import sys, os, platform, time, copy, re, asyncio
import threading, ast
import shutil, random, traceback, requests
from datetime import datetime, timedelta
from typing import Optional, List
import secrets, subprocess
messages: list = []
sys.path.insert(
    0, os.path.abspath("../..")
)  # Adds the parent directory to the system path - for litellm local dev

try:
    import uvicorn
    import fastapi
    import appdirs
    import backoff
    import yaml
    import rq
except ImportError:
    import sys

    subprocess.check_call(
        [
            sys.executable,
            "-m",
            "pip",
            "install",
            "uvicorn",
            "fastapi",
            "appdirs",
            "backoff",
            "pyyaml", 
            "rq"
        ]
    )
    import uvicorn
    import fastapi
    import appdirs
    import backoff
    import yaml

import random

list_of_messages = [
    "'The thing I wish you improved is...'",
    "'A feature I really want is...'",
    "'The worst thing about this product is...'",
    "'This product would be better if...'",
    "'I don't like how this works...'",
    "'It would help me if you could add...'",
    "'This feature doesn't meet my needs because...'",
    "'I get frustrated when the product...'",
]


def generate_feedback_box():
    box_width = 60

    # Select a random message
    message = random.choice(list_of_messages)

    print()
    print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m")
    print("\033[1;37m" + "#" + " " * box_width + "#\033[0m")
    print("\033[1;37m" + "# {:^59} #\033[0m".format(message))
    print(
        "\033[1;37m"
        + "# {:^59} #\033[0m".format("https://github.com/BerriAI/litellm/issues/new")
    )
    print("\033[1;37m" + "#" + " " * box_width + "#\033[0m")
    print("\033[1;37m" + "#" + "-" * box_width + "#\033[0m")
    print()
    print(" Thank you for using LiteLLM! - Krrish & Ishaan")
    print()
    print()
    print()
    print(
        "\033[1;31mGive Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new\033[0m"
    )
    print()
    print()

import litellm
from litellm.caching import DualCache
litellm.suppress_debug_info = True
from fastapi import FastAPI, Request, HTTPException, status, Depends
from fastapi.routing import APIRouter
from fastapi.encoders import jsonable_encoder
from fastapi.responses import StreamingResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security.api_key import APIKeyHeader
import json
import logging
# from litellm.proxy.queue import start_rq_worker_in_background

app = FastAPI(docs_url="/", title="LiteLLM API")
router = APIRouter()
origins = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

user_api_base = None
user_model = None
user_debug = False
user_max_tokens = None
user_request_timeout = None
user_temperature = None
user_telemetry = True
user_config = None
user_headers = None
local_logging = True # writes logs to a local api_log.json file for debugging
experimental = False
#### GLOBAL VARIABLES ####
llm_router: Optional[litellm.Router] = None
llm_model_list: Optional[list] = None
general_settings: dict = {}
log_file = "api_log.json"
worker_config = None
master_key = None
prisma_client = None
user_api_key_cache = DualCache()
### REDIS QUEUE ### 
async_result = None
celery_app_conn = None 
celery_fn = None # Redis Queue for handling requests
#### HELPER FUNCTIONS ####
def print_verbose(print_statement):
    global user_debug
    if user_debug:
        print(print_statement)

def usage_telemetry(
    feature: str,
):  # helps us know if people are using this feature. Set `litellm --telemetry False` to your cli call to turn this off
    if user_telemetry:
        data = {"feature": feature}  # "local_proxy_server"
        threading.Thread(
            target=litellm.utils.litellm_telemetry, args=(data,), daemon=True
        ).start()

api_key_header = APIKeyHeader(name="Authorization", auto_error=False)

async def user_api_key_auth(request: Request, api_key: str = fastapi.Security(api_key_header)):
    global master_key, prisma_client, llm_model_list
    if master_key is None:
        return {
            "api_key": None
        } 
    try: 
        route = request.url.path

        # note: never string compare api keys, this is vulenerable to a time attack. Use secrets.compare_digest instead
        is_master_key_valid = secrets.compare_digest(api_key, master_key) or secrets.compare_digest(api_key, "Bearer " + master_key)
        if is_master_key_valid:
            return {
                "api_key": master_key
            }
        
        if (route == "/key/generate" or route == "/key/delete" or route == "/key/info") and not is_master_key_valid:
            raise Exception(f"If master key is set, only master key can be used to generate, delete or get info for new keys")

        if prisma_client: 
            ## check for cache hit (In-Memory Cache)
            valid_token = user_api_key_cache.get_cache(key=api_key)
            if valid_token is None: 
                ## check db 
                if "Bearer " in api_key:
                    cleaned_api_key = api_key[len("Bearer "):]
                valid_token = await prisma_client.litellm_verificationtoken.find_first(
                    where={
                        "token": cleaned_api_key,
                        "expires": {"gte": datetime.utcnow()}  # Check if the token is not expired
                    }
                )
                ## save to cache for 60s
                user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60)
            else: 
                print(f"API Key Cache Hit!")
            if valid_token:
                litellm.model_alias_map = valid_token.aliases
                config = valid_token.config
                if config != {}:
                    model_list = config.get("model_list", [])
                    llm_model_list =  model_list
                    print("\n new llm router model list", llm_model_list)
                if len(valid_token.models) == 0: # assume an empty model list means all models are allowed to be called
                    return {
                        "api_key": valid_token.token
                    }
                else: 
                    data = await request.json()
                    model = data.get("model", None)
                    if model in litellm.model_alias_map:
                        model = litellm.model_alias_map[model]
                    if model and model not in valid_token.models:
                        raise Exception(f"Token not allowed to access model")
                return {
                    "api_key": valid_token.token
                } 
            else: 
                raise Exception(f"Invalid token")
    except Exception as e: 
        print(f"An exception occurred - {e}")
    raise HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail={"error": "invalid user key"},
    )

def prisma_setup(database_url: Optional[str]): 
    global prisma_client
    if database_url:
        try: 
            import os 
            print("LiteLLM: DATABASE_URL Set in config, trying to 'pip install prisma'")
            os.environ["DATABASE_URL"] = database_url
            subprocess.run(['prisma', 'generate'])
            subprocess.run(['prisma', 'db', 'push', '--accept-data-loss']) # this looks like a weird edge case when prisma just wont start on render. we need to have the --accept-data-loss
            # Now you can import the Prisma Client
            from prisma import Client
            prisma_client = Client()
        except Exception as e:
            print("Error when initializing prisma, Ensure you run pip install prisma", e)

def celery_setup(use_queue: bool): 
    global celery_fn, celery_app_conn, async_result
    print(f"value of use_queue: {use_queue}")
    if use_queue:
        from litellm.proxy.queue.celery_worker import start_worker
        from litellm.proxy.queue.celery_app import celery_app, process_job
        from celery.result import AsyncResult
        start_worker(os.getcwd())
        celery_fn = process_job
        async_result = AsyncResult
        celery_app_conn = celery_app

def cost_tracking(): 
    global prisma_client, master_key
    if prisma_client is not None and master_key is not None:
        if isinstance(litellm.success_callback, list):
            print("setting litellm success callback to track cost")
            if (track_cost_callback) not in litellm.success_callback: # type: ignore
                litellm.success_callback.append(track_cost_callback) # type: ignore
            else:
                litellm.success_callback = track_cost_callback # type: ignore

def track_cost_callback(
    kwargs,                                       # kwargs to completion
    completion_response: litellm.ModelResponse,           # response from completion
    start_time = None,
    end_time = None,                              # start/end time for completion
):
    try:
        # init logging config
        print("in custom callback tracking cost", llm_model_list)
        # check if it has collected an entire stream response
        if "complete_streaming_response" in kwargs:
            # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost 
            completion_response=kwargs["complete_streaming_response"]
            input_text = kwargs["messages"]
            output_text = completion_response["choices"][0]["message"]["content"]
            response_cost = litellm.completion_cost(
                model = kwargs["model"],
                messages = input_text,
                completion=output_text
            )
            print("streaming response_cost", response_cost)
        # for non streaming responses
        else:
            # we pass the completion_response obj
            if kwargs["stream"] != True:
                input_text = kwargs.get("messages", "")
                if isinstance(input_text, list): 
                    input_text = "".join(m["content"] for m in input_text)
                response_cost = litellm.completion_cost(completion_response=completion_response, completion=input_text)
                print("regular response_cost", response_cost)
        print(f"metadata in kwargs: {kwargs}")
        user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None)
        if user_api_key: 
            asyncio.run(update_prisma_database(token=user_api_key, response_cost=response_cost))
    except Exception as e:
        print(f"error in tracking cost callback - {str(e)}")

async def update_prisma_database(token, response_cost):
    global prisma_client
    try:
        print(f"Enters prisma db call, token: {token}")
        # Fetch the existing cost for the given token
        existing_spend = await prisma_client.litellm_verificationtoken.find_unique(
            where={
                "token": token
            }
        )
        print(f"existing spend: {existing_spend}")
        # Calculate the new cost by adding the existing cost and response_cost
        new_spend = existing_spend.spend + response_cost

        print(f"new cost: {new_spend}")
        # Update the cost column for the given token
        await prisma_client.litellm_verificationtoken.update(
            where={
                "token": token
            },
            data={
                "spend": new_spend
            }
        )
        print(f"Prisma database updated for token {token}. New cost: {new_spend}")

    except Exception as e:
        print(f"Error updating Prisma database: {traceback.format_exc()}")
        pass

def run_ollama_serve():
    try:
        command = ['ollama', 'serve']

        with open(os.devnull, 'w') as devnull:
            process = subprocess.Popen(command, stdout=devnull, stderr=devnull)
    except Exception as e:
        print(f"""
            LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
        """)

def load_router_config(router: Optional[litellm.Router], config_file_path: str):
    global master_key
    config = {}
    try: 
        if os.path.exists(config_file_path):
            with open(config_file_path, 'r') as file:
                config = yaml.safe_load(file)
        else:
            raise Exception(f"Path to config does not exist, 'os.path.exists({config_file_path})' returned False")
    except Exception as e:
        raise Exception(f"Exception while reading Config: {e}")
    
    print(f"Loaded config YAML:\n{json.dumps(config, indent=2)}")

    ## ENVIRONMENT VARIABLES
    environment_variables = config.get('environment_variables', None)
    if environment_variables: 
        for key, value in environment_variables.items(): 
            os.environ[key] = value

    ## GENERAL SERVER SETTINGS (e.g. master key,..)
    general_settings = config.get("general_settings", {})
    if general_settings is None: 
        general_settings = {}
    if general_settings: 
        ### MASTER KEY ###
        master_key = general_settings.get("master_key", None)
        ### CONNECT TO DATABASE ###
        database_url = general_settings.get("database_url", None)
        prisma_setup(database_url=database_url)
        ## COST TRACKING ## 
        cost_tracking()
        ### START REDIS QUEUE ###
        use_queue = general_settings.get("use_queue", False)
        celery_setup(use_queue=use_queue)

    ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..)
    litellm_settings = config.get('litellm_settings', None)
    if litellm_settings: 
        for key, value in litellm_settings.items(): 
            if key == "cache":
                # ANSI escape code for blue text
                blue_color_code = "\033[94m"
                reset_color_code = "\033[0m"
                print(f"{blue_color_code}\nSetting Cache on Proxy")
                from litellm.caching import Cache
                cache_type = value["type"]
                cache_host = os.environ.get("REDIS_HOST")
                cache_port = os.environ.get("REDIS_PORT")
                cache_password = os.environ.get("REDIS_PASSWORD")

                # Assuming cache_type, cache_host, cache_port, and cache_password are strings
                print(f"{blue_color_code}Cache Type:{reset_color_code} {cache_type}")
                print(f"{blue_color_code}Cache Host:{reset_color_code} {cache_host}")
                print(f"{blue_color_code}Cache Port:{reset_color_code} {cache_port}")
                print(f"{blue_color_code}Cache Password:{reset_color_code} {cache_password}")
                print()

                litellm.cache = Cache(
                    type=cache_type,
                    host=cache_host,
                    port=cache_port,
                    password=cache_password
                )
                
    ## MODEL LIST
    model_list = config.get('model_list', None)
    if model_list:
        router = litellm.Router(model_list=model_list, num_retries=2)
        print(f"\033[32mLiteLLM: Proxy initialized with Config, Set models:\033[0m")
        for model in model_list:
            print(f"\033[32m    {model.get('model_name', '')}\033[0m")
            litellm_model_name = model["litellm_params"]["model"]
            if "ollama" in litellm_model_name: 
                run_ollama_serve()
    return router, model_list, general_settings

async def generate_key_helper_fn(duration_str: str, models: list, aliases: dict, config: dict, spend: float):
    token = f"sk-{secrets.token_urlsafe(16)}"
    def _duration_in_seconds(duration: str): 
        match = re.match(r"(\d+)([smhd]?)", duration)
        if not match:
            raise ValueError("Invalid duration format")

        value, unit = match.groups()
        value = int(value)

        if unit == "s":
            return value
        elif unit == "m":
            return value * 60
        elif unit == "h":
            return value * 3600
        elif unit == "d":
            return value * 86400
        else:
            raise ValueError("Unsupported duration unit")

    duration = _duration_in_seconds(duration=duration_str)
    expires = datetime.utcnow() + timedelta(seconds=duration)
    aliases_json = json.dumps(aliases)
    config_json = json.dumps(config)
    try:
        db = prisma_client
        # Create a new verification token (you may want to enhance this logic based on your needs)
        verification_token_data = {
            "token": token, 
            "expires": expires,
            "models": models,
            "aliases": aliases_json,
            "config": config_json,
            "spend": spend
        }
        print(f"verification_token_data: {verification_token_data}")
        new_verification_token = await db.litellm_verificationtoken.create( # type: ignore
           {**verification_token_data} # type: ignore
        )
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
    return {"token": new_verification_token.token, "expires": new_verification_token.expires}

async def delete_verification_token(tokens: List[str]):
    global prisma_client
    try: 
        if prisma_client: 
            # Assuming 'db' is your Prisma Client instance
            deleted_tokens = await prisma_client.litellm_verificationtoken.delete_many(
                where={"token": {"in": tokens}}
            )
        else: 
            raise Exception
    except Exception as e: 
        traceback.print_exc()
        raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
    return deleted_tokens

async def generate_key_cli_task(duration_str):
    task = asyncio.create_task(generate_key_helper_fn(duration_str=duration_str))
    await task

def save_worker_config(**data): 
    import json
    os.environ["WORKER_CONFIG"] = json.dumps(data)

def initialize(
    model,
    alias,
    api_base,
    api_version,
    debug,
    temperature,
    max_tokens,
    request_timeout,
    max_budget,
    telemetry,
    drop_params,
    add_function_to_prompt,
    headers,
    save,
    config, 
    use_queue
):
    global user_model, user_api_base, user_debug, user_max_tokens, user_request_timeout, user_temperature, user_telemetry, user_headers, experimental, llm_model_list, llm_router, general_settings
    generate_feedback_box()
    user_model = model
    user_debug = debug
    dynamic_config = {"general": {}, user_model: {}}
    if config:
        llm_router, llm_model_list, general_settings = load_router_config(router=llm_router, config_file_path=config)
    if headers:  # model-specific param
        user_headers = headers
        dynamic_config[user_model]["headers"] = headers
    if api_base:  # model-specific param
        user_api_base = api_base
        dynamic_config[user_model]["api_base"] = api_base
    if api_version:
        os.environ[
            "AZURE_API_VERSION"
        ] = api_version  # set this for azure - litellm can read this from the env
    if max_tokens:  # model-specific param
        user_max_tokens = max_tokens
        dynamic_config[user_model]["max_tokens"] = max_tokens
    if temperature:  # model-specific param
        user_temperature = temperature
        dynamic_config[user_model]["temperature"] = temperature
    if request_timeout:
        user_request_timeout = request_timeout
        dynamic_config[user_model]["request_timeout"] = request_timeout
    if alias:  # model-specific param
        dynamic_config[user_model]["alias"] = alias
    if drop_params == True:  # litellm-specific param
        litellm.drop_params = True
        dynamic_config["general"]["drop_params"] = True
    if add_function_to_prompt == True:  # litellm-specific param
        litellm.add_function_to_prompt = True
        dynamic_config["general"]["add_function_to_prompt"] = True
    if max_budget:  # litellm-specific param
        litellm.max_budget = max_budget
        dynamic_config["general"]["max_budget"] = max_budget
    if debug==True:  # litellm-specific param
        litellm.set_verbose = True
    if use_queue: 
        celery_setup(use_queue=use_queue)
    if experimental: 
        pass
    user_telemetry = telemetry
    usage_telemetry(feature="local_proxy_server")
    curl_command = """
    curl --location 'http://0.0.0.0:8000/chat/completions' \\
    --header 'Content-Type: application/json' \\
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'
    \n
    """
    print()
    print(f"\033[1;34mLiteLLM: Test your local proxy with: \"litellm --test\" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n")
    print(f"\033[1;34mLiteLLM: Curl Command Test for your local proxy\n {curl_command} \033[0m\n")
    print("\033[1;34mDocs: https://docs.litellm.ai/docs/simple_proxy\033[0m\n")
# for streaming
def data_generator(response):
    print_verbose("inside generator")
    for chunk in response:
        print_verbose(f"returned chunk: {chunk}")
        try:
            yield f"data: {json.dumps(chunk.dict())}\n\n"
        except:
            yield f"data: {json.dumps(chunk)}\n\n"

def litellm_completion(*args, **kwargs):
    global user_temperature, user_request_timeout, user_max_tokens, user_api_base
    call_type = kwargs.pop("call_type")
    # override with user settings, these are params passed via cli
    if user_temperature: 
        kwargs["temperature"] = user_temperature
    if user_request_timeout:
        kwargs["request_timeout"] = user_request_timeout
    if user_max_tokens: 
        kwargs["max_tokens"] = user_max_tokens
    if user_api_base: 
        kwargs["api_base"] = user_api_base
    ## ROUTE TO CORRECT ENDPOINT ## 
    router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
    try:
        if llm_router is not None and kwargs["model"] in router_model_names: # model in router model list 
            if call_type == "chat_completion":
                response = llm_router.completion(*args, **kwargs)
            elif call_type == "text_completion":
                response = llm_router.text_completion(*args, **kwargs)
        else: 
            if call_type == "chat_completion":
                response = litellm.completion(*args, **kwargs)
            elif call_type == "text_completion":
                response = litellm.text_completion(*args, **kwargs)
    except Exception as e:
        raise e
    if 'stream' in kwargs and kwargs['stream'] == True: # use generate_responses to stream responses
        return StreamingResponse(data_generator(response), media_type='text/event-stream')
    return response

@app.on_event("startup")
async def startup_event():
    global prisma_client
    import json
    worker_config = json.loads(os.getenv("WORKER_CONFIG"))
    initialize(**worker_config)
    if prisma_client: 
        await prisma_client.connect()

@app.on_event("shutdown")
async def shutdown_event():
    global prisma_client
    if prisma_client:
        print("Disconnecting from Prisma")
        await prisma_client.disconnect()

#### API ENDPOINTS ####
@router.get("/v1/models", dependencies=[Depends(user_api_key_auth)])
@router.get("/models", dependencies=[Depends(user_api_key_auth)])  # if project requires model list
def model_list():
    global llm_model_list, general_settings    
    all_models = []
    if general_settings.get("infer_model_from_keys", False):
        all_models = litellm.utils.get_valid_models()
    if llm_model_list: 
        print(f"llm model list: {llm_model_list}")
        all_models += [m["model_name"] for m in llm_model_list]
    if user_model is not None:
        all_models += user_model
    ### CHECK OLLAMA MODELS ### 
    try:
        response = requests.get("http://0.0.0.0:11434/api/tags")
        models = response.json()["models"]
        ollama_models = ["ollama/" + m["name"].replace(":latest", "") for m in models]
        all_models.extend(ollama_models)
    except Exception as e: 
        pass
    return dict(
        data=[
            {
                "id": model,
                "object": "model",
                "created": 1677610602,
                "owned_by": "openai",
            }
            for model in all_models
        ],
        object="list",
    )

@router.post("/v1/completions", dependencies=[Depends(user_api_key_auth)])
@router.post("/completions", dependencies=[Depends(user_api_key_auth)])
@router.post("/engines/{model:path}/completions", dependencies=[Depends(user_api_key_auth)])
async def completion(request: Request, model: Optional[str] = None, user_api_key_dict: dict = Depends(user_api_key_auth)):
    try: 
        body = await request.body()
        body_str = body.decode()
        try:
            data = ast.literal_eval(body_str)
        except: 
            data = json.loads(body_str)
        data["model"] = (
            general_settings.get("completion_model", None) # server default
            or user_model # model name passed via cli args
            or model # for azure deployments
            or data["model"] # default passed in http request
        )
        if user_model:
            data["model"] = user_model
        data["call_type"] = "text_completion"
        data["metadata"] = {"user_api_key": user_api_key_dict["api_key"]}
        return litellm_completion(
            **data
        )
    except Exception as e: 
        print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`")
        error_traceback = traceback.format_exc()
        error_msg = f"{str(e)}\n\n{error_traceback}"
        try:
            status = e.status_code  # type: ignore
        except:
            status = 500
        raise HTTPException(
            status_code=status,
            detail=error_msg
        )
                              

@router.post("/v1/chat/completions", dependencies=[Depends(user_api_key_auth)])
@router.post("/chat/completions", dependencies=[Depends(user_api_key_auth)])
@router.post("/openai/deployments/{model:path}/chat/completions", dependencies=[Depends(user_api_key_auth)]) # azure compatible endpoint
async def chat_completion(request: Request, model: Optional[str] = None, user_api_key_dict: dict = Depends(user_api_key_auth)):
    global general_settings
    try: 
        body = await request.body()
        body_str = body.decode()
        try:
            data = ast.literal_eval(body_str)
        except: 
            data = json.loads(body_str)
        print(f"receiving data: {data}")
        data["model"] = (
            general_settings.get("completion_model", None) # server default
            or user_model # model name passed via cli args
            or model # for azure deployments
            or data["model"] # default passed in http request
        )
        data["call_type"] = "chat_completion"
        data["metadata"] = {"user_api_key": user_api_key_dict["api_key"]}
        return litellm_completion(
            **data
        )
    except Exception as e: 
        print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`")
        error_traceback = traceback.format_exc()
        error_msg = f"{str(e)}\n\n{error_traceback}"
        try:
            status = e.status_code # type: ignore
        except:
            status = 500
        raise HTTPException(
            status_code=status,
            detail=error_msg
        )

@router.post("/v1/embeddings", dependencies=[Depends(user_api_key_auth)])
@router.post("/embeddings", dependencies=[Depends(user_api_key_auth)])
async def embeddings(request: Request, user_api_key_dict: dict = Depends(user_api_key_auth)): 
    try: 
        data = await request.json()
        print(f"data: {data}")
        data["model"] = (
            general_settings.get("embedding_model", None) # server default
            or user_model # model name passed via cli args
            or data["model"] # default passed in http request
        )
        if user_model:
            data["model"] = user_model
        data["metadata"] = {"user_api_key": user_api_key_dict["api_key"]}
        ## ROUTE TO CORRECT ENDPOINT ##
        router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
        if llm_router is not None and data["model"] in router_model_names: # model in router model list 
            response = await llm_router.aembedding(**data)
        else:
            response = await litellm.aembedding(**data)
        return response
    except Exception as e:
        traceback.print_exc()
        raise e
    except Exception as e: 
        pass

@router.post("/key/generate", dependencies=[Depends(user_api_key_auth)])
async def generate_key_fn(request: Request): 
    data = await request.json()

    duration_str = data.get("duration", "1h")  # Default to 1 hour if duration is not provided
    models = data.get("models", []) # Default to an empty list (meaning allow token to call all models)
    aliases = data.get("aliases", {}) # Default to an empty dict (no alias mappings, on top of anything in the config.yaml model_list)
    config = data.get("config", {})
    spend = data.get("spend", 0)
    if isinstance(models, list):
        response = await generate_key_helper_fn(duration_str=duration_str, models=models, aliases=aliases, config=config, spend=spend)
        return {"key": response["token"], "expires": response["expires"]}
    else: 
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail={"error": "models param must be a list"},
        )

@router.post("/key/delete", dependencies=[Depends(user_api_key_auth)])
async def delete_key_fn(request: Request): 
    try: 
        data = await request.json()

        keys = data.get("keys", [])

        if not isinstance(keys, list):
            if isinstance(keys, str): 
                keys = [keys]
            else: 
                raise Exception(f"keys must be an instance of either a string or a list")
        
        deleted_keys = await delete_verification_token(tokens=keys)
        assert len(keys) == deleted_keys
        return {"deleted_keys": keys}
    except Exception as e: 
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail={"error": str(e)},
        )

@router.get("/key/info", dependencies=[Depends(user_api_key_auth)])
async def info_key_fn(key: str = fastapi.Query(..., description="Key in the request parameters")): 
    global prisma_client
    try: 
        key_info = await prisma_client.litellm_verificationtoken.find_unique(
            where={
                "token": key
            }
        )

        return {"key": key, "info": key_info}
    except Exception as e: 
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail={"error": str(e)},
        )


@router.get("/test")
async def test_endpoint(request: Request): 
    return {"route": request.url.path}

#### EXPERIMENTAL QUEUING #### 
@router.post("/queue/request", dependencies=[Depends(user_api_key_auth)])
async def async_queue_request(request: Request): 
    global celery_fn, llm_model_list
    if celery_fn is not None: 
        body = await request.body()
        body_str = body.decode()
        try:
            data = ast.literal_eval(body_str)
        except: 
            data = json.loads(body_str)
        data["model"] = (
            general_settings.get("completion_model", None) # server default
            or user_model # model name passed via cli args
            or data["model"] # default passed in http request
        )
        data["llm_model_list"] = llm_model_list
        print(f"data: {data}")
        job = celery_fn.apply_async(kwargs=data)
        return {"id": job.id, "url": f"/queue/response/{job.id}", "eta": 5, "status": "queued"}
    else: 
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail={"error": "Queue not initialized"},
        )

@router.get("/queue/response/{task_id}", dependencies=[Depends(user_api_key_auth)])
async def async_queue_response(request: Request, task_id: str): 
    global celery_app_conn, async_result
    try: 
        if celery_app_conn is not None and async_result is not None: 
            job = async_result(task_id, app=celery_app_conn) 
            if job.ready():
                return {"status": "finished", "result": job.result}
            else:
                return {'status': 'queued'}
        else:
            raise Exception()
    except Exception as e: 
        return {"status": "finished", "result": str(e)}


@router.get("/ollama_logs", dependencies=[Depends(user_api_key_auth)])
async def retrieve_server_log(request: Request):
    filepath = os.path.expanduser("~/.ollama/logs/server.log")
    return FileResponse(filepath)


@router.get("/")
async def home(request: Request):
    return "LiteLLM: RUNNING"

@app.get("/routes")
async def get_routes():
    """
    Get a list of available routes in the FastAPI application.
    """
    routes = []
    for route in app.routes:
        route_info = {
            "path": route.path,
            "methods": route.methods,
            "name": route.name,
            "endpoint": route.endpoint.__name__ if route.endpoint else None,
        }
        routes.append(route_info)

    return {"routes": routes}


app.include_router(router)