# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import os

from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
from comps.cores.mega.utils import handle_message
from comps.cores.proto.api_protocol import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatCompletionResponseChoice,
    ChatMessage,
    UsageInfo,
)
from comps.cores.proto.docarray import LLMParams
from fastapi import Request
from fastapi.responses import StreamingResponse

MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
EMBEDDING_SERVICE_PORT = int(os.getenv("EMBEDDING_SERVICE_PORT", 6000))
WEB_RETRIEVER_SERVICE_HOST_IP = os.getenv("WEB_RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
WEB_RETRIEVER_SERVICE_PORT = int(os.getenv("WEB_RETRIEVER_SERVICE_PORT", 7000))
RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
RERANK_SERVICE_PORT = int(os.getenv("RERANK_SERVICE_PORT", 8000))
LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
LLM_SERVICE_PORT = int(os.getenv("LLM_SERVICE_PORT", 9000))


def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
    next_data = {}
    if self.services[cur_node].service_type == ServiceType.EMBEDDING:
        next_data = {"text": inputs["input"], "embedding": data["data"][0]["embedding"], "k": 1}
        return next_data

    else:
        return data


class SearchQnAService:
    def __init__(self, host="0.0.0.0", port=8000):
        self.host = host
        self.port = port
        ServiceOrchestrator.align_outputs = align_outputs
        self.megaservice = ServiceOrchestrator()
        self.endpoint = str(MegaServiceEndpoint.SEARCH_QNA)

    def add_remote_service(self):
        embedding = MicroService(
            name="embedding",
            host=EMBEDDING_SERVICE_HOST_IP,
            port=EMBEDDING_SERVICE_PORT,
            endpoint="/v1/embeddings",
            use_remote_service=True,
            service_type=ServiceType.EMBEDDING,
        )
        web_retriever = MicroService(
            name="web_retriever",
            host=WEB_RETRIEVER_SERVICE_HOST_IP,
            port=WEB_RETRIEVER_SERVICE_PORT,
            endpoint="/v1/web_retrieval",
            use_remote_service=True,
            service_type=ServiceType.WEB_RETRIEVER,
        )
        rerank = MicroService(
            name="rerank",
            host=RERANK_SERVICE_HOST_IP,
            port=RERANK_SERVICE_PORT,
            endpoint="/v1/reranking",
            use_remote_service=True,
            service_type=ServiceType.RERANK,
        )
        llm = MicroService(
            name="llm",
            host=LLM_SERVICE_HOST_IP,
            port=LLM_SERVICE_PORT,
            endpoint="/v1/chat/completions",
            use_remote_service=True,
            service_type=ServiceType.LLM,
        )
        self.megaservice.add(embedding).add(web_retriever).add(rerank).add(llm)
        self.megaservice.flow_to(embedding, web_retriever)
        self.megaservice.flow_to(web_retriever, rerank)
        self.megaservice.flow_to(rerank, llm)

    async def handle_request(self, request: Request):
        data = await request.json()
        stream_opt = data.get("stream", True)
        chat_request = ChatCompletionRequest.parse_obj(data)
        prompt = handle_message(chat_request.messages)
        parameters = LLMParams(
            max_tokens=chat_request.max_tokens if chat_request.max_tokens else 1024,
            top_k=chat_request.top_k if chat_request.top_k else 10,
            top_p=chat_request.top_p if chat_request.top_p else 0.95,
            temperature=chat_request.temperature if chat_request.temperature else 0.01,
            frequency_penalty=chat_request.frequency_penalty if chat_request.frequency_penalty else 0.0,
            presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
            repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
            stream=stream_opt,
        )
        result_dict, runtime_graph = await self.megaservice.schedule(
            initial_inputs={"input": prompt}, llm_parameters=parameters
        )
        for node, response in result_dict.items():
            # Here it suppose the last microservice in the megaservice is LLM.
            if (
                isinstance(response, StreamingResponse)
                and node == list(self.megaservice.services.keys())[-1]
                and self.megaservice.services[node].service_type == ServiceType.LLM
            ):
                return response
        last_node = runtime_graph.all_leaves()[-1]
        print(f"================= result: {result_dict[last_node]}")
        response = result_dict[last_node]["choices"][0]["text"]
        choices = []
        usage = UsageInfo()
        choices.append(
            ChatCompletionResponseChoice(
                index=0,
                message=ChatMessage(role="assistant", content=response),
                finish_reason="stop",
            )
        )
        return ChatCompletionResponse(model="searchqna", choices=choices, usage=usage)

    def start(self):
        self.service = MicroService(
            self.__class__.__name__,
            service_role=ServiceRoleType.MEGASERVICE,
            host=self.host,
            port=self.port,
            endpoint=self.endpoint,
            input_datatype=ChatCompletionRequest,
            output_datatype=ChatCompletionResponse,
        )
        self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
        self.service.start()


if __name__ == "__main__":
    searchqna = SearchQnAService(port=MEGA_SERVICE_PORT)
    searchqna.add_remote_service()
    searchqna.start()