import reflex as rx
import json
import requests
from typing import Optional, List
from pydantic import BaseModel, Field 
# from rerank import ReRanker

# https://hub.guardrailsai.com/validator/guardrails/toxic_language
from guardrails.hub import ToxicLanguage
from guardrails import Guard

# guardrails hub install hub://guardrails/detect_pii
from guardrails.hub import DetectPII

# https://hub.guardrailsai.com/validator/guardrails/qa_relevance_llm_eval
from guardrails.hub import QARelevanceLLMEval

import logging
logger = logging.getLogger("uvicorn").info

from .summary import summarize_it


def IsPii(answer: str) -> bool:
    guard = Guard().use(DetectPII, 
                        ["EMAIL_ADDRESS", "PHONE_NUMBER"], 
                        "exception",
                        )
    try:
        guard.validate(answer) 
        return True
    
    except Exception as e:
        print(e)
        return False

def IsToxic(query: str, threshold=0.5) -> bool:

    # https://hub.guardrailsai.com/validator/guardrails/toxic_language
    # Use the Guard with the validator
    guard = Guard().use(
            ToxicLanguage, 
            threshold=threshold, # high for highly toxic only
            validation_method="sentence", 
            on_fail="exception" 
        )

    try:
        guard.validate(query)
        return False
    
    except Exception as e:
        print(e)  # will output the toxic question
        return True

def IsRelevant(answer: str, query: str, model: str="gpt-3.5-turbo") -> bool:
    
    guard = Guard().use(
        QARelevanceLLMEval,
        llm_callable=model,
        on_fail="exception",
    )

    try:
        guard.validate(
            answer,
            metadata={"original_prompt": query},
        )  
        return True
    except Exception as e:
        print(e)
        return False