import openai
import os
import asyncio
from asyncio import Queue
import logging

from .compute_task import ComputeTask,ComputeTaskResult,ComputeTaskState
from .compute_node import ComputeNode

logger = logging.getLogger(__name__)

class OpenAI_ComputeNode(ComputeNode):
    _instance = None
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(OpenAI_ComputeNode, cls).__new__(cls)
            cls._instance.is_start = False
        return cls._instance
    
    def __init__(self) -> None:
        super().__init__()
        if self.is_start is True:
            logger.warn("OpenAI_ComputeNode is already start")
            return
        
        self.is_start = True
        #openai.organization = "org-AoKrOtF2myemvfiFfnsSU8rF" #buckycloud
        self.openai_api_key = ""
        self.node_id = "openai_node"

        self.task_queue = Queue()

        if os.getenv("OPENAI_API_KEY") is not None: 
            openai.api_key = os.getenv("OPENAI_API_KEY")
        else:
            openai.api_key = self.openai_api_key
        
        self.start()
    
    async def push_task(self,task:ComputeTask,proiority:int = 0):
        logger.info(f"openai_node push task: {task.display()}")
        self.task_queue.put_nowait(task)
    
    async def remove_task(self,task_id:str):
        pass
    
    def _run_task(self,task:ComputeTask):
        task.state = ComputeTaskState.RUNNING
        # switch tsak type
        if task.task_type == "llm_completion":
            mode_name = task.params["model_name"]
            # max_token_size = task.params["max_token_size"]
            prompts = task.params["prompts"]

            mode_name = task.params["model_name"]
	        # max_token_size = task.params["max_token_size"]
            prompts = task.params["prompts"]

            logger.info(f"call openai {mode_name} prompts: {prompts}")
            resp = openai.ChatCompletion.create(model=mode_name,
                                                messages=prompts,
                                                max_tokens=4000,
                                                temperature=1.2)
            logger.info(f"openai response: {resp}")

            status_code = resp["choices"][0]["finish_reason"]
            if status_code != "stop":
                task.state = ComputeTaskState.ERROR
                task.error_str =f"The status code was {status_code}."
                return None

            result = ComputeTaskResult()    
            result.set_from_task(task)
            result.worker_id = self.node_id
            result.result_str = resp["choices"][0]["message"]["content"]
            result.result = resp["choices"][0]["message"]

            return result
        if task.task_type == "text_embedding":
            model_name = task.params["model_name"]
            input = task.params["input"]
            logger.info(f"call openai {model_name} input: {input}")

            resp = openai.Embeding.create(model=model_name,
                                            input=input)
            logger.info(f"openai response: {resp}")

            result = ComputeTaskResult()    
            result.set_from_task(task)
            result.worker_id = self.node_id
            result.result = resp["data"][0]["embedding"]

            return result
            
    def start(self):
        async def _run_task_loop():
            while True:
                logger.info("openai_node is waiting for task...")
                task = await self.task_queue.get()
                logger.info(f"openai_node get task: {task.display()}")
                result = self._run_task(task)
                if result is not None:
                    task.state = ComputeTaskState.DONE
                    task.result = result
        
        asyncio.create_task(_run_task_loop())

    def display(self) -> str:
        return f"OpenAI_ComputeNode: {self.node_id}"
    
    def get_task_state(self,task_id:str):
        pass


    def get_capacity(self):
        pass


    def is_support(self, task: ComputeTask) -> bool:
        if task.task_type == "llm_completion":
            return True
        if task.task_type == "text_embedding":
            if task.params["model_name"] == "text-embedding-ada-002":
                return True
        return False


    def is_local(self) -> bool:
        return False