app.py

import streamlit as st
from transformers import pipeline

# Load sentiment analysis model
model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
sentiment_analysis = pipeline("text-classification", model=model)


# Streamlit app
def main():
    # Set app title and description
    st.title("Sentiment Analysis App")
    st.write("Enter text to predict sentiment.")

    # User input
    text = st.text_area("Text", "")

    # Predict sentiment
    if st.button("Predict"):
        if text.strip() != "":
            sentiment = predict_sentiment(text)
            score = sentiment['score'] * 100
            st.metric(label = f"Sentiment: {sentiment['label']}", value = f"Score: {score:.2f}%")
            #st.write(f"Sentiment: {sentiment['label']}")
            #st.write(f"Score: {sentiment['score']}")
        else:
            st.warning("Please enter some text.")


def predict_sentiment(text):
    result = sentiment_analysis(text)[0]
    return result


if __name__ == "__main__":
    main()