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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('π') | |
with col4: | |
dislike = st.button('π') | |
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() |