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| # install required packages | |
| import subprocess | |
| import sys | |
| def install(package): | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", package]) | |
| install("tensorflow") | |
| install("numpy") | |
| install("transformers") | |
| # import related packages | |
| import streamlit as st | |
| import numpy as np | |
| import tensorflow as tf | |
| import transformers | |
| from transformers import DistilBertTokenizer | |
| from transformers import TFDistilBertForSequenceClassification | |
| # print the header message | |
| st.header("Welcome to the STEM NLP application!") | |
| # fetch the pre-trained model | |
| model = TFDistilBertForSequenceClassification.from_pretrained("kaixinwang/NLP") | |
| # build the tokenizer | |
| MODEL_NAME = 'distilbert-base-uncased' | |
| # tokenizer = DistilBertTokenizer.from_pretrained(MODEL_NAME) | |
| tokenizer = DistilBertTokenizer.from_pretrained("kaixinwang/NLP") | |
| mapping = {0:"Negative", 1:"Positive"} | |
| # prompt for the user input | |
| x = st.text_input("To get started, enter your review/text below and hit ENTER:") | |
| if x: | |
| st.write("Determining the sentiment...") | |
| # utterance tokenization | |
| encoding = tokenizer([x], truncation=True, padding=True) | |
| encoded = tf.data.Dataset.from_tensor_slices((dict(encoding), np.ones(1))) | |
| # make the prediction | |
| preds = model.predict(encoded.batch(1)).logits | |
| prob = tf.nn.softmax(preds, axis=1).numpy() | |
| prob_max = np.argmax(prob, axis=1) | |
| # display the output | |
| st.write("Your review is:", x) | |
| content = "Sentiment: %s, prediction score: %.4f" %(mapping[prob_max[0]], prob[0][prob_max][0]) | |
| st.write(content) | |
| # st.write("Sentiment:", mapping[prob_max[0]], "Prediction Score:", prob[0][prob_max][0]) |