import os
import json
import bcrypt
import pandas as pd
import numpy as np
from typing import List
from pathlib import Path
from langchain_huggingface import HuggingFaceEndpoint
from langchain.schema.runnable.config import RunnableConfig
from langchain.schema import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent, create_csv_agent

import chainlit as cl
from chainlit.input_widget import TextInput, Select, Switch, Slider

from deep_translator import GoogleTranslator

@cl.password_auth_callback
def auth_callback(username: str, password: str):
    auth = json.loads(os.environ['CHAINLIT_AUTH_LOGIN'])
    ident = next(d['ident'] for d in auth if d['ident'] == username)
    pwd = next(d['pwd'] for d in auth if d['ident'] == username)
    resultLogAdmin = bcrypt.checkpw(username.encode('utf-8'), bcrypt.hashpw(ident.encode('utf-8'), bcrypt.gensalt())) 
    resultPwdAdmin = bcrypt.checkpw(password.encode('utf-8'), bcrypt.hashpw(pwd.encode('utf-8'), bcrypt.gensalt())) 
    resultRole = next(d['role'] for d in auth if d['ident'] == username)
    if resultLogAdmin and resultPwdAdmin and resultRole == "admindatapcc":
        return cl.User(
            identifier=ident + " : 🧑‍💼 Admin Datapcc", metadata={"role": "admin", "provider": "credentials"}
        )
    elif resultLogAdmin and resultPwdAdmin and resultRole == "userdatapcc":
        return cl.User(
            identifier=ident + " : 🧑‍🎓 User Datapcc", metadata={"role": "user", "provider": "credentials"}
        )
        
@cl.step(type="tool")
async def LLMistral():
    os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.environ['HUGGINGFACEHUB_API_TOKEN']
    repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
    llm = HuggingFaceEndpoint(
        repo_id=repo_id, max_new_tokens=5300, temperature=0.1, task="text2text-generation", streaming=True
    )
    return llm

@cl.set_chat_profiles
async def chat_profile():
    return [
        cl.ChatProfile(name="Traitement des données d'enquête : «Expé CFA : questionnaire auprès des professionnels de la branche de l'agencement»",markdown_description="Vidéo exploratoire autour de l'événement",icon="/public/logo-ofipe.png",),
    ]
    
@cl.set_starters
async def set_starters():
    return [
        cl.Starter(
            label="Répartition du nombre de CAA dans les entreprises",
            message="Quel est le nombre de chargé.e d'affaires en agencement dans chaque type d'entreprises?",
            icon="/public/request-theme.svg",
            )
    ]
    
@cl.on_message
async def on_message(message: cl.Message):
    await cl.Message(f"> SURVEYIA").send()
    llm = await LLMistral()
    agent = create_csv_agent(
        llm,
        "./public/ExpeCFA_LP_CAA.csv",
        verbose=True,
        allow_dangerous_code=True,
        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
    )
    cb = cl.AsyncLangchainCallbackHandler()
    try:
        res = await agent.acall("Réponds en langue française à la question suivante :\n" + message.content + "\nDétaille la réponse en faisant une analyse complète en 2000 mots minimum.", callbacks=[cb])
        await cl.Message(author="COPILOT",content=GoogleTranslator(source='auto', target='fr').translate(res['output'])).send()
    except ValueError as e:
        res = str(e)
        resArray = res.split(":")
        ans = ''
        if str(res).find('parsing') != -1:
            for i in range(2,len(resArray)):
                ans += resArray[i]
            await cl.Message(author="COPILOT",content=ans.replace("`","")).send()
        else:
            await cl.Message(author="COPILOT",content="Reformulez votre requête, s'il vous plait 😃").send()