#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on 2024-07-19 19:52:59 Friday

@author: Nikhil Kapila
"""

# import os, pickle, torch
# import matplotlib.pyplot as plt
# from sklearn.metrics import mean_absolute_percentage_error

# def plot_graph(y, predicted):
#     plt.figure(figsize=(14, 7))
#     plt.plot(y, label='Actual Values')
#     plt.plot(predicted, label='Predicted Values', linestyle='dashed')
#     plt.xlabel('Date')
#     plt.ylabel('Value')
#     plt.title('Actual vs Predicted Values')
#     plt.legend()
#     # plt.show()
#     return plt

# def model_folder_name(ft, type):
#     return f'bgd_{type}_{ft}_LSTM'

# def fetch_model(ft, type, dir='metaflow_models'):
#     file = f'{dir}/{model_folder_name(ft,type)}/{model_folder_name(ft,type)}.pth'
#     return torch.load(file)

# def fetch_scaler(ft, type, dir='metaflow_models'):
#     scaler_dir = f'{dir}/{model_folder_name(ft,type)}/scaler_{model_folder_name(ft, type)}.pkl'
#     print(scaler_dir)
#     # scaler_dir = f'{dir}/{scaler_name}'
#     with open(scaler_dir, 'rb') as file:
#         scaler = pickle.load(file)
#     return scaler

# def mape(y_true, y_pred):
#     return mean_absolute_percentage_error(y_true=y_true, y_pred=y_pred)*100