# import
from tensorflow.python.keras.utils.generic_utils import default
import streamlit as st
from newspaper import Article
from transformers import pipeline

# set config
st.set_page_config(layout="wide", page_title="SummarizeLink")

# load the summarization model (cache for faster loading)
@st.cache(allow_output_mutation=True)
def load_summarize_model():
    # model = pipeline("summarization", model='sshleifer/distilbart-cnn-12-6')
    model = pipeline("summarization")
    return model

# loading the model
summ = load_summarize_model()

# define the down functions
def download_and_parse_article(url):
    """Downloads and parses an article from a URL.

    Parameters
    ----------
    url : str
        The URL of the article to download and parse.

    Returns
    -------
    article : newspaper.Article
        The article downloaded and parsed.
    """
    # define the article
    article = Article(url)
    # download and parse the article
    article.download()
    article.parse()
    # return the article
    return article.text

# APP
# set title and subtitle
st.title("SummarizeLink")
st.markdown("Paste any article link below and click on the 'Summarize' button.")
st.markdown("*Note:* We truncate the text incase the article is lengthy! 🖖")
# create the input text box and setting panel
link = st.text_area('Paste your link here...', "https://towardsdatascience.com/a-guide-to-the-knowledge-graphs-bfb5c40272f1", height=50)
button = st.button("Summarize")
min_length = st.sidebar.slider('Min summary length', min_value=10, max_value=100, value=50, step=10)
max_length = st.sidebar.slider('Max summary length', min_value=30, max_value=700, value=100, step=10)
num_beams = st.sidebar.slider('Beam length', min_value=1, max_value=10, value=5, step=1)

# if button is clicked
with st.spinner("Parsing article and Summarizing..."):
    if button and link:
        # get the text
        text = download_and_parse_article(link)  
        # summarize the text
        summary = summ(text, 
                       truncation=True,
                       max_length = max_length, 
                       min_length = min_length, 
                       num_beams=num_beams, 
                       do_sample=True,
                       early_stopping=True, 
                       repetition_penalty=1.5, 
                       length_penalty=1.5)[0]
        # display the summary
        st.markdown("**Summary:**")
        st.write(summary['summary_text'])