import html
import os
from typing import AnyStr

import nltk
import streamlit as st
import validators
from transformers import pipeline
from validators import ValidationFailure


def main() -> None:
    nltk.download("punkt")
    # header
    st.title(":bookmark_tabs: Terms Of Service Summarizer :bookmark_tabs:")
    st.markdown("The app aims to extract the main information from Terms Of Conditions, which are often too long and "
                "difficult to understand. ")
    st.markdown("To test it just copy-paste a Terms Of Conditions in the textarea or select one of the examples that "
                "we have prepared for you, then you will see the summary represented as the most important sentences.")
    st.markdown("If you want more info in how we built our NLP algorithm check the documentation in the following "
                "GitHub repo: :point_right: https://github.com/balditommaso/TermsOfServiceSummarization :point_left:")
    st.markdown(":skull_and_crossbones: NOTE :skull_and_crossbones::")
    st.markdown("the App is still under development and we do not give any guarantee on the quality of the summaries, "
                "so we suggest a careful reading of the document.")

    @st.cache(allow_output_mutation=True, suppress_st_warning=True, show_spinner=False)
    def create_pipeline():
        with st.spinner("Loading the model..."):
            tos_pipeline = pipeline(task="summarization",
                                    model="ML-unipi/bart-large-tos",
                                    tokenizer="ML-unipi/bart-large-tos"
                                    )
        return tos_pipeline

    def display_summary(summary_sentences: list) -> None:
        st.subheader("Summary :male-detective:")
        for senetence in summary_sentences:
            st.markdown(f"<li>{senetence}</li>", unsafe_allow_html=True)

    def is_valid_url(url: str) -> bool:
        result = validators.url(url)
        if isinstance(result, ValidationFailure):
            return False
        return True

    def get_list_files() -> list:
        names = []
        for file in os.listdir("./samples/"):
            if file.endswith(".txt"):
                names.append(file.replace(".txt", ""))

        return names

    def fetch_file_content(filename: str) -> AnyStr:
        with open(f"./samples/{filename.lower()}.txt", "r") as file:
            text = file.read()
        return text

    if "target_text" not in st.session_state:
        st.session_state.target_text = ""
    if "sentence_lenght" not in st.session_state:
        st.session_state.sentence_length = 15
    if "sample_choice" not in st.session_state:
        st.session_state.sentence_length = ""

    st.header("Input")

    # sentences_length = st.number_input(
    #     label="How many senetences to be extracted:",
    #     min_value=5,
    #     max_value=15,
    #     step=1,
    #     value=st.session_state.sentence_length
    # )

    sample_choice = st.selectbox(
        label="Select a sample:",
        options=get_list_files()
    )

    st.session_state.target_text = fetch_file_content(sample_choice)
    target_text_input = st.text_area(
        value=st.session_state.target_text,
        label="Paste your own Term Of Service:",
        height=240
    )

    summarize_button = st.button(label="Try it!")

    # @st.cache(suppress_st_warning=True,
    #           show_spinner=False,
    #           allow_output_mutation=True,
    #           hash_funcs={"torch.nn.parameter.Parameter": lambda _: None,
    #                       "tokenizers.Tokenizer": lambda _: None,
    #                       "tokenizers.AddedToken": lambda _: None,
    #                       }
    #           )


    # def summary_from_cache(summary_sentence: tuple) -> tuple:
    #     with st.spinner("Summarizing in progress..."):
    #         return tuple(summarizer.abstractive_summary(list(summary_sentence)))

    if summarize_button:
        output = pipeline(st.session_state.target_text)
        output(output[0])


if __name__ == "__main__":
    main()