import streamlit as st from transformers import pipeline def main(): available_models = { "Google Pegasus": "suriya7/bart-finetuned-text-summarization", "Facebook Bart" : "Azma-AI/bart-large-text-summarizer", } history = [] summary_read = '' if 'history' not in st.session_state: st.session_state['history'] = [] def summarize_text(text, max_length, model, model_name ): global summary_read summarizer = pipeline('summarization', model=model) summary = summarizer(text, max_length=max_length+10, min_length=max_length, do_sample=False) st.write(summary[0]['summary_text']) print(summary[0]['summary_text']) summary_read = summary[0]['summary_text'] st.session_state['history'].append({ 'original text' : text, 'summary': summary[0]['summary_text'], 'model': model_name, 'word_limit': max_length-10, }) st.title('Text Summarizer') text = st.text_area("Enter Text:", value='', height=None, max_chars=None, key=None) max_length = st.slider("Max Length:", min_value=10, max_value=100, step=1) model_name = st.selectbox("Choose a model:", list(available_models.keys())) model_choice = available_models[model_name] col1, col2, col3, col4, col5 = st.columns([1,1,1,1,1]) with col1: st.write(" ") with col2: st.write(" ") with col3: like = st.button('👍') with col4: dislike = st.button('👎') with col5: st.write(" ") if st.button('Summarize'): if text: max_length = max_length+10 print(max_length) summarize_text(text, max_length, model_choice, model_name) else: st.write("Please enter text for summarization.") for i, item in enumerate(st.session_state['history']): st.sidebar.markdown(f'{i+1}.') for key, value in item.items(): st.sidebar.markdown(f'{key}: {value}') if __name__ == "__main__": main()