from backend.ai.llm import hugging_llm
from langchain_core.prompts import ChatPromptTemplate
from backend.prompts.all_prompt import SUMMARY_SYSTEM_PROMPT
from backend.repository.models import CallHistory
from backend.schemas.summary_schema import CallSummary, CallCreate
from sqlalchemy.orm import Session

from backend.schemas.summary_schema import CallRequest

# LLM transcript 요약 시키기
def summary_call(transcript: str):

    summary_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", SUMMARY_SYSTEM_PROMPT),
            ("human", "상담내용\n{transcript}"),
        ]
    )

    structured_llm = hugging_llm.with_structured_output(CallSummary)
    summary_chain = summary_prompt | structured_llm

    result = summary_chain.invoke({"transcript" : transcript})
    return result

def save_call_history(db, data):
    """상담 저장"""
    history = CallHistory(
        customer_id = data.customer_id,
        transcript = data.transcript,
        summary = data.summary,
        category = data.category,
        sentiment = data.sentiment,
        customer_issue = data.customer_issue,
        resolution = data.resolution
    )

    db.add(history)
    db.commit()
    db.refresh(history)

    return history

def evaluate_call():
    """상담  평가"""

    pass

def save_call_evaluation():
    """상담 평가 내용 저장"""

    pass

def create_call_history(req: CallRequest, db:Session):
    # 요약
    # CallSummary
    summary = summary_call(req.transcript)
    print("summary",summary)
    # 데이터베이스 저장용 객체
    call_data = CallCreate(
        customer_id = req.customer_id,
        transcript = req.transcript,
        summary = summary.summary,
        category = summary.category,
        sentiment = summary.sentiment,
        customer_issue = summary.customer_issue,
        resolution = summary.resolution
    )

    return save_call_history(db = db, data = call_data)