quickcode00

from sumy.parsers.html import HtmlParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
import requests

quickcode00

import streamlit as st

st.title('News Summarizer')

# Gives option between top stories and search term
search_choice = st.sidebar.radio('', options=['Top Headlines', 'Search Term'])

# Takes user input for max number of sentences per summary
sentences_count = st.sidebar.slider('Max sentences per summary:', min_value=1, 
                                                                  max_value=10, 

quickcode00

import streamlit as st

st.title('News Summarizer')

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def search_articles(sentences_count: int, **kwargs) -> list:
    """
    Sends GET request to News API /v2/everything endpoint,
    and summarizes data at each URL
    
    Inputs
    ----------
    sentences_count: specifies max number of sentences 
                     for return value
    kwargs: see News API 

quickcode00

import streamlit as st

st.title('News Summarizer')

# Gives option between top stories and search term
search_choice = st.sidebar.radio('', options=['Top Headlines', 'Search Term'])

# Takes user input for max number of sentences per summary
sentences_count = st.sidebar.slider('Max sentences per summary:', min_value=1, 
                                                                  max_value=10, 

quickcode00

def summarize_news_api(articles: list, sentences_count: int) -> list:
    """
    summarizes text at URL for each element of articles dict 
    (return value from news_api_request) and adds a new element 
    articles dict where the key is 'summary' and the value is 
    the summarized text
    
    Inputs
    ----------
    articles: list of dict returned from news_api_request()

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url = 'https://en.wikipedia.org/wiki/Automatic_summarization'
summarize_html(url, 10)

quickcode00

import os
from app_functions import get_top_headlines, search_articles
import streamlit as st

API_KEY = os.environ['NEWS_API_KEY']

st.title('News Summarizer')

search_choice = st.sidebar.radio('', options=['Top Headlines', 'Search Term'])
sentences_count = st.sidebar.slider('Max sentences per summary:', min_value=1, 

quickcode00

from sumy.parsers.html import HtmlParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words
import requests


def summarize_html(url: str, sentences_count: int, language: str = 'english') -> str:
    """

quickcode00

streamlit run streamlit_example.py