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import streamlit as st
from transformers import pipeline
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the sentiment analysis model from our BERT model
classifier = pipeline("text-classification", model = "MarieAngeA13/Sentiment-Analysis-BERT")
# Create a Streamlit app
st.title('Sentiment Analysis with BERT')
st.write('Enter some text and we will predict its sentiment!')
# Add a text input box for the user to enter text
text_input = st.text_input('Enter text here')
# When the user submits text, run the sentiment analysis model on it
if st.button('Submit'):
# Predict the sentiment of the text using our own BERT model
output = classifier(text_input)
best_prediction = output[0]
sentiment = best_prediction['label']
confidence = best_prediction['score']
# Display the sentiment prediction to the user
st.write(f'Sentiment: {sentiment}')
st.write(f'Confidence: {round(confidence, 2)}') |