"""

    AUTHOR:

        Khushal P Soonderji

    DATE:

        Thursday, 5th Dec., 2024

    OBJECTIVE:

        To use LLMs to perform activities like chat completion, text summarization, etc.

    REFERENCES:

        N/A

    DOWNLOADS:

        N/A

    NOTES:

        N/A

"""


#   *****************************************************************************************************************
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# ***                                                   IMPORT                                                      ***
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#   *****************************************************************************************************************


# To make sibling directories accessible for imports:
import sys
sys.path.append(".")
sys.path.append("..")

# For using Quart:
from quart import Blueprint, current_app, request

# My utils:
from utils_v2.string import json
from utils_v2.api.codes import StatusCodes, HttpCodes
from utils_v2.api.response import ResponseModel
from utils_v2.api.async_quart import (
    set_api_version,
    read_input,
    get_session_info,
    log_request_to_mongo,
    log_chain_to_mongo,
    should_not_be_under_maintenance,
    only_whitelisted_ips,
    limit_rate,
    validate_input,
    handle_cancelled_request
)

# Common:
from shared import constants

# Data Models:
from models.api.ai.llm import LLMRequestHeaders
from models.core.ai.llm import LLMInput
from models.core.user import CoreUserInfoModel

# For asynchronous activities:
import asyncio


#   *****************************************************************************************************************
#  *****                                                                                                          ****
# ***                                           MACROS / ONE-TIME INIT                                              ***
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#   *****************************************************************************************************************


# Related to Quart:
llm_invoke_bp = Blueprint("llm_invoke", __name__)


#   *****************************************************************************************************************
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# ***                                                VARIABLES                                                      ***
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# --- Nothing Yet


#   *****************************************************************************************************************
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# ***                                                  FUNCTIONS                                                    ***
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@llm_invoke_bp.record_once
def init(blueprint_setup_state):

    # This gets called when the blueprint is registered.
    # Consider this to be a one-time setup for the whole blueprint:
    pass


# ---------------------------------------------------------------------------------------------------------------------


@llm_invoke_bp.route("/llm/invoke", methods = ["GET"])
@set_api_version(api_version = "1.0.0")
@read_input(sanitize_headers = False, sanitize_data = False)
@get_session_info(key = "X-Session-Token", session_coro = "get_session")
@log_request_to_mongo(
    attr_name = "logs_mongo",
    project = constants.PROJECT_NAME,
    log_type = constants.MODULE_NAME,
    operation = "llmInvokeApi",
    log_input = True,
    log_output = True,
    sensitive_keys = ["sessionToken", "X-Session-Token"]
)
@log_chain_to_mongo(attr_name = "logs_mongo")
@should_not_be_under_maintenance(attr_name = "is_under_maintenance")
@validate_input(
    header_validator = lambda x: LLMRequestHeaders(**x).model_dump(),
    data_validator = lambda x: LLMInput(**x)
)
@handle_cancelled_request()
async def invoke_llm(
        inbound_headers: dict | LLMRequestHeaders = None,
        inbound_data: dict | LLMInput = None,
        inbound_files: dict = None,
        **kwargs
):

    """
    Use this to invoke an LLM for text completion kind of activities.
    :param inbound_headers: auto-extracted by the decorators.
    :param inbound_data: auto-extracted by the decorators.
    :param inbound_files: auto-extracted by the decorators.
    :param kwargs: Any number of extra inputs supplied by the decorators.
    :return: A standard response structure.
    """

    # ┏┓
    # ┃┃┏┓┏┓┏┓┏┓┏┓┏┏┓┏┏
    # ┣┛┛ ┗ ┣┛┛ ┗┛┗┗ ┛┛
    #       ┛

    # If the session token is invalid/expired:
    if kwargs.get("session_info") is None:
        return constants.API_RESPONSE_UNAUTHORIZED

    # ┳      ┓
    # ┃┏┓┓┏┏┓┃┏┏┓
    # ┻┛┗┗┛┗┛┛┗┗

    # Call the LLM and see if its service worked or not:
    llm_response = await current_app.llm.invoke(
        mongo_data_conn = current_app.data_mongo,
        user_info = CoreUserInfoModel(**kwargs["session_info"]),
        llm_input = inbound_data
    )
    success = False if llm_response.output is None else True

    # ┳┓
    # ┣┫┏┓┏┏┓┏┓┏┓┏┏┓
    # ┛┗┗ ┛┣┛┗┛┛┗┛┗
    #      ┛

    # Done here:
    return ResponseModel(
        status_code = StatusCodes.OK if success else StatusCodes.FAILED,
        http_code = HttpCodes.SUCCESS if success else HttpCodes.INTERNAL_SERVER_ERROR,
        data = {
            "ts": llm_response.ts.isoformat(),
            "client": llm_response.client,
            "model": llm_response.model,
            "output": llm_response.output,
            "tokens": llm_response.tokens.model_dump(),
            "invocationId": str(llm_response.invocationId)
        } if success else None
    )


#   *****************************************************************************************************************
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# ***                                               MAIN PROGRAM                                                    ***
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#   *****************************************************************************************************************


if __name__ == "__main__":

    pass