import datetime
import os
import time
import logging
import nltk
import validators
import streamlit as st
from summarizer import summarizer_init, summarizer_summarize
from config import MODELS
from warnings import filterwarnings

filterwarnings("ignore")
from utils import (
    clean_text,
    fetch_article_text,
    preprocess_text_for_abstractive_summarization,
    read_text_from_file,
)

# summarizer = None
# from rouge import Rouge


logger = logging.getLogger(__name__)

def initialize_app():
    nltk.download("punkt")
    SESSION_DEFAULTS = {
        "model_type": "local",
        "model_name": "long-t5 v1",
        "summarizer_type": "Map Reduce",
        "is_parameters_changed":False,
        # "user_question":'',
        'openai_api_key':'',
    }

    for k, v in SESSION_DEFAULTS.items():
        if k not in st.session_state:
            st.session_state[k] = v

    # init_summarizer(st.session_state.model_name,api_key=None)

@st.cache_resource
def init_summarizer(model_name,api_key=None):
    with st.spinner(
            text="initialising the summarizer. This might take a few seconds ..."
        ):
        model_type = "local"
        if model_name == "OpenAI":
            model_type = "openai"

        model_path = MODELS[model_name]
        if model_type == "openai":
            #validation logic
            api_key = st.session_state.openai_api_key
            tokenizer,base_summarizer = summarizer_init(model_path,model_type,api_key)
        else:
            logger.info(f"Model for summarization : {model_path}")
            tokenizer,base_summarizer = summarizer_init(model_path, model_type)

        alert =  st.success("summarizer initialised")
        time.sleep(1) # Wait for 1 seconds
        alert.empty() # Clear the alert
        return model_type, tokenizer, base_summarizer

def update_parameters_change():
    st.session_state.is_parameters_changed = True


def parameters_change_button(model_name, summarizer_type):
    st.session_state.model_name = model_name
    st.session_state.summarizer_type = summarizer_type
    st.session_state.is_parameters_changed = False
    # init_summarizer(model_name,api_key=None)
    alert =  st.success("chat parameters updated")
    time.sleep(2) # Wait for 1 seconds
    alert.empty() # Clear the alert

import re
def is_valid_open_ai_api_key(secretKey):
    if re.search("^sk-[a-zA-Z0-9]{32,}$", secretKey ): 
        return True
    else: return False

def side_bar():
    with st.sidebar:
        st.subheader("Model parameters")

        with st.form('param_form'):
            # st.info('Info: use openai chat model for best results')
            model_name = st.selectbox(
                "Chat model",
                MODELS,
                #  options=["long-t5 v0", "long-t5 v1",  "pegasus-x-large v1", "OpenAI"],
                key="Model Name",
                help="Select the LLM model for summarization",
                # on_change=update_parameters_change,
            )

            summarizer_type = st.selectbox(
                "Summarizer Type for Long Text", options=["Map Reduce", "Refine"]
            )

            submitted = st.form_submit_button(
                "Save Parameters",
                # on_click=update_parameters_change
                )
       
            if submitted:
                parameters_change_button(model_name, summarizer_type)


        st.markdown("\n")
        if st.session_state.model_name == 'openai':
            with st.form('openai api key'):
                api_key = st.text_input(
                    "Enter openai api key", 
                    type="password",
                    value=st.session_state.openai_api_key,
                    help="enter an openai api key created from 'https://platform.openai.com/account/api-keys'",
                )

                submit_key = st.form_submit_button(
                    "Save key",
                    # on_click=update_parameters_change
                    )
        
                if submit_key:
                    st.session_state.openai_api_key = api_key
                    # st.text(st.session_state.openai_api_key)
                    alert =  st.success("openai api key updated")
                    time.sleep(1) # Wait for 3 seconds
                    alert.empty() # Clear the alert
        st.markdown(
            "### How to use\n"
            "1. Select the LLM model\n"  # noqa: E501
            "1. If selected model asks for a api key enter a valid api key.\n"  # noqa: E501
            "1. Enter a text or a url to get a  summary."
        )
        st.markdown("---")
        st.markdown("""
           This app supports text in the following formats:
            - Raw text in text box 
            - URL of article/news to be summarized 
            - .txt, .pdf, .docx file formats
        """)


def load_app():
    st.title("Text Summarizer 📝")

    inp_text = st.text_input("Enter text or a url here")
    st.markdown(
        "<h4 style='text-align: center; color: green;'>OR</h4>",
        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)
        logger.info("Text Input Type: URL")
        text, cleaned_txt = fetch_article_text(url=inp_text)
    elif uploaded_file:
        logger.info("Text Input Type: FILE")
        cleaned_txt = read_text_from_file(uploaded_file)
        cleaned_txt = clean_text(cleaned_txt)
    else:
        logger.info("Text Input Type: INPUT TEXT")
        cleaned_txt = clean_text(inp_text)

    # view summarized text (expander)
    with st.expander("View input text"):
        if is_url:
            st.write(cleaned_txt[0])
        else:
            st.write(cleaned_txt)

    submitted = st.button("Summarize")

    if submitted:
        if is_url:
            text_to_summarize = " ".join([txt for txt in cleaned_txt])
        else:
            text_to_summarize = cleaned_txt

        submit_text_to_summarize(text_to_summarize)

def submit_text_to_summarize(text_to_summarize):
    summarized_text, time = get_summary(text_to_summarize)
    display_output(summarized_text,time)


def get_summary(text_to_summarize):
    model_name = st.session_state.model_name
    summarizer_type = st.session_state.summarizer_type
    model_type, tokenizer, base_summarizer = init_summarizer(model_name,api_key=None)

    logger.info(f"Model Name: {model_name}")
    logger.info(f"Summarization Type for Long Text: {summarizer_type}")

    with st.spinner(
        text="Creating summary. This might take a few seconds ..."
    ):
        if summarizer_type == "Refine":
            # summarized_text, time = summarizer.summarize(text_to_summarize,"refine")
            summarized_text, time = summarizer_summarize(model_type,tokenizer, base_summarizer, text_to_summarize ,summarizer_type = "refine")
            return summarized_text, time
        else : 
            # summarized_text, time = summarizer.summarize(text_to_summarize,"map_reduce")
            summarized_text, time = summarizer_summarize(model_type,tokenizer, base_summarizer, text_to_summarize ,summarizer_type = "map_reduce")
            return summarized_text, time


def display_output(summarized_text,time):
    logger.info(f"SUMMARY: {summarized_text}")
    logger.info(f"Summary took {time}s")
    st.subheader("Summarized text")
    st.info(f"{summarized_text}")
    st.info(f"Time: {time}s")


def main():
   
    initialize_app()
    side_bar()
    load_app()
    # chat_body()


if __name__ == "__main__":
    main()
    # text_to_summarize, model_name, summarizer_type, summarize = load_app()
    # summarized_text,time = get_summary(text_to_summarize, model_name, summarizer_type, summarize)
    # display_output(summarized_text,time)