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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, do_sample=False)[0]["generated_text"]
return summary
# Sentiment Classification
def sentiment_classification(summary):
sentiment_model = pipeline("text-classification", model="wxrrrrrrr/finetuned_sentiment_analysis")
result = sentiment_model(summary, max_length=100, do_sample=False)[0]['label']
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() |