inclusive-ml commited on
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  1. app.py +18 -0
  2. requirements.txt +2 -0
app.py ADDED
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+ import streamlit as st
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+ from transformers import pipeline
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+ @st.cache(allow_output_mutation=True)
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+ def summarize_model():
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+ model = pipeline("summarization")
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+ return model
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+ summ = summarize_model()
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+ st.title("Summarize Your Text")
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+ st.subheader("Paste any article in the text area below and click on the 'Summarize Text' button to get the summarized textual data")
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+ st.subheader("This application is using HuggingFace's transformers pre-trained model for text summarization.")
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+ sentence = st.text_area('Paste your copied data here...', height=100)
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+ button = st.button("Summarize Text")
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+ max_lengthy = st.sidebar.slider('Maximum summary length (words)', min_value=30, max_value=700, value=100, step=10)
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+ num_beamer = st.sidebar.slider('Speed vs quality of Summary (1 is fastest but less accurate)', min_value=1, max_value=8, value=4, step=1)
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+ with st.spinner("Summarizing..."):
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+ if button and sentence:
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+ summary = summ(sentence, max_length = max_lengthy, min_length = 50, num_beams=num_beamer, do_sample=True,early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)[0]
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+ st.write(summary['summary_text'])
requirements.txt ADDED
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+ tensorflow==2.3.0
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+ transformers