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