"""
LightRAG FastAPI Server
"""

from fastapi import FastAPI, Depends, HTTPException, status
import asyncio
import os
import logging
import logging.config
import signal
import sys
import uvicorn
import pipmaster as pm
from fastapi.staticfiles import StaticFiles
from fastapi.responses import RedirectResponse
from pathlib import Path
import configparser
from ascii_colors import ASCIIColors
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from dotenv import load_dotenv
from lightrag.api.utils_api import (
    get_combined_auth_dependency,
    display_splash_screen,
    check_env_file,
)
from .config import (
    global_args,
    update_uvicorn_mode_config,
    get_default_host,
)
from lightrag.utils import get_env_value
from lightrag import LightRAG, __version__ as core_version
from lightrag.api import __api_version__
from lightrag.types import GPTKeywordExtractionFormat
from lightrag.utils import EmbeddingFunc
from lightrag.constants import (
    DEFAULT_LOG_MAX_BYTES,
    DEFAULT_LOG_BACKUP_COUNT,
    DEFAULT_LOG_FILENAME,
)
from lightrag.api.routers.document_routes import (
    DocumentManager,
    create_document_routes,
    run_scanning_process,
)
from lightrag.api.routers.query_routes import create_query_routes
from lightrag.api.routers.graph_routes import create_graph_routes
from lightrag.api.routers.ollama_api import OllamaAPI

from lightrag.utils import logger, set_verbose_debug
from lightrag.kg.shared_storage import (
    get_namespace_data,
    get_pipeline_status_lock,
    initialize_pipeline_status,
    cleanup_keyed_lock,
    finalize_share_data,
)
from fastapi.security import OAuth2PasswordRequestForm
from lightrag.api.auth import auth_handler

# use the .env that is inside the current folder
# allows to use different .env file for each lightrag instance
# the OS environment variables take precedence over the .env file
load_dotenv(dotenv_path=".env", override=False)


webui_title = os.getenv("WEBUI_TITLE")
webui_description = os.getenv("WEBUI_DESCRIPTION")

# Initialize config parser
config = configparser.ConfigParser()
config.read("config.ini")

# Global authentication configuration
auth_configured = bool(auth_handler.accounts)


def setup_signal_handlers():
    """Setup signal handlers for graceful shutdown"""

    def signal_handler(sig, frame):
        print(f"\n\nReceived signal {sig}, shutting down gracefully...")
        print(f"Process ID: {os.getpid()}")

        # Release shared resources
        finalize_share_data()

        # Exit with success status
        sys.exit(0)

    # Register signal handlers
    signal.signal(signal.SIGINT, signal_handler)  # Ctrl+C
    signal.signal(signal.SIGTERM, signal_handler)  # kill command


def create_app(args):
    # Setup logging
    logger.setLevel(args.log_level)
    set_verbose_debug(args.verbose)

    # Verify that bindings are correctly setup
    if args.llm_binding not in [
        "lollms",
        "ollama",
        "openai",
        "azure_openai",
        "aws_bedrock",
    ]:
        raise Exception("llm binding not supported")

    if args.embedding_binding not in [
        "lollms",
        "ollama",
        "openai",
        "azure_openai",
        "aws_bedrock",
        "jina",
    ]:
        raise Exception("embedding binding not supported")

    # Set default hosts if not provided
    if args.llm_binding_host is None:
        args.llm_binding_host = get_default_host(args.llm_binding)

    if args.embedding_binding_host is None:
        args.embedding_binding_host = get_default_host(args.embedding_binding)

    # Add SSL validation
    if args.ssl:
        if not args.ssl_certfile or not args.ssl_keyfile:
            raise Exception(
                "SSL certificate and key files must be provided when SSL is enabled"
            )
        if not os.path.exists(args.ssl_certfile):
            raise Exception(f"SSL certificate file not found: {args.ssl_certfile}")
        if not os.path.exists(args.ssl_keyfile):
            raise Exception(f"SSL key file not found: {args.ssl_keyfile}")

    # Check if API key is provided either through env var or args
    api_key = os.getenv("LIGHTRAG_API_KEY") or args.key

    # Initialize document manager with workspace support for data isolation
    doc_manager = DocumentManager(args.input_dir, workspace=args.workspace)

    @asynccontextmanager
    async def lifespan(app: FastAPI):
        """Lifespan context manager for startup and shutdown events"""
        # Store background tasks
        app.state.background_tasks = set()

        try:
            # Initialize database connections
            await rag.initialize_storages()
            await initialize_pipeline_status()

            # Data migration regardless of storage implementation
            await rag.check_and_migrate_data()

            pipeline_status = await get_namespace_data("pipeline_status")

            should_start_autoscan = False
            async with get_pipeline_status_lock():
                # Auto scan documents if enabled
                if args.auto_scan_at_startup:
                    if not pipeline_status.get("autoscanned", False):
                        pipeline_status["autoscanned"] = True
                        should_start_autoscan = True

            # Only run auto scan when no other process started it first
            if should_start_autoscan:
                # Create background task
                task = asyncio.create_task(run_scanning_process(rag, doc_manager))
                app.state.background_tasks.add(task)
                task.add_done_callback(app.state.background_tasks.discard)
                logger.info(f"Process {os.getpid()} auto scan task started at startup.")

            ASCIIColors.green("\nServer is ready to accept connections! 🚀\n")

            yield

        finally:
            # Clean up database connections
            await rag.finalize_storages()

            # Clean up shared data
            finalize_share_data()

    # Initialize FastAPI
    app_kwargs = {
        "title": "LightRAG Server API",
        "description": (
            "Providing API for LightRAG core, Web UI and Ollama Model Emulation"
            + "(With authentication)"
            if api_key
            else ""
        ),
        "version": __api_version__,
        "openapi_url": "/openapi.json",  # Explicitly set OpenAPI schema URL
        "docs_url": "/docs",  # Explicitly set docs URL
        "redoc_url": "/redoc",  # Explicitly set redoc URL
        "lifespan": lifespan,
    }

    # Configure Swagger UI parameters
    # Enable persistAuthorization and tryItOutEnabled for better user experience
    app_kwargs["swagger_ui_parameters"] = {
        "persistAuthorization": True,
        "tryItOutEnabled": True,
    }

    app = FastAPI(**app_kwargs)

    def get_cors_origins():
        """Get allowed origins from global_args
        Returns a list of allowed origins, defaults to ["*"] if not set
        """
        origins_str = global_args.cors_origins
        if origins_str == "*":
            return ["*"]
        return [origin.strip() for origin in origins_str.split(",")]

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

    # Create combined auth dependency for all endpoints
    combined_auth = get_combined_auth_dependency(api_key)

    # Create working directory if it doesn't exist
    Path(args.working_dir).mkdir(parents=True, exist_ok=True)
    if args.llm_binding == "lollms" or args.embedding_binding == "lollms":
        from lightrag.llm.lollms import lollms_model_complete, lollms_embed
    if args.llm_binding == "ollama" or args.embedding_binding == "ollama":
        from lightrag.llm.ollama import ollama_model_complete, ollama_embed
        from lightrag.llm.binding_options import OllamaLLMOptions
    if args.llm_binding == "openai" or args.embedding_binding == "openai":
        from lightrag.llm.openai import openai_complete_if_cache, openai_embed
        from lightrag.llm.binding_options import OpenAILLMOptions
    if args.llm_binding == "azure_openai" or args.embedding_binding == "azure_openai":
        from lightrag.llm.azure_openai import (
            azure_openai_complete_if_cache,
            azure_openai_embed,
        )
    if args.llm_binding == "aws_bedrock" or args.embedding_binding == "aws_bedrock":
        from lightrag.llm.bedrock import bedrock_complete_if_cache, bedrock_embed
    if args.embedding_binding == "ollama":
        from lightrag.llm.binding_options import OllamaEmbeddingOptions
    if args.embedding_binding == "jina":
        from lightrag.llm.jina import jina_embed

    async def openai_alike_model_complete(
        prompt,
        system_prompt=None,
        history_messages=None,
        keyword_extraction=False,
        **kwargs,
    ) -> str:
        keyword_extraction = kwargs.pop("keyword_extraction", None)
        if keyword_extraction:
            kwargs["response_format"] = GPTKeywordExtractionFormat
        if history_messages is None:
            history_messages = []

        # Use OpenAI LLM options if available, otherwise fallback to global temperature
        if args.llm_binding == "openai":
            openai_options = OpenAILLMOptions.options_dict(args)
            kwargs.update(openai_options)
        else:
            kwargs["temperature"] = args.temperature

        return await openai_complete_if_cache(
            args.llm_model,
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            base_url=args.llm_binding_host,
            api_key=args.llm_binding_api_key,
            **kwargs,
        )

    async def azure_openai_model_complete(
        prompt,
        system_prompt=None,
        history_messages=None,
        keyword_extraction=False,
        **kwargs,
    ) -> str:
        keyword_extraction = kwargs.pop("keyword_extraction", None)
        if keyword_extraction:
            kwargs["response_format"] = GPTKeywordExtractionFormat
        if history_messages is None:
            history_messages = []

        # Use OpenAI LLM options if available, otherwise fallback to global temperature
        if args.llm_binding == "azure_openai":
            openai_options = OpenAILLMOptions.options_dict(args)
            kwargs.update(openai_options)
        else:
            kwargs["temperature"] = args.temperature

        return await azure_openai_complete_if_cache(
            args.llm_model,
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            base_url=args.llm_binding_host,
            api_key=os.getenv("AZURE_OPENAI_API_KEY"),
            api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"),
            **kwargs,
        )

    async def bedrock_model_complete(
        prompt,
        system_prompt=None,
        history_messages=None,
        keyword_extraction=False,
        **kwargs,
    ) -> str:
        keyword_extraction = kwargs.pop("keyword_extraction", None)
        if keyword_extraction:
            kwargs["response_format"] = GPTKeywordExtractionFormat
        if history_messages is None:
            history_messages = []

        # Use global temperature for Bedrock
        kwargs["temperature"] = args.temperature

        return await bedrock_complete_if_cache(
            args.llm_model,
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            **kwargs,
        )

    embedding_func = EmbeddingFunc(
        embedding_dim=args.embedding_dim,
        func=lambda texts: (
            lollms_embed(
                texts,
                embed_model=args.embedding_model,
                host=args.embedding_binding_host,
                api_key=args.embedding_binding_api_key,
            )
            if args.embedding_binding == "lollms"
            else (
                ollama_embed(
                    texts,
                    embed_model=args.embedding_model,
                    host=args.embedding_binding_host,
                    api_key=args.embedding_binding_api_key,
                    options=OllamaEmbeddingOptions.options_dict(args),
                )
                if args.embedding_binding == "ollama"
                else (
                    azure_openai_embed(
                        texts,
                        model=args.embedding_model,  # no host is used for openai,
                        api_key=args.embedding_binding_api_key,
                    )
                    if args.embedding_binding == "azure_openai"
                    else (
                        bedrock_embed(
                            texts,
                            model=args.embedding_model,
                        )
                        if args.embedding_binding == "aws_bedrock"
                        else (
                            jina_embed(
                                texts,
                                dimensions=args.embedding_dim,
                                base_url=args.embedding_binding_host,
                                api_key=args.embedding_binding_api_key,
                            )
                            if args.embedding_binding == "jina"
                            else openai_embed(
                                texts,
                                model=args.embedding_model,
                                base_url=args.embedding_binding_host,
                                api_key=args.embedding_binding_api_key,
                            )
                        )
                    )
                )
            )
        ),
    )

    # Configure rerank function if model and API are configured
    rerank_model_func = None
    if args.rerank_binding_api_key and args.rerank_binding_host:
        from lightrag.rerank import custom_rerank

        async def server_rerank_func(
            query: str, documents: list, top_n: int = None, **kwargs
        ):
            """Server rerank function with configuration from environment variables"""
            return await custom_rerank(
                query=query,
                documents=documents,
                model=args.rerank_model,
                base_url=args.rerank_binding_host,
                api_key=args.rerank_binding_api_key,
                top_n=top_n,
                **kwargs,
            )

        rerank_model_func = server_rerank_func
        logger.info(
            f"Rerank model configured: {args.rerank_model} (can be enabled per query)"
        )
    else:
        logger.info(
            "Rerank model not configured. Set RERANK_BINDING_API_KEY and RERANK_BINDING_HOST to enable reranking."
        )

    # Create ollama_server_infos from command line arguments
    from lightrag.api.config import OllamaServerInfos

    ollama_server_infos = OllamaServerInfos(
        name=args.simulated_model_name, tag=args.simulated_model_tag
    )

    # Initialize RAG
    if args.llm_binding in ["lollms", "ollama", "openai", "aws_bedrock"]:
        rag = LightRAG(
            working_dir=args.working_dir,
            workspace=args.workspace,
            llm_model_func=(
                lollms_model_complete
                if args.llm_binding == "lollms"
                else (
                    ollama_model_complete
                    if args.llm_binding == "ollama"
                    else bedrock_model_complete
                    if args.llm_binding == "aws_bedrock"
                    else openai_alike_model_complete
                )
            ),
            llm_model_name=args.llm_model,
            llm_model_max_async=args.max_async,
            summary_max_tokens=args.max_tokens,
            chunk_token_size=int(args.chunk_size),
            chunk_overlap_token_size=int(args.chunk_overlap_size),
            llm_model_kwargs=(
                {
                    "host": args.llm_binding_host,
                    "timeout": args.timeout,
                    "options": OllamaLLMOptions.options_dict(args),
                    "api_key": args.llm_binding_api_key,
                }
                if args.llm_binding == "lollms" or args.llm_binding == "ollama"
                else {}
            ),
            embedding_func=embedding_func,
            kv_storage=args.kv_storage,
            graph_storage=args.graph_storage,
            vector_storage=args.vector_storage,
            doc_status_storage=args.doc_status_storage,
            vector_db_storage_cls_kwargs={
                "cosine_better_than_threshold": args.cosine_threshold
            },
            enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
            enable_llm_cache=args.enable_llm_cache,
            rerank_model_func=rerank_model_func,
            max_parallel_insert=args.max_parallel_insert,
            max_graph_nodes=args.max_graph_nodes,
            addon_params={"language": args.summary_language},
            ollama_server_infos=ollama_server_infos,
        )
    else:  # azure_openai
        rag = LightRAG(
            working_dir=args.working_dir,
            workspace=args.workspace,
            llm_model_func=azure_openai_model_complete,
            chunk_token_size=int(args.chunk_size),
            chunk_overlap_token_size=int(args.chunk_overlap_size),
            llm_model_kwargs={
                "timeout": args.timeout,
            },
            llm_model_name=args.llm_model,
            llm_model_max_async=args.max_async,
            summary_max_tokens=args.max_tokens,
            embedding_func=embedding_func,
            kv_storage=args.kv_storage,
            graph_storage=args.graph_storage,
            vector_storage=args.vector_storage,
            doc_status_storage=args.doc_status_storage,
            vector_db_storage_cls_kwargs={
                "cosine_better_than_threshold": args.cosine_threshold
            },
            enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
            enable_llm_cache=args.enable_llm_cache,
            rerank_model_func=rerank_model_func,
            max_parallel_insert=args.max_parallel_insert,
            max_graph_nodes=args.max_graph_nodes,
            addon_params={"language": args.summary_language},
            ollama_server_infos=ollama_server_infos,
        )

    # Add routes
    app.include_router(
        create_document_routes(
            rag,
            doc_manager,
            api_key,
        )
    )
    app.include_router(create_query_routes(rag, api_key, args.top_k))
    app.include_router(create_graph_routes(rag, api_key))

    # Add Ollama API routes
    ollama_api = OllamaAPI(rag, top_k=args.top_k, api_key=api_key)
    app.include_router(ollama_api.router, prefix="/api")

    @app.get("/")
    async def redirect_to_webui():
        """Redirect root path to /webui"""
        return RedirectResponse(url="/webui")

    @app.get("/auth-status")
    async def get_auth_status():
        """Get authentication status and guest token if auth is not configured"""

        if not auth_handler.accounts:
            # Authentication not configured, return guest token
            guest_token = auth_handler.create_token(
                username="guest", role="guest", metadata={"auth_mode": "disabled"}
            )
            return {
                "auth_configured": False,
                "access_token": guest_token,
                "token_type": "bearer",
                "auth_mode": "disabled",
                "message": "Authentication is disabled. Using guest access.",
                "core_version": core_version,
                "api_version": __api_version__,
                "webui_title": webui_title,
                "webui_description": webui_description,
            }

        return {
            "auth_configured": True,
            "auth_mode": "enabled",
            "core_version": core_version,
            "api_version": __api_version__,
            "webui_title": webui_title,
            "webui_description": webui_description,
        }

    @app.post("/login")
    async def login(form_data: OAuth2PasswordRequestForm = Depends()):
        if not auth_handler.accounts:
            # Authentication not configured, return guest token
            guest_token = auth_handler.create_token(
                username="guest", role="guest", metadata={"auth_mode": "disabled"}
            )
            return {
                "access_token": guest_token,
                "token_type": "bearer",
                "auth_mode": "disabled",
                "message": "Authentication is disabled. Using guest access.",
                "core_version": core_version,
                "api_version": __api_version__,
                "webui_title": webui_title,
                "webui_description": webui_description,
            }
        username = form_data.username
        if auth_handler.accounts.get(username) != form_data.password:
            raise HTTPException(
                status_code=status.HTTP_401_UNAUTHORIZED, detail="Incorrect credentials"
            )

        # Regular user login
        user_token = auth_handler.create_token(
            username=username, role="user", metadata={"auth_mode": "enabled"}
        )
        return {
            "access_token": user_token,
            "token_type": "bearer",
            "auth_mode": "enabled",
            "core_version": core_version,
            "api_version": __api_version__,
            "webui_title": webui_title,
            "webui_description": webui_description,
        }

    @app.get("/health", dependencies=[Depends(combined_auth)])
    async def get_status():
        """Get current system status"""
        try:
            pipeline_status = await get_namespace_data("pipeline_status")

            if not auth_configured:
                auth_mode = "disabled"
            else:
                auth_mode = "enabled"

            # Cleanup expired keyed locks and get status
            keyed_lock_info = cleanup_keyed_lock()

            return {
                "status": "healthy",
                "working_directory": str(args.working_dir),
                "input_directory": str(args.input_dir),
                "configuration": {
                    # LLM configuration binding/host address (if applicable)/model (if applicable)
                    "llm_binding": args.llm_binding,
                    "llm_binding_host": args.llm_binding_host,
                    "llm_model": args.llm_model,
                    # embedding model configuration binding/host address (if applicable)/model (if applicable)
                    "embedding_binding": args.embedding_binding,
                    "embedding_binding_host": args.embedding_binding_host,
                    "embedding_model": args.embedding_model,
                    "max_tokens": args.max_tokens,
                    "kv_storage": args.kv_storage,
                    "doc_status_storage": args.doc_status_storage,
                    "graph_storage": args.graph_storage,
                    "vector_storage": args.vector_storage,
                    "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract,
                    "enable_llm_cache": args.enable_llm_cache,
                    "workspace": args.workspace,
                    "max_graph_nodes": args.max_graph_nodes,
                    # Rerank configuration (based on whether rerank model is configured)
                    "enable_rerank": rerank_model_func is not None,
                    "rerank_model": args.rerank_model
                    if rerank_model_func is not None
                    else None,
                    "rerank_binding_host": args.rerank_binding_host
                    if rerank_model_func is not None
                    else None,
                    # Environment variable status (requested configuration)
                    "summary_language": args.summary_language,
                    "force_llm_summary_on_merge": args.force_llm_summary_on_merge,
                    "max_parallel_insert": args.max_parallel_insert,
                    "cosine_threshold": args.cosine_threshold,
                    "min_rerank_score": args.min_rerank_score,
                    "related_chunk_number": args.related_chunk_number,
                    "max_async": args.max_async,
                    "embedding_func_max_async": args.embedding_func_max_async,
                    "embedding_batch_num": args.embedding_batch_num,
                },
                "auth_mode": auth_mode,
                "pipeline_busy": pipeline_status.get("busy", False),
                "keyed_locks": keyed_lock_info,
                "core_version": core_version,
                "api_version": __api_version__,
                "webui_title": webui_title,
                "webui_description": webui_description,
            }
        except Exception as e:
            logger.error(f"Error getting health status: {str(e)}")
            raise HTTPException(status_code=500, detail=str(e))

    # Custom StaticFiles class for smart caching
    class SmartStaticFiles(StaticFiles):  # Renamed from NoCacheStaticFiles
        async def get_response(self, path: str, scope):
            response = await super().get_response(path, scope)

            if path.endswith(".html"):
                response.headers["Cache-Control"] = (
                    "no-cache, no-store, must-revalidate"
                )
                response.headers["Pragma"] = "no-cache"
                response.headers["Expires"] = "0"
            elif (
                "/assets/" in path
            ):  # Assets (JS, CSS, images, fonts) generated by Vite with hash in filename
                response.headers["Cache-Control"] = (
                    "public, max-age=31536000, immutable"
                )
            # Add other rules here if needed for non-HTML, non-asset files

            # Ensure correct Content-Type
            if path.endswith(".js"):
                response.headers["Content-Type"] = "application/javascript"
            elif path.endswith(".css"):
                response.headers["Content-Type"] = "text/css"

            return response

    # Webui mount webui/index.html
    static_dir = Path(__file__).parent / "webui"
    static_dir.mkdir(exist_ok=True)
    app.mount(
        "/webui",
        SmartStaticFiles(
            directory=static_dir, html=True, check_dir=True
        ),  # Use SmartStaticFiles
        name="webui",
    )

    return app


def get_application(args=None):
    """Factory function for creating the FastAPI application"""
    if args is None:
        args = global_args
    return create_app(args)


def configure_logging():
    """Configure logging for uvicorn startup"""

    # Reset any existing handlers to ensure clean configuration
    for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]:
        logger = logging.getLogger(logger_name)
        logger.handlers = []
        logger.filters = []

    # Get log directory path from environment variable
    log_dir = os.getenv("LOG_DIR", os.getcwd())
    log_file_path = os.path.abspath(os.path.join(log_dir, DEFAULT_LOG_FILENAME))

    print(f"\nLightRAG log file: {log_file_path}\n")
    os.makedirs(os.path.dirname(log_dir), exist_ok=True)

    # Get log file max size and backup count from environment variables
    log_max_bytes = get_env_value("LOG_MAX_BYTES", DEFAULT_LOG_MAX_BYTES, int)
    log_backup_count = get_env_value("LOG_BACKUP_COUNT", DEFAULT_LOG_BACKUP_COUNT, int)

    logging.config.dictConfig(
        {
            "version": 1,
            "disable_existing_loggers": False,
            "formatters": {
                "default": {
                    "format": "%(levelname)s: %(message)s",
                },
                "detailed": {
                    "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
                },
            },
            "handlers": {
                "console": {
                    "formatter": "default",
                    "class": "logging.StreamHandler",
                    "stream": "ext://sys.stderr",
                },
                "file": {
                    "formatter": "detailed",
                    "class": "logging.handlers.RotatingFileHandler",
                    "filename": log_file_path,
                    "maxBytes": log_max_bytes,
                    "backupCount": log_backup_count,
                    "encoding": "utf-8",
                },
            },
            "loggers": {
                # Configure all uvicorn related loggers
                "uvicorn": {
                    "handlers": ["console", "file"],
                    "level": "INFO",
                    "propagate": False,
                },
                "uvicorn.access": {
                    "handlers": ["console", "file"],
                    "level": "INFO",
                    "propagate": False,
                    "filters": ["path_filter"],
                },
                "uvicorn.error": {
                    "handlers": ["console", "file"],
                    "level": "INFO",
                    "propagate": False,
                },
                "lightrag": {
                    "handlers": ["console", "file"],
                    "level": "INFO",
                    "propagate": False,
                    "filters": ["path_filter"],
                },
            },
            "filters": {
                "path_filter": {
                    "()": "lightrag.utils.LightragPathFilter",
                },
            },
        }
    )


def check_and_install_dependencies():
    """Check and install required dependencies"""
    required_packages = [
        "uvicorn",
        "tiktoken",
        "fastapi",
        # Add other required packages here
    ]

    for package in required_packages:
        if not pm.is_installed(package):
            print(f"Installing {package}...")
            pm.install(package)
            print(f"{package} installed successfully")


def main():
    # Check if running under Gunicorn
    if "GUNICORN_CMD_ARGS" in os.environ:
        # If started with Gunicorn, return directly as Gunicorn will call get_application
        print("Running under Gunicorn - worker management handled by Gunicorn")
        return

    # Check .env file
    if not check_env_file():
        sys.exit(1)

    # Check and install dependencies
    check_and_install_dependencies()

    from multiprocessing import freeze_support

    freeze_support()

    # Configure logging before parsing args
    configure_logging()
    update_uvicorn_mode_config()
    display_splash_screen(global_args)

    # Setup signal handlers for graceful shutdown
    setup_signal_handlers()

    # Create application instance directly instead of using factory function
    app = create_app(global_args)

    # Start Uvicorn in single process mode
    uvicorn_config = {
        "app": app,  # Pass application instance directly instead of string path
        "host": global_args.host,
        "port": global_args.port,
        "log_config": None,  # Disable default config
    }

    if global_args.ssl:
        uvicorn_config.update(
            {
                "ssl_certfile": global_args.ssl_certfile,
                "ssl_keyfile": global_args.ssl_keyfile,
            }
        )

    print(
        f"Starting Uvicorn server in single-process mode on {global_args.host}:{global_args.port}"
    )
    uvicorn.run(**uvicorn_config)


if __name__ == "__main__":
    main()
