import streamlit as st
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
from transformers import AutoModel
#from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

from Scrapper_Summarizer import get_full_article_dawn, get_full_article_tnews, get_full_article_brecorder, summarizer

# summarizer = pipeline("summarization", model="mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization")

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to scrape full article and summarize it
def get_full_article(url):
    try:
        response = requests.get(url, verify=False)
        soup = BeautifulSoup(response.text, 'html.parser')
        content_div = soup.find('div', class_='story__content')

        if content_div:
            paragraphs = content_div.find_all('p')
            full_text = ' '.join([para.get_text(strip=True) for para in paragraphs])

            # Limiting text length for summarization
            summary_obj = summarizer(full_text[:1020])

            # Convert the summary object to a string
            summary = summary_obj[0]['summary_text'] if summary_obj else ""
            st.success("Summary generated successfully!")
            return summary
        else:
            st.error("Content not found in the article.")
            return "Content not found."
    except Exception as e:
        st.error(f"Error fetching the article: {e}")
        return "Error fetching the article."



def article_sum():
    # App title
    st.title("📰 Article Summarizer")
    st.write("Provide the URL of the article you'd like summarized below, and we'll fetch and summarize it for you!")

    # Input URL from user
    url = st.text_input("Enter the article URL:", "")

    # Sidebar with buttons for different sources
    st.sidebar.title("Choose a Source")

    # Button for "The News"
    if st.sidebar.button("The News"):
        if url:
            with st.spinner('Fetching and summarizing the article from The News...'):
                full_text = get_full_article_tnews(url)
                summary_obj = summarizer(full_text[:1020])

                # Convert the summary object to a string
                summary = summary_obj[0]['summary_text'] if summary_obj else ""
                st.write(summary)
        else:
            st.sidebar.error("Please enter the URL of an article from The News.")

    # Button for "The Dawn"
    if st.sidebar.button("The Dawn"):
        if url:
            with st.spinner('Fetching and summarizing the article from The Dawn...'):
                full_text = get_full_article_dawn(url)
                summary_obj = summarizer(full_text[:1020])

                # Convert the summary object to a string
                summary = summary_obj[0]['summary_text'] if summary_obj else ""
                st.write(summary)
        else:
            st.sidebar.error("Please enter the URL of an article from The Dawn.")

    # Button for "Business Recorder"
    if st.sidebar.button("Business Recorder"):
        if url:
            with st.spinner('Fetching and summarizing the article from Business Recorder...'):
                full_text= get_full_article_brecorder(url)
                summary_obj = summarizer(full_text[:1020])

                # Convert the summary object to a string
                summary = summary_obj[0]['summary_text'] if summary_obj else ""
                st.write(summary)
        else:
            st.sidebar.error("Please enter the URL of an article from Business Recorder.")

    # Sidebar details and credits
    st.sidebar.title("About")
    st.sidebar.write(
        "This utility fetches articles from a given URL and summarizes them using a pre-trained summarization model.")
    st.sidebar.markdown("### Model Used")
    st.sidebar.info("Model: `sshleifer/distilbart-cnn-12-6` (BART-based summarizer)")
    st.sidebar.markdown("---")
    st.sidebar.write("Created by Strategy")