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import streamlit as st
from transformers import pipeline
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
def summarize(data, modelname):
if (modelname == 'Bart'):
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
print("world")
output = summarizer(data, max_length=300, min_length=30, do_sample=False)
return output[0]["summary_text"]
elif (modelname == 'Pegasus'):
model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
else:
summarizer = pipeline("summarization", model="lidiya/bart-large-xsum-samsum")
print("world")
output = summarizer(data, max_length=300, min_length=30, do_sample=False)
return output[0]["summary_text"]
# Create tokens - number representation of our text
tokens = tokenizer(data, truncation=True, padding="longest", return_tensors="pt")
summary = model.generate(**tokens)
return tokenizer.decode(summary[0])
uploaded_file = st.file_uploader("Choose a file",help=" you can choose .txt file")
data = ""
output = ""
if uploaded_file is not None:
# To read file as bytes:
bytes_data = uploaded_file.getvalue()
data = bytes_data.decode("utf-8")
modelname = st.radio("Choose your model",
["Bart", "Pegasus" ,"Meeting summary(bart-large-cnn-samsum)"],
help=" you can choose between 3 models to summarize your text. More to come!", )
col1, col2 = st.columns(2)
with col1:
st.header("Copy paste your text or Upload file")
if (uploaded_file is not None):
with st.expander("Text to summarize", expanded=True):
st.write(
data
)
else:
with st.expander("Text to summarize", expanded=True):
data = st.text_area("Paste your text below (max 500 words)", height=100, )
MAX_WORDS = 500
import re
res = len(re.findall(r"\w+", data))
Summarizebtn = st.button("Summarize")
if (Summarizebtn):
output = summarize(data, modelname)
with col2:
st.header("Summary")
if (len(output) > 0):
with st.expander("", expanded=True):
st.write(output)
elif(Summarizebtn):
st.balloons()
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