app/main.py

"""
CSM-1B TTS API main application.
Provides an OpenAI-compatible API for the CSM-1B text-to-speech model.
"""
import os
import time
import tempfile
import logging
from logging.handlers import RotatingFileHandler
import traceback
import asyncio
import glob
import torch
import uvicorn
from contextlib import asynccontextmanager
from fastapi import FastAPI, Depends, HTTPException, Request, Response
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse, FileResponse
from fastapi.staticfiles import StaticFiles
from app.api.routes import router as api_router
from app.db import connect_to_mongo, close_mongo_connection

# Setup logging
os.makedirs("logs", exist_ok=True)
log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

# Console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(log_format))

# File handler
file_handler = RotatingFileHandler(
    "logs/csm_tts_api.log", 
    maxBytes=10*1024*1024,  # 10MB
    backupCount=5
)
file_handler.setFormatter(logging.Formatter(log_format))

# Configure root logger
logging.basicConfig(
    level=logging.INFO,
    format=log_format,
    handlers=[console_handler, file_handler]
)
logger = logging.getLogger(__name__)
logger.info("Starting CSM-1B TTS API")

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan manager for startup and shutdown events."""
    # STARTUP EVENT
    logger.info("Starting application initialization")
    app.state.startup_time = time.time()
    app.state.generator = None  # Will be populated later if model loads
    app.state.logger = logger  # Make logger available to routes
    
    # Initialize database
    from app.db import init_db
    logger.info("Initializing database...")
    await init_db()
    logger.info("Database initialized")
    
    # Create necessary directories - use persistent locations
    APP_DIR = "/app"
    os.makedirs(os.path.join(APP_DIR, "models"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "tokenizers"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "voice_memories"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "voice_references"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "voice_profiles"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "cloned_voices"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "audio_cache"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "static"), exist_ok=True)
    os.makedirs(os.path.join(APP_DIR, "storage/audio"), exist_ok=True)  # For audio files
    os.makedirs(os.path.join(APP_DIR, "storage/text"), exist_ok=True)   # For text files
    os.makedirs(os.path.join(APP_DIR, "audiobooks"), exist_ok=True)  # Add audiobooks directory
    
    # Set tokenizer cache
    try:
        os.environ["TRANSFORMERS_CACHE"] = os.path.join(APP_DIR, "tokenizers")
        logger.info(f"Set tokenizer cache to: {os.environ['TRANSFORMERS_CACHE']}")
    except Exception as e:
        logger.error(f"Error setting tokenizer cache: {e}")
    
    # Install additional dependencies if needed
    try:
        import scipy
        import soundfile
        logger.info("Audio processing dependencies available")
    except ImportError as e:
        logger.warning(f"Audio processing dependency missing: {e}. Some audio enhancements may not work.")
        logger.warning("Consider installing: pip install scipy soundfile")
    
    # Check CUDA availability
    cuda_available = torch.cuda.is_available()
    if cuda_available:
        device_count = torch.cuda.device_count()
        device_name = torch.cuda.get_device_name(0) if device_count > 0 else "unknown"
        logger.info(f"CUDA is available: {device_count} device(s). Using {device_name}")
        # Report CUDA memory
        if hasattr(torch.cuda, 'get_device_properties'):
            total_memory = torch.cuda.get_device_properties(0).total_memory
            logger.info(f"Total CUDA memory: {total_memory / (1024**3):.2f} GB")
    else:
        logger.warning("CUDA is not available. Using CPU (this will be slow)")
    
    # Determine device and device mapping
    device = "cuda" if cuda_available else "cpu"
    device_map = os.environ.get("CSM_DEVICE_MAP", None)  # Options: "auto", "balanced", "sequential"
    if device_map and cuda_available:
        if torch.cuda.device_count() > 1:
            logger.info(f"Using device mapping strategy: {device_map} across {torch.cuda.device_count()} GPUs")
        else:
            logger.info("Device mapping requested but only one GPU available, ignoring device_map")
            device_map = None
    else:
        device_map = None
    
    logger.info(f"Using device: {device}")
    app.state.device = device
    app.state.device_map = device_map
    
    # Check if model file exists
    model_path = os.path.join(APP_DIR, "models", "ckpt.pt")
    if not os.path.exists(model_path):
        # Try to download at runtime if not present
        logger.info("Model not found. Attempting to download...")
        try:
            from huggingface_hub import hf_hub_download, login
            # Check for token in environment
            hf_token = os.environ.get("HF_TOKEN", "").strip()
            if hf_token:
                logger.info("Logging in to Hugging Face using provided token")
                try:
                    login(token=hf_token)
                    logger.info("Successfully logged in to Hugging Face")
                except Exception as e:
                    logger.error(f"Error logging in to Hugging Face: {e}")
                    logger.error("Will attempt to download model without authentication")
            else:
                logger.warning("No Hugging Face token provided. Some models may not be accessible")

            # Attempt to download the model
            try:
                logger.info("Downloading CSM-1B model from Hugging Face...")
                download_start = time.time()
                model_path = hf_hub_download(
                    repo_id="sesame/csm-1b", 
                    filename="ckpt.pt",
                    local_dir=os.path.join(APP_DIR, "models"),
                    token=hf_token if hf_token else None
                )
                download_time = time.time() - download_start
                logger.info(f"Model downloaded to {model_path} in {download_time:.2f} seconds")
            except Exception as e:
                error_stack = traceback.format_exc()
                logger.error(f"Error downloading model: {str(e)}\n{error_stack}")
                logger.error("Please ensure you have a valid Hugging Face token with access to the model")
                logger.error("Starting without model - API will return 503 Service Unavailable")
        except Exception as e:
            error_stack = traceback.format_exc()
            logger.error(f"Error downloading model: {str(e)}\n{error_stack}")
            logger.error("Please build the image with HF_TOKEN to download the model")
            logger.error("Starting without model - API will return 503 Service Unavailable")
    else:
        logger.info(f"Found existing model at {model_path}")
        logger.info(f"Model size: {os.path.getsize(model_path) / (1024 * 1024):.2f} MB")
    
    # Load the model
    try:
        logger.info("Loading CSM-1B model...")
        load_start = time.time()
        from app.generator import load_csm_1b
        app.state.generator = load_csm_1b(model_path, device, device_map)
        load_time = time.time() - load_start
        logger.info(f"Model loaded successfully in {load_time:.2f} seconds")
        
        # Store sample rate in app state
        app.state.sample_rate = app.state.generator.sample_rate
        logger.info(f"Model sample rate: {app.state.sample_rate} Hz")
        
        # Initialize voice enhancement system (this will create proper voice profiles)
        logger.info("Initializing voice enhancement system...")
        try:
            from app.voice_enhancement import initialize_voice_profiles, save_voice_profiles
            initialize_voice_profiles()
            app.state.voice_enhancement_enabled = True
            logger.info("Voice profiles initialized successfully")
        except Exception as e:
            error_stack = traceback.format_exc()
            logger.error(f"Error initializing voice profiles: {str(e)}\n{error_stack}")
            logger.warning("Voice enhancement features will be limited")
            app.state.voice_enhancement_enabled = False
        
        # Initialize voice memory system for consistent generation
        logger.info("Initializing voice memory system...")
        try:
            from app.voice_memory import initialize_voices
            initialize_voices(app.state.sample_rate)
            app.state.voice_memory_enabled = True
            logger.info("Voice memory system initialized")
        except Exception as e:
            logger.warning(f"Error initializing voice memory: {e}")
            app.state.voice_memory_enabled = False
        
        # Initialize voice cloning system
        try:
            logger.info("Initializing voice cloning system...")
            from app.voice_cloning import VoiceCloner, CLONED_VOICES_DIR
            # Update the cloned voices directory to use the persistent volume
            app.state.cloned_voices_dir = os.path.join(APP_DIR, "cloned_voices")  # Store path in app state for access
            os.makedirs(app.state.cloned_voices_dir, exist_ok=True)
            CLONED_VOICES_DIR = app.state.cloned_voices_dir  # Update the module constant
            
            # Initialize the voice cloner with proper device
            app.state.voice_cloner = VoiceCloner(app.state.generator, device=device)
            
            # Make sure existing voices are loaded
            app.state.voice_cloner._load_existing_voices()
            
            # Log the available voices
            cloned_voices = app.state.voice_cloner.list_voices()
            logger.info(f"Voice cloning system initialized with {len(cloned_voices)} existing voices")
            for voice in cloned_voices:
                logger.info(f"  - {voice.name} (ID: {voice.id}, Speaker ID: {voice.speaker_id})")
            
            # Flag for voice cloning availability
            app.state.voice_cloning_enabled = True
        except Exception as e:
            error_stack = traceback.format_exc()
            logger.error(f"Error initializing voice cloning: {e}\n{error_stack}")
            logger.warning("Voice cloning features will not be available")
            app.state.voice_cloning_enabled = False
        
        # Create prompt templates for consistent generation
        logger.info("Setting up prompt engineering templates...")
        try:
            from app.prompt_engineering import initialize_templates
            app.state.prompt_templates = initialize_templates()
            logger.info("Prompt templates initialized")
        except Exception as e:
            error_stack = traceback.format_exc()
            logger.error(f"Error initializing prompt templates: {e}\n{error_stack}")
            logger.warning("Voice consistency features will be limited")
        
        # Generate voice reference samples (runs in background to avoid blocking startup)
        async def generate_samples_async():
            try:
                logger.info("Starting voice reference generation (background task)...")
                from app.voice_enhancement import create_voice_segments
                create_voice_segments(app.state)
                logger.info("Voice reference generation completed")
            except Exception as e:
                error_stack = traceback.format_exc()
                logger.error(f"Error in voice reference generation: {str(e)}\n{error_stack}")
        
        # Start as a background task
        asyncio.create_task(generate_samples_async())
        
        # Initialize voice cache for all voices (standard + cloned)
        app.state.voice_cache = {}
        
        # Add standard voices
        standard_voices = ["alloy", "echo", "fable", "onyx", "nova", "shimmer"]
        for voice in standard_voices:
            app.state.voice_cache[voice] = []
        
        # Add cloned voices to cache if they exist
        if app.state.voice_cloning_enabled and hasattr(app.state, "voice_cloner"):
            for voice in app.state.voice_cloner.list_voices():
                app.state.voice_cache[voice.id] = []
                # Also add by name for more flexible lookup
                app.state.voice_cache[voice.name] = []
        
        # Create mapping from voice name/id to speaker_id for easy lookup
        app.state.voice_speaker_map = {
            "alloy": 0, "echo": 1, "fable": 2, "onyx": 3, "nova": 4, "shimmer": 5
        }
        
        # Add cloned voices to the speaker map
        if app.state.voice_cloning_enabled and hasattr(app.state, "voice_cloner"):
            for voice in app.state.voice_cloner.list_voices():
                app.state.voice_speaker_map[voice.id] = voice.speaker_id
                app.state.voice_speaker_map[voice.name] = voice.speaker_id
                app.state.voice_speaker_map[str(voice.speaker_id)] = voice.speaker_id
        
        # Compile voice information for API
        app.state.available_voices = standard_voices.copy()
        if app.state.voice_cloning_enabled and hasattr(app.state, "voice_cloner"):
            for voice in app.state.voice_cloner.list_voices():
                app.state.available_voices.append(voice.id)
                app.state.available_voices.append(voice.name)
        
        # Store model information for API endpoints
        app.state.model_info = {
            "name": "CSM-1B",
            "device": device,
            "device_map": device_map,
            "sample_rate": app.state.sample_rate,
            "standard_voices": standard_voices,
            "cloned_voices": [v.id for v in app.state.voice_cloner.list_voices()] if app.state.voice_cloning_enabled else [],
            "voice_enhancement_enabled": app.state.voice_enhancement_enabled,
            "voice_memory_enabled": app.state.voice_memory_enabled,
            "voice_cloning_enabled": app.state.voice_cloning_enabled,
            "streaming_enabled": True
        }
        
        # Create a function to access all voices in a standardized format
        def get_all_available_voices():
            """Helper function to get all available voices for API endpoints"""
            # Standard voices with fixed descriptions
            all_voices = [
                {"voice_id": "alloy", "name": "Alloy", "description": "Balanced and natural"},
                {"voice_id": "echo", "name": "Echo", "description": "Resonant and deeper"},
                {"voice_id": "fable", "name": "Fable", "description": "Bright and higher-pitched"},
                {"voice_id": "onyx", "name": "Onyx", "description": "Deep and authoritative"},
                {"voice_id": "nova", "name": "Nova", "description": "Warm and smooth"},
                {"voice_id": "shimmer", "name": "Shimmer", "description": "Light and airy"}
            ]
            
            # Add cloned voices if available
            if app.state.voice_cloning_enabled and hasattr(app.state, "voice_cloner"):
                for voice in app.state.voice_cloner.list_voices():
                    all_voices.append({
                        "voice_id": voice.id,
                        "name": voice.name,
                        "description": voice.description or f"Cloned voice: {voice.name}"
                    })
            
            return all_voices
        
        app.state.get_all_voices = get_all_available_voices
        
        # Add helper function to lookup voice info
        def get_voice_info(voice_identifier):
            """Look up voice information based on name, ID, or speaker_id"""
            # Check standard voices
            if voice_identifier in standard_voices:
                return {
                    "type": "standard",
                    "voice_id": voice_identifier,
                    "name": voice_identifier,
                    "speaker_id": standard_voices.index(voice_identifier)
                }
            
            # Look for cloned voice
            if not app.state.voice_cloning_enabled or not hasattr(app.state, "voice_cloner"):
                return None
                
            # Check by ID
            if voice_identifier in app.state.voice_cloner.cloned_voices:
                voice = app.state.voice_cloner.cloned_voices[voice_identifier]
                return {
                    "type": "cloned",
                    "voice_id": voice.id,
                    "name": voice.name,
                    "speaker_id": voice.speaker_id
                }
                
            # Check by name
            for v_id, voice in app.state.voice_cloner.cloned_voices.items():
                if voice.name == voice_identifier:
                    return {
                        "type": "cloned",
                        "voice_id": voice.id,
                        "name": voice.name,
                        "speaker_id": voice.speaker_id
                    }
                    
            # Check by speaker_id (string representation)
            try:
                speaker_id = int(voice_identifier)
                # Check if any cloned voice has this speaker_id
                for v_id, voice in app.state.voice_cloner.cloned_voices.items():
                    if voice.speaker_id == speaker_id:
                        return {
                            "type": "cloned", 
                            "voice_id": voice.id,
                            "name": voice.name,
                            "speaker_id": speaker_id
                        }
            except (ValueError, TypeError):
                pass
                
            # No match found
            return None
            
        app.state.get_voice_info = get_voice_info
        
        # Set up audio cache
        app.state.audio_cache_enabled = os.environ.get("ENABLE_AUDIO_CACHE", "true").lower() == "true"
        if app.state.audio_cache_enabled:
            app.state.audio_cache_dir = os.path.join(APP_DIR, "audio_cache")
            logger.info(f"Audio cache enabled, cache dir: {app.state.audio_cache_dir}")
        
        # Log GPU utilization after model loading
        if cuda_available:
            memory_allocated = torch.cuda.memory_allocated() / (1024**3)
            memory_reserved = torch.cuda.memory_reserved() / (1024**3)
            logger.info(f"GPU memory: {memory_allocated:.2f} GB allocated, {memory_reserved:.2f} GB reserved")
            
            if torch.cuda.device_count() > 1 and device_map:
                logger.info("Multi-GPU setup active with the following memory usage:")
                for i in range(torch.cuda.device_count()):
                    memory_allocated = torch.cuda.memory_allocated(i) / (1024**3)
                    memory_reserved = torch.cuda.memory_reserved(i) / (1024**3)
                    logger.info(f"GPU {i}: {memory_allocated:.2f} GB allocated, {memory_reserved:.2f} GB reserved")
        
        # Set up scheduled tasks
        try:
            # Create a background task for periodic voice profile backup
            async def periodic_voice_profile_backup(interval_hours=6):
                """Periodically save voice profiles to persistent storage."""
                while True:
                    try:
                        # Wait for the specified interval
                        await asyncio.sleep(interval_hours * 3600)
                        
                        # Log the backup
                        timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
                        logger.info(f"Scheduled voice profile backup started at {timestamp}")
                        
                        # Save voice profiles
                        if hasattr(app.state, "voice_enhancement_enabled") and app.state.voice_enhancement_enabled:
                            from app.voice_enhancement import save_voice_profiles
                            save_voice_profiles()
                            logger.info("Voice profiles saved successfully")
                            
                        # Save voice memories
                        if hasattr(app.state, "voice_memory_enabled") and app.state.voice_memory_enabled:
                            from app.voice_memory import VOICE_MEMORIES
                            for voice_name, memory in VOICE_MEMORIES.items():
                                memory.save()
                            logger.info("Voice memories saved successfully")
                            
                    except Exception as e:
                        logger.error(f"Error in periodic voice profile backup: {e}")
            
            # Start the scheduled task
            asyncio.create_task(periodic_voice_profile_backup(interval_hours=6))
            logger.info("Started scheduled voice profile backup task")
            
        except Exception as e:
            logger.warning(f"Failed to set up scheduled tasks: {e}")
        
        logger.info(f"CSM-1B TTS API is ready on {device} with sample rate {app.state.sample_rate}")
        logger.info(f"Standard voices: {standard_voices}")
        cloned_count = len(app.state.voice_cloner.list_voices()) if app.state.voice_cloning_enabled else 0
        logger.info(f"Cloned voices: {cloned_count}")
        
    except Exception as e:
        error_stack = traceback.format_exc()
        logger.error(f"Error loading model: {str(e)}\n{error_stack}")
        app.state.generator = None
    
    # Calculate total startup time
    startup_time = time.time() - app.state.startup_time
    logger.info(f"Application startup completed in {startup_time:.2f} seconds")
    
    yield  # This is where the application runs
    
    # SHUTDOWN EVENT
    logger.info("Application shutdown initiated")
    
    # Clean up model resources
    if hasattr(app.state, "generator") and app.state.generator is not None:
        try:
            # Clean up CUDA memory if available
            if torch.cuda.is_available():
                logger.info("Clearing CUDA cache")
                torch.cuda.empty_cache()
                torch.cuda.synchronize()
        except Exception as e:
            logger.error(f"Error during CUDA cleanup: {e}")
    
    # Save voice profiles if they've been updated
    try:
        if hasattr(app.state, "voice_enhancement_enabled") and app.state.voice_enhancement_enabled:
            from app.voice_enhancement import save_voice_profiles
            logger.info("Saving voice profiles...")
            save_voice_profiles()
            logger.info("Voice profiles saved successfully")
    except Exception as e:
        logger.error(f"Error saving voice profiles: {e}")
    
    # Save voice memories if they've been updated
    try:
        if hasattr(app.state, "voice_memory_enabled") and app.state.voice_memory_enabled:
            from app.voice_memory import VOICE_MEMORIES
            logger.info("Saving voice memories...")
            for voice_name, memory in VOICE_MEMORIES.items():
                memory.save()
            logger.info("Voice memories saved successfully")
    except Exception as e:
        logger.error(f"Error saving voice memories: {e}")
    
    # Clean up any temporary files
    try:
        for temp_file in glob.glob(os.path.join(tempfile.gettempdir(), "csm_tts_*")):
            try:
                os.remove(temp_file)
                logger.info(f"Removed temporary file: {temp_file}")
            except:
                pass
    except Exception as e:
        logger.warning(f"Error cleaning up temporary files: {e}")
    
    logger.info("Application shutdown complete")
    
# Initialize FastAPI app
app = FastAPI(
    title="CSM-1B TTS API",
    description="OpenAI-compatible TTS API using the CSM-1B model from Sesame",
    version="1.0.0",
    lifespan=lifespan
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Create static and other required directories
os.makedirs("/app/static", exist_ok=True)
os.makedirs("/app/cloned_voices", exist_ok=True)

# Mount the static files directory
app.mount("/static", StaticFiles(directory="/app/static"), name="static")

# Include routers
app.include_router(api_router, prefix="/api/v1")

# Add OpenAI compatible route
app.include_router(api_router, prefix="/v1")

# Add voice cloning routes
from app.api.voice_cloning_routes import router as voice_cloning_router
app.include_router(voice_cloning_router, prefix="/api/v1")
app.include_router(voice_cloning_router, prefix="/v1")

# Add streaming routes
from app.api.streaming import router as streaming_router
app.include_router(streaming_router, prefix="/api/v1")
app.include_router(streaming_router, prefix="/v1")

# Add audiobook routes
from app.api.audiobook_routes import router as audiobook_router
app.include_router(audiobook_router, prefix="/api/v1")
app.include_router(audiobook_router, prefix="/v1")

# Add realtime conversation routes
from app.api.realtime import router as realtime_router
app.include_router(realtime_router, prefix="/api/v1")
app.include_router(realtime_router, prefix="/v1")

# Middleware for request timing
@app.middleware("http")
async def add_process_time_header(request: Request, call_next):
    """Middleware to track request processing time."""
    start_time = time.time()
    response = await call_next(request)
    process_time = time.time() - start_time
    response.headers["X-Process-Time"] = str(process_time)
    logger.debug(f"Request to {request.url.path} processed in {process_time:.3f} seconds")
    return response

# Health check endpoint
@app.get("/health", include_in_schema=False)
async def health_check(request: Request):
    """Health check endpoint that returns the status of the API."""
    model_status = "healthy" if hasattr(request.app.state, "generator") and request.app.state.generator is not None else "unhealthy"
    uptime = time.time() - getattr(request.app.state, "startup_time", time.time())

    # Get voice information
    standard_voices = ["alloy", "echo", "fable", "onyx", "nova", "shimmer"]
    cloned_voices = []
    
    if hasattr(request.app.state, "voice_cloner") and request.app.state.voice_cloner is not None:
        cloned_voices = [
            {"id": v.id, "name": v.name, "speaker_id": v.speaker_id}
            for v in request.app.state.voice_cloner.list_voices()
        ]
    
    # Get CUDA memory stats if available
    cuda_stats = None
    if torch.cuda.is_available():
        cuda_stats = {
            "allocated_gb": torch.cuda.memory_allocated() / (1024**3),
            "reserved_gb": torch.cuda.memory_reserved() / (1024**3)
        }
    
    return {
        "status": model_status,
        "uptime": f"{uptime:.2f} seconds",
        "device": getattr(request.app.state, "device", "unknown"),
        "model": "CSM-1B",
        "standard_voices": standard_voices,
        "cloned_voices": cloned_voices,
        "cloned_voices_count": len(cloned_voices),
        "sample_rate": getattr(request.app.state, "sample_rate", 0),
        "enhancements": "enabled" if hasattr(request.app.state, "model_info") and 
                      request.app.state.model_info.get("voice_enhancement_enabled", False) else "disabled",
        "streaming": "enabled",
        "cuda": cuda_stats,
        "version": "1.0.0"
    }

# Version endpoint
@app.get("/version", include_in_schema=False)
async def version():
    """Version endpoint that returns API version information."""
    return {
        "api_version": "1.0.0",
        "model_version": "CSM-1B",
        "compatible_with": "OpenAI TTS v1",
        "enhancements": "voice consistency and audio quality v1.0",
        "voice_cloning": "enabled" if hasattr(app.state, "voice_cloner") else "disabled",
        "streaming": "enabled"
    }

# Voice cloning UI endpoint
@app.get("/voice-cloning", include_in_schema=False)
async def voice_cloning_ui():
    """Voice cloning UI endpoint."""
    return FileResponse("/app/static/voice-cloning.html")

# Streaming demo endpoint
@app.get("/streaming-demo", include_in_schema=False)
async def streaming_demo():
    """Streaming TTS demo endpoint."""
    return FileResponse("/app/static/streaming-demo.html")

@app.get("/", include_in_schema=False)
async def root():
    """Root endpoint that redirects to docs."""
    logger.debug("Root endpoint accessed, redirecting to docs")
    return RedirectResponse(url="/docs")

@app.on_event("startup")
async def startup_db_client():
    """Initialize MongoDB connection on startup."""
    await connect_to_mongo()

@app.on_event("shutdown")
async def shutdown_db_client():
    """Close MongoDB connection on shutdown."""
    await close_mongo_connection()

if __name__ == "__main__":
    # Get port from environment or use default
    port = int(os.environ.get("PORT", 7860))
    
    # Development mode flag
    dev_mode = os.environ.get("DEV_MODE", "false").lower() == "true"
    
    # Log level (default to INFO, but can be overridden)
    log_level = os.environ.get("LOG_LEVEL", "INFO").upper()
    logging.getLogger().setLevel(log_level)
    
    # Check for audio enhancement and voice cloning flags
    enable_enhancements = os.environ.get("ENABLE_ENHANCEMENTS", "true").lower() == "true"
    enable_voice_cloning = os.environ.get("ENABLE_VOICE_CLONING", "true").lower() == "true"
    
    if not enable_enhancements:
        logger.warning("Voice enhancements disabled by environment variable")
    if not enable_voice_cloning:
        logger.warning("Voice cloning disabled by environment variable")
    
    logger.info(f"Voice enhancements: {'enabled' if enable_enhancements else 'disabled'}")
    logger.info(f"Voice cloning: {'enabled' if enable_voice_cloning else 'disabled'}")
    logger.info(f"Streaming: enabled")
    logger.info(f"Log level: {log_level}")
    
    if dev_mode:
        logger.info(f"Running in development mode with auto-reload enabled on port {port}")
        uvicorn.run(
            "app.main:app", 
            host="0.0.0.0", 
            port=port, 
            reload=True, 
            log_level=log_level.lower()
        )
    else:
        logger.info(f"Running in production mode on port {port}")
        uvicorn.run(
            "app.main:app", 
            host="0.0.0.0", 
            port=port, 
            reload=False, 
            log_level=log_level.lower()
        )