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import streamlit as st
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from transformers import pipeline
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@st.cache_resource
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def load_summarizer():
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model = pipeline("summarization", device=0)
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return model
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def generate_chunks(inp_str):
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max_chunk = 500
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inp_str = inp_str.replace('.', '.<eos>')
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inp_str = inp_str.replace('?', '?<eos>')
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inp_str = inp_str.replace('!', '!<eos>')
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sentences = inp_str.split('<eos>')
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current_chunk = 0
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chunks = []
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for sentence in sentences:
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if len(chunks) == current_chunk + 1:
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if len(chunks[current_chunk]) + len(sentence.split(' ')) <= max_chunk:
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chunks[current_chunk].extend(sentence.split(' '))
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else:
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current_chunk += 1
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chunks.append(sentence.split(' '))
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else:
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chunks.append(sentence.split(' '))
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for chunk_id in range(len(chunks)):
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chunks[chunk_id] = ' '.join(chunks[chunk_id])
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return chunks
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summarizer = load_summarizer()
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st.title("Summarize Text")
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sentence = st.text_area('Please paste your article :', height=30)
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button = st.button("Summarize")
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max = st.sidebar.slider('Select max', 50, 500, step=10, value=150)
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min = st.sidebar.slider('Select min', 10, 450, step=10, value=50)
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do_sample = st.sidebar.checkbox("Do sample", value=False)
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with st.spinner("Generating Summary.."):
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if button and sentence:
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chunks = generate_chunks(sentence)
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res = summarizer(chunks,
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max_length=max,
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min_length=min,
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do_sample=do_sample)
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text = ' '.join([summ['summary_text'] for summ in res])
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st.write(text)
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