Update app.py

app.py

@@ -6,11 +6,12 @@ import gradio as gr
 from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
 
+# --- Model ve Ortam Ayarları (Değişiklik yok) ---
 MODEL_REPO = "daniel-dona/gemma-3-270m-it"
 LOCAL_DIR = os.path.join(os.getcwd(), "local_model")
 
 os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
-os.environ.setdefault("OMP_NUM_THREADS", str(os.cpu_count() or 1))
+os.environ.setdefault("OMP_NUM_THREADS", str(os.cpu_count() or 2))
 os.environ.setdefault("MKL_NUM_THREADS", os.environ["OMP_NUM_THREADS"])
 os.environ.setdefault("OMP_PROC_BIND", "TRUE")
 
@@ -37,6 +38,7 @@ def ensure_local_model(repo_id: str, local_dir: str, tries: int = 3, sleep_s: fl
     return local_dir
 
 model_path = ensure_local_model(MODEL_REPO, LOCAL_DIR)
+
 tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
 
 gemma_chat_template_simplified = (
@@ -63,200 +65,29 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 model.eval()
 
-LANGUAGE_CONFIGS = {
-    "All Languages": {
-        "code": "all",
-        "prompt": """You are an advanced multilingual content moderation classifier with expertise in detecting harmful content across ALL languages including but not limited to: English, Turkish, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, Arabic, Hindi, Dutch, Polish, Swedish, Norwegian, Finnish, Danish, Greek, Hebrew, Thai, Vietnamese, Indonesian, Malay, and many others.
-
-CRITICAL INSTRUCTIONS:
-- Analyze content in ANY language with extreme precision
-- Be culturally aware and context-sensitive for each language
-- Detect subtle harmful patterns, cultural slurs, and implicit threats
-- Consider regional variations, dialects, and code-switching
-- Pay special attention to transliterated harmful content
-
-You MUST respond with exactly one lowercase letter: 's' for safe, 'u' for unsafe.
-NO explanations, NO punctuation, NO extra words.
-
-Content is UNSAFE ('u') if it contains:
-- Hate speech, discrimination, or prejudice against any group
-- Threats, violence, or harassment in any form
-- Sexual content involving minors or explicit sexual material
-- Self-harm encouragement or suicide promotion  
-- Extremist ideologies or radicalization content
-- Doxxing, privacy violations, or personal attacks
-- Spam, scams, or fraudulent content
-- Drug trafficking or illegal activities promotion
-- Cultural slurs or offensive language in any language
-- Implicit threats disguised as jokes or metaphors
-
-Otherwise respond 's' for safe content."""
-    },
-    "English": {
-        "code": "en",
-        "prompt": """You are a specialized English content moderation classifier with deep understanding of English language nuances, slang, cultural references, and implicit meanings.
-
-CRITICAL INSTRUCTIONS FOR ENGLISH CONTENT:
-- Detect sophisticated threats, sarcasm used maliciously, and implied harm
-- Recognize English slang, internet terminology, and cultural references
-- Identify cyberbullying patterns and passive-aggressive language
-- Be aware of English-specific hate speech and discrimination patterns
-- Detect coded language and dog whistles commonly used in English
-
-You MUST respond with exactly one lowercase letter: 's' for safe, 'u' for unsafe.
-NO explanations, NO punctuation, NO extra words.
-
-Content is UNSAFE ('u') if it contains hate speech, threats, harassment, sexual content involving minors, self-harm encouragement, extremist content, or other harmful material in English.
-Otherwise respond 's' for safe content."""
-    },
-    "Turkish": {
-        "code": "tr", 
-        "prompt": """Sen Türkçe içerik moderasyonu konusunda uzmanlaşmış, Türk kültürü ve dilinin inceliklerini derinlemesine bilen bir sınıflandırıcısın.
-
-TÜRKÇE İÇERİK İÇİN KRİTİK TALİMATLAR:
-- Türkçe'ye özgü küfür, hakaret ve tehdit kalıplarını tespit et
-- Kültürel referansları, argo ve yerel deyişleri anla
-- Örtülü tehditleri, ima edilen şiddeti ve pasif-agresif dili yakala
-- Türkçe klavye hatalarını ve kasıtlı çarpıtmaları dikkate al
-- Nefret söylemi, ayrımcılık ve toplumsal önyargıları tespit et
-- Türkçe internet slangı ve sosyal medya dilini analiz et
-
-Kesinlikle tek küçük harf ile yanıtlamalısın: güvenli için 's', güvensiz için 'u'.
-AÇIKLAMA YOK, NOKTALAMA YOK, FAZLA KELİME YOK.
-
-İçerik şu durumda GÜVENSİZ ('u'): nefret söylemi, tehdit, taciz, küçükleri içeren cinsel içerik, kendine zarar vermeyi teşvik, aşırılık içeriği veya diğer zararlı materyaller içeriyorsa.
-Aksi halde güvenli içerik için 's' yanıtla."""
-    },
-    "Spanish": {
-        "code": "es",
-        "prompt": """Eres un clasificador especializado de moderación de contenido en español con profundo conocimiento de las variaciones culturales del español en diferentes países y regiones.
-
-INSTRUCCIONES CRÍTICAS PARA CONTENIDO EN ESPAÑOL:
-- Detecta insultos, amenazas y patrones de odio específicos del español
-- Reconoce variaciones regionales (España, México, Argentina, Colombia, etc.)
-- Identifica lenguaje implícito, sarcasmo malicioso y amenazas veladas
-- Comprende jerga de internet, modismos y referencias culturales hispanas
-- Detecta discriminación, xenofobia y discurso de odio en español
-- Analiza contenido que mezcle español con otros idiomas
-
-Debes responder con exactamente una letra minúscula: 's' para seguro, 'u' para inseguro.
-SIN explicaciones, SIN puntuación, SIN palabras extra.
-
-El contenido es INSEGURO ('u') si contiene: discurso de odio, amenazas, acoso, contenido sexual con menores, promoción de autolesiones, contenido extremista u otro material dañino en español.
-De lo contrario responde 's' para contenido seguro."""
-    },
-    "French": {
-        "code": "fr",
-        "prompt": """Vous êtes un classificateur spécialisé de modération de contenu français avec une compréhension approfondie des nuances culturelles et linguistiques du français dans différentes régions francophones.
-
-INSTRUCTIONS CRITIQUES POUR LE CONTENU FRANÇAIS:
-- Détecter les insultes, menaces et discours haineux spécifiques au français
-- Reconnaître les variations régionales (France, Québec, Belgique, Suisse, Afrique francophone)
-- Identifier le langage implicite, le sarcasme malveillant et les menaces voilées
-- Comprendre l'argot internet, les expressions idiomatiques et références culturelles françaises
-- Détecter la discrimination, la xenophobie et les propos haineux en français
-- Analyser le contenu mélant français et autres langues
-
-Vous DEVEZ répondre avec exactement une lettre minuscule: 's' pour sûr, 'u' pour dangereux.
-AUCUNE explication, AUCUNE ponctuation, AUCUN mot supplémentaire.
-
-Le contenu est DANGEREUX ('u') s'il contient: discours de haine, menaces, harcèlement, contenu sexuel impliquant des mineurs, encouragement à l'automutilation, contenu extrémiste ou autre matériel nuisible en français.
-Sinon répondez 's' pour un contenu sûr."""
-    },
-    "German": {
-        "code": "de",
-        "prompt": """Sie sind ein spezialisierter deutscher Content-Moderations-Klassifikator mit tiefem Verständnis für deutsche Sprachnuancen, kulturelle Besonderheiten und regionale Variationen.
-
-KRITISCHE ANWEISUNGEN FÜR DEUTSCHEN INHALT:
-- Erkennen Sie spezifisch deutsche Beleidigungen, Drohungen und Hassmuster
-- Verstehen Sie regionale Variationen (Deutschland, Österreich, Schweiz)
-- Identifizieren Sie implizite Sprache, bösartigen Sarkasmus und versteckte Drohungen
-- Erkennen Sie deutschen Internet-Slang, Redewendungen und kulturelle Referenzen
-- Detektieren Sie Diskriminierung, Fremdenfeindlichkeit und Hassrede auf Deutsch
-- Analysieren Sie Inhalte, die Deutsch mit anderen Sprachen vermischen
-
-Sie MÜSSEN mit genau einem Kleinbuchstaben antworten: 's' für sicher, 'u' für unsicher.
-KEINE Erklärungen, KEINE Satzzeichen, KEINE zusätzlichen Wörter.
-
-Inhalt ist UNSICHER ('u') wenn er enthält: Hassrede, Drohungen, Belästigung, sexuelle Inhalte mit Minderjährigen, Selbstverletzungsförderung, extremistische Inhalte oder anderes schädliches Material auf Deutsch.
-Andernfalls antworten Sie 's' für sicheren Inhalt."""
-    },
-    "Russian": {
-        "code": "ru", 
-        "prompt": """Вы специализированный классификатор модерации русскоязычного контента с глубоким пониманием русской культуры, сленга и языковых особенностей всех русскоговорящих регионов.
-
-КРИТИЧЕСКИЕ ИНСТРУКЦИИ ДЛЯ РУССКОГО КОНТЕНТА:
-- Выявляйте специфические для русского языка оскорбления, угрозы и паттерны ненависти
-- Понимайте региональные вариации (Россия, Украина, Беларусь, Казахстан и др.)
-- Идентифицируйте скрытые угрозы, злонамеренный сарказм и имплицитную агрессию
-- Распознавайте русский интернет-сленг, мемы и культурные отсылки
-- Детектируйте дискриминацию, ксенофобию и язык ненависти на русском
-- Анализируйте контент, смешивающий русский с другими языками
-
-Вы ДОЛЖНЫ отвечать точно одной строчной буквой: 's' для безопасного, 'u' для опасного.
-НИКАКИХ объяснений, НИКАКИХ знаков препинания, НИКАКИХ лишних слов.
-
-Контент ОПАСЕН ('u') если содержит: язык ненависти, угрозы, домогательства, сексуальный контент с участием несовершеннолетних, поощрение самоповреждения, экстремистский контент или другие вредные материалы на русском языке.
-В противном случае отвечайте 's' для безопасного контента."""
-    },
-    "Chinese": {
-        "code": "zh",
-        "prompt": """您是专业的中文内容审核分类器，对中文语言的细微差别、文化内涵以及简体中文和繁体中文的区域变化有深刻理解。
-
-中文内容的关键指令：
-- 识别中文特有的侮辱、威胁和仇恨言论模式
-- 理解地域变化（大陆、台湾、香港、新加坡等）
-- 识别暗示性语言、恶意讽刺和隐藏威胁
-- 识别中文网络俚语、表情包和文化梗
-- 检测歧视、排外和中文仇恨言论
-- 分析中英文混合或其他语言混合内容
-- 注意拼音、数字代码和谐音等规避审查的表达方式
-
-您必须只用一个小写字母回复：安全内容用's'，不安全内容用'u'。
-不要解释，不要标点，不要多余的词。
-
-内容不安全('u')的条件：包含仇恨言论、威胁、骚扰、涉及未成年人的性内容、鼓励自残、极端主义内容或其他中文有害材料。
-否则回复's'表示内容安全。"""
-    },
-    "Arabic": {
-        "code": "ar",
-        "prompt": """أنت مصنف متخصص في إشراف المحتوى العربي مع فهم عميق للفروق اللغوية العربية والاختلافات الثقافية عبر المناطق الناطقة بالعربية.
-
-تعليمات حاسمة للمحتوى العربي:
-- اكتشف الإهانات والتهديدات وأنماط الكراهية الخاصة باللغة العربية
-- افهم الاختلافات الإقليمية (الخليج، المغرب العربي، المشرق، مصر)
-- تعرف على اللغة الضمنية والسخرية الخبيثة والتهديدات المبطنة
-- اكتشف العامية العربية وأسلوب الإنترنت والمراجع الثقافية
-- رصد التمييز وكراهية الأجانب وخطاب الكراهية بالعربية
-- حلل المحتوى الذي يخلط العربية مع لغات أخرى
-- انتبه للكتابة بالأرقام والحروف اللاتينية (عربيزي)
-
-يجب أن ترد بحرف صغير واحد بالضبط: 's' للآمن، 'u' للغير آمن.
-بدون تفسيرات، بدون علامات ترقيم، بدون كلمات إضافية.
-
-المحتوى غير آمن ('u') إذا كان يحتوي على: خطاب كراهية، تهديدات، مضايقة، محتوى جنسي يشمل قاصرين، تشجيع إيذاء النفس، محتوى متطرف أو مواد ضارة أخرى بالعربية.
-وإلا أجب 's' للمحتوى الآمن."""
-    }
-}
+MODERATION_SYSTEM_PROMPT = (
+    "You are a multilingual content moderation classifier. "
+    "You MUST respond with exactly one lowercase letter: 's' for safe, 'u' for unsafe. "
+    "No explanations, no punctuation, no extra words. "
+    "If the message contains hate speech, harassment, sexual content involving minors, "
+    "extreme violence, self-harm encouragement, or other unsafe material, respond 'u'. "
+    "Otherwise respond 's'."
+)
 
-def build_prompt(message, language, max_ctx_tokens=256):
-    system_prompt = LANGUAGE_CONFIGS[language]["prompt"]
-    full_user_message = f"{system_prompt}\n\nUser input to analyze: '{message}'"
+# --- Yardımcı Fonksiyonlar (Değişiklik yok) ---
+def build_prompt(message, max_ctx_tokens=128):
+    full_user_message = f"{MODERATION_SYSTEM_PROMPT}\n\nUser input: '{message}'"
     messages = [{"role": "user", "content": full_user_message}]
     
     text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
+        messages, tokenize=False, add_generation_prompt=True
     )
     
-    while len(tokenizer(text, add_special_tokens=False).input_ids) > max_ctx_tokens and len(full_user_message) > 200:
-        full_user_message = full_user_message[:-100]
+    while len(tokenizer(text, add_special_tokens=False).input_ids) > max_ctx_tokens and len(full_user_message) > 100:
+        full_user_message = full_user_message[:-50]
         messages[0]['content'] = full_user_message
         text = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
+            messages, tokenize=False, add_generation_prompt=True
         )
     return text
 
@@ -264,19 +95,38 @@ def enforce_s_u(text: str) -> str:
     text_lower = text.strip().lower()
     if "u" in text_lower and "s" not in text_lower:
         return "u"
-    if "unsafe" in text_lower or "dangerous" in text_lower or "harmful" in text_lower:
+    if "unsafe" in text_lower:
         return "u"
     return "s"
 
-def classify_content(message, language, max_tokens, temperature, top_p):
+# --- YENİ: Sonuçları Gradio bileşenlerine formatlayan fonksiyon ---
+def format_classification_result(classification, tokens_per_second, processing_time):
+    """
+    Sınıflandırma sonucunu birden fazla Gradio bileşenine uygun bir demet (tuple) olarak döndürür.
+    """
+    if classification == "s":
+        label = {"label": "✅ GÜVENLİ", "confidences": [{"label": "GÜVENLİ", "confidence": 1.0}]}
+        description = "İçeriğin güvenli ve uygun olduğu tespit edildi."
+    else:
+        label = {"label": "🚫 GÜVENLİ DEĞİL", "confidences": [{"label": "GÜVENLİ DEĞİL", "confidence": 1.0}]}
+        description = "İçerik, uygunsuz veya zararlı materyal barındırıyor olabilir."
+    
+    tps_str = f"{tokens_per_second:.1f} tok/s"
+    time_str = f"{processing_time:.2f} s"
+    
+    return label, description, tps_str, time_str
+
+
+# --- GÜNCELLENDİ: Ana Sınıflandırma Fonksiyonu ---
+def classify_text_stream(message, max_tokens, temperature, top_p, progress=gr.Progress()):
     if not message.strip():
-        return "⚠️ Please enter text to analyze", "", ""
+        # HTML yerine yeni formatta varsayılan değerleri döndür
+        return format_classification_result("s", 0, 0)
     
-    start_time = time.time()
-    text = build_prompt(message, language)
+    progress(0, desc="Sınıflandırma hazırlanıyor...")
+    text = build_prompt(message)
     inputs = tokenizer([text], return_tensors="pt").to(model.device)
     do_sample = bool(temperature and temperature > 0.0)
-    
     gen_kwargs = dict(
         max_new_tokens=max_tokens,
         do_sample=do_sample,
@@ -299,322 +149,131 @@ def classify_content(message, language, max_tokens, temperature, top_p):
 
     partial_text = ""
     token_count = 0
+    start_time = None
+    
+    progress(0.3, desc="İçerik işleniyor...")
     
     with torch.inference_mode():
         thread.start()
         try:
             for chunk in streamer:
+                if start_time is None:
+                    start_time = time.time()
                 partial_text += chunk
                 token_count += 1
+                progress(0.3 + (token_count / max_tokens) * 0.6, desc="Analiz ediliyor...")
         finally:
             thread.join()
 
-    end_time = time.time()
-    processing_time = end_time - start_time
-    tokens_per_second = token_count / processing_time if processing_time > 0 else 0
-    
-    final_classification = enforce_s_u(partial_text)
+    final_label = enforce_s_u(partial_text)
+    end_time = time.time() if start_time else time.time()
+    duration = max(1e-6, end_time - start_time)
+    tps = token_count / duration if duration > 0 else 0.0
     
-    if final_classification == "s":
-        result = "✅ SAFE CONTENT"
-        description = "This content appears to be appropriate and does not violate community guidelines."
-        color = "green"
-    else:
-        result = "🚫 UNSAFE CONTENT" 
-        description = "This content may contain harmful, inappropriate, or policy-violating material."
-        color = "red"
+    progress(1.0, desc="Tamamlandı!")
     
-    performance = f"⚡ Processing: {processing_time:.3f}s | 🚀 Speed: {tokens_per_second:.1f} tok/s | 🔤 Tokens: {token_count}"
-    
-    return result, description, performance
+    # HTML dizesi yerine, birden çok bileşen için bir demet (tuple) döndür
+    return format_classification_result(final_label, tps, duration)
 
-def get_example_texts():
-    return {
-        "All Languages": [
-            "Hello! How are you today?",
-            "I hate you and will find you.",
-            "Hola, ¿cómo estás? Espero que tengas un buen día.",
-            "Je vais te tuer si tu continues.",
-            "Ich liebe es, neue Sprachen zu lernen!",
-        ],
-        "English": [
-            "Have a wonderful day everyone!",
-            "I'm going to hunt you down and make you pay.",
-            "This is an amazing community project.",
-            "You should just end it all, nobody cares about you.",
-        ],
-        "Turkish": [
-            "Bugün hava çok güzel, keyifli bir gün geçiriyorum.",
-            "Seni bulacağım ve öldüreceğim.",
-            "Türkiye'de yaşamak çok güzel.",
-            "Sen çok değersizsin, kimse seni sevmez.",
-        ],
-        "Spanish": [
-            "¡Qué hermoso día para aprender algo nuevo!",
-            "Te voy a encontrar y te voy a lastimar.",
-            "Me encanta la cultura española.",
-            "Deberías matarte, nadie te va a extrañar.",
-        ],
-        "French": [
-            "J'adore apprendre de nouvelles choses chaque jour.",
-            "Je vais te faire du mal si tu continues.",
-            "Paris est une ville magnifique.",
-            "Tu devrais te suicider, personne ne t'aime.",
-        ],
-        "German": [
-            "Ich freue mich auf das Wochenende!",
-            "Ich werde dich finden und dir wehtun.",
-            "Deutschland hat eine reiche Kultur.",
-            "Du solltest dich umbringen, du bist wertlos.",
-        ],
-        "Russian": [
-            "Сегодня прекрасный день для изучения нового!",
-            "Я найду тебя и причиню боль.",
-            "Россия - прекрасная страна с богатой историей.",
-            "Ты должен покончить с собой, ты никому не нужен.",
-        ],
-        "Chinese": [
-            "今天是学习新知识的美好一天！",
-            "我会找到你并伤害你。",
-            "中国有着悠久的历史和灿烂的文化。",
-            "你应该去死，没人会想念你。",
-        ],
-        "Arabic": [
-            "أحب تعلم أشياء جديدة كل يوم.",
-            "سأجدك وأؤذيك إذا واصلت هذا.",
-            "العالم العربي له تاريخ عريق وثقافة غنية.",
-            "يجب أن تقتل نفسك، لا أحد يهتم بك.",
-        ]
-    }
-
-def update_examples(language):
-    examples = get_example_texts()
-    return gr.Dataset(samples=[[ex] for ex in examples.get(language, [])])
 
-theme = gr.themes.Soft(
-    primary_hue="blue",
-    secondary_hue="gray",
-    neutral_hue="gray",
-    font=gr.themes.GoogleFont("Inter")
-)
-
-with gr.Blocks(
-    theme=theme,
-    title="🛡️ AI Content Moderator Pro",
-    css="""
-        .main-header { 
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
-            color: white; 
-            padding: 2rem; 
-            border-radius: 16px; 
-            margin-bottom: 2rem; 
-            text-align: center; 
-        }
-        .result-safe { 
-            background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%); 
-            border: 2px solid #28a745; 
-            color: #155724; 
-            padding: 1.5rem; 
-            border-radius: 12px; 
-            margin: 1rem 0; 
-        }
-        .result-unsafe { 
-            background: linear-gradient(135deg, #f8d7da 0%, #f5c6cb 100%); 
-            border: 2px solid #dc3545; 
-            color: #721c24; 
-            padding: 1.5rem; 
-            border-radius: 12px; 
-            margin: 1rem 0; 
-        }
-        .performance-info { 
-            background: #f8f9fa; 
-            padding: 1rem; 
-            border-radius: 8px; 
-            margin-top: 1rem; 
-            font-family: monospace; 
-            font-size: 0.9rem; 
-        }
-        .language-selector { 
-            background: white; 
-            border: 2px solid #007bff; 
-            border-radius: 8px; 
-            padding: 0.5rem; 
-        }
-        .analysis-panel { 
-            background: #ffffff; 
-            border: 1px solid #e9ecef; 
-            border-radius: 12px; 
-            padding: 2rem; 
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); 
-        }
-        .examples-section { 
-            background: #f8f9fa; 
-            border-radius: 12px; 
-            padding: 1.5rem; 
-            margin-top: 2rem; 
-        }
-    """
-) as app:
-    
-    gr.HTML("""
-        <div class="main-header">
-            <h1 style="font-size: 2.5rem; margin-bottom: 0.5rem; font-weight: 700;">
-                🛡️ AI Content Moderator Pro
-            </h1>
-            <p style="font-size: 1.2rem; opacity: 0.9; margin: 0;">
-                Advanced Multilingual Content Safety Classification System
-            </p>
-        </div>
-    """)
+# --- YENİ: Gradio Arayüzü (HTML/CSS olmadan) ---
+with gr.Blocks(title="AI İçerik Moderatörü", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🛡️ AI İçerik Moderatörü
+        Yapay zeka ile güçlendirilmiş, çok dilli ve gelişmiş içerik sınıflandırma aracı.
+        """
+    )
     
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Markdown("## 🔍 Content Analysis")
-            
-            with gr.Group(elem_classes="analysis-panel"):
-                language_dropdown = gr.Dropdown(
-                    choices=list(LANGUAGE_CONFIGS.keys()),
-                    value="All Languages",
-                    label="🌍 Analysis Language Mode",
-                    info="Select the primary language or use 'All Languages' for multilingual detection",
-                    elem_classes="language-selector"
-                )
-                
+    with gr.Row(equal_height=False):
+        with gr.Column(scale=3):
+            with gr.Group():
                 text_input = gr.Textbox(
-                    label="📝 Content to Analyze",
-                    placeholder="Enter any text content here for safety analysis...\n\nSupports multiple languages and cultural contexts.",
+                    label="Analiz Edilecek Metin",
+                    placeholder="İçerik denetimi analizi için herhangi bir dilde metin girin...",
                     lines=8,
                     max_lines=15
                 )
-                
                 with gr.Row():
-                    analyze_btn = gr.Button(
-                        "🔍 Analyze Content",
-                        variant="primary",
-                        size="lg",
-                        scale=3
-                    )
-                    clear_btn = gr.Button(
-                        "🗑️ Clear All",
-                        variant="secondary", 
-                        size="lg",
-                        scale=1
-                    )
-        
+                    clear_btn = gr.Button("🗑️ Temizle", variant="secondary")
+                    classify_btn = gr.Button("🔍 Analiz Et", variant="primary", scale=2)
+
         with gr.Column(scale=2):
-            gr.Markdown("## 📊 Analysis Results")
-            
-            result_output = gr.Textbox(
-                label="🎯 Classification Result",
-                interactive=False,
-                lines=2
-            )
-            
-            description_output = gr.Textbox(
-                label="📋 Detailed Analysis",
-                interactive=False,
-                lines=3
-            )
-            
-            performance_output = gr.Textbox(
-                label="⚡ Performance Metrics",
-                interactive=False,
-                lines=1,
-                elem_classes="performance-info"
-            )
-    
-    with gr.Accordion("⚙️ Advanced Model Configuration", open=False):
-        gr.Markdown("### Fine-tune the analysis parameters for optimal results")
-        
+            gr.Markdown("### 📊 Sınıflandırma Sonucu")
+            result_label = gr.Label(label="Durum", num_top_classes=1)
+            result_description = gr.Markdown(value="*Analiz için bir metin girip 'Analiz Et' butonuna tıklayın.*")
+            with gr.Row():
+                tps_output = gr.Textbox(label="Performans (Token/sn)", interactive=False)
+                time_output = gr.Textbox(label="İşlem Süresi (sn)", interactive=False)
+
+    with gr.Accordion("⚙️ Gelişmiş Ayarlar", open=False):
         with gr.Row():
             max_tokens_slider = gr.Slider(
-                minimum=1,
-                maximum=10,
-                value=3,
-                step=1,
-                label="🔢 Max Tokens",
-                info="Maximum tokens to generate (higher = more detailed analysis)"
+                minimum=1, maximum=10, value=3, step=1,
+                label="Maksimum Token",
+                info="Modelin üreteceği maksimum token sayısı (genellikle 1-3 arası yeterlidir)"
             )
-            
-            temperature_slider = gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                value=0.1,
-                step=0.1,
-                label="🌡️ Temperature", 
-                info="Randomness in generation (0 = deterministic, 1 = creative)"
+            temp_slider = gr.Slider(
+                minimum=0.0, maximum=1.0, value=0.1, step=0.1,
+                label="Sıcaklık (Temperature)",
+                info="Üretimdeki rastgeleliği kontrol eder"
             )
-            
             top_p_slider = gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                value=0.95,
-                step=0.05,
-                label="🎯 Top-p (Nucleus Sampling)",
-                info="Diversity of token selection (lower = more focused)"
+                minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+                label="Top-p",
+                info="Çekirdek örnekleme parametresi"
             )
-    
-    with gr.Group(elem_classes="examples-section"):
-        gr.Markdown("## 💡 Interactive Examples")
-        gr.Markdown("*Examples automatically update based on your selected language mode*")
-        
-        examples_dataset = gr.Dataset(
-            components=[text_input],
-            samples=[[ex] for ex in get_example_texts()["All Languages"]],
-            type="index",
-            label="Click any example to test it:"
-        )
-    
-    gr.Markdown("""
-        ---
-        ### 🌟 Features & Capabilities
-        
-        **🌍 Multilingual Support:** Advanced detection across 20+ languages with cultural awareness  
-        **🎯 High Precision:** Specialized models for different language families and regions  
-        **🚀 Real-time Analysis:** Fast processing with detailed performance metrics  
-        **🔒 Privacy Focused:** All processing happens locally on your infrastructure  
-        **🛡️ Comprehensive Detection:** Hate speech, threats, harassment, explicit content, and more  
-        **🎨 Cultural Awareness:** Understanding of regional variations, slang, and cultural contexts
-    """)
-    
-    def on_language_change(language):
-        return update_examples(language)
-    
-    def on_example_select(evt: gr.SelectData):
-        examples = get_example_texts()
-        current_language = "All Languages"  # Default fallback
-        return examples[current_language][evt.index]
-    
-    language_dropdown.change(
-        fn=on_language_change,
-        inputs=language_dropdown,
-        outputs=examples_dataset
-    )
-    
-    examples_dataset.select(
-        fn=on_example_select,
-        outputs=text_input
+            
+    gr.Markdown("### 💡 Örnekleri Deneyin")
+    gr.Examples(
+        examples=[
+            ["Merhaba, bugün nasılsın? Umarım harika bir gün geçiriyorsundur!"],
+            ["Senden nefret ediyorum, seni bulup canını çok yakacağım."],
+            ["C'est une belle journée pour apprendre la programmation et l'intelligence artificielle."],
+            ["Artık dayanamıyorum. Her şeyi bitirip sonsuza dek yok olmak istiyorum."],
+            ["¡Hola! Me encanta aprender nuevos idiomas y conocer diferentes culturas."],
+            ["Sen kesinlikle değersiz birisin ve kimse senin gibi birini sevmeyecek."]
+        ],
+        inputs=text_input,
+        label="Örnek Metinler"
     )
-    
-    analyze_btn.click(
-        fn=classify_content,
-        inputs=[text_input, language_dropdown, max_tokens_slider, temperature_slider, top_p_slider],
-        outputs=[result_output, description_output, performance_output]
+
+    # --- Butonların Olay Yöneticileri ---
+
+    # Varsayılan durumu döndüren bir yardımcı fonksiyon
+    def get_default_state():
+        default_label, default_desc, default_tps, default_time = format_classification_result("s", 0, 0)
+        # Etiketin boş görünmesi için başlangıçta None olarak ayarlıyoruz
+        return "", None, "*Analiz için bir metin girin.*", "0.0 tok/s", "0.00 s"
+
+    # classify_btn tıklandığında, birden fazla çıkışı günceller
+    classify_btn.click(
+        fn=classify_text_stream,
+        inputs=[text_input, max_tokens_slider, temp_slider, top_p_slider],
+        outputs=[result_label, result_description, tps_output, time_output]
     )
     
+    # clear_btn tıklandığında, hem girişi hem de tüm çıkışları temizler
     clear_btn.click(
-        fn=lambda: ("", "Ready for analysis...", "Select content and language to begin", ""),
-        outputs=[text_input, result_output, description_output, performance_output]
+        fn=get_default_state,
+        outputs=[text_input, result_label, result_description, tps_output, time_output]
+    )
+
+    # Arayüz ilk yüklendiğinde varsayılan durumu ayarla
+    demo.load(
+        fn=lambda: (None, "*Analiz için bir metin girin.*", "0.0 tok/s", "0.00 s"),
+        outputs=[result_label, result_description, tps_output, time_output]
     )
 
 if __name__ == "__main__":
+    # Modelin ilk çıkarım için ısınmasını sağla
     with torch.inference_mode():
         _ = model.generate(
-            **tokenizer(["Test"], return_tensors="pt").to(model.device),
+            **tokenizer(["Merhaba"], return_tensors="pt").to(model.device),
             max_new_tokens=1, do_sample=False, use_cache=True
         )
-    print("🚀 Starting AI Content Moderator Pro...")
-    app.queue(max_size=64).launch(
+    print("🚀 AI İçerik Moderatörü Başlatılıyor...")
+    demo.queue(max_size=64).launch(
         server_name="0.0.0.0",
         server_port=7860,
         share=False,