| import numpy as np |
| import tensorflow as tf |
| from tensorflow import keras |
| from tensorflow.keras.preprocessing.sequence import pad_sequences |
| from tensorflow.keras.datasets import imdb |
| from huggingface_hub import from_pretrained_keras |
| import gradio as gr |
| from typing import Dict |
|
|
| class KerasIMDBTokenizer: |
|
|
| def __init__(self, vocab_size: int = 20000) -> None: |
|
|
| |
| self.START_CHAR = 1 |
| self.OOV_CHAR = 2 |
| self.INDEX_FROM = 3 |
|
|
| self.word_index: dict[str, int] = imdb.get_word_index() |
| self.word_index = { |
| token: input_id + self.INDEX_FROM |
| for token, input_id in self.word_index.items() if input_id <= vocab_size |
| } |
|
|
| def tokenize_and_pad(self, text: str, maxlen: int = 200) -> np.ndarray: |
| tokens = text.split() |
| input_ids = [self.word_index.get(token.lower(), self.OOV_CHAR) for token in tokens] |
| input_ids.insert(0, self.START_CHAR) |
| |
| return pad_sequences([input_ids], maxlen=maxlen) |
|
|
| model = from_pretrained_keras("keras-io/text-classification-with-transformer", compile=False) |
| tokenizer = KerasIMDBTokenizer() |
|
|
| def sentiment_analysis(model_input: str) -> Dict[str, float]: |
| tokenized = tokenizer.tokenize_and_pad(model_input) |
|
|
| prediction = model.predict(tokenized)[0] |
| ret = { |
| "negative": float(prediction[0]), |
| "positive": float(prediction[1]) |
| } |
| return ret |
|
|
|
|
| model_input = gr.Textbox("Input text here", show_label=False) |
| model_output = gr.Label("Sentiment Analysis Result", num_top_classes=2, show_label=True, label="Sentiment Analysis Result") |
|
|
|
|
| examples = [ |
| ( |
| "Story of a man who has unnatural feelings for a pig. " |
| "Starts out with a opening scene that is a terrific example of absurd comedy. " |
| "A formal orchestra audience is turned into an insane, violent mob by the crazy chantings of it's singers. " |
| "Unfortunately it stays absurd the WHOLE time with no general narrative eventually making it just too off putting. " |
| "Even those from the era should be turned off. " |
| "The cryptic dialogue would make Shakespeare seem easy to a third grader. " |
| "On a technical level it's better than you might think with some good cinematography by future great Vilmos Zsigmond. " |
| "Future stars Sally Kirkland and Frederic Forrest can be seen briefly." |
| ), |
| ( |
| "I came in in the middle of this film so I had no idea about any credits or even its title till I looked it up here, " |
| "where I see that it has received a mixed reception by your commentators. " |
| "I'm on the positive side regarding this film but one thing really caught my attention as I watched: " |
| "the beautiful and sensitive score written in a Coplandesque Americana style. " |
| "My surprise was great when I discovered the score to have been written by none other than John Williams himself. " |
| "True he has written sensitive and poignant scores such as Schindler's List but one usually associates " |
| "his name with such bombasticities as Star Wars. " |
| "But in my opinion what Williams has written for this movie surpasses anything I've ever heard of his " |
| "for tenderness, sensitivity and beauty, fully in keeping with the tender and lovely plot of the movie. " |
| "And another recent score of his, for Catch Me if You Can, shows still more wit and sophistication. " |
| "As to Stanley and Iris, I like education movies like How Green was my Valley and Konrack, " |
| "that one with John Voigt and his young African American charges in South Carolina, " |
| "and Danny deVito's Renaissance Man, etc. They tell a necessary story of intellectual and spiritual awakening, " |
| "a story which can't be told often enough. This one is an excellent addition to that genre." |
| ) |
| ] |
|
|
| title = "Text classification with Transformer" |
| description = "Implement a Transformer block as a Keras layer and use it for text classification." |
| article = ( |
| "Author: Xin Sui " |
| "Based on <a href=\"https://keras.io/examples/nlp/text_classification_with_transformer\">this</a> " |
| "keras example by <a href=\"https://twitter.com/NandanApoorv\">Apoorv Nandan</a>. " |
| "HuggingFace Model <a href=\"https://huggingface.co/keras-io/text-classification-with-transformer\">here</a>" |
| ) |
|
|
|
|
| app = gr.Interface( |
| sentiment_analysis, |
| inputs=model_input, |
| outputs=model_output, |
| examples=examples, |
| title=title, |
| description=description, |
| article=article, |
| allow_flagging='never', |
| analytics_enabled=False, |
| ) |
|
|
| app.launch(enable_queue=True) |
