Create app.py

app.py

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+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  # pyright: reportMissingImports=false
+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:
+
+        # Parameters used in `keras.datasets.imdb.load_data`
+        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)
+        # pad_sequences only accepts a list of sequences
+        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)