def predict(x, temp):
    probs = F.softmax(x / temp, dim = 0)
    probs = np.squeeze(probs.detach().cpu().numpy())
    ind = np.random.choice(vocab_len, 1, p = probs)
    return ind[0]
 
generated_text = ['there','is','no','one','love']
curr_len = 0
embeds = []
is_end = word_to_int[';']

qt_gen.eval()

for i in generated_text:
    embeds.append(emb[word_to_int[i]])

while(curr_len < 50):
    curr_len += 1
    input_tensor = torch.Tensor(embeds).view(1,5,128).float().to(device)
    h_h, h_c = qt_gen.zero_states(1)
    output, (h_h, h_c) = qt_gen(input_tensor, (h_h,h_c))
    word_ind = predict(output[-1], 1.6)
    embeds[0][:4].tolist().extend(emb[word_ind])
    generated_text.append(int_to_word[word_ind])
    if word_ind == is_end:
        break
  
print(' '.join(generated_text))

torch.cuda.empty_cache()