import os
import logging
from faster_whisper import WhisperModel
import sqlite3
from datetime import datetime
from pydub import AudioSegment
from pathlib import Path

logger = logging.getLogger(__name__)

# ЖЕСТКИЙ ФИКС ПУТЕЙ К БИНАРНИКАМ
AudioSegment.converter = "/usr/bin/ffmpeg"
AudioSegment.ffprobe = "/usr/bin/ffprobe"

class VoiceService:
    def __init__(self, db_path: str, model_size: str = "small"):
        self.db_path = db_path
        self.model_size = model_size
        logger.info(f"💾 Загрузка модели Whisper ({model_size})...")
        self.model = WhisperModel(model_size, device="cpu", compute_type="int8")
        self._init_db()

    def _init_db(self):
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS voice_recognition_logs (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                user_id INTEGER,
                username TEXT,
                message_type TEXT,
                duration INTEGER,
                text TEXT,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        conn.commit()
        conn.close()

    def transcribe(self, file_path: str, user_id: int, username: str, msg_type: str) -> str:
        f_path = Path(file_path)
        if not f_path.exists():
            raise FileNotFoundError(f"Файл не найден: {file_path}")

        try:
            logger.info(f"🔄 Конвертация {f_path.name}...")
            wav_path = str(f_path.with_suffix('.wav'))
            
            # Конвертируем
            audio = AudioSegment.from_file(str(f_path))
            duration = int(len(audio) / 1000)
            audio.export(wav_path, format="wav")

            logger.info(f"🧠 Расшифровка ({duration}с)...")
            segments, info = self.model.transcribe(wav_path, beam_size=5)
            text = " ".join([segment.text for segment in segments]).strip()

            # Чистим за собой
            if os.path.exists(wav_path): os.remove(wav_path)
            
            self._log_to_db(user_id, username, msg_type, duration, text)
            return text
        except Exception as e:
            logger.error(f"❌ Ошибка транскрибации: {e}", exc_info=True)
            raise e

    def _log_to_db(self, user_id, username, msg_type, duration, text):
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            cursor.execute('''
                INSERT INTO voice_recognition_logs (user_id, username, message_type, duration, text)
                VALUES (?, ?, ?, ?, ?)
            ''', (user_id, username, msg_type, duration, text))
            conn.commit()
            conn.close()
        except Exception as e:
            logger.error(f"DB Log error: {e}")