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