from .clustering import *
from typing import List
import textdistance as td
from .utils import UnionFind, ArticleList
from .academic_query import AcademicQuery
import streamlit as st
from tokenizers import Tokenizer


class LiteratureResearchTool:
    def __init__(self, cluster_config: Configuration = None):
        self.literature_search = AcademicQuery
        self.cluster_pipeline = ClusterPipeline(cluster_config)

    def __postprocess_clusters__(self, clusters: ClusterList) ->ClusterList:
        '''
        add top-5 keyphrases to each cluster
        :param clusters:
        :return: clusters
        '''
        def condition(x, y):
            return td.ratcliff_obershelp(x, y) > 0.8

        def valid_keyphrase(x:str):
            return x is not None and x != '' and not x.isspace()

        for cluster in clusters:
            cluster.top_5_keyphrases = []
            keyphrases = cluster.get_keyphrases()
            keyphrases = list(keyphrases.keys())
            keyphrases = list(filter(valid_keyphrase,keyphrases))
            unionfind = UnionFind(keyphrases, condition)
            unionfind.union_step()

            keyphrases = sorted(list(unionfind.get_unions().values()), key=len, reverse=True)[:5]  # top-5 keyphrases: list

            for i in keyphrases:
                tmp = '/'.join(i)
                cluster.top_5_keyphrases.append(tmp)

        return clusters

    def __call__(self,
                 query: str,
                 num_papers: int,
                 start_year: int,
                 end_year: int,
                 platforms: List[str] = ['IEEE', 'Arxiv', 'Paper with Code'],
                 best_k: int = 5,
                 loading_ctx_manager = None,
                 ):


        for platform in platforms:
            if loading_ctx_manager:
                with loading_ctx_manager():
                    clusters, articles = self.__platformPipeline__(platform,query,num_papers,start_year,end_year,best_k)
            else:
                clusters, articles = self.__platformPipeline__(platform, query, num_papers, start_year, end_year,best_k)

            clusters.sort()
            yield clusters,articles



    def __platformPipeline__(self,platforn_name:str,
                             query: str,
                             num_papers: int,
                             start_year: int,
                             end_year: int,
                             best_k: int = 5
                             ) -> (ClusterList,ArticleList):

        @st.cache(hash_funcs={Tokenizer: Tokenizer.__hash__})
        def ieee_process(
                query: str,
                num_papers: int,
                start_year: int,
                end_year: int,
                best_k: int = 5
        ):
            articles = ArticleList.parse_ieee_articles(
            self.literature_search.ieee(query, start_year, end_year, num_papers))  # ArticleList
            abstracts = articles.getAbstracts()  # List[str]
            clusters = self.cluster_pipeline(abstracts, best_k=best_k)
            clusters = self.__postprocess_clusters__(clusters)
            return clusters, articles

        @st.cache(hash_funcs={Tokenizer: Tokenizer.__hash__})
        def arxiv_process(
                query: str,
                num_papers: int,
                best_k: int = 5
        ):
            articles = ArticleList.parse_arxiv_articles(
            self.literature_search.arxiv(query, num_papers))  # ArticleList
            abstracts = articles.getAbstracts()  # List[str]
            clusters = self.cluster_pipeline(abstracts, best_k=best_k)
            clusters = self.__postprocess_clusters__(clusters)
            return clusters, articles

        @st.cache(hash_funcs={Tokenizer: Tokenizer.__hash__})
        def pwc_process(
                query: str,
                num_papers: int,
                best_k: int = 5
        ):
            articles = ArticleList.parse_pwc_articles(
            self.literature_search.paper_with_code(query, num_papers))  # ArticleList
            abstracts = articles.getAbstracts()  # List[str]
            clusters = self.cluster_pipeline(abstracts, best_k=best_k)
            clusters = self.__postprocess_clusters__(clusters)
            return clusters, articles

        if platforn_name == 'IEEE':
            return ieee_process(query,num_papers,start_year,end_year,best_k)
        elif platforn_name == 'Arxiv':
            return arxiv_process(query,num_papers,best_k)
        elif platforn_name == 'Paper with Code':
            return pwc_process(query,num_papers,best_k)
        else:
            raise RuntimeError('This platform is not supported. Please open an issue on the GitHub.')