app.py

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)}')