src/model.py

import copy
from pathlib import Path
import warnings
import lightning.pytorch as pl
import numpy as np
import pandas as pd
import torch
from prophet.serialize import model_to_json, model_from_json
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import pickle
# at beginning of the script
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

class Model_Load:
    def __init__(self):
        pass
    def energy_model_load(self,model_option):
        if model_option=='TFT':
          best_model_path='models/consumer_final_10/lightning_logs/lightning_logs/version_0/checkpoints/epoch=5-step=49260.ckpt'
          best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
          print('Model Load Sucessfully.')
          return best_tft
        elif model_option=='Prophet':
          best_model_path='models/fb_energy_model.json'
          with open(best_model_path, 'r') as fin:
             model = model_from_json(fin.read())
          return model

        # elif model_option=='ten consumer':
        #   best_model_path='consumer_10/lightning_logs/lightning_logs/version_0/checkpoints/epoch=11-step=98544.ckpt'
        #   best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
        #   print('Model Load Sucessfully.') 
        # elif model_option=='fifty consumer':
        #     raise Exception('Model not present')
      
            
    def store_model_load(self,model_option):
        if model_option=='TFT':
          # best_model_path="models/store_item_10_lead_1_v2/lightning_logs/lightning_logs/version_2/checkpoints/epoch=7-step=4472.ckpt"
          # best_model_path="models/store_item_10_lead_1_v3/lightning_logs/lightning_logs/version_0/checkpoints/epoch=7-step=4472.ckpt"
          # best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
          best_tft=pickle.load(open("models/cpu_finalized_model_v1.sav", 'rb'))
          print('Model Load Sucessfully.')
          return best_tft
        elif model_option=='Prophet':
          best_model_path='models/fb_store_model_new.json'
          with open(best_model_path, 'r') as fin:
             model = model_from_json(fin.read())
          return model
        
        # elif model_option=='Item 50 TFT':
        #   raise Exception('Model not present')
        # elif model_option=='FB Prophet':
        #   raise Exception('Model not present')
      
      


if __name__=='__main__':
    obj=Model_Load()
    obj.load()