app.py

import streamlit as st
import sumy

# using sumy library for summarization
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lex_rank import LexRankSummarizer
from sumy.summarizers.text_rank import TextRankSummarizer
from sumy.nlp.tokenizers import Tokenizer
import pandas as pd
import matplotlib.pyplot as plt
# import seaborn
from transformers import BartForConditionalGeneration, BartTokenizer
from transformers import T5ForConditionalGeneration, T5Tokenizer
from rouge import Rouge
import altair as at
import torch
from Text_analysis import *
from Metadata import *
from app_utils import *


HTML_BANNER = """
    <div style="background-color:lightgreen;padding:10px;border-radius:10px">
    <h1 style="color:white;text-align:center;">Summary app </h1>
    </div>
    """


def main():
    menu=['Summarization','Text-Analysis','Meta-Data']
    choice=st.sidebar.selectbox("Menu",menu)


    if choice=='Summarization':
        stc.html(HTML_BANNER)
        st.subheader('summarization')
        raw_text=st.text_area("Enter the text you want to summarize")
        if st.button("Summarize"):
            with st.expander("Original Text"):
                st.write(raw_text)
            c1, c2 = st.columns(2)

            with c1:
               
                with st.expander("LexRank Summary"):
                    try:
                        summary = sumy_summarizer(raw_text)
                        document_len={"Original":len(raw_text),
                                  "Summary":len(summary)
                                   }
                        st.write(document_len)
                        st.write(summary)
                        st.info("Rouge Score")
                        score=evaluate_summary(summary,raw_text)
                        st.write(score.T)
                        st.subheader(" ")
                        score['metrics']=score.index
                        c=at.Chart(score).mark_bar().encode(
                        x='metrics',y='rouge-1'
                        )
                        st.altair_chart(c)
                    except:
                        st.warning('Insufficient data')

                    

            with c2:
                with st.expander("TextRank Summary"):
                    try:
                        text_summary=sumy_text_summarizer(raw_text)
                        document_len={"Original":len(raw_text),
                                  "Summary":len(summary)
                                   }
                        st.write(document_len)
                        st.write(text_summary)

                        st.info("Rouge Score")
                        score=evaluate_summary(text_summary,raw_text)
                        st.write(score.T)
                        st.subheader(" ")
                        score['metrics']=score.index
                        c=at.Chart(score).mark_bar().encode(
                            x='metrics',y='rouge-1'
                        )
                        st.altair_chart(c)
                        
                    except:
                          st.warning('Insufficient data')
                        

            st.subheader("Bart Sumary")
            with st.expander("Bart Summary"):
                try:
                    bart_summ = bart_summary(raw_text)
                    document_len={"Original":len(raw_text),
                                  "Summary":len(summary)
                                   }
                    st.write(document_len)
                    st.write(bart_summ)
                    st.info("Rouge Score")
                    score=evaluate_summary(bart_summ,raw_text)
                    st.write(score.T)
                    st.subheader(" ")
                    score['metrics']=score.index
                    c=at.Chart(score).mark_bar().encode(
                        x='metrics',y='rouge-1'
                    )
                    st.altair_chart(c)
                except:
                      st.warning('Insufficient data')

            st.subheader("T5 Sumarization")
            with st.expander("T5 Summary"):
                T5_sum = T5_summary(raw_text)
                document_len={"Original":len(raw_text),
                                  "Summary":len(summary)
                                   }
                st.write(document_len)
                st.write(T5_sum)
                st.info("Rouge Score")
                score=evaluate_summary(T5_sum,raw_text)
                st.write(score.T)
                st.subheader(" ")
                score['metrics']=score.index
                c=at.Chart(score).mark_bar().encode(
                        x='metrics',y='rouge-1'
                    )
                st.altair_chart(c)


    elif choice=='Text-Analysis':
        text_analysis()
    else:
        metadata()


if __name__=='__main__':
    main()