backend.py

import os
import time
import uuid
import json
import requests
import subprocess
import asyncio
import threading
import hashlib
import re
from datetime import datetime, timedelta
from typing import Optional, Dict, List, Tuple
from dataclasses import dataclass, asdict
from concurrent.futures import ThreadPoolExecutor
import sqlite3
from contextlib import contextmanager
from dotenv import load_dotenv
from azure.storage.blob import BlobServiceClient
import tempfile
import shutil

# Load Environment
load_dotenv()

def _require_env_var(varname):
    value = os.environ.get(varname)
    if not value or value.strip() == "" or "your" in value.lower():
        raise ValueError(f"Environment variable {varname} is missing or invalid. Check your .env file.")
    return value

# Environment variables - Enhanced for AI services
AZURE_SPEECH_KEY = _require_env_var("AZURE_SPEECH_KEY")
AZURE_SPEECH_KEY_ENDPOINT = _require_env_var("AZURE_SPEECH_KEY_ENDPOINT").rstrip('/')
AZURE_REGION = _require_env_var("AZURE_REGION")
AZURE_BLOB_CONNECTION = _require_env_var("AZURE_BLOB_CONNECTION")
AZURE_CONTAINER = _require_env_var("AZURE_CONTAINER")
AZURE_BLOB_SAS_TOKEN = _require_env_var("AZURE_BLOB_SAS_TOKEN")
ALLOWED_LANGS = json.loads(os.environ.get("ALLOWED_LANGS", "{}"))
API_VERSION = os.environ.get("API_VERSION", "v3.2")

# New AI-specific environment variables
AZURE_OPENAI_ENDPOINT = os.environ.get("AZURE_OPENAI_ENDPOINT", "")
AZURE_OPENAI_KEY = os.environ.get("AZURE_OPENAI_KEY", "")
AZURE_OPENAI_DEPLOYMENT = os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4o-mini")

# Containers for different types of data
TRANSCRIPTS_CONTAINER = AZURE_CONTAINER
AI_SUMMARIES_CONTAINER = os.environ.get("AI_SUMMARIES_CONTAINER", f"{AZURE_CONTAINER}-summaries")
CHAT_RESPONSES_CONTAINER = os.environ.get("CHAT_RESPONSES_CONTAINER", f"{AZURE_CONTAINER}-chats")

# Directories
UPLOAD_DIR = "uploads"
DB_DIR = "database"
os.makedirs(UPLOAD_DIR, exist_ok=True)
os.makedirs(DB_DIR, exist_ok=True)

AUDIO_FORMATS = [
    "wav", "mp3", "ogg", "opus", "flac", "wma", "aac", "alaw", "mulaw", "amr", "webm", "speex"
]

@dataclass
class User:
    user_id: str
    email: str
    username: str
    password_hash: str
    created_at: str
    last_login: Optional[str] = None
    is_active: bool = True
    gdpr_consent: bool = False
    data_retention_agreed: bool = False
    marketing_consent: bool = False

@dataclass
class TranscriptionJob:
    job_id: str
    user_id: str
    original_filename: str
    audio_url: str
    language: str
    status: str  # pending, processing, completed, failed
    created_at: str
    completed_at: Optional[str] = None
    transcript_text: Optional[str] = None
    transcript_url: Optional[str] = None
    error_message: Optional[str] = None
    azure_trans_id: Optional[str] = None
    settings: Optional[Dict] = None

@dataclass
class SummaryJob:
    job_id: str
    user_id: str
    original_files: List[str]
    summary_type: str
    user_prompt: str
    status: str  # pending, processing, completed, failed
    created_at: str
    completed_at: Optional[str] = None
    summary_text: Optional[str] = None
    processed_files: Optional[Dict] = None
    extracted_images: Optional[List[str]] = None
    transcript_text: Optional[str] = None
    error_message: Optional[str] = None
    settings: Optional[Dict] = None
    chat_response_url: Optional[str] = None

class AuthManager:
    """Handle user authentication and PDPA compliance"""
    
    @staticmethod
    def hash_password(password: str) -> str:
        """Hash password using SHA-256 with salt"""
        salt = "azure_ai_conference_service_salt_2024"  # In production, use environment variable
        return hashlib.sha256((password + salt).encode()).hexdigest()
    
    @staticmethod
    def verify_password(password: str, password_hash: str) -> bool:
        """Verify password against hash"""
        return AuthManager.hash_password(password) == password_hash
    
    @staticmethod
    def validate_email(email: str) -> bool:
        """Validate email format"""
        pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        return re.match(pattern, email) is not None
    
    @staticmethod
    def validate_username(username: str) -> bool:
        """Validate username format"""
        # Username: 3-30 characters, alphanumeric and underscore only
        pattern = r'^[a-zA-Z0-9_]{3,30}$'
        return re.match(pattern, username) is not None
    
    @staticmethod
    def validate_password(password: str) -> Tuple[bool, str]:
        """Validate password strength"""
        if len(password) < 8:
            return False, "Password must be at least 8 characters long"
        if not re.search(r'[A-Z]', password):
            return False, "Password must contain at least one uppercase letter"
        if not re.search(r'[a-z]', password):
            return False, "Password must contain at least one lowercase letter"
        if not re.search(r'\d', password):
            return False, "Password must contain at least one number"
        return True, "Password is valid"

class DatabaseManager:
    def __init__(self, db_path: str = None):
        self.db_path = db_path or os.path.join(DB_DIR, "ai_conference_service.db")
        self.blob_service = BlobServiceClient.from_connection_string(AZURE_BLOB_CONNECTION)
        self.db_blob_name = "shared/database/ai_conference_service.db"  # Shared database location
        self._lock = threading.Lock()
        self._last_backup_time = 0
        self._backup_interval = 30  # Backup every 30 seconds at most
        
        # Download existing database from blob storage or create new one
        self.init_database()
    
    def _download_db_from_blob(self):
        """Download database from Azure Blob Storage if it exists"""
        try:
            blob_client = self.blob_service.get_blob_client(container=TRANSCRIPTS_CONTAINER, blob=self.db_blob_name)
            
            # Check if blob exists
            if blob_client.exists():
                print("📥 Downloading existing shared database from Azure Blob Storage...")
                
                # Create temporary file
                with tempfile.NamedTemporaryFile(delete=False) as temp_file:
                    temp_path = temp_file.name
                
                # Download blob to temporary file
                with open(temp_path, "wb") as download_file:
                    download_file.write(blob_client.download_blob().readall())
                
                # Move to final location
                os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
                shutil.move(temp_path, self.db_path)
                
                print("✅ Shared database downloaded successfully")
                return True
            else:
                print("🔍 No existing shared database found in blob storage, will create new one")
                return False
                
        except Exception as e:
            print(f"⚠️ Warning: Could not download shared database from blob storage: {e}")
            print("🔍 Will create new local database")
            return False
    
    def _upload_db_to_blob(self):
        """Upload database to Azure Blob Storage with rate limiting"""
        try:
            current_time = time.time()
            if current_time - self._last_backup_time < self._backup_interval:
                return  # Skip backup if too recent
            
            if not os.path.exists(self.db_path):
                return
            
            blob_client = self.blob_service.get_blob_client(container=TRANSCRIPTS_CONTAINER, blob=self.db_blob_name)
            
            with open(self.db_path, "rb") as data:
                blob_client.upload_blob(data, overwrite=True)
            
            self._last_backup_time = current_time
            
        except Exception as e:
            print(f"⚠️ Warning: Could not upload shared database to blob storage: {e}")
    
    def _store_chat_response(self, job_id: str, response_content: str, user_id: str) -> str:
        """Store AI chat response in dedicated blob container"""
        try:
            # Create chat response blob name with user isolation
            chat_blob_name = f"users/{user_id}/chats/summary_{job_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt"
            
            # Create temporary file
            temp_path = os.path.join(tempfile.gettempdir(), f"chat_response_{job_id}.txt")
            with open(temp_path, "w", encoding="utf-8") as f:
                f.write(response_content)
            
            # Upload to chat responses container
            chat_blob_client = self.blob_service.get_blob_client(
                container=CHAT_RESPONSES_CONTAINER, 
                blob=chat_blob_name
            )
            
            with open(temp_path, "rb") as data:
                chat_blob_client.upload_blob(data, overwrite=True)
            
            # Clean up temp file
            os.remove(temp_path)
            
            # Create SAS URL
            sas = AZURE_BLOB_SAS_TOKEN.lstrip("?")
            chat_url = f"{chat_blob_client.url}?{sas}"
            
            print(f"💬 Chat response stored for user {user_id[:8]}...")
            return chat_url
            
        except Exception as e:
            print(f"⚠️ Error storing chat response: {e}")
            return ""
    
    @contextmanager
    def get_connection(self):
        with self._lock:
            conn = sqlite3.connect(self.db_path, timeout=30.0)
            conn.row_factory = sqlite3.Row
            try:
                yield conn
            finally:
                conn.close()
                # Auto-backup after any database operation (rate limited)
                threading.Thread(target=self._upload_db_to_blob, daemon=True).start()
    
    def init_database(self):
        # Try to download existing database first
        self._download_db_from_blob()
        
        # Initialize database structure - Enhanced for AI services
        with self.get_connection() as conn:
            # Users table (same as before)
            conn.execute("""

                CREATE TABLE IF NOT EXISTS users (

                    user_id TEXT PRIMARY KEY,

                    email TEXT UNIQUE NOT NULL,

                    username TEXT UNIQUE NOT NULL,

                    password_hash TEXT NOT NULL,

                    created_at TEXT NOT NULL,

                    last_login TEXT,

                    is_active BOOLEAN DEFAULT 1,

                    gdpr_consent BOOLEAN DEFAULT 0,

                    data_retention_agreed BOOLEAN DEFAULT 0,

                    marketing_consent BOOLEAN DEFAULT 0

                )

            """)
            
            # Enhanced transcriptions table
            conn.execute("""

                CREATE TABLE IF NOT EXISTS transcriptions (

                    job_id TEXT PRIMARY KEY,

                    user_id TEXT NOT NULL,

                    original_filename TEXT NOT NULL,

                    audio_url TEXT,

                    language TEXT NOT NULL,

                    status TEXT NOT NULL,

                    created_at TEXT NOT NULL,

                    completed_at TEXT,

                    transcript_text TEXT,

                    transcript_url TEXT,

                    error_message TEXT,

                    azure_trans_id TEXT,

                    settings TEXT,

                    file_size INTEGER DEFAULT 0,

                    processing_duration REAL DEFAULT 0.0,

                    FOREIGN KEY (user_id) REFERENCES users (user_id)

                )

            """)
            
            # New AI summaries table
            conn.execute("""

                CREATE TABLE IF NOT EXISTS ai_summaries (

                    job_id TEXT PRIMARY KEY,

                    user_id TEXT NOT NULL,

                    original_files TEXT NOT NULL,

                    summary_type TEXT NOT NULL,

                    user_prompt TEXT NOT NULL,

                    status TEXT NOT NULL,

                    created_at TEXT NOT NULL,

                    completed_at TEXT,

                    summary_text TEXT,

                    processed_files TEXT,

                    extracted_images TEXT,

                    transcript_text TEXT,

                    error_message TEXT,

                    settings TEXT,

                    chat_response_url TEXT,

                    input_token_count INTEGER DEFAULT 0,

                    output_token_count INTEGER DEFAULT 0,

                    processing_duration REAL DEFAULT 0.0,

                    FOREIGN KEY (user_id) REFERENCES users (user_id)

                )

            """)
            
            # Create comprehensive indexes
            conn.execute("CREATE INDEX IF NOT EXISTS idx_users_email ON users(email)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_users_username ON users(username)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_transcriptions_user_id ON transcriptions(user_id)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_transcriptions_status ON transcriptions(status)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_transcriptions_created_at ON transcriptions(created_at DESC)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_transcriptions_user_created ON transcriptions(user_id, created_at DESC)")
            
            # AI summaries indexes
            conn.execute("CREATE INDEX IF NOT EXISTS idx_ai_summaries_user_id ON ai_summaries(user_id)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_ai_summaries_status ON ai_summaries(status)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_ai_summaries_created_at ON ai_summaries(created_at DESC)")
            conn.execute("CREATE INDEX IF NOT EXISTS idx_ai_summaries_user_created ON ai_summaries(user_id, created_at DESC)")
            
            conn.commit()
            print("✅ Enhanced database schema initialized (transcriptions + AI summaries)")
    
    # User management methods (same as before)
    def create_user(self, email: str, username: str, password: str, gdpr_consent: bool = True, 

                   data_retention_agreed: bool = True, marketing_consent: bool = False) -> Tuple[bool, str, Optional[str]]:
        """Create new user account"""
        try:
            # Validate inputs
            if not AuthManager.validate_email(email):
                return False, "Invalid email format", None
            
            if not AuthManager.validate_username(username):
                return False, "Username must be 3-30 characters, alphanumeric and underscore only", None
            
            is_valid, message = AuthManager.validate_password(password)
            if not is_valid:
                return False, message, None
            
            if not gdpr_consent:
                return False, "GDPR consent is required to create an account", None
            
            if not data_retention_agreed:
                return False, "Data retention agreement is required", None
            
            user_id = str(uuid.uuid4())
            password_hash = AuthManager.hash_password(password)
            
            with self.get_connection() as conn:
                # Check if email or username already exists
                existing = conn.execute(
                    "SELECT email, username FROM users WHERE email = ? OR username = ?",
                    (email, username)
                ).fetchone()
                
                if existing:
                    if existing['email'] == email:
                        return False, "Email already registered", None
                    else:
                        return False, "Username already taken", None
                
                # Create user
                user = User(
                    user_id=user_id,
                    email=email,
                    username=username,
                    password_hash=password_hash,
                    created_at=datetime.now().isoformat(),
                    gdpr_consent=gdpr_consent,
                    data_retention_agreed=data_retention_agreed,
                    marketing_consent=marketing_consent
                )
                
                conn.execute("""

                    INSERT INTO users 

                    (user_id, email, username, password_hash, created_at, is_active, 

                     gdpr_consent, data_retention_agreed, marketing_consent)

                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)

                """, (
                    user.user_id, user.email, user.username, user.password_hash,
                    user.created_at, user.is_active, user.gdpr_consent,
                    user.data_retention_agreed, user.marketing_consent
                ))
                conn.commit()
                
                print(f"👤 New user registered: {username} ({email})")
                return True, "Account created successfully", user_id
                
        except Exception as e:
            print(f"❌ Error creating user: {str(e)}")
            return False, f"Registration failed: {str(e)}", None
    
    def authenticate_user(self, login: str, password: str) -> Tuple[bool, str, Optional[User]]:
        """Authenticate user by email or username"""
        try:
            with self.get_connection() as conn:
                # Find user by email or username
                user_row = conn.execute("""

                    SELECT * FROM users 

                    WHERE (email = ? OR username = ?) AND is_active = 1

                """, (login, login)).fetchone()
                
                if not user_row:
                    return False, "Invalid credentials", None
                
                # Verify password
                if not AuthManager.verify_password(password, user_row['password_hash']):
                    return False, "Invalid credentials", None
                
                # Update last login
                conn.execute(
                    "UPDATE users SET last_login = ? WHERE user_id = ?",
                    (datetime.now().isoformat(), user_row['user_id'])
                )
                conn.commit()
                
                # Convert to User object
                user = User(
                    user_id=user_row['user_id'],
                    email=user_row['email'],
                    username=user_row['username'],
                    password_hash=user_row['password_hash'],
                    created_at=user_row['created_at'],
                    last_login=datetime.now().isoformat(),
                    is_active=bool(user_row['is_active']),
                    gdpr_consent=bool(user_row['gdpr_consent']),
                    data_retention_agreed=bool(user_row['data_retention_agreed']),
                    marketing_consent=bool(user_row['marketing_consent'])
                )
                
                print(f"🔐 User logged in: {user.username} ({user.email})")
                return True, "Login successful", user
                
        except Exception as e:
            print(f"❌ Authentication error: {str(e)}")
            return False, f"Login failed: {str(e)}", None
    
    def get_user_by_id(self, user_id: str) -> Optional[User]:
        """Get user by ID"""
        try:
            with self.get_connection() as conn:
                user_row = conn.execute(
                    "SELECT * FROM users WHERE user_id = ? AND is_active = 1",
                    (user_id,)
                ).fetchone()
                
                if user_row:
                    return User(
                        user_id=user_row['user_id'],
                        email=user_row['email'],
                        username=user_row['username'],
                        password_hash=user_row['password_hash'],
                        created_at=user_row['created_at'],
                        last_login=user_row['last_login'],
                        is_active=bool(user_row['is_active']),
                        gdpr_consent=bool(user_row['gdpr_consent']),
                        data_retention_agreed=bool(user_row['data_retention_agreed']),
                        marketing_consent=bool(user_row['marketing_consent'])
                    )
        except Exception as e:
            print(f"❌ Error getting user: {str(e)}")
        return None
    
    def update_user_consent(self, user_id: str, marketing_consent: bool) -> bool:
        """Update user marketing consent"""
        try:
            with self.get_connection() as conn:
                conn.execute(
                    "UPDATE users SET marketing_consent = ? WHERE user_id = ?",
                    (marketing_consent, user_id)
                )
                conn.commit()
                return True
        except Exception as e:
            print(f"❌ Error updating consent: {str(e)}")
            return False
    
    def delete_user_account(self, user_id: str) -> bool:
        """Delete user account and all associated data (GDPR compliance)"""
        try:
            with self.get_connection() as conn:
                # Delete all transcriptions
                conn.execute("DELETE FROM transcriptions WHERE user_id = ?", (user_id,))
                # Delete all AI summaries
                conn.execute("DELETE FROM ai_summaries WHERE user_id = ?", (user_id,))
                # Deactivate user (for audit trail) rather than delete
                conn.execute(
                    "UPDATE users SET is_active = 0, email = ?, username = ? WHERE user_id = ?",
                    (f"deleted_{user_id}@deleted.com", f"deleted_{user_id}", user_id)
                )
                conn.commit()
                print(f"🗑️ Complete user account deleted: {user_id}")
                return True
        except Exception as e:
            print(f"❌ Error deleting user account: {str(e)}")
            return False
    
    def delete_user_summary_data(self, user_id: str) -> bool:
        """Delete user's AI summary data specifically"""
        try:
            with self.get_connection() as conn:
                conn.execute("DELETE FROM ai_summaries WHERE user_id = ?", (user_id,))
                conn.commit()
                print(f"🗑️ User AI summary data deleted: {user_id}")
                return True
        except Exception as e:
            print(f"❌ Error deleting user AI summary data: {str(e)}")
            return False
    
    # Transcription methods (enhanced)
    def save_job(self, job: TranscriptionJob):
        with self.get_connection() as conn:
            conn.execute("""

                INSERT OR REPLACE INTO transcriptions 

                (job_id, user_id, original_filename, audio_url, language, status, 

                 created_at, completed_at, transcript_text, transcript_url, error_message, 

                 azure_trans_id, settings, file_size, processing_duration)

                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)

            """, (
                job.job_id, job.user_id, job.original_filename, job.audio_url,
                job.language, job.status, job.created_at, job.completed_at,
                job.transcript_text, job.transcript_url, job.error_message,
                job.azure_trans_id, json.dumps(job.settings) if job.settings else None,
                0, 0.0  # file_size and processing_duration will be updated later
            ))
            conn.commit()
    
    # AI Summary methods (new)
    def save_summary_job(self, job: SummaryJob):
        """Save or update AI summary job"""
        with self.get_connection() as conn:
            conn.execute("""

                INSERT OR REPLACE INTO ai_summaries 

                (job_id, user_id, original_files, summary_type, user_prompt, status, 

                 created_at, completed_at, summary_text, processed_files, extracted_images, 

                 transcript_text, error_message, settings, chat_response_url, 

                 input_token_count, output_token_count, processing_duration)

                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)

            """, (
                job.job_id, job.user_id, json.dumps(job.original_files), job.summary_type,
                job.user_prompt, job.status, job.created_at, job.completed_at,
                job.summary_text, json.dumps(job.processed_files) if job.processed_files else None,
                json.dumps(job.extracted_images) if job.extracted_images else None,
                job.transcript_text, job.error_message,
                json.dumps(job.settings) if job.settings else None,
                job.chat_response_url, 0, 0, 0.0
            ))
            conn.commit()
    
    def get_summary_job(self, job_id: str) -> Optional[SummaryJob]:
        """Get AI summary job by ID"""
        with self.get_connection() as conn:
            row = conn.execute(
                "SELECT * FROM ai_summaries WHERE job_id = ?", (job_id,)
            ).fetchone()
            if row:
                return self._row_to_summary_job(row)
        return None
    
    def get_user_summary_jobs(self, user_id: str, limit: int = 50) -> List[SummaryJob]:
        """Get AI summary jobs for a specific user"""
        with self.get_connection() as conn:
            rows = conn.execute("""

                SELECT * FROM ai_summaries 

                WHERE user_id = ? 

                ORDER BY created_at DESC 

                LIMIT ?

            """, (user_id, limit)).fetchall()
            return [self._row_to_summary_job(row) for row in rows]
    
    def _row_to_summary_job(self, row) -> SummaryJob:
        """Convert database row to SummaryJob object"""
        return SummaryJob(
            job_id=row['job_id'],
            user_id=row['user_id'],
            original_files=json.loads(row['original_files']) if row['original_files'] else [],
            summary_type=row['summary_type'],
            user_prompt=row['user_prompt'],
            status=row['status'],
            created_at=row['created_at'],
            completed_at=row['completed_at'],
            summary_text=row['summary_text'],
            processed_files=json.loads(row['processed_files']) if row['processed_files'] else None,
            extracted_images=json.loads(row['extracted_images']) if row['extracted_images'] else None,
            transcript_text=row['transcript_text'],
            error_message=row['error_message'],
            settings=json.loads(row['settings']) if row['settings'] else None,
            chat_response_url=row['chat_response_url']
        )
    
    def get_job(self, job_id: str) -> Optional[TranscriptionJob]:
        with self.get_connection() as conn:
            row = conn.execute(
                "SELECT * FROM transcriptions WHERE job_id = ?", (job_id,)
            ).fetchone()
            if row:
                return self._row_to_job(row)
        return None
    
    def get_user_jobs(self, user_id: str, limit: int = 50) -> List[TranscriptionJob]:
        """Get all transcription jobs for a specific user - PDPA compliant"""
        with self.get_connection() as conn:
            rows = conn.execute("""

                SELECT * FROM transcriptions 

                WHERE user_id = ? 

                ORDER BY created_at DESC 

                LIMIT ?

            """, (user_id, limit)).fetchall()
            return [self._row_to_job(row) for row in rows]
    
    def get_all_jobs(self, limit: int = 100) -> List[TranscriptionJob]:
        """Get all transcription jobs across all users (for admin/global view)"""
        with self.get_connection() as conn:
            rows = conn.execute("""

                SELECT * FROM transcriptions 

                ORDER BY created_at DESC 

                LIMIT ?

            """, (limit,)).fetchall()
            return [self._row_to_job(row) for row in rows]
    
    def get_pending_jobs(self) -> List[TranscriptionJob]:
        """Get pending transcription jobs across all users for background processing"""
        with self.get_connection() as conn:
            rows = conn.execute(
                "SELECT * FROM transcriptions WHERE status IN ('pending', 'processing')"
            ).fetchall()
            return [self._row_to_job(row) for row in rows]
    
    def get_pending_summary_jobs(self) -> List[SummaryJob]:
        """Get pending AI summary jobs for background processing"""
        with self.get_connection() as conn:
            rows = conn.execute(
                "SELECT * FROM ai_summaries WHERE status IN ('pending', 'processing')"
            ).fetchall()
            return [self._row_to_summary_job(row) for row in rows]
    
    def get_user_stats(self, user_id: str) -> Dict:
        """Get comprehensive statistics for a specific user (transcriptions)"""
        with self.get_connection() as conn:
            stats = {}
            
            # Total transcription jobs
            result = conn.execute("""

                SELECT COUNT(*) FROM transcriptions WHERE user_id = ?

            """, (user_id,)).fetchone()
            stats['total_jobs'] = result[0] if result else 0
            
            # Transcription jobs by status
            result = conn.execute("""

                SELECT status, COUNT(*) FROM transcriptions 

                WHERE user_id = ? 

                GROUP BY status

            """, (user_id,)).fetchall()
            stats['by_status'] = {row[0]: row[1] for row in result}
            
            # Recent transcription activity (last 7 days)
            week_ago = (datetime.now() - timedelta(days=7)).isoformat()
            result = conn.execute("""

                SELECT COUNT(*) FROM transcriptions 

                WHERE user_id = ? AND created_at >= ?

            """, (user_id, week_ago)).fetchone()
            stats['recent_jobs'] = result[0] if result else 0
            
            return stats
    
    def get_user_summary_stats(self, user_id: str) -> Dict:
        """Get comprehensive statistics for a specific user (AI summaries)"""
        with self.get_connection() as conn:
            stats = {}
            
            # Total AI summary jobs
            result = conn.execute("""

                SELECT COUNT(*) FROM ai_summaries WHERE user_id = ?

            """, (user_id,)).fetchone()
            stats['total_jobs'] = result[0] if result else 0
            
            # AI summary jobs by status
            result = conn.execute("""

                SELECT status, COUNT(*) FROM ai_summaries 

                WHERE user_id = ? 

                GROUP BY status

            """, (user_id,)).fetchall()
            stats['by_status'] = {row[0]: row[1] for row in result}
            
            # Recent AI summary activity (last 7 days)
            week_ago = (datetime.now() - timedelta(days=7)).isoformat()
            result = conn.execute("""

                SELECT COUNT(*) FROM ai_summaries 

                WHERE user_id = ? AND created_at >= ?

            """, (user_id, week_ago)).fetchone()
            stats['recent_jobs'] = result[0] if result else 0
            
            return stats
    
    def export_user_data(self, user_id: str) -> Dict:
        """Export comprehensive user data including AI summaries"""
        try:
            with self.get_connection() as conn:
                # Get user info
                user_row = conn.execute(
                    "SELECT * FROM users WHERE user_id = ?", (user_id,)
                ).fetchone()
                
                # Get all transcriptions
                transcription_rows = conn.execute(
                    "SELECT * FROM transcriptions WHERE user_id = ?", (user_id,)
                ).fetchall()
                
                # Get all AI summaries
                summary_rows = conn.execute(
                    "SELECT * FROM ai_summaries WHERE user_id = ?", (user_id,)
                ).fetchall()
                
                export_data = {
                    "export_date": datetime.now().isoformat(),
                    "export_type": "comprehensive_ai_conference_service",
                    "user_info": dict(user_row) if user_row else {},
                    "transcriptions": [dict(row) for row in transcription_rows],
                    "ai_summaries": [dict(row) for row in summary_rows],
                    "transcription_statistics": self.get_user_stats(user_id),
                    "ai_summary_statistics": self.get_user_summary_stats(user_id),
                    "service_features": [
                        "speech_transcription",
                        "ai_summarization", 
                        "computer_vision",
                        "multi_modal_analysis"
                    ]
                }
                
                return export_data
                
        except Exception as e:
            print(f"❌ Error exporting comprehensive user data: {str(e)}")
            return {}
    
    def _row_to_job(self, row) -> TranscriptionJob:
        settings = json.loads(row['settings']) if row['settings'] else None
        return TranscriptionJob(
            job_id=row['job_id'],
            user_id=row['user_id'],
            original_filename=row['original_filename'],
            audio_url=row['audio_url'],
            language=row['language'],
            status=row['status'],
            created_at=row['created_at'],
            completed_at=row['completed_at'],
            transcript_text=row['transcript_text'],
            transcript_url=row['transcript_url'],
            error_message=row['error_message'],
            azure_trans_id=row['azure_trans_id'],
            settings=settings
        )

class TranscriptionManager:
    def __init__(self):
        self.db = DatabaseManager()
        self.executor = ThreadPoolExecutor(max_workers=5)
        self.blob_service = BlobServiceClient.from_connection_string(AZURE_BLOB_CONNECTION)
        self._job_status_cache = {}  # Cache to track status changes
        
        # Start background worker
        self.running = True
        self.worker_thread = threading.Thread(target=self._background_worker, daemon=True)
        self.worker_thread.start()
        
        print("✅ Enhanced Transcription Manager initialized with AI integration")
    
    def _log_status_change(self, job_id: str, old_status: str, new_status: str, filename: str = "", user_id: str = ""):
        """Only log when status actually changes"""
        cache_key = f"{job_id}_{old_status}_{new_status}"
        if cache_key not in self._job_status_cache:
            self._job_status_cache[cache_key] = True
            user_display = f"[{user_id[:8]}...]" if user_id else ""
            if filename:
                print(f"🔄 {user_display} Job {job_id[:8]}... ({filename}): {old_status} → {new_status}")
            else:
                print(f"🔄 {user_display} Job {job_id[:8]}...: {old_status} → {new_status}")
    
    def _background_worker(self):
        """Enhanced background worker to process both transcriptions and AI summaries"""
        iteration_count = 0
        while self.running:
            try:
                # Process transcription jobs
                pending_transcription_jobs = self.db.get_pending_jobs()
                pending_summary_jobs = self.db.get_pending_summary_jobs()
                
                # Only log if there are jobs to process
                if (pending_transcription_jobs or pending_summary_jobs) and iteration_count % 6 == 0:  # Log every minute
                    active_transcripts = len([j for j in pending_transcription_jobs if j.status == 'processing'])
                    queued_transcripts = len([j for j in pending_transcription_jobs if j.status == 'pending'])
                    active_summaries = len([j for j in pending_summary_jobs if j.status == 'processing'])
                    queued_summaries = len([j for j in pending_summary_jobs if j.status == 'pending'])
                    
                    if any([active_transcripts, queued_transcripts, active_summaries, queued_summaries]):
                        print(f"📊 Background worker: 🎙️ {active_transcripts} transcribing, {queued_transcripts} queued | 🤖 {active_summaries} summarizing, {queued_summaries} queued")
                
                # Process transcription jobs
                for job in pending_transcription_jobs:
                    if job.status == 'pending':
                        self.executor.submit(self._process_transcription_job, job.job_id)
                    elif job.status == 'processing' and job.azure_trans_id:
                        self.executor.submit(self._check_transcription_status, job.job_id)
                
                # Note: AI summary jobs are processed by ai_summary_manager
                
                time.sleep(10)  # Check every 10 seconds
                iteration_count += 1
                
            except Exception as e:
                print(f"❌ Background worker error: {e}")
                time.sleep(30)
    
    def submit_transcription(

        self, 

        file_bytes: bytes, 

        original_filename: str,

        user_id: str,

        language: str,

        settings: Dict

    ) -> str:
        """Submit a new transcription job for authenticated user"""
        job_id = str(uuid.uuid4())
        
        print(f"🚀 [{user_id[:8]}...] New transcription: {original_filename} ({len(file_bytes):,} bytes)")
        
        # Create job record
        job = TranscriptionJob(
            job_id=job_id,
            user_id=user_id,
            original_filename=original_filename,
            audio_url="",  # Will be set after upload
            language=language,
            status="pending",
            created_at=datetime.now().isoformat(),
            settings=settings
        )
        
        # Save job to database
        self.db.save_job(job)
        
        # Submit file processing to thread pool
        self.executor.submit(self._prepare_audio_file, job_id, file_bytes, original_filename, settings)
        
        return job_id
    
    def _prepare_audio_file(self, job_id: str, file_bytes: bytes, original_filename: str, settings: Dict):
        """Prepare audio file and upload to blob storage with user-specific paths"""
        try:
            job = self.db.get_job(job_id)
            if not job:
                return
            
            user_id = job.user_id
            
            # Save original file
            src_ext = original_filename.split('.')[-1].lower() if '.' in original_filename else "bin"
            upload_path = os.path.join(UPLOAD_DIR, f"{job_id}_original.{src_ext}")
            
            with open(upload_path, "wb") as f:
                f.write(file_bytes)
            
            # Determine if conversion is needed
            audio_format = settings.get('audio_format', 'wav')
            
            # Check if file is already in target format and specs
            if src_ext == audio_format and audio_format == 'wav':
                # Check if it's already 16kHz mono (Azure Speech preferred format)
                try:
                    probe_cmd = [
                        'ffprobe', '-v', 'quiet', '-print_format', 'json', 
                        '-show_streams', upload_path
                    ]
                    result = subprocess.run(probe_cmd, capture_output=True, text=True, timeout=30)
                    
                    if result.returncode == 0:
                        import json
                        probe_data = json.loads(result.stdout)
                        audio_stream = probe_data.get('streams', [{}])[0]
                        
                        sample_rate = int(audio_stream.get('sample_rate', 0))
                        channels = int(audio_stream.get('channels', 0))
                        
                        # If already optimal format, use as-is
                        if sample_rate == 16000 and channels == 1:
                            out_path = upload_path  # Use original file
                        else:
                            print(f"🔄 [{user_id[:8]}...] Converting {original_filename} to 16kHz mono")
                            out_path = os.path.join(UPLOAD_DIR, f"{job_id}_converted.{audio_format}")
                            self._convert_to_audio(upload_path, out_path, audio_format)
                    else:
                        out_path = os.path.join(UPLOAD_DIR, f"{job_id}_converted.{audio_format}")
                        self._convert_to_audio(upload_path, out_path, audio_format)
                        
                except Exception as e:
                    print(f"⚠️ [{user_id[:8]}...] Audio probing failed for {original_filename}: {e}")
                    out_path = os.path.join(UPLOAD_DIR, f"{job_id}_converted.{audio_format}")
                    self._convert_to_audio(upload_path, out_path, audio_format)
            else:
                # Different format, need conversion
                print(f"🔄 [{user_id[:8]}...] Converting {original_filename}: {src_ext} → {audio_format}")
                out_path = os.path.join(UPLOAD_DIR, f"{job_id}_converted.{audio_format}")
                
                try:
                    self._convert_to_audio(upload_path, out_path, audio_format)
                except Exception as e:
                    print(f"❌ [{user_id[:8]}...] Audio conversion failed for {original_filename}: {str(e)}")
                    job.status = "failed"
                    job.error_message = f"Audio conversion failed: {str(e)}"
                    job.completed_at = datetime.now().isoformat()
                    self.db.save_job(job)
                    
                    # Clean up files
                    try:
                        os.remove(upload_path)
                    except:
                        pass
                    return
            
            # Upload to blob storage with user-specific paths
            try:
                # Upload the processed audio file to user-specific path
                final_audio_name = f"users/{user_id}/audio/{job_id}.{audio_format}"
                audio_url = self._upload_blob(out_path, final_audio_name, TRANSCRIPTS_CONTAINER)
                
                # Upload original file to blob storage (only if different from processed)
                if out_path != upload_path:
                    orig_blob_name = f"users/{user_id}/originals/{job_id}_{original_filename}"
                    self._upload_blob(upload_path, orig_blob_name, TRANSCRIPTS_CONTAINER)
                else:
                    # If we used the original file as-is, still store it as original
                    orig_blob_name = f"users/{user_id}/originals/{job_id}_{original_filename}"
                    self._upload_blob(upload_path, orig_blob_name, TRANSCRIPTS_CONTAINER)
                
                print(f"☁️ [{user_id[:8]}...] {original_filename} uploaded to user-specific blob storage")
                
                # Update job with audio URL
                job.audio_url = audio_url
                job.status = "pending"
                self.db.save_job(job)
                
            except Exception as e:
                print(f"❌ [{user_id[:8]}...] Blob upload failed for {original_filename}: {str(e)}")
                job.status = "failed"
                job.error_message = f"Blob storage upload failed: {str(e)}"
                job.completed_at = datetime.now().isoformat()
                self.db.save_job(job)
                
            # Clean up local files
            try:
                if os.path.exists(upload_path):
                    os.remove(upload_path)
                if out_path != upload_path and os.path.exists(out_path):
                    os.remove(out_path)
            except Exception as e:
                print(f"⚠️ [{user_id[:8]}...] Warning: Could not clean up local files for {original_filename}: {e}")
                
        except Exception as e:
            print(f"❌ File preparation error for {original_filename}: {e}")
            job = self.db.get_job(job_id)
            if job:
                job.status = "failed"
                job.error_message = f"File preparation failed: {str(e)}"
                job.completed_at = datetime.now().isoformat()
                self.db.save_job(job)
    
    def _process_transcription_job(self, job_id: str):
        """Process a transcription job"""
        try:
            job = self.db.get_job(job_id)
            if not job or job.status != 'pending' or not job.audio_url:
                return
            
            user_id = job.user_id
            
            old_status = job.status
            # Update status to processing
            job.status = "processing"
            self.db.save_job(job)
            
            self._log_status_change(job_id, old_status, job.status, job.original_filename, job.user_id)
            
            # Create Azure transcription
            settings = job.settings or {}
            azure_trans_id = self._create_transcription(
                job.audio_url,
                job.language,
                settings.get('diarization_enabled', False),
                settings.get('speakers', 2),
                settings.get('profanity', 'masked'),
                settings.get('punctuation', 'automatic'),
                settings.get('timestamps', True),
                settings.get('lexical', False),
                settings.get('language_id_enabled', False),
                settings.get('candidate_locales', None)
            )
            
            # Update job with Azure transcription ID
            job.azure_trans_id = azure_trans_id
            self.db.save_job(job)
            
        except Exception as e:
            print(f"❌ Transcription submission failed for job {job_id[:8]}...: {str(e)}")
            job = self.db.get_job(job_id)
            if job:
                old_status = job.status
                job.status = "failed"
                job.error_message = f"Transcription submission failed: {str(e)}"
                job.completed_at = datetime.now().isoformat()
                self.db.save_job(job)
                self._log_status_change(job_id, old_status, job.status, job.original_filename, job.user_id)
    
    def _check_transcription_status(self, job_id: str):
        """Check status of Azure transcription"""
        try:
            job = self.db.get_job(job_id)
            if not job or job.status != 'processing' or not job.azure_trans_id:
                return
            
            # Check Azure transcription status
            url = f"{AZURE_SPEECH_KEY_ENDPOINT}/speechtotext/{API_VERSION}/transcriptions/{job.azure_trans_id}"
            headers = {"Ocp-Apim-Subscription-Key": AZURE_SPEECH_KEY}
            
            r = requests.get(url, headers=headers)
            data = r.json()
            
            if data.get("status") == "Succeeded":
                # Get transcription result
                content_url = self._get_transcription_result_url(job.azure_trans_id)
                if content_url:
                    transcript = self._fetch_transcript(content_url)
                    
                    # Save transcript to user-specific blob storage
                    transcript_blob_name = f"users/{job.user_id}/transcripts/{job_id}.txt"
                    transcript_path = os.path.join(UPLOAD_DIR, f"{job_id}_transcript.txt")
                    
                    with open(transcript_path, "w", encoding="utf-8") as f:
                        f.write(transcript)
                    
                    transcript_url = self._upload_blob(transcript_path, transcript_blob_name, TRANSCRIPTS_CONTAINER)
                    
                    # Update job
                    old_status = job.status
                    job.status = "completed"
                    job.transcript_text = transcript
                    job.transcript_url = transcript_url
                    job.completed_at = datetime.now().isoformat()
                    self.db.save_job(job)
                    
                    self._log_status_change(job_id, old_status, job.status, job.original_filename, job.user_id)
                    print(f"✅ [{job.user_id[:8]}...] Transcription completed: {job.original_filename}")
                    
                    # Clean up
                    try:
                        os.remove(transcript_path)
                    except:
                        pass
                        
            elif data.get("status") in ("Failed", "FailedWithPartialResults"):
                error_message = ""
                if "properties" in data and "error" in data["properties"]:
                    error_message = data["properties"]["error"].get("message", "")
                elif "error" in data:
                    error_message = data["error"].get("message", "")
                
                old_status = job.status
                job.status = "failed"
                job.error_message = f"Azure transcription failed: {error_message}"
                job.completed_at = datetime.now().isoformat()
                self.db.save_job(job)
                
                self._log_status_change(job_id, old_status, job.status, job.original_filename, job.user_id)
                print(f"❌ [{job.user_id[:8]}...] Transcription failed: {job.original_filename} - {error_message}")
                
        except Exception as e:
            print(f"❌ Status check failed for job {job_id[:8]}...: {str(e)}")
            job = self.db.get_job(job_id)
            if job:
                old_status = job.status
                job.status = "failed"
                job.error_message = f"Status check failed: {str(e)}"
                job.completed_at = datetime.now().isoformat()
                self.db.save_job(job)
                self._log_status_change(job_id, old_status, job.status, job.original_filename, job.user_id)
    
    def get_job_status(self, job_id: str) -> Optional[TranscriptionJob]:
        """Get current transcription job status"""
        return self.db.get_job(job_id)
    
    def get_user_history(self, user_id: str, limit: int = 50) -> List[TranscriptionJob]:
        """Get user's transcription history - PDPA compliant"""
        return self.db.get_user_jobs(user_id, limit)
    
    def get_all_history(self, limit: int = 100) -> List[TranscriptionJob]:
        """Get all transcription history across all users (admin view)"""
        return self.db.get_all_jobs(limit)
    
    def get_user_stats(self, user_id: str) -> Dict:
        """Get user transcription statistics"""
        return self.db.get_user_stats(user_id)
    
    def get_user_summary_stats(self, user_id: str) -> Dict:
        """Get user AI summary statistics"""
        return self.db.get_user_summary_stats(user_id)
    
    def download_transcript(self, job_id: str, user_id: str) -> Optional[str]:
        """Download transcript content - with user verification for PDPA compliance"""
        job = self.db.get_job(job_id)
        if job and job.user_id == user_id and job.transcript_text:
            return job.transcript_text
        return None
    
    # AI Summary integration methods
    def save_summary_job(self, job: SummaryJob):
        """Save AI summary job to database"""
        self.db.save_summary_job(job)
    
    def get_summary_job(self, job_id: str) -> Optional[SummaryJob]:
        """Get AI summary job by ID"""
        return self.db.get_summary_job(job_id)
    
    def get_user_summary_history(self, user_id: str, limit: int = 50) -> List[SummaryJob]:
        """Get user's AI summary history"""
        return self.db.get_user_summary_jobs(user_id, limit)
    
    # Authentication methods
    def register_user(self, email: str, username: str, password: str, gdpr_consent: bool = True,

                     data_retention_agreed: bool = True, marketing_consent: bool = False) -> Tuple[bool, str, Optional[str]]:
        """Register new user"""
        return self.db.create_user(email, username, password, gdpr_consent, data_retention_agreed, marketing_consent)
    
    def login_user(self, login: str, password: str) -> Tuple[bool, str, Optional[User]]:
        """Login user"""
        return self.db.authenticate_user(login, password)
    
    def get_user(self, user_id: str) -> Optional[User]:
        """Get user by ID"""
        return self.db.get_user_by_id(user_id)
    
    def update_user_consent(self, user_id: str, marketing_consent: bool) -> bool:
        """Update user marketing consent"""
        return self.db.update_user_consent(user_id, marketing_consent)
    
    def export_user_data(self, user_id: str) -> Dict:
        """Export comprehensive user data including AI summaries"""
        return self.db.export_user_data(user_id)
    
    def delete_user_account(self, user_id: str) -> bool:
        """Delete user account and all data"""
        return self.db.delete_user_account(user_id)
    
    def delete_user_summary_data(self, user_id: str) -> bool:
        """Delete user's AI summary data specifically"""
        return self.db.delete_user_summary_data(user_id)
    
    # Helper methods
    def _convert_to_audio(self, input_path, output_path, audio_format="wav"):
        """Convert audio/video file to specified audio format"""
        # Ensure output directory exists
        os.makedirs(os.path.dirname(output_path), exist_ok=True)
        
        if audio_format in {"wav", "alaw", "mulaw"}:
            cmd = [
                "ffmpeg", "-y", "-i", input_path,
                "-ar", "16000", "-ac", "1",
                output_path
            ]
        else:
            cmd = [
                "ffmpeg", "-y", "-i", input_path,
                output_path
            ]
        
        try:
            result = subprocess.run(
                cmd, 
                stdout=subprocess.PIPE, 
                stderr=subprocess.PIPE, 
                timeout=300,  # 5 minute timeout
                text=True
            )
            
            if result.returncode != 0:
                error_output = result.stderr
                raise Exception(f"FFmpeg conversion failed: {error_output}")
                
            # Verify output file exists and has content
            if not os.path.exists(output_path):
                raise Exception(f"Output file was not created: {output_path}")
            
            file_size = os.path.getsize(output_path)
            if file_size == 0:
                raise Exception(f"Output file is empty: {output_path}")
            
        except subprocess.TimeoutExpired:
            raise Exception(f"FFmpeg conversion timed out after 5 minutes")
        except Exception as e:
            if "FFmpeg conversion failed" in str(e):
                raise  # Re-raise our detailed error
            else:
                raise Exception(f"FFmpeg error: {str(e)}")
    
    def _upload_blob(self, local_file, blob_name, container_name=None):
        """Upload file to specified blob container"""
        if container_name is None:
            container_name = TRANSCRIPTS_CONTAINER
            
        blob_client = self.blob_service.get_blob_client(container=container_name, blob=blob_name)
        with open(local_file, "rb") as data:
            blob_client.upload_blob(data, overwrite=True)
        sas = AZURE_BLOB_SAS_TOKEN.lstrip("?")
        return f"{blob_client.url}?{sas}"
    
    def _create_transcription(self, audio_url, language, diarization_enabled, speakers, 

                            profanity, punctuation, timestamps, lexical, 

                            language_id_enabled=False, candidate_locales=None):
        """Create Azure Speech transcription job"""
        url = f"{AZURE_SPEECH_KEY_ENDPOINT}/speechtotext/{API_VERSION}/transcriptions"
        headers = {
            "Ocp-Apim-Subscription-Key": AZURE_SPEECH_KEY,
            "Content-Type": "application/json"
        }

        properties = {
            "profanityFilterMode": profanity,
            "punctuationMode": punctuation,
            "wordLevelTimestampsEnabled": timestamps,
            "displayFormWordLevelTimestampsEnabled": timestamps,
            "lexical": lexical
        }
        if diarization_enabled:
            properties["diarizationEnabled"] = True
            properties["diarization"] = {
                "speakers": {
                    "minCount": 1,
                    "maxCount": int(speakers)
                }
            }
        if language_id_enabled and candidate_locales:
            properties["languageIdentification"] = {
                "mode": "continuous",
                "candidateLocales": candidate_locales
            }
        
        properties = {k: v for k, v in properties.items() if v is not None}
        body = {
            "displayName": f"AI_Conference_Transcription_{uuid.uuid4()}",
            "description": "Enhanced batch speech-to-text with AI integration support",
            "locale": language,
            "contentUrls": [audio_url],
            "properties": properties,
            "customProperties": {}
        }
        r = requests.post(url, headers=headers, json=body)
        r.raise_for_status()
        trans_id = r.headers["Location"].split("/")[-1].split("?")[0]
        return trans_id
    
    def _get_transcription_result_url(self, trans_id):
        """Get transcription result URL from Azure"""
        url = f"{AZURE_SPEECH_KEY_ENDPOINT}/speechtotext/{API_VERSION}/transcriptions/{trans_id}"
        headers = {"Ocp-Apim-Subscription-Key": AZURE_SPEECH_KEY}
        
        r = requests.get(url, headers=headers)
        data = r.json()
        
        if data.get("status") == "Succeeded":
            files_url = None
            if "links" in data and "files" in data["links"]:
                files_url = data["links"]["files"]
            if files_url:
                r2 = requests.get(files_url, headers=headers)
                file_list = r2.json().get("values", [])
                for f in file_list:
                    if f.get("kind", "").lower() == "transcription":
                        return f["links"]["contentUrl"]
        return None
    
    def _fetch_transcript(self, content_url):
        """Enhanced transcript fetching with improved timestamp handling"""
        r = requests.get(content_url)
        try:
            j = r.json()
            out = []
            
            def get_text(phrase):
                if 'nBest' in phrase and phrase['nBest']:
                    return phrase['nBest'][0].get('display', '') or phrase.get('display', '')
                return phrase.get('display', '')

            def safe_offset(val):
                try:
                    return int(val)
                except (ValueError, TypeError):
                    return None

            def format_time(seconds):
                """Format seconds into HH:MM:SS format"""
                try:
                    td = timedelta(seconds=int(seconds))
                    hours, remainder = divmod(td.total_seconds(), 3600)
                    minutes, seconds = divmod(remainder, 60)
                    return f"{int(hours):02d}:{int(minutes):02d}:{int(seconds):02d}"
                except:
                    return "00:00:00"

            # Check if this is a diarization result or regular transcription
            if 'recognizedPhrases' in j:
                for phrase in j['recognizedPhrases']:
                    speaker_id = phrase.get('speaker', 0)  # Default to speaker 0 if not present
                    text = get_text(phrase)
                    
                    if not text.strip():
                        continue
                    
                    # Try to get timestamp from multiple possible locations
                    timestamp_seconds = None
                    
                    # Method 1: Direct offset from phrase
                    if 'offset' in phrase and phrase['offset'] is not None:
                        offset_100ns = safe_offset(phrase['offset'])
                        if offset_100ns is not None:
                            timestamp_seconds = offset_100ns / 10_000_000
                    
                    # Method 2: Offset from first word
                    if timestamp_seconds is None and 'words' in phrase and phrase['words']:
                        first_word = phrase['words'][0]
                        if 'offset' in first_word and first_word['offset'] is not None:
                            offset_100ns = safe_offset(first_word['offset'])
                            if offset_100ns is not None:
                                timestamp_seconds = offset_100ns / 10_000_000
                    
                    # Method 3: offsetInTicks (alternative field name)
                    if timestamp_seconds is None and 'offsetInTicks' in phrase:
                        offset_ticks = safe_offset(phrase['offsetInTicks'])
                        if offset_ticks is not None:
                            timestamp_seconds = offset_ticks / 10_000_000
                    
                    # Format output based on whether we have speaker diarization and timestamps
                    if timestamp_seconds is not None:
                        time_str = format_time(timestamp_seconds)
                        if 'speaker' in phrase:
                            # Speaker diarization with timestamp
                            out.append(f"[{time_str}] Speaker {speaker_id}: {text}")
                        else:
                            # Just timestamp, no speaker
                            out.append(f"[{time_str}] {text}")
                    else:
                        # No timestamp available
                        if 'speaker' in phrase:
                            out.append(f"Speaker {speaker_id}: {text}")
                        else:
                            out.append(text)
                
                if out:
                    return '\n\n'.join(out)
            
            # Fallback: handle combined results or other formats
            if 'combinedRecognizedPhrases' in j:
                combined_results = []
                for combined_phrase in j['combinedRecognizedPhrases']:
                    text = combined_phrase.get('display', '')
                    if text.strip():
                        combined_results.append(text)
                
                if combined_results:
                    return '\n\n'.join(combined_results)
            
            # Last resort: return raw JSON for debugging
            return json.dumps(j, ensure_ascii=False, indent=2)
            
        except Exception as e:
            return f"Unable to parse transcription result: {str(e)}\n\nRaw response: {r.text[:1000]}..."

# Global transcription manager instance
transcription_manager = TranscriptionManager()

# Backward compatibility functions
def allowed_file(filename):
    """Check if file extension is supported"""
    if not filename or filename in ["upload.unknown", ""]:
        return True  # Let FFmpeg handle unknown formats
    
    if '.' not in filename:
        return True  # No extension, let FFmpeg try
    
    ext = filename.rsplit('.', 1)[1].lower()
    supported_extensions = set(AUDIO_FORMATS) | {
        'mp4', 'mov', 'avi', 'mkv', 'webm', 'm4a', '3gp', 'f4v', 
        'wmv', 'asf', 'rm', 'rmvb', 'flv', 'mpg', 'mpeg', 'mts', 'vob',
        # Additional formats for AI processing
        'pdf', 'docx', 'doc', 'pptx', 'ppt', 'xlsx', 'xls', 'csv', 'txt', 'json',
        'jpg', 'jpeg', 'png', 'bmp', 'gif', 'tiff', 'webp'
    }
    
    return ext in supported_extensions

def generate_user_session():
    """Generate a unique user session ID - kept for compatibility"""
    return str(uuid.uuid4())