import os
import pandas as pd
import numpy as np
from scraper.top100_extractor import programming_crime_list
from typing import Optional

DF_HIST_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'price_history_data.csv')
DF_HIST: Optional[pd.DataFrame] = None


def lazy_load_history():
    global DF_HIST
    if DF_HIST is None:
        DF_HIST = pd.read_csv(DF_HIST_PATH, index_col=0)


def get_closing_price_hist(tickers: list[str]) -> list[dict]:
    lazy_load_history()
    ticker_series = pd.Series(tickers)
    df = DF_HIST.loc[DF_HIST.symbol.isin(ticker_series), :] \
        .rename(columns={"Closing Price": "price", "symbol": "ticker"}) \
        .reset_index(drop=True)
    df = df.pivot(index='date', columns='ticker', values='price').reset_index(drop=False)
    return df.replace({ np.nan: None }).to_dict('records')