import os
import cython
from joblib import load
from numpy import append, expand_dims
from pandas import read_json, to_datetime, Timedelta
from tensorflow.keras.models import load_model


cdef class Utilities:
    async def forecasting_utils(self, int sequence_length,
        int days, str model_name, str algorithm, bint with_pred) -> tuple:
        cdef str model_path = os.path.join(f'./resources/algorithms/{algorithm}/models',
            f'{model_name}.keras')
        model = load_model(model_path)

        cdef str dataframe_path = os.path.join(f'./resources/algorithms/{algorithm}/posttrained',
            f'{model_name}-posttrained.json')
        dataframe = read_json(dataframe_path)
        dataframe.set_index('Date', inplace=True)

        minmax_scaler = load(os.path.join(f'./resources/algorithms/{algorithm}/pickles',
            f'{model_name}_minmax_scaler.pickle'))
        standard_scaler = load(os.path.join(f'./resources/algorithms/{algorithm}/pickles',
            f'{model_name}_standard_scaler.pickle'))

        if with_pred:
            # Prediction
            lst_seq = dataframe[-sequence_length:].values
            lst_seq = expand_dims(lst_seq, axis=0)

            predicted_prices = {}
            last_date = to_datetime(dataframe.index[-1])

            for _ in range(days):
                predicted_price = model.predict(lst_seq)
                last_date = last_date + Timedelta(days=1)

                predicted_prices[last_date] = minmax_scaler.inverse_transform(predicted_price)
                predicted_prices[last_date] = standard_scaler.inverse_transform(predicted_prices[last_date])

                lst_seq = append(lst_seq[:, 1:, :], [predicted_price], axis=1)

            predictions = [
                {'date': date.strftime('%Y-%m-%d'), 'price': float(price)} \
                    for date, price in predicted_prices.items()
            ]

        else: predictions = []

        # Actual
        df_date = dataframe.index[-sequence_length:].values
        df_date = [to_datetime(date) for date in df_date]

        dataframe[['Close']] = minmax_scaler.inverse_transform(dataframe)
        dataframe[['Close']] = standard_scaler.inverse_transform(dataframe)
        df_close = dataframe.iloc[-sequence_length:]['Close'].values

        actuals = [
            {'date': date.strftime('%Y-%m-%d'), 'price': close} \
                for date, close in zip(df_date, df_close)
        ]
        
        return actuals, predictions