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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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from unidecode import unidecode |
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import tensorflow as tf |
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import cloudpickle |
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from transformers import DistilBertTokenizerFast |
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import os |
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def load_model(): |
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interpreter = tf.lite.Interpreter(model_path=os.path.join("models/news_classification_hf_distilbert.tflite")) |
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with open("models/news_classification_labelencoder.bin", "rb") as model_file_obj: |
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label_encoder = cloudpickle.load(model_file_obj) |
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model_checkpoint = "distilbert-base-uncased" |
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tokenizer = DistilBertTokenizerFast.from_pretrained(model_checkpoint) |
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return interpreter, label_encoder, tokenizer |
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interpreter, label_encoder, tokenizer = load_model() |
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def inference(text): |
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tflite_pred = "Can't Predict" |
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if text != "": |
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tokens = tokenizer(text, max_length=80, padding="max_length", truncation=True, return_tensors="tf") |
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interpreter.allocate_tensors() |
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input_details = interpreter.get_input_details() |
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output_details = interpreter.get_output_details()[0] |
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attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids'] |
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interpreter.set_tensor(input_details[0]["index"], attention_mask) |
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interpreter.set_tensor(input_details[1]["index"], input_ids) |
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interpreter.invoke() |
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tflite_pred = interpreter.get_tensor(output_details["index"])[0] |
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tflite_pred_argmax = np.argmax(tflite_pred) |
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tflite_pred = f"{label_encoder.inverse_transform([tflite_pred_argmax])[0].upper()} ({str(np.round(tflite_pred[tflite_pred_argmax], 5))})" |
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return tflite_pred |
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def main(): |
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st.title("News Headline Classification") |
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lang_trained = 'TECHNOLOGY, HEALTH, WORLD, ENTERTAINMENT, SPORTS, BUSINESS, NATION, SCIENCE' |
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st.write(f'Model is fine-tuned on the following categories \n{lang_trained}') |
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review = st.text_area("Enter a news headline:", "", height=100) |
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if st.button("Submit"): |
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result = inference(review) |
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st.write(result) |
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if __name__ == "__main__": |
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main() |
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