app/services/models.py

# app/services/models.py - TO'LIQ YAXSHILANGAN VERSIYA
"""
AI Modellari (STT, TTS, LLM) bilan ishlash uchun funksiyalar

YAXSHILANISHLAR:
1. ✅ JSON Parsing - ROBUST va xatoliklarga bardoshli
2. ✅ Multi-language - To'liq 3 til qo'llab-quvvatlash (uzb, eng, rus)
3. ✅ TTS - Speed o'chirildi, faqat til parametri
4. ✅ Error handling - Hamma joyda try-except
5. ✅ Fallback responses - Xatolik bo'lsa default javob qaytarish
"""

import subprocess
import numpy as np
import soundfile as sf
import io
import os
import torch
import torchaudio 
import google.generativeai as genai
import logging
import json
import re
from typing import Optional, Generator, Dict
from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor

from app.core.config import GEMINI_API_KEY, SYSTEM_INSTRUCTION
from app.utils.translit import lotin_to_kirill, clean_cyrillic_text


# Logging sozlash
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- SOZLAMALAR ---
genai.configure(api_key=GEMINI_API_KEY)
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
TORCH_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32

# Model holati tekshirish
MODELS = {}
MODEL_STATUS = {
    "stt": False,
    "tts_uzb": False,
    "tts_eng": False,
    "tts_rus": False,
    "llm": False
}

# Audio format validatsiyasi
SUPPORTED_AUDIO_FORMATS = {
    'webm': 'audio/webm',
    'wav': 'audio/wav',
    'mp3': 'audio/mpeg',
    'ogg': 'audio/ogg',
    'm4a': 'audio/mp4'
}

MAX_AUDIO_SIZE = 100 * 1024 * 1024  # 100MB
MIN_AUDIO_DURATION = 0.5  # 0.5 sekund
MAX_AUDIO_DURATION = 300  # 5 minut


# ==================== MODEL YUKLASH ====================

def load_models():
    """Barcha modellarni yuklab, statuslarini yangilaydi"""
    logger.info("🚀 Modellar Hugging Face Hub'dan yuklanmoqda...")
    
    # ========== STT MODELI ==========
    try:
        logger.info("   📥 STT (islomov/rubaistt_v2_medium) modeli yuklanmoqda...")
        stt_model_id = "islomov/rubaistt_v2_medium"
        
        stt_model = AutoModelForSpeechSeq2Seq.from_pretrained(
            stt_model_id, 
            torch_dtype=TORCH_DTYPE, 
            low_cpu_mem_usage=True, 
            use_safetensors=True
        )
        stt_model.to(DEVICE)
        stt_processor = AutoProcessor.from_pretrained(stt_model_id)
        
        MODELS["stt_pipeline"] = pipeline(
            "automatic-speech-recognition",
            model=stt_model,
            tokenizer=stt_processor.tokenizer,
            feature_extractor=stt_processor.feature_extractor,
            max_new_tokens=128,
            torch_dtype=TORCH_DTYPE,
            device=DEVICE,
        )
        
        MODEL_STATUS["stt"] = True
        logger.info("   ✅ STT modeli tayyor")
        
    except Exception as e:
        logger.error(f"   ❌ STT modelini yuklashda xatolik: {e}")
        MODEL_STATUS["stt"] = False

    # ========== TTS MODELLARI (3 ta: uzb, eng, rus) ==========
    
    # TTS O'ZBEKCHA
    try:
        logger.info("   🎧 TTS O'ZBEKCHA (facebook/mms-tts-uzb-script_cyrillic) modeli yuklanmoqda...")
        tts_model_path = "facebook/mms-tts-uzb-script_cyrillic"
        
        MODELS["tts_uzb_pipeline"] = pipeline(
            "text-to-speech",
            model=tts_model_path,
            device=DEVICE
        )
        
        MODEL_STATUS["tts_uzb"] = True
        logger.info("   ✅ TTS O'ZBEK modeli tayyor")
        
    except Exception as e:
        logger.error(f"   ❌ TTS O'ZBEK modelini yuklashda xatolik: {e}")
        MODEL_STATUS["tts_uzb"] = False

    # TTS INGLIZCHA
    try:
        logger.info("   🎧 TTS INGLIZCHA (facebook/mms-tts-eng) modeli yuklanmoqda...")
        tts_eng_path = "facebook/mms-tts-eng"
        
        MODELS["tts_eng_pipeline"] = pipeline(
            "text-to-speech",
            model=tts_eng_path,
            device=DEVICE
        )
        
        MODEL_STATUS["tts_eng"] = True
        logger.info("   ✅ TTS INGLIZ modeli tayyor")
        
    except Exception as e:
        logger.error(f"   ❌ TTS INGLIZ modelini yuklashda xatolik: {e}")
        MODEL_STATUS["tts_eng"] = False

    # TTS RUSCHA
    try:
        logger.info("   🎧 TTS RUSCHA (facebook/mms-tts-rus) modeli yuklanmoqda...")
        tts_rus_path = "facebook/mms-tts-rus"
        
        MODELS["tts_rus_pipeline"] = pipeline(
            "text-to-speech",
            model=tts_rus_path,
            device=DEVICE
        )
            
        MODEL_STATUS["tts_rus"] = True
        logger.info("   ✅ TTS RUS modeli tayyor")
        
    except Exception as e:
        logger.error(f"   ❌ TTS RUS modelini yuklashda xatolik: {e}")
        MODEL_STATUS["tts_rus"] = False

    # ========== LLM MODELI (Gemini) ==========
    try:
        logger.info("   🧠 LLM (Gemini) modeli yuklanmoqda...")
        # Sizning kodingizda "gemini-2.0-flash-exp" ishlatilgan ekan, shuni qoldiramiz
        MODELS["llm"] = genai.GenerativeModel("gemini-2.0-flash-exp") 
        MODEL_STATUS["llm"] = True
        logger.info("   ✅ LLM modeli tayyor")
        
    except Exception as e:
        logger.error(f"   ❌ LLM modelini yuklashda xatolik: {e}")
        MODEL_STATUS["llm"] = False

    # ========== NATIJA ==========
    if not any(MODEL_STATUS.values()):
        raise RuntimeError("❌ CRITICAL: Hech qanday model yuklanmadi. Loyiha ishlay olmaydi.")
    
    logger.info("=" * 60)
    logger.info("✅ Modellar yuklash yakunlandi:")
    for model_name, status in MODEL_STATUS.items():
        logger.info(f"   {model_name}: {'✅ Tayyor' if status else '❌ Yuklanmadi'}")
    logger.info("=" * 60)


def check_model_status() -> dict:
    """Model holatlarini qaytaradi"""
    return MODEL_STATUS.copy()


# ==================== TIL ANIQLASH ====================

def detect_language(text: str) -> str:
    """
    Matndan tilni aniqlaydi
    
    Args:
        text: Tahlil qilinadigan matn
        
    Returns:
        "uzb" | "eng" | "rus"
    """
    if not text or len(text.strip()) < 3:
        return "uzb"  # Default o'zbekcha
    
    text_lower = text.lower()
    
    # Ingliz tilining kalit so'zlari
    english_keywords = [
        'hello', 'help', 'my', 'heart', 'pain', 'can\'t', 'breathe', 
        'chest', 'head', 'stomach', 'feel', 'sick', 'please', 
        'i', 'am', 'the', 'and', 'have', 'is', 'it', 'hurts'
    ]
    
    # Rus tilining kalit so'zlari
    russian_keywords = [
        'привет', 'помогите', 'болит', 'сердце', 'голова', 'живот',
        'не могу', 'дышать', 'помощь', 'температура', 'у меня',
        'я', 'мне', 'очень', 'плохо'
    ]
    
    # O'zbek tilining kalit so'zlari
    uzbek_keywords = [
        'salom', 'assalomu', 'yordam', 'yurak', 'bosh', 'qorin',
        'og\'rig\'i', 'nafas', 'harorat', 'yomon', 'bemor',
        'menga', 'men', 'juda'
    ]
    
    # Kirill alifbosini tekshirish
    cyrillic_chars = sum(1 for c in text if '\u0400' <= c <= '\u04FF')
    total_chars = len([c for c in text if c.isalpha()])
    
    if total_chars > 0:
        cyrillic_ratio = cyrillic_chars / total_chars
        
        # Agar 50%+ kirill bo'lsa
        if cyrillic_ratio > 0.5:
            # Rus yoki o'zbek kirill
            rus_count = sum(1 for keyword in russian_keywords if keyword in text_lower)
            uzb_count = sum(1 for keyword in uzbek_keywords if keyword in text_lower)
            
            if rus_count > uzb_count:
                return "rus"
            else:
                return "uzb"
    
    # Lotin alifbosi - ingliz yoki o'zbek
    eng_count = sum(1 for keyword in english_keywords if keyword in text_lower)
    uzb_count = sum(1 for keyword in uzbek_keywords if keyword in text_lower)
    
    if eng_count > uzb_count and eng_count >= 2:
        return "eng"
    
    # Default: o'zbekcha
    return "uzb"


# ==================== STT (Speech-to-Text) ====================

def transcribe_audio_from_bytes(audio_bytes: bytes) -> str:
    """
    Xotiradagi audio baytlarni (WEBM, MP3, etc) qabul qilib,
    FFmpeg yordamida WAV formatiga o'giradi va matnga aylantiradi.
    
    YANGILANGAN: Ruscha transkripsiyani avtomatik kirilga o'tkazadi
    
    Args:
        audio_bytes: Audio baytlar
        
    Returns:
        Transkripsiya qilingan matn (ruscha bo'lsa kirill formatda)
    """
    logger.info(f"🎙️ Audio baytlar transkripsiya uchun qabul qilindi. Hajmi: {len(audio_bytes)} bayt")
    
    if not MODEL_STATUS["stt"]:
        logger.error("STT modeli yuklanmagan.")
        raise RuntimeError("STT modeli ishlamaydi")

    try:
        # 1-QADAM: FFmpeg yordamida formatni o'zgartirish (in-memory)
        # Biz FFmpeg'ga kiruvchi ma'lumotni stdin'dan olishni va
        # natijani stdout'ga 16kHz'li WAV formatida chiqarishni buyuramiz.
        ffmpeg_command = [
            "ffmpeg",
            "-i", "pipe:0",          # Kiruvchi ma'lumot standart kiritishdan (stdin)
            "-f", "wav",             # Chiquvchi format: WAV
            "-ac", "1",              # Kanallar soni: 1 (mono)
            "-ar", "16000",          # Chastota: 16000Hz (Whisper uchun standart)
            "pipe:1"                 # Chiquvchi ma'lumot standart chiqarishga (stdout)
        ]

        logger.info("FFmpeg bilan audio konvertatsiya boshlanmoqda...")
        process = subprocess.run(
            ffmpeg_command, 
            input=audio_bytes, 
            capture_output=True, 
            check=True
        )
        wav_audio_bytes = process.stdout
        logger.info(f"✅ FFmpeg muvaffaqiyatli yakunlandi. WAV hajmi: {len(wav_audio_bytes)} bayt.")

        # 2-QADAM: WAV baytlarini NumPy array'ga o'tkazish
        audio_stream = io.BytesIO(wav_audio_bytes)
        audio, sampling_rate = sf.read(audio_stream)
        logger.debug(f"WAV ma'lumot NumPy array'ga o'girildi. Shape: {audio.shape}, Rate: {sampling_rate}")

        # 3-QADAM: Whisper modeliga uzatish
        generate_kwargs = {"language": "uzbek", "task": "transcribe"}
        logger.info("🚀 Whisper modeliga transkripsiya uchun so'rov yuborilmoqda...")
        
        outputs = MODELS["stt_pipeline"](
            audio,
            chunk_length_s=30,
            generate_kwargs=generate_kwargs
        )
        result_text = outputs.get("text", "").strip()
        logger.info(f"✅ Transkripsiya yakunlandi. Natija: '{result_text}'")
        
        # ========== ✅ YANGI: RUSCHA KIRILGA O'TKAZISH ==========
        detected_lang = detect_language(result_text)
        
        if detected_lang == "rus":
            # Ruscha lotin → kirill konvertatsiya
            from app.utils.translit import russian_latin_to_cyrillic
            result_text_cyrillic = russian_latin_to_cyrillic(result_text)
            logger.info(f"🔄 Ruscha kirilga o'tkazildi: '{result_text_cyrillic}'")
            return result_text_cyrillic
        elif detected_lang == "uzb":
            # O'zbekcha - lotin qoldiramiz (kerak bo'lsa kirilga o'tkazish mumkin)
            return result_text
        else:
            # Ingliz yoki boshqa tillar - o'zgartirmasdan qaytarish
            return result_text

    except subprocess.CalledProcessError as e:
        # FFmpeg xatolik bersa, uni log'ga yozamiz
        logger.error(f"❌ FFmpeg xatoligi: {e.stderr.decode()}", exc_info=True)
        raise RuntimeError(f"FFmpeg audio konvertatsiya qila olmadi.")
    except Exception as e:
        logger.error(f"❌ STT transkripsiya (baytlardan) xatoligi: {e}", exc_info=True)
        raise e


def transcribe_audio(audio_path: str) -> Generator[str, None, None]:
    """
    Audio faylni o'qib, uni matnga aylantiradi
    
    Args:
        audio_path: Audio fayl yo'li
        
    Yields:
        str: Transkripsiya qilingan matn
    """
    try:
        logger.info(f"Fayldan audio o'qilmoqda: {audio_path}")
        with open(audio_path, "rb") as f:
            audio_bytes = f.read()
        
        text_piece = transcribe_audio_from_bytes(audio_bytes)
        
        if text_piece:
            yield text_piece
        else:
            yield "Ovoz aniqlanmadi"

    except FileNotFoundError as e:
        logger.error(f"❌ Fayl topilmadi: {e}")
        yield f"Fayl topilmadi: {str(e)}"
        
    except ValueError as e:
        logger.error(f"❌ Validatsiya xatoligi: {e}")
        yield f"Xatolik: {str(e)}"
        
    except Exception as e:
        logger.error(f"❌ Fayldan STT transkripsiya xatoligi: {e}", exc_info=True)
        yield f"Ovozni tanishda xatolik: {str(e)}"


# ==================== JSON PARSING (ROBUST) ====================

def extract_json_from_response(response_text: str) -> Dict:
    """
    LLM javobidan JSON'ni ajratib oladi (ROBUST va xatoliklarga bardoshli)
    
    VAZIFA-1: Bu funksiya Gemini'dan kelgan javobni har qanday formatda bo'lsa ham
    JSON'ga parse qilishga harakat qiladi. Agar parse qilib bo'lmasa, default javob qaytaradi.
    
    Args:
        response_text: Gemini'dan kelgan raw text
        
    Returns:
        Dict: Parse qilingan JSON yoki default response
    """
    try:
        # 1. To'g'ridan-to'g'ri parse qilishga harakat
        try:
            return json.loads(response_text)
        except json.JSONDecodeError:
            pass
        
        # 2. {...} qavslar ichini topishga harakat (nested brackets ham)
        json_match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', response_text, re.DOTALL)
        
        if json_match:
            json_str = json_match.group(0)
            try:
                return json.loads(json_str)
            except json.JSONDecodeError:
                pass
        
        # 3. ```json ... ``` code block ichini topishga harakat
        code_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', response_text, re.DOTALL)
        if code_block_match:
            json_str = code_block_match.group(1)
            try:
                return json.loads(json_str)
            except json.JSONDecodeError:
                pass
        
        # 4. Hech narsa topilmasa - default qaytarish
        logger.warning(f"⚠️ JSON topilmadi, default qaytarilyapti. Response: {response_text[:200]}...")
        return {
            "risk_level": "sariq",
            "response_text": "Kechirasiz, javobni qayta ishlashda muammo yuz berdi. Iltimos, boshqacha aytib ko'ring.",
            "language": "uzb",
            "address_extracted": None,
            "district_extracted": None,
            "symptoms_extracted": None,
            "analysis_notes": "JSON parsing failed, returned default"
        }
        
    except Exception as e:
        logger.error(f"❌ JSON parsing xatoligi: {e}")
        return {
            "risk_level": "sariq",
            "response_text": "Texnik xatolik yuz berdi. Iltimos, qayta urinib ko'ring.",
            "language": "uzb",
            "address_extracted": None,
            "district_extracted": None,
            "symptoms_extracted": None,
            "analysis_notes": f"Exception in JSON parsing: {str(e)}"
        }


# ==================== LLM (Gemini) ====================

def get_gemini_response(prompt: str, stream: bool = False) -> Dict:
    """
    Gemini dan javob oladi va uni ROBUST JSON sifatida tahlil qiladi
    
    VAZIFA-1 & VAZIFA-2: Bu funksiya Gemini'ga so'rov yuborib, javobni
    xatoliklarga bardoshli tarzda JSON'ga parse qiladi va kerakli maydonlarni
    to'ldiradi (shu jumladan "language" maydoni).
    
    Args:
        prompt: Bemorning so'rovi va suhbat tarixi
        stream: Stream rejimi (hozircha qo'llab-quvvatlanmaydi)
        
    Returns:
        Dict: Parse qilingan va validatsiya qilingan JSON
    """
    try:
        if not MODEL_STATUS["llm"]:
            raise RuntimeError("LLM modeli ishlamaydi")
        
        if stream:
            raise NotImplementedError("JSON tahlili uchun stream rejimi qo'llab-quvvatlanmaydi")

        full_prompt = f"{SYSTEM_INSTRUCTION}\n\nSuhbat Tarixi:\n{prompt}"
        
        logger.info("🧠 Gemini'ga so'rov yuborilmoqda...")
        response = MODELS["llm"].generate_content(full_prompt)
        
        logger.info(f"✅ Gemini javobi qabul qilindi ({len(response.text)} belgi)")
        logger.debug(f"Raw response: {response.text[:200]}...")
        
        # ROBUST JSON PARSING (VAZIFA-1)
        response_data = extract_json_from_response(response.text)
        
        # VAZIFA-2: Kerakli maydonlar mavjudligini tekshirish va default qiymatlar
        if "risk_level" not in response_data or response_data["risk_level"] not in ["qizil", "sariq", "yashil"]:
            logger.warning(f"⚠️ risk_level noto'g'ri: {response_data.get('risk_level')}, default: sariq")
            response_data["risk_level"] = "sariq"
        
        if "response_text" not in response_data or not response_data["response_text"].strip():
            logger.warning("⚠️ Gemini 'response_text' maydonini qaytarmadi. Fallback javob shakllantirilmoqda.")
            
            risk = response_data.get("risk_level")
            action = response_data.get("action")
            lang = response_data.get("language", "uzb") # Tilni ham hisobga olamiz
            
            # Vaziyatga qarab aqlli javob berish
            if risk == "yashil" and action == "offer_doctor_recommendation":
                if lang == "rus":
                    response_data["response_text"] = "Понятно, не о чем беспокоиться. Хотите, я порекомендую вам подходящего врача?"
                elif lang == "eng":
                    response_data["response_text"] = "I understand, no need to worry. Would you like me to recommend a suitable doctor for you?"
                else: # uzb
                    response_data["response_text"] = "Tushunarli, xavotirga o'rin yo'q. Sizga mos shifokor tavsiya qilishimni xohlaysizmi?"
            
            elif risk in ["qizil", "sariq"]:
                if lang == "rus":
                    response_data["response_text"] = "Понимаю, это серьезно. Пожалуйста, назовите ваш точный адрес, мы отправляем бригаду."
                elif lang == "eng":
                    response_data["response_text"] = "I understand, this is serious. Please tell me your exact address, we are sending a team."
                else: # uzb
                    response_data["response_text"] = "Tushundim, bu jiddiy holat. Iltimos, aniq manzilingizni ayting, brigada yuboryapmiz."

            else:
                # Agar hech qaysi holatga tushmasa, umumiy javob
                if lang == "rus":
                    response_data["response_text"] = "Извините, я не совсем вас поняла. Пожалуйста, повторите."
                elif lang == "eng":
                    response_data["response_text"] = "Sorry, I didn't quite understand. Please repeat."
                else: # uzb
                    response_data["response_text"] = "Kechirasiz, sizni to'liq tushunmadim. Iltimos, qaytadan ayting."
        
        # VAZIFA-2: CRITICAL - "language" maydoni ALBATTA BO'LISHI KERAK
        if "language" not in response_data or response_data["language"] not in ["uzb", "eng", "rus"]:
            # Fallback: response_text dan til aniqlash
            detected_lang = detect_language(response_data.get("response_text", ""))
            logger.warning(f"⚠️ Gemini 'language' qaytarmadi, fallback: {detected_lang}")
            response_data["language"] = detected_lang
        
        # Qolgan maydonlarni tekshirish
        for field in ["address_extracted", "district_extracted", "symptoms_extracted", "pre_arrival_instruction_text", "analysis_notes"]:
            if field not in response_data:
                response_data[field] = None
        
        logger.info(f"✅ Gemini javobi to'liq validatsiya qilindi: risk={response_data['risk_level']}, lang={response_data['language']}")
        
        return response_data
        
    except json.JSONDecodeError as e:
        logger.error(f"❌ JSON decode xatoligi: {e}")
        logger.error(f"Response matn: {response.text if 'response' in locals() else 'N/A'}")
        
        return {
            "risk_level": "sariq",
            "response_text": "Kechirasiz, javobni qayta ishlashda muammo yuz berdi. Iltimos, boshqacha aytib ko'ring.",
            "language": "uzb",
            "address_extracted": None,
            "district_extracted": None,
            "symptoms_extracted": None,
            "analysis_notes": f"JSON decode error: {str(e)}"
        }
            
    except Exception as e:
        logger.error(f"❌ LLM kutilmagan xatolik: {e}", exc_info=True)
        
        return {
            "risk_level": "sariq",
            "response_text": "Texnik xatolik yuz berdi. Iltimos, bir oz kuting va qayta urinib ko'ring.",
            "language": "uzb",
            "address_extracted": None,
            "district_extracted": None,
            "symptoms_extracted": None,
            "analysis_notes": f"Unexpected error: {str(e)}"
        }


# ==================== TTS (Text-to-Speech) ====================

def synthesize_speech(text: str, output_path: str, language: str = "uzb") -> bool:
    """
    Matnni ovozga aylantiradi (KO'P TILLI: uzb, eng, rus)
    
    YANGILANGAN: 
    - output_path validatsiyasi qo'shildi
    - Ruscha lotin → kirill konverter qo'shildi
    
    Args:
        text: Ovozga aylantirilishi kerak bo'lgan matn
        output_path: Saqlash uchun fayl yo'li (masalan: "static/audio/tts_case_025.wav")
        language: "uzb" | "eng" | "rus"
        
    Returns:
        bool: Muvaffaqiyatli bo'lsa True, aks holda False
    """
    try:
        # ========== VALIDATSIYA ==========
        
        # output_path tekshirish
        if not output_path or not output_path.strip():
            logger.error("❌ output_path bo'sh!")
            return False
        
        if not output_path.endswith('.wav'):
            logger.warning(f"⚠️ output_path .wav bilan tugamaydi: {output_path}")
            output_path += '.wav'
        
        # Model mavjudligini tekshirish
        if language == "eng" and not MODEL_STATUS.get("tts_eng", False):
            logger.warning("⚠️ TTS_ENG modeli yo'q, TTS_UZB ishlatilmoqda")
            language = "uzb"
        
        if language == "rus" and not MODEL_STATUS.get("tts_rus", False):
            logger.warning("⚠️ TTS_RUS modeli yo'q, TTS_UZB ishlatilmoqda")
            language = "uzb"
        
        if language == "uzb" and not MODEL_STATUS.get("tts_uzb", False):
            raise RuntimeError("TTS_UZB modeli ishlamaydi")
        
        if not text or not text.strip():
            raise ValueError("Bo'sh matn ovozga aylantirilmaydi")
        
        # Matn uzunligini tekshirish
        if len(text) > 1000:
            logger.warning(f"⚠️ Matn juda uzun ({len(text)} belgi), qisqartirilmoqda...")
            text = text[:1000] + "..."
        
        # ========== MATNNI TAYYORLASH ==========
        
        if language == "uzb":
            # O'zbekcha uchun kirill kerak
            from app.utils.translit import lotin_to_kirill, clean_cyrillic_text
            cyrillic_text = lotin_to_kirill(text)
            cleaned_text = clean_cyrillic_text(cyrillic_text)
            
        elif language == "eng":
            # Inglizcha uchun faqat tozalash
            cleaned_text = text.strip()
            
        elif language == "rus":
            # ✅ YANGI: Ruscha uchun kirill kerak
            from app.utils.translit import russian_latin_to_cyrillic, clean_cyrillic_text
            
            # Kirill nisbatini tekshirish
            cyrillic_count = sum(1 for c in text if '\u0400' <= c <= '\u04FF')
            total_chars = len([c for c in text if c.isalpha()])
            cyrillic_ratio = cyrillic_count / max(total_chars, 1)
            
            if cyrillic_ratio < 0.5:  # Agar 50%dan kam kirill bo'lsa
                logger.info("🔄 Ruscha matn lotindan kirilga o'tkazilmoqda...")
                text = russian_latin_to_cyrillic(text)
            
            cleaned_text = clean_cyrillic_text(text)
            
        else:
            cleaned_text = text.strip()
        
        if not cleaned_text.strip():
            raise ValueError("Tozalangan matn bo'sh")
        
        logger.info(f"🗣️ TTS ({language.upper()}): '{cleaned_text[:50]}{'...' if len(cleaned_text) > 50 else ''}'")

        # ========== MODEL TANLASH ==========
        
        pipeline_key = f"tts_{language}_pipeline"
        if pipeline_key not in MODELS:
            raise RuntimeError(f"{pipeline_key} topilmadi")

        # ========== OVOZ GENERATSIYA ==========
        
        output = MODELS[pipeline_key](cleaned_text)
        
        # Audio formatini to'g'rilash
        import torch
        import torchaudio
        
        audio_data = torch.tensor(output["audio"])
        
        if audio_data.dim() == 3:
            audio_data = audio_data.squeeze(0)
        elif audio_data.dim() == 1:
            audio_data = audio_data.unsqueeze(0)
        
        # ========== FAYLGA SAQLASH ==========
        
        # Papka yaratish (xavfsiz)
        output_dir = os.path.dirname(output_path)
        if output_dir:  # Bo'sh bo'lsa yaratmaydi
            os.makedirs(output_dir, exist_ok=True)
            logger.info(f"📁 Papka tekshirildi: {output_dir}")
        
        # Audio faylni saqlash
        torchaudio.save(
            output_path,
            src=audio_data,
            sample_rate=output["sampling_rate"]
        )
        
        logger.info(f"✅ Ovoz fayli saqlandi: {output_path}")
        return True
        
    except Exception as e:
        logger.error(f"❌ TTS xatoligi: {e}", exc_info=True)
        return False