#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 12 10:19:31 2021

@author: cuxac
"""




#from scipy.spatial import cKDTree as KDTree
import faiss
from scipy.special import softmax
import numpy as np
import sys
import json
from collections import defaultdict#,Counter
import operator
from more_itertools import locate
import statistics
import pickle
import fasttext


model=fasttext.load_model("modelhal0EN2.bin")
D=np.load("HALen_matrixKDT.npy")

dico_nd_pkl=open("dico_nd.pkl","rb")    
dico_nd=pickle.load(dico_nd_pkl)

d=200
quantizer = faiss.IndexFlatL2(d)
quantizer.add(D)

verb_class={"spi":"Sciences de l'ingénieur [physics]","shs":"Sciences de l'Homme et Société","sdv":"Sciences du Vivant [q-bio]","sdu":"Planète et Univers [physics]","sde":"Sciences de l'environnement","scco":"Sciences cognitives","phys":"Physique [physics]","nlin":"Science non linéaire [physics]","math":"Mathématiques [math]","info":"Informatique [cs]","chim":"Chimie","stat":"Statistiques","qfin":"Économie et finance quantitative [q-fin]"}

kdtree=KDTree(D)


distlist=[]
list_defis=[]

n=0
K=50

for line in sys.stdin:
    data = json.loads(line)
    text=data['value']

    mv=model.get_sentence_vector(text.strip())
    t=np.asmatrix(mv)

    Dis, Ind = quantizer.search(t,K)
    ppv=Dis[0],Ind[0] #ppv=Dis et Ind

    #ppv=kdtree.query(mv,k=K,p=2)#,distance_upper_bound=0.05)
    #dmax_k=kdtree.query(mv,k=[K],p=2)[0][0]

    dmax_k=Dis[0][K-1]
    dN_ppv=ppv[0]/dmax_k
    distlist.append(ppv[0][0]/dmax_k)
 
    list_defis=list(ppv[1])
    list_defis_label=list(dico_nd[i] for i in list_defis)
    
    r=zip(list_defis_label,list(ppv[0]))
    dis=defaultdict(list)
    for i in set(r):
        dis[i[0]].append(1/i[1])
    for k in dis.keys():     
        dis[k]=sum(dis[k])
    ddd=np.array(list(dis.values()))
    sm=softmax(ddd)
    lab=list(dis.keys())
    res=zip(lab,sm)
    d4=dict(res)
    classmax=max(d4.items(),key=operator.itemgetter(1))[0]
    cmax=classmax.split('_')[1].split('.')[0]
    

    if ppv[0][0]<100:

        mm=max(list_defis_label,key=list_defis_label.count)

        ind=list(locate(list_defis_label, lambda a: a ==mm))
        indd=list(dN_ppv)
        dmax=[indd[i] for i in ind]
        dist_mean=statistics.mean(dmax)
        prob0=round((len(ind))/(K+len(set(list_defis_label))),3)
        prob=d4[classmax]
        if len(ind)<25:
            dist_mean=str(round(dist_mean,3))+' / '+str(len(ind))
        
        data['value']=verb_class[cmax]#,prob
        sys.stdout.write(json.dumps(data))
        sys.stdout.write('\n')
        
        n+=1