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| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import pickle | |
| from huggingface_hub import from_pretrained_keras | |
| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
| model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb") | |
| with open('tokenizer.pickle', 'rb') as file: | |
| tokenizer = pickle.load(file) | |
| def decide(text): | |
| tokenized_text = tokenizer.texts_to_sequences([text]) | |
| padded_tokens = pad_sequences(tokenized_text, maxlen= 200) | |
| result = model.predict(padded_tokens)[0][0] | |
| if result >= 0.6 : | |
| return "Positive review" | |
| elif result <= 0.4: | |
| return "Negative review" | |
| else: | |
| return "Neutral review" | |
| example_sentence_1 = "I hate the movie, they made no effort in making the movie. Waste of time!" | |
| example_sentence_2 = "Awesome movie! Loved the way in which the hero acted." | |
| examples = [[example_sentence_1], [example_sentence_2]] | |
| description = "Write out a movie review to know the underlying sentiment." | |
| article = "<div style='text-align: center;'><a href='https://huggingface.co/DrishtiSharma' target='_blank'>Space by Drishti Sharma</a><br><a href='https://keras.io/examples/nlp/bidirectional_lstm_imdb/' target='_blank'>Keras example by François Chollet</a></div>" | |
| gr.Interface(decide, inputs= gr.inputs.Textbox( lines=1, placeholder=None, default="", label=None), outputs='text', examples=examples, | |
| title="Sentiment analysis of movie reviews", theme = "grass", description=description, allow_flagging="auto", | |
| flagging_dir='flagging records', article = article).launch(enable_queue = True, | |
| inline=False, share = True) | |