Update app.py

app.py

@@ -1,69 +1,63 @@
 import os
-import subprocess
-import sys
-import io
 import gradio as gr
 import numpy as np
-import random
 import spaces
 import torch
-from diffusers import Flux2Pipeline, Flux2Transformer2DModel
-from diffusers import BitsAndBytesConfig as DiffBitsAndBytesConfig
-import requests
-from PIL import Image
-import json
+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
 
-subprocess.check_call([sys.executable, "-m", "pip", "install", "spaces==0.43.0"])
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# ==========================================
+# 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"
 
-hf_client = InferenceClient(
-    api_key=os.environ.get("HF_TOKEN"),
-)
-VLM_MODEL = "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT"
-
-SYSTEM_PROMPT_TEXT_ONLY = """You are an expert prompt engineer for FLUX.2 by Black Forest Labs. Rewrite user prompts to be more descriptive while strictly preserving their core subject and intent.
-
-Guidelines:
-1. Structure: Keep structured inputs structured (enhance within fields). Convert natural language to detailed paragraphs.
-2. Details: Add concrete visual specifics - form, scale, textures, materials, lighting (quality, direction, color), shadows, spatial relationships, and environmental context.
-3. Text in Images: Put ALL text in quotation marks, matching the prompt's language. Always provide explicit quoted text for objects that would contain text in reality (signs, labels, screens, etc.) - without it, the model generates gibberish.
-
-Output only the revised prompt and nothing else."""
-
-SYSTEM_PROMPT_WITH_IMAGES = """You are FLUX.2 by Black Forest Labs, an image-editing expert. You convert editing requests into one concise instruction (50-80 words, ~30 for brief requests).
-
-Rules:
-- Single instruction only, no commentary
-- Use clear, analytical language (avoid "whimsical," "cascading," etc.)
-- Specify what changes AND what stays the same (face, lighting, composition)
-- Reference actual image elements
-- Turn negatives into positives ("don't change X" → "keep X")
-- Make abstractions concrete ("futuristic" → "glowing cyan neon, metallic panels")
-- Keep content PG-13
+# بارگذاری مدل تشخیص محتوای نامناسب (Safety Checker)
+print("Loading Safety Checker...")
+safety_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=-1)
 
-Output only the final instruction in plain text and nothing else."""
+# کلاینت‌های هوش مصنوعی
+hf_client = InferenceClient(api_key=os.environ.get("HF_TOKEN"))
+VLM_MODEL = "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT"
 
-def remote_text_encoder(prompts):
-    from gradio_client import Client
-    
-    client = Client("multimodalart/mistral-text-encoder")
-    result = client.predict(
-        prompt=prompts,
-        api_name="/encode_text"
-    )
-    
-    # Load returns a tensor, usually on CPU by default
-    prompt_embeds = torch.load(result[0])
-    return prompt_embeds
+# 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"
+]
 
-# Load model
+# ==========================================
+# 2. بارگذاری مدل FLUX.2
+# ==========================================
+print("Loading FLUX.2 Pipeline...")
 repo_id = "black-forest-labs/FLUX.2-dev"
 
 dit = Flux2Transformer2DModel.from_pretrained(
@@ -80,94 +74,157 @@ pipe = Flux2Pipeline.from_pretrained(
 )
 pipe.to(device)
 
-# Pull pre-compiled Flux2 Transformer blocks from HF hub
+# 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:
-            # Image + Text Editing Mode
             system_content = SYSTEM_PROMPT_WITH_IMAGES
-            
-            # Construct user message with text and 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}
-            ]
+                user_content.append({"type": "image_url", "image_url": {"url": data_uri}})
+            messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}]
         else:
-            # Text Only Mode
             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
-        )
+            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 update_dimensions_from_image(image_list):
-    """Update width/height sliders based on uploaded image aspect ratio.
-    Keeps one side at 1024 and scales the other proportionally, with both sides as multiples of 8."""
-    if image_list is None or len(image_list) == 0:
-        return 1024, 1024  # Default dimensions
-    
-    # Get the first image to determine dimensions
-    img = image_list[0][0]  # Gallery returns list of tuples (image, caption)
-    img_width, img_height = img.size
-    
-    aspect_ratio = img_width / img_height
-    
-    if aspect_ratio >= 1:  # Landscape or square
-        new_width = 1024
-        new_height = int(1024 / aspect_ratio)
-    else:  # Portrait
-        new_height = 1024
-        new_width = int(1024 * aspect_ratio)
-    
-    # Round to nearest multiple of 8
-    new_width = round(new_width / 8) * 8
-    new_height = round(new_height / 8) * 8
-    
-    # Ensure within valid range (minimum 256, maximum 1024)
-    new_width = max(256, min(1024, new_width))
-    new_height = max(256, min(1024, new_height))
-    
-    return new_width, new_height
-
-# Updated duration function to match generate_image arguments (including progress)
 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)):
-    # Move embeddings to GPU only when inside the GPU decorated function
     prompt_embeds = prompt_embeds.to(device)
-    
     generator = torch.Generator(device=device).manual_seed(seed)
     
     pipe_kwargs = {
@@ -180,191 +237,355 @@ def generate_image(prompt_embeds, image_list, width, height, num_inference_steps
         "height": height,
     }
     
-    # Progress bar for the actual generation steps
-    if progress:
-        progress(0, desc="Starting generation...")
-        
+    if progress: progress(0, desc="Starting generation...")
     image = pipe(**pipe_kwargs).images[0]
     return image
 
-def infer(prompt, input_images=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=50, guidance_scale=2.5, prompt_upsampling=False, progress=gr.Progress(track_tqdm=True)):
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    # Prepare image list (convert None or empty gallery to None)
+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 = []
-        for item in input_images:
-            image_list.append(item[0])
-
-    # 1. Upsampling (Network bound - No GPU needed)
-    final_prompt = prompt
-    if prompt_upsampling:
-        progress(0.05, desc="Upsampling prompt...")
-        final_prompt = upsample_prompt_logic(prompt, image_list)
-        print(f"Original Prompt: {prompt}")
-        print(f"Upsampled Prompt: {final_prompt}")
-
-    # 2. Text Encoding (Network bound - No GPU needed)
-    progress(0.1, desc="Encoding prompt...")
-    # This returns CPU tensors
-    prompt_embeds = remote_text_encoder(final_prompt)
-    
-    # 3. Image Generation (GPU bound)
-    progress(0.3, desc="Waiting for GPU...")
-    image = generate_image(
-        prompt_embeds, 
-        image_list, 
-        width, 
-        height, 
-        num_inference_steps, 
-        guidance_scale, 
-        seed, 
-        progress
-    )
+        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)
     
-    return image, 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)
 
-examples = [
-    ["Create a vase on a table in living room, the color of the vase is a gradient of color, starting with #02eb3c color and finishing with #edfa3c. The flowers inside the vase have the color #ff0088"],
-    ["Photorealistic infographic showing the complete Berlin TV Tower (Fernsehturm) from ground base to antenna tip, full vertical view with entire structure visible including concrete shaft, metallic sphere, and antenna spire. Slight upward perspective angle looking up toward the iconic sphere, perfectly centered on clean white background. Left side labels with thin horizontal connector lines: the text '368m' in extra large bold dark grey numerals (#2D3748) positioned at exactly the antenna tip with 'TOTAL HEIGHT' in small caps below. The text '207m' in extra large bold with 'TELECAFÉ' in small caps below, with connector line touching the sphere precisely at the window level. Right side label with horizontal connector line touching the sphere's equator: the text '32m' in extra large bold dark grey numerals with 'SPHERE DIAMETER' in small caps below. Bottom section arranged in three balanced columns: Left - Large text '986' in extra bold dark grey with 'STEPS' in caps below. Center - 'BERLIN TV TOWER' in bold caps with 'FERNSEHTURM' in lighter weight below. Right - 'INAUGURATED' in bold caps with 'OCTOBER 3, 1969' below. All typography in modern sans-serif font (such as Inter or Helvetica), color #2D3748, clean minimal technical diagram style. Horizontal connector lines are thin, precise, and clearly visible, touching the tower structure at exact corresponding measurement points. Professional architectural elevation drawing aesthetic with dynamic low angle perspective creating sense of height and grandeur, poster-ready infographic design with perfect visual hierarchy."],
-    ["Soaking wet capybara taking shelter under a banana leaf in the rainy jungle, close up photo"],
-    ["A kawaii die-cut sticker of a chubby orange cat, featuring big sparkly eyes and a happy smile with paws raised in greeting and a heart-shaped pink nose. The design should have smooth rounded lines with black outlines and soft gradient shading with pink cheeks."],
-]
+        if subscription_status != 'paid' and remaining <= 0:
+             btn_run_update = gr.update(visible=False)
+             btn_upg_update = gr.update(visible=True)
 
-examples_images = [
-    # ["Replace the top of the person from image 1 with the one from image 2", ["person1.webp", "woman2.webp"]],
-    ["The person from image 1 is petting the cat from image 2, the bird from image 3 is next to them", ["woman1.webp", "cat_window.webp", "bird.webp"]]
-]
+        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)
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 1200px;
+
+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 }, '*');
 }
-.gallery-container img{
-    object-fit: contain;
+"""
+
+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>
 """
 
-with gr.Blocks() as demo:
+# ==========================================
+# 6. ساخت رابط کاربری (Gradio Blocks)
+# ==========================================
+
+with gr.Blocks(css=css_code) 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(f"""# FLUX.2 [dev]
-FLUX.2 [dev] is a 32B model rectified flow capable of generating, editing and combining images based on text instructions model [[model](https://huggingface.co/black-forest-labs/FLUX.2-dev)], [[blog](https://bfl.ai/blog/flux-2)]
-        """)
+        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="Prompt",
-                        show_label=False,
-                        max_lines=2,
-                        placeholder="Enter your prompt",
-                        container=False,
-                        scale=3
+                        label="توصیف تصویر (به فارسی)",
+                        show_label=True,
+                        max_lines=3,
+                        placeholder="یک منظره زیبا از...",
+                        rtl=True
                     )
-                    
-                    run_button = gr.Button("Run", scale=1)
-                    
-                with gr.Accordion("Input image(s) (optional)", open=True):
+                
+                with gr.Accordion("بارگذاری تصویر (اختیاری برای ویرایش/ایده)", open=False):
                     input_images = gr.Gallery(
-                        label="Input Image(s)",
+                        label="تصاویر ورودی",
                         type="pil",
                         columns=3,
                         rows=1,
+                        height=200
                     )
                 
-                with gr.Accordion("Advanced Settings", open=False):
-                    prompt_upsampling = gr.Checkbox(
-                        label="Prompt Upsampling",
-                        value=True,
-                        info="Automatically enhance the prompt using a VLM"
-                    )
-        
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0,
-                    )
-                    
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    
-                    with gr.Row():
-                        
-                        width = gr.Slider(
-                            label="Width",
-                            minimum=256,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=8,
-                            value=1024,
-                        )
-                        
-                        height = gr.Slider(
-                            label="Height",
-                            minimum=256,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=8,
-                            value=1024,
-                        )
-                    
-                    with gr.Row():
-                        
-                        num_inference_steps = gr.Slider(
-                            label="Number of inference steps",
-                            minimum=1,
-                            maximum=100,
-                            step=1,
-                            value=30,
-                        )
-                        
-                        guidance_scale = gr.Slider(
-                            label="Guidance scale",
-                            minimum=0.0,
-                            maximum=10.0,
-                            step=0.1,
-                            value=4,
-                        )
+                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.Column():
-                result = gr.Image(label="Result", show_label=False)
-            
-        
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples=True,
-            cache_mode="lazy"
-        )
+                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)
 
-        gr.Examples(
-            examples=examples_images,
-            fn=infer,
-            inputs=[prompt, input_images],
-            outputs=[result, seed],
-            cache_examples=True,
-            cache_mode="lazy"
-        )
+            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")
 
-    # Auto-update dimensions when images are uploaded
+    # اتصال رویدادها
+    
+    # 1. آپدیت ابعاد بر اساس تصویر آپلودی
     input_images.upload(
         fn=update_dimensions_from_image,
         inputs=[input_images],
         outputs=[width, height]
     )
 
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+    # 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],
-        outputs=[result, seed]
+        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]
     )
 
-demo.launch(css=css)
+    # 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)