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import streamlit
import pandas as pd
#import torch
from transformers import pipeline
import streamlit as st
def app():
st.title("Text Summarization 🤓")
st.markdown("This is a Web application that Summarizes Text 😎")
upload_file = st.file_uploader('Upload a file containing Text data')
button = st.button("Summarize")
st.cache(allow_output_mutation=True)
def facebook_bart_model():
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
return summarizer
summarizer= facebook_bart_model()
def text_summarizer(text):
a = summarizer(text, max_length=150, min_length=30, do_sample=False)
return a[0]['summary_text']
# Check to see if a file has been uploaded
if upload_file is not None and button:
st.success("Summarizing Text, Please wait...")
# If it has then do the following:
# Read the file to a dataframe using pandas
df = pd.read_csv(upload_file)
# Create a section for the dataframe header
df1 = df.copy()
df1['summarized_text'] = df1['Dialog'].apply(text_summarizer)
df2 = df1[['Name','summarized_text']]
st.write(df2.head(5))
@st.cache
def convert_df(dataframe):
return dataframe.to_csv().encode('utf-8')
csv = convert_df(df2)
st.download_button(label="Download CSV", data=csv, file_name='summarized_output.csv', mime='text/csv')
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if __name__ == "__main__":
app()
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