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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)) | |
| 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') | |
| if __name__ == "__main__": | |
| app() | |