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fintweet-GPT-Search
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##Variables

import os

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)