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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('👍')
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() |