from utils import gen_query_acc, gen_sql_query
import sys
from word_mapping import *
filename = 'glove/glove.42B.300d.txt'
agg_checkpoint_name = 'saved_models/agg_predictor.pth'
select_checkpoint_name = 'saved_models/sel_predictor.pth'
cond_checkpoint_name = 'saved_models/cond_predictor.pth'
N_word = 300
batch_size = 10
hidden_dim = 100
n_epochs = 5
table_name = 'EMPLOYEE'
word_embed = load_word_emb(filename)
word_emb = WordEmbedding(N_word, word_embed)
model = Model(hidden_dim, N_word, word_emb)
model.agg_predictor.load_state_dict(torch.load(agg_checkpoint_name))
model.cond_predictor.load_state_dict(torch.load(cond_checkpoint_name))
model.sel_predictor.load_state_dict(torch.load(select_checkpoint_name))
model.eval()
sentence = sys.argv[1]
sentence = process_sentence(sentence)
question = [sentence.split(' ')]
from net_utils import run_lstm
from model import Model
import torch.nn as nn
import torch.optim as optim
import torch
import numpy as np
from utils import train_model,test_model
from model import Model
from torch.autograd import Variable
import torch.nn.functional as F
N_word = 50
batch_size =10
hidden_dim = 100
word_embed = load_word_emb('glove/glove.6B.50d.txt')
train = SQLDataset('train')
#train , valid = SQLDataset('train') , SQLDataset('dev')
train_dataloader = DataLoader(train,batch_size=batch_size,shuffle=True,num_workers=1,collate_fn=collate_fn)
#valid_dataloader = DataLoader(valid,batch_size=batch_size,shuffle=True,num_workers=1,collate_fn=collate_fn)
#test = SQLDataset('test')
#test_dataloader = DataLoader(test,batch_size = batch_size, shuffle=True, num_workers=1,collate_fn=collate_fn)