# import bot
from kibernikto.bots.cybernoone import Kibernikto

bot_class = Kibernikto

from kibernikto.telegram import dispatcher
from kibernikto.telegram import commands
from kibernikto.telegram import middleware_service_group
from kibernikto.telegram.payment import middleware_subscription

dispatcher.start(bot_class=bot_class)

import asyncio
from kibernikto.interactors import DEFAULT_CONFIG, OpenAIExecutor

config = DEFAULT_CONFIG  # <--- setting from env
config.key = "yr_key"
config.url = "https://api.deepseek.com"
config.model = "deepseek-chat"
config.max_tokens = 760
config.who_am_i = "Your are mister Kibernikto"

executor = OpenAIExecutor(unique_id="kbnkt", config=config)  # <--- executor instance creation
response = asyncio.run(executor.request_llm(message="Good morning mister kibernikto!"))
print(response)

import asyncio
import logging
import traceback
from typing import Literal

from openai import NOT_GIVEN, AsyncOpenAI

from kibernikto.bots.cybernoone import Kibernikto
from kibernikto.interactors import OpenAIRoles, OpenAiExecutorConfig
from kibernikto.telegram.telegram_bot import KiberniktoChatInfo

DEFAULT_SYSTEM = """
You are a noble independent cybernetic assistant named {0}.
You have access to LLM-agents for solving various tasks via delegate_task function.
When receiving a request, you must: 
1. Determine if any agents can be useful for completing the task. 
2. Use these agents (for example: when discussing files, always try to refer to the document agent) to obtain necessary information or perform actions. 
3. Provide the user with an accurate and helpful response to their request.

'delegate_task' function
Use 'delegate_task' function to delegate tasks to the appropriate AI agents according to user orders and your common sense.

[AGENTS DESCRIPTION GOES HERE] 
"""

__GLOBAL_ASYNC_CLIENT = None


def get_client(config):
    global __GLOBAL_ASYNC_CLIENT
    if __GLOBAL_ASYNC_CLIENT is None:
        __GLOBAL_ASYNC_CLIENT = AsyncOpenAI(base_url=config.url, api_key=config.key, max_retries=config.max_retries)
    return __GLOBAL_ASYNC_CLIENT


class Kiberkalki(Kibernikto):
    TOOL_SEPARATOR = "||"

    def __init__(self, master_id: str, username: str, config: OpenAiExecutorConfig, key: str = NOT_GIVEN,
                 chat_info: KiberniktoChatInfo = None):
        # better not to change from env.
        config.who_am_i = DEFAULT_SYSTEM
        # for running delegation tasks not to delegate new until done
        self.delegate_task_info = None
        # for additional notifications like payment etc
        self.last_notification = None
        self.session_call_interation = 0

        openai_client = get_client(config)

        # Your experts, same OpenAIExecutor instances as this one. Are being called from tools using delegate task.
        self.knowledge_expert = KnowledgeExpert(unique_key=key)
        self.web_expert = WebExpert(unique_key=key)
        self.scheduler_expert = SchedulerExpert(unique_key=key, chat_info=chat_info)
        self.conversation_expert = ConversationExpert(unique_key=key, chat_info=chat_info)
        self.report_expert = ReportExpert(unique_key=key)
        self.image_expert = ImageExpert(unique_key=key, chat_info=chat_info)

        # any other params u may need
        self.tts_enabled = False
        self.attached_file = None

        super().__init__(config=config,
                         username=username,
                         master_id=master_id,
                         key=key,
                         hide_errors=False,
                         chat_info=chat_info,
                         client=openai_client)
        self.load_history()  # persistent history

    @property
    def default_headers(self):
        hidden_key = "Kibernikto1"
        return {
            "X-Title": f"{self.full_config.app_id}.{hidden_key}",
        }

    async def request_llm(self, message, save_to_history=True,
                          response_type: Literal['text', 'json_object'] = 'text',
                          additional_content=None, **kwargs):

        # enhance yr message as u with
        enhanced_message = f"{message} today at Hollywood"
        print(f"- REQUEST with {self.model}\n{enhanced_message} \n======")
        try:
            response_message = await super().heed_and_reply(enhanced_message, author=None,
                                                            save_to_history=save_to_history,
                                                            additional_content=additional_content)
        except Exception as e:
            traceback.print_exc()
            response_message = "ERROR! 😈"
        return response_message

    def get_cur_system_message(self):
        cur_system_dict = self.about_me.copy()

        cur_system_dict['content'] += "Any content to put to system prompt at every call"

        return cur_system_dict

    def get_config_key(self) -> int:
        """
        :return: the key to be used for db entries separation for different yser configs
        """
        return int(self.unique_id)

    def _reset(self):
        super()._reset()
        self.update_system_chat_data()

    def save_to_history(self, this_message: dict, usage_dict: dict = None, author=NOT_GIVEN):
        if usage_dict is None:
            return None
        log_key: int = self.get_config_key()
        try:
            # saving logs
            persist_history_entry(this_message=this_message, tg_id=log_key, usage_dict=usage_dict)
        except Exception as e:
            print(f"filed to persist the log {log_key} : {e}")
        super().save_to_history(this_message, usage_dict, author=author)

    def update_system_chat_data(self):
        """
        Adding slave agents data to system message
        """
        wai = self.full_config.who_am_i.format(self.full_config.name)

        self.about_me = dict(role=OpenAIRoles.system.value, content=f"{wai}")
        print(self.about_me)

    def load_history(self):
        log_key = self.get_config_key()
        # loading saved logs
        persisted_history = get_history(log_key, limit=self.max_messages - 4)
        logging.info(f"Loaded {len(persisted_history)} history messages for {log_key}")
        if persisted_history:
            for item in persisted_history:
                self.messages.append(item)