرید نسخه نامحدود", variant="primary", elem_classes="upgrade-btn", elem_id="upgrade-btn", visible=False)

                with gr.Accordion("تنظیمات پیشرفته", open=False):
                    prompt_upsampling = gr.Checkbox(label="بهبود خودکار پرامپت (هوشمند)", value=True)
                    seed = gr.Slider(label="دانه تصادفی (Seed)", minimum=0, maximum=MAX_SEED, step=1, value=0)
                    randomize_seed = gr.Checkbox(label="Seed تصادفی", value=True)
                    with gr.Row():
                        width = gr.Slider(label="عرض (Width)", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024)
                        height = gr.Slider(label="ارتفاع (Height)", minimum=256, maximum=MAX_IMAGE_SIZE, step=8, value=1024)
                    with gr.Row():
                        num_inference_steps = gr.Slider(label="تعداد مراحل (Steps)", minimum=1, maximum=50, step=1, value=28)
                        guidance_scale = gr.Slider(label="میزان وفاداری (Guidance)", minimum=1.0, maximum=10.0, step=0.1, value=3.5)

            with gr.Column():
                result = gr.Image(label="تصویر نهایی", show_label=True, interactive=False)
                download_button = gr.Button("📥 دانلود تصویر", variant="secondary", elem_id="download-btn")

    # اتصال رویدادها

    # 1. آپدیت ابعاد بر اساس تصویر آپلودی
    input_images.upload(
        fn=update_dimensions_from_image,
        inputs=[input_images],
        outputs=[width, height]
    )

    # 2. بررسی اولیه اعتبار
    fingerprint_box.change(
        fn=check_initial_quota,
        inputs=[fingerprint_box, status_box_input],
        outputs=[run_button, upgrade_button, status_box]
    )

    # 3. اجرای مدل
    run_button.click(
        fn=infer,
        inputs=[
            prompt, input_images, seed, randomize_seed, width, height,
            num_inference_steps, guidance_scale, prompt_upsampling,
            fingerprint_box, status_box_input
        ],
        outputs=[result, seed, status_box, run_button, upgrade_button]
    )

    # 4. دکمه‌های دانلود و ارتقا
    upgrade_button.click(fn=None, js=js_upgrade_func)
    download_button.click(fn=None, inputs=[result], js=js_download_func)

if __name__ == "__main__":
    demo.queue(max_size=30).launch(show_error=True)