app.py

import streamlit as st
from transformers import pipeline


# Summarization
def summarization(text):
    text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
    summary = text_model(text, max_length=100, temperature=1.0)[0]["generated_text"]
    return summary

    
# Sentiment Classification
def sentiment_classification(summary):
    sentiment_model = pipeline("text-classification", model="wxrrrrrrr/finetunde_sentiment_analysis")
    result = sentiment_model(summary, max_length=100, truncation=True)[0]['label']
    if result != 'negative':
        result = 'positive'
    return result


def main():
    st.set_page_config(page_title="Your Text Analysis", page_icon="🦜")
    st.header("Tell me your comments!")
    text_input = st.text_input("Enter your text here:")

    if text_input:
        # Stage 1: Summarization
        st.text('Processing text...')
        summary = summarization(text_input)
        # st.write(summary)

        # Stage 2: Sentiment Classification
        st.text('Analyzing sentiment...')
        sentiment = sentiment_classification(summary)
        st.write(sentiment)

        # Display the classification result
        st.write("Sentiment:", sentiment)
    
if __name__ == '__main__':
    main()