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| import gradio as gr | |
| import tensorflow as tf | |
| import re | |
| from tensorflow import keras | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| from tensorflow.keras.layers import TextVectorization | |
| import pickle | |
| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
| def custom_standardization(input_data): | |
| lowercase = tf.strings.lower(input_data) | |
| stripped_html = tf.strings.regex_replace(lowercase, "<br />", " ") | |
| return tf.strings.regex_replace( | |
| stripped_html, "[%s]" % re.escape("!#$%&'()*+,-./:;<=>?@\^_`{|}~"), "" | |
| ) | |
| count_vect = pickle.load(open('countvect.pkl', 'rb')) | |
| tokenizer = pickle.load(open('tokenizer.pkl', 'rb')) | |
| from_disk = pickle.load(open('tv_layer.pkl', 'rb')) | |
| text_vectorization = TextVectorization.from_config(from_disk['config']) | |
| text_vectorization.set_weights(from_disk['weights']) | |
| lr_model = pickle.load(open('logistic_model.pkl', 'rb')) | |
| lstm_model = keras.models.load_model('lstm_model.h5') | |
| bert_classifier_model = keras.models.load_model('bert_classifier.h5') | |
| def get_bert_end_to_end(model): | |
| inputs_string = keras.Input(shape=(1,), dtype="string") | |
| indices = text_vectorization(inputs_string) | |
| outputs = model(indices) | |
| end_to_end_model = keras.Model(inputs_string, outputs, name="end_to_end_model") | |
| optimizer = keras.optimizers.Adam(learning_rate=0.001) | |
| end_to_end_model.compile( | |
| optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"] | |
| ) | |
| return end_to_end_model | |
| bert_end_model = get_bert_end_to_end(bert_classifier_model) | |
| def get_lr_results(text): | |
| sample_vec = count_vect.transform([text]) | |
| return lr_model.predict(sample_vec)[0] | |
| def get_lstm_results(text): | |
| tokenized_text = tokenizer.texts_to_sequences([text]) | |
| padded_tokens = pad_sequences(tokenized_text, maxlen=200) | |
| return lstm_model.predict(padded_tokens)[0][0] | |
| def get_bert_results(text): | |
| return bert_end_model.predict([text])[0][0] | |
| def decide(text): | |
| lr_result = get_lr_results(text) | |
| lstm_result = get_lstm_results(text) | |
| bert_result = get_bert_results(text) | |
| results = [ | |
| lr_result.round(2), | |
| lstm_result.round(2), | |
| bert_result.round(2)] | |
| if lstm_result >= 0.6: | |
| return "Positive review (LR: {}, LSTM: {}, BERT: {}".format(*results) | |
| elif lstm_result <= 0.4: | |
| return "Negative review (LR: {}, LSTM: {}, BERT: {}".format(*results) | |
| else: | |
| return "Neutral review (LR: {}, LSTM: {}, BERT: {}".format(*results) | |
| example_sentence_1 = "I hate this toaster, they made no effort in making it. So cheap, it almost immediately broke!" | |
| example_sentence_2 = "Great toaster! We love the way it toasted my bread so quickly. Very high quality components too." | |
| examples = [[example_sentence_1], [example_sentence_2]] | |
| description = "Write out a product review to know the underlying sentiment." | |
| gr.Interface(decide, | |
| inputs=gr.inputs.Textbox(lines=1, placeholder=None, default="", label=None), | |
| outputs='text', | |
| examples=examples, | |
| title="Sentiment analysis of product reviews", | |
| theme="grass", description=description, | |
| allow_flagging="auto", | |
| flagging_dir='flagging records').launch(enable_queue=True, inline=False, share=True) | |