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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=3, language:str='english') -> str:
    parser = HtmlParser.from_url(url, Tokenizer(language))

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pip install sumy
pip install streamlit

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import nltk

nltk.download()

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echo "export NLTK_DATA=<PATH-TO-YOUR-DATA> >> ~/.zshenv"

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def summarize_html(url: str, sentences_count: int, language: str = 'english') -> str:
    """
    Summarizes text from URL
    
    Inputs
    ----------
    url: URL for full text
    sentences_count: specifies max number of sentences for return value
    language: specifies language of text
    

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def news_api_request(url: str, **kwargs) -> list:
    """
    Sends GET request to News API endpoint
    
    Inputs
    ----------
    url: full URL for endpoint
    kwargs: please refer to 
            News API documentations: 
            https://newsapi.org/docs/endpoints/

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url = 'https://newsapi.org/v2/top-headlines/'
articles = news_api_request(url, apiKey=api_key, sortBy='publishedAt', country='us')
summaries = summarize_news_api(articles)
print(summaries[0])

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{'source': {'id': None, 'name': None},
 'author': '',
 'title': '',
 'description': '',
 'url': '',
 'urlToImage': '',
 'publishedAt': '',
 'content': '',
 'summary': ''}

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import streamlit as st

name = st.text_input('Enter Name')

if not name:
  name = 'World'

st.write(f'Hello {name}')

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streamlit run streamlit_example.py