sentiment_model_test_user_text.py

from string import punctuation

def tokenize_review(test_review):
    test_review = test_review.lower() # lowercase
    # get rid of punctuation
    test_text = ''.join([c for c in test_review if c not in punctuation])

    # splitting by spaces
    test_words = test_text.split()

    # tokens
    test_ints = []
    test_ints.append([vocab_to_int[word] for word in test_words])

    return test_ints

# test code and generate tokenized review
test_ints = tokenize_review(test_review_neg)
print(test_ints)


# test sequence padding
seq_length=200
features = pad_features(test_ints, seq_length)

print(features)


# test conversion to tensor and pass into your model
feature_tensor = torch.from_numpy(features)
print(feature_tensor.size())


def predict(net, test_review, sequence_length=200):
    
    net.eval()
    
    # tokenize review
    test_ints = tokenize_review(test_review)
    
    # pad tokenized sequence
    seq_length=sequence_length
    features = pad_features(test_ints, seq_length)
    
    # convert to tensor to pass into your model
    feature_tensor = torch.from_numpy(features)
    
    batch_size = feature_tensor.size(0)
    
    # initialize hidden state
    h = net.init_hidden(batch_size)
    
    if(train_on_gpu):
        feature_tensor = feature_tensor.cuda()
    
    # get the output from the model
    output, h = net(feature_tensor, h)
    
    # convert output probabilities to predicted class (0 or 1)
    pred = torch.round(output.squeeze()) 
    # printing output value, before rounding
    print('Prediction value, pre-rounding: {:.6f}'.format(output.item()))
    
    # print custom response
    if(pred.item()==1):
        print("Positive review detected!")
    else:
        print("Negative review detected.")