import nltk
import validators
import streamlit as st
from transformers import AutoTokenizer, pipeline

# local modules
from extractive_summarizer.model_processors import Summarizer
from utils import (
    clean_text,
    fetch_article_text,
    preprocess_text_for_abstractive_summarization,
    read_text_from_file,
)

if __name__ == "__main__":
    # ---------------------------------
    # Main Application
    # ---------------------------------
    st.title("Text Summarizer")

    
    summarize_type = st.sidebar.selectbox(
        "Summarization type", options=["Extractive", "Abstractive"]
    )

    st.markdown(
        "Enter a text or a url to get a concise summary of the article while conserving the overall meaning. This app supports text in the following formats:"
    )
    st.markdown(
        """- Raw text in text box 
- URL of article/news to be summarized 
- .txt, .pdf, .docx file formats"""
    )
    st.markdown("---")
    # ---------------------------
    # SETUP & Constants
    nltk.download("punkt")
    abs_tokenizer_name = "facebook/bart-large-cnn"
    abs_model_name = "facebook/bart-large-cnn"
    abs_tokenizer = AutoTokenizer.from_pretrained(abs_tokenizer_name)
    abs_max_length = 90
    abs_min_length = 30
    # ---------------------------

    inp_text = st.text_input("Enter text or a url here")
    st.markdown(
        "<h3 style='text-align: center; color: green;'>OR</h3>",
        unsafe_allow_html=True,
    )
    uploaded_file = st.file_uploader(
        "Upload a .txt, .pdf, .docx file for summarization"
    )

    is_url = validators.url(inp_text)
    if is_url:
        # complete text, chunks to summarize (list of sentences for long docs)
        text, clean_txt = fetch_article_text(url=inp_text)
    elif uploaded_file:
        clean_txt = read_text_from_file(uploaded_file)
        clean_txt = clean_text(clean_txt)
    else:
        clean_txt = clean_text(inp_text)

    # view summarized text (expander)
    with st.expander("View input text"):
        if is_url:
            st.write(clean_txt[0])
        else:
            st.write(clean_txt)
    summarize = st.button("Summarize")

    # called on toggle button [summarize]
    if summarize:
        if summarize_type == "Extractive":
            if is_url:
                text_to_summarize = " ".join([txt for txt in clean_txt])
            else:
                text_to_summarize = clean_txt
            # extractive summarizer

            with st.spinner(
                text="Creating extractive summary. This might take a few seconds ..."
            ):
                ext_model = Summarizer()
                summarized_text = ext_model(text_to_summarize, num_sentences=5)

        elif summarize_type == "Abstractive":
            with st.spinner(
                text="Creating abstractive summary. This might take a few seconds ..."
            ):
                text_to_summarize = clean_txt
                abs_summarizer = pipeline(
                    "summarization", model=abs_model_name, tokenizer=abs_tokenizer_name
                )

                if is_url is False:
                    # list of chunks
                    text_to_summarize = preprocess_text_for_abstractive_summarization(
                        tokenizer=abs_tokenizer, text=clean_txt
                    )

                tmp_sum = abs_summarizer(
                    text_to_summarize,
                    max_length=abs_max_length,
                    min_length=abs_min_length,
                    do_sample=False,
                )

                summarized_text = " ".join([summ["summary_text"] for summ in tmp_sum])

        # final summarized output
        st.subheader("Summarized text")
        st.info(summarized_text)