import torch
import validators
import streamlit as st
from transformers import pipeline, T5Tokenizer, T5ForConditionalGeneration

# local modules
from extractive_summarizer.model_processors import Summarizer
from src.utils import clean_text, fetch_article_text
from src.abstractive_summarizer import abstractive_summarizer

# abstractive summarizer model
@st.cache()
def load_abs_model():
    tokenizer = T5Tokenizer.from_pretrained("t5-large")
    model = T5ForConditionalGeneration.from_pretrained("t5-base")
    return tokenizer, model


if __name__ == "__main__":
    # ---------------------------------
    # Main Application
    # ---------------------------------
    st.title("Text Summarizer 📝")
    summarize_type = st.sidebar.selectbox(
        "Summarization type", options=["Extractive", "Abstractive"]
    )

    inp_text = st.text_input("Enter text or a url here")

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

    # view summarized text (expander)
    with st.expander("View input text"):
        st.write(inp_text)

    summarize = st.button("Summarize")

    # called on toggle button [summarize]
    if summarize:
        if summarize_type == "Extractive":
            # 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=6)

        elif summarize_type == "Abstractive":
            with st.spinner(
                text="Creating abstractive summary. This might take a few seconds ..."
            ):
                abs_tokenizer, abs_model = load_abs_model()
                summarized_text = abstractive_summarizer(
                    abs_tokenizer, abs_model, text_to_summarize
                )
        elif summarize_type == "Abstractive" and is_url:
            abs_url_summarizer = pipeline("summarization")
            tmp_sum = abs_url_summarizer(
                text_to_summarize, max_length=120, min_length=30, 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)