##Variables

import os
import streamlit as st
import pathlib

from langchain.embeddings import HuggingFaceEmbeddings,HuggingFaceInstructEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chat_models.openai import ChatOpenAI
from langchain import VectorDBQA
import pandas as pd

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)

@st.experimental_singleton(suppress_st_warning=True)
def get_latest_file():
    '''Get the latest file from output folder'''

    # set the directory path
    directory_path = "output/"
    
    # create a list of all text files in the directory and sort by modification time
    text_files = sorted(pathlib.Path(directory_path).glob("*.txt"), key=lambda f: f.stat().st_mtime)
    
    # get the latest modified file
    latest_file = text_files[-1]
    
    # open the file and read its contents
    with open(latest_file, "r") as f:
        file_contents = f.read()

    return file_contents

@st.experimental_singleton(suppress_st_warning=True)
def process_tweets(file,embed_model,query):
    '''Process file with latest tweets'''

    # Split tweets int chunks
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    texts = text_splitter.split_text(file)

    model = bi_enc_dict[embed_model]

    if model == "hkunlp/instructor-large":
        emb = HuggingFaceInstructEmbeddings(model_name=model,
                                            query_instruction='Represent the Financial question for retrieving supporting documents: ',
                                            embed_instruction='Represent the Financial document for retrieval: ')
        
    elif model == "sentence-transformers/all-mpnet-base-v2":
        emb = HuggingFaceEmbeddings(model_name=model)

    docsearch = FAISS.from_texts(texts, emb)

    chain_type_kwargs = {"prompt": prompt}
    chain = VectorDBQA.from_chain_type(
    ChatOpenAI(temperature=0), 
    chain_type="stuff", 
    vectorstore=docsearch,
    chain_type_kwargs=chain_type_kwargs
    )

    result = chain({"query": query}, return_only_outputs=True)

    return result

CONFIG = {
    "bearer_token": os.environ.get("bearer_token")
              }

sent_model_id = 'nickmuchi/optimum-finbert-tone-finetuned-fintwitter-classification'
topic_model_id = 'nickmuchi/optimum-finbert-tone-finetuned-finance-topic-classification'
task = 'text-classification'

sentiments = {"0": "Bearish", "1": "Bullish", "2": "Neutral"}

topics = {
    "0": "Analyst Update",
    "1": "Fed | Central Banks",
    "2": "Company | Product News",
    "3": "Treasuries | Corporate Debt",
    "4": "Dividend",
    "5": "Earnings",
    "6": "Energy | Oil",
    "7": "Financials",
    "8": "Currencies",
    "9": "General News | Opinion",
    "10": "Gold | Metals | Materials",
    "11": "IPO",
    "12": "Legal | Regulation",
    "13": "M&A | Investments",
    "14": "Macro",
    "15": "Markets",
    "16": "Politics",
    "17": "Personnel Change",
    "18": "Stock Commentary",
    "19": "Stock Movement",
}

user_name = [
    "Investing.com",
    "(((The Daily Shot)))",
    "Bloomberg Markets",
    "FirstSquawk",
    "MarketWatch",
    "markets",
    "FinancialTimes",
    "CNBC",
    "ReutersBiz",
    "BreakingNews",
    "LiveSquawk",
    "NYSE",
    "WSJmarkets",
    "FT",
    "TheStreet",
    "ftfinancenews",
    "BloombergTV",
    "Nasdaq",
    "NYSE",
    "federalreserve",
    "NewYorkFed",
    "sffed",
    "WSJCentralBanks",
    "RichmondFed",
    "ecb",
    "stlouisfed",
    "WorldBank",
    "MarketCurrents",
    "OpenOutcrier",
    "BullTradeFinder",
    "WallStChatter",
    "Briefingcom",
    "SeekingAlpha",
    "realDonaldTrump",
    "AswathDamodaran",
    "ukarlewitz",
    "alphatrends",
    "Investor666",
    "ACInvestorBlog",
    "ZorTrades",
    "ScottNations",
    "TradersCorner",
    "TraderGoalieOne",
    "option_snipper",
    "jasonleavitt",
    "LMT978",
    "OptionsHawk",
    "andrewbtodd",
    "Terri1618",
    "SunriseTrader",
    "traderstewie",
    "TMLTrader",
    "IncredibleTrade",
    "NYFedResearch",
    "YahooFinance",
    "business",
    "economics",
    "IMFNews",
    "Market_Screener",
    "QuickTake",
    "NewsFromBW",
    "BNCommodities",
]

user_id = [
    "988955288",
    "423769635",
    "69620713",
    "59393368",
    "3295423333",
    "624413",
    "69620713",
    "4898091",
    "20402945",
    "15110357",
    "6017542",
    "21323268",
    "28164923",
    "18949452",
    "15281391",
    "11014272",
    "35002876",
    "18639734",
    "21323268",
    "26538229",
    "15072071",
    "117237387",
    "327484803",
    "16532451",
    "83466368",
    "71567590",
    "27860681",
    "15296897",
    "2334614718",
    "2222635612",
    "3382363841",
    "72928001",
    "23059499",
    "25073877",
    "33216611",
    "37284991",
    "15246621",
    "293458690",
    "55561590",
    "18560146",
    "244978426",
    "85523269",
    "276714687",
    "2806294664",
    "16205561",
    "1064700308",
    "61342056",
    "184126162",
    "405820375",
    "787439438964068352",
    "52166809",
    "2715646770",
    "47247213",
    "374672240",
    "19546277",
    "34713362",
    "144274618",
    "25098482",
    "102325185",
    "252751061",
    "976297820532518914",
    "804556370",
]

def convert_user_names(user_name: list):
    '''convert user_names to tweepy format'''
    users = []
    for user in user_name:
        users.append(f"from:{user}")
    
    return " OR ".join(users)