"""
Create a text summarization app using stremlit with a GUI
"""

import streamlit as st
import spacy
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from heapq import nlargest

# load the model
nlp = spacy.load('en_core_web_sm')

# add the stop words
stopwords = list(STOP_WORDS)

# add punctuation to stop words
stopwords = stopwords + list(punctuation)

# add words that aren't in the NLTK stopwords list
other_exclusions = ["'s", "n't", "'m", "'re", "'ve", "'d", "'ll"]
stopwords = stopwords + other_exclusions

# function to get the keywords
def get_summary(text):
    doc = nlp(text)
    tokens = [token.text for token in doc]
    word_frequencies = {}
    for word in doc:
        if word.text.lower() not in stopwords:
            if word.text.lower() not in word_frequencies.keys():
                word_frequencies[word.text] = 1
            else:
                word_frequencies[word.text] += 1
    # get the weighted frequencies
    max_frequency = max(word_frequencies.values())
    for word in word_frequencies.keys():
        word_frequencies[word] = word_frequencies[word]/max_frequency
    # get the sentences
    sentence_tokens = [sent for sent in doc.sents]
    sentence_scores = {}
    for sent in sentence_tokens:
        for word in sent:
            if word.text.lower() in word_frequencies.keys():
                if sent not in sentence_scores.keys():
                    sentence_scores[sent] = word_frequencies[word.text.lower()]
                else:
                    sentence_scores[sent] += word_frequencies[word.text.lower()]
    # get the summary
    summary_sentences = nlargest(7, sentence_scores, key=sentence_scores.get)
    final_sentences = [w.text for w in summary_sentences]
    summary = ' '.join(final_sentences)
    return summary

# main function
def main():
    st.title('Text Summarizer')
    st.subheader('Summarize your text')
    message = st.text_area('Enter your text here', 'Type here')
    summary_options = st.selectbox('Choose the summarizer', ['Gensim', 'Spacy'])
    if st.button('Summarize'):
        if summary_options == 'Gensim':
            summary_result = get_summary(message)
        elif summary_options == 'Spacy':
            summary_result = nlp(message)
            summary_result = ' '.join([sent.text for sent in summary_result.sents])
        else:
            st.write('Select a summarizer')
        st.success(summary_result)

if __name__ == '__main__':
    main()