import http.client as http_client
import json
import logging
import os
import pprint
import re
import time
import string

import streamlit as st

import streamlit.components.v1 as components
from typing import Callable, Optional, Tuple, Union
from pyserini import util
from pyserini.search import LuceneSearcher, FaissSearcher, AutoQueryEncoder


VERSION = '1.0'
st.set_page_config(page_title="Miracl Search - French", layout="wide")

os.makedirs(os.path.join(os.getcwd(),".streamlit"), exist_ok = True)
with open(os.path.join(os.getcwd(),".streamlit/config.toml"), "w") as file:
    file.write(
        '[theme]\nbase="light"'
    )

Searcher = Union[FaissSearcher, LuceneSearcher]
LANG_MAPPING = {'French':'fr'}


st.sidebar.markdown(
"""
<style>
.aligncenter {
    text-align: center;
    font-weight: bold;
    font-size: 30px;
}
</style>
<p class="aligncenter">MIRACL French Demo</p>
<p class="aligncenter">🌍🙌🌏</p>
<p style="text-align: center;"> MIRACL is a multilingual dataset for ad hoc retrieval that consists of 18 different languages, collectively encompassing over three billion native speakers around the world.</p>
""",
unsafe_allow_html=True,
)

st.sidebar.markdown(
"""
<style>
.aligncenter {
    text-align: center;
}
</style>
<p style='text-align: center'>
<a href="https://github.com/project-miracl" >GitHub</a> | <a href="https://arxiv.org/abs/2210.09984" >Paper</a>
</p>
""",
unsafe_allow_html=True,
)

query = st.sidebar.text_input(label='Search query', value='')
language = 'French'

max_results = st.sidebar.slider(
    "Maximum Number of Results",
    min_value=1,
    max_value=1000,
    step=1,
    value=10,
    help="Maximum Number of Documents to return",
)


def _load_sparse_searcher(language: str, k1: Optional[float]=None, b: Optional[float]=None) -> (Searcher):
    searcher = LuceneSearcher(f'lucene-index.miracl-v1.0-{language}.20221004.2b2856')
    searcher.set_language(language)
    if k1 is not None and b is not None:
        searcher.set_bm25(k1, b)
        retriever_name = f'BM25 (k1={k1}, b={b})'
    else:
        retriever_name = 'BM25'

    return searcher

def search(query, language, num_results=10):
    searcher = _load_sparse_searcher(language=LANG_MAPPING[language])

    t_0 = time.time()
    search_results = searcher.search(query, k=num_results)
    search_time = time.time() - t_0

    results_dict ={"docs": [], "doc_ids": [], "score":[], "lang": language}
    for i, result in enumerate(search_results):
        result = json.loads(result.raw)
        results_dict["docs"].append(result["text"])
        results_dict["doc_ids"].append(result["docid"])
        results_dict["score"].append(search_results[i].score)

    return results_dict, search_time



def highlight_string(paragraph: str, highlight_terms: list) -> str:
    for term in highlight_terms:
        paragraph = re.sub(f"\\b{term}\\b", f"<b>{term}</b>", paragraph, flags=re.I)
    return paragraph

def process_results(hits: dict, highlight_terms: list) -> str:
    hit_list = []
    for i in range(len(hits['doc_ids'])):
        res_head = f"""
                    <div class='searchresult'>
                        <h2>{i+1}. Document ID: {hits['doc_ids'][i]}</h2>
                        <p>Language: <string>{hits['lang']}</string>, Score: {round(hits['score'][i], 2)}</p>              
                        <p>{highlight_string(hits['docs'][i], highlight_terms)}</p>
                    </div>
                    <hr>
                    """
        hit_list.append(res_head)
    return " ".join(hit_list)



if st.sidebar.button("Search"):
    hits, search_time = search(query, language, max_results)
    html_results = process_results(hits, [])
    rendered_results = f"""
            <div id="searchresultsarea">
                <br>
                <p id="searchresultsnumber">About {max_results} results</p>
                {html_results}
            </div>
                        """
    st.markdown("""
                <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css" rel="stylesheet" 
                    integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
                """,
                unsafe_allow_html=True)
    st.markdown(
        """
        <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">                                  
        """,
        unsafe_allow_html=True)
    st.markdown(
        f"""
        <div class="row no-gutters mt-3 align-items-center">
            <h2> Search Results </h2>
        </div> 
        """,
        unsafe_allow_html=True)
    components.html(
        """
        <style>
        #searchresultsarea {
            font-family: 'Arial';
        }
        
        #searchresultsnumber {
            font-size: 0.8rem;
            color: gray;
        }
        
        .searchresult h2 {
            font-size: 19px;
            line-height: 18px;
            font-weight: normal;
            color: rgb(7, 111, 222);
            margin-bottom: 0px;
            margin-top: 25px;
        }
        
        .searchresult a {
            font-size: 12px;
            line-height: 12px;
            color: green;
            margin-bottom: 0px;
        }
    
        .dark-mode {
          color: white;
        }
        </style>
        <script>
        function load_image(id){
            console.log(id)
            var x = document.getElementById(id);
            console.log(x)
            if (x.style.display === "none") {
                x.style.display = "block";
            } else {
                x.style.display = "none";
            }
        };
        function myFunction() {
           var element = document.body;
           element.classList.toggle("dark-mode");
        }
        </script>
        <button onclick="myFunction()">Toggle dark mode</button>
        """ + rendered_results, height=800, scrolling=True
        )