import os

from dotenv import load_dotenv
from fastapi import FastAPI, Response, Body, Security
from fastapi.security import APIKeyHeader
from pydantic import BaseModel, model_validator
from typing import List
import json

from conversation.conversation_store import ConversationStore
from rag_langchain import LangChainRAG

load_dotenv()
api_keys = [os.environ["API_API_KEY"]]

api = FastAPI()

conversation_store = ConversationStore()

api_key_header = APIKeyHeader(name="Authorization", auto_error=True)
prompt_id = "summarize_rag_1"
check_prompt_id = "check_control_challenge_step_back"
rewrite_prompt_id = "first"
default_llm = "gpt-4o 128k"


class QModel(BaseModel):
    q: str
    retrieval_count: int = 10
    temperature: str = "0.2"
    llm: str = default_llm

    @classmethod
    @model_validator(mode='before')
    def validate_to_json(cls, value):
        if isinstance(value, str):
            return cls(**json.loads(value))
        return value


class AModel(BaseModel):
    q: str
    a: str
    sources: List[str]
    oid: str


class EmoModel(BaseModel):
    qid: str
    helpfulness: str

    @classmethod
    @model_validator(mode='before')
    def validate_to_json(cls, value):
        if isinstance(value, str):
            return cls(**json.loads(value))
        return value


@api.get("/")
async def read_root():
    return "Empty"


@api.post("/qa", response_model=AModel)
async def qa(api_key: str = Security(api_key_header), data: QModel = Body(...)):
    if not valid_api_key(api_key):
        return Response(status_code=401)

    rag = LangChainRAG(
        config={
            "retrieve_documents": data.retrieval_count,
            "temperature": data.temperature,
            "prompt_id": prompt_id,
            "check_prompt_id": check_prompt_id,
            "rewrite_prompt_id": rewrite_prompt_id
        }
    )

    answer, check_result, sources = await rag.rag_chain(data.q, data.llm)

    oid = conversation_store.save_content(
        q=data.q,
        a=answer,
        sources=list(map(lambda doc: doc.page_content, sources)),
        params=
        {
            "prompt_id": prompt_id,
            "check_prompt_id": check_prompt_id,
            "rewrite_prompt_id": rewrite_prompt_id,
            "check_result": check_result,
            "temperature": data.temperature,
            "retrieve_document_count": str(data.retrieval_count),
        }
    )

    return AModel(
                a=answer,
                q=data.q,
                sources=list(map(lambda doc: doc.page_content, sources)),
                oid=oid
            )


@api.post("/emo")
async def emo(api_key: str = Security(api_key_header), json_body: EmoModel = Body(...)):
    if not valid_api_key(api_key):
        return Response(status_code=401)

    conversation = conversation_store.get(json_body.qid)
    new_params = conversation.params
    new_params["user_grading"] = str(json_body.helpfulness)
    conversation_store.update(
        oid=json_body["qid"],
        q=conversation.conversation[0].q,
        a=conversation.conversation[0].a,
        sources=conversation.conversation[0].sources,
        params=new_params
    )


def valid_api_key(api_key: str) -> bool:
    return api_key in api_keys