ILPformat.py

import signal
import json
import sys
import makesets
import jsonrpclib
from simplejson import loads
import pickle
from random import randint
import RevisedEntityFileCreator as EF

OUT=None

class StanfordNLP:
    def __init__(self, port_number=8080):
        #self.server = jsonrpclib.Server("http://localhost:%d" % port_number)
        self.server = jsonrpclib.Server("http://localhost:8080") 


    def parse(self, text):
        return loads(self.server.parse(text))

nlp = StanfordNLP()

def cleannum(n):
    return ''.join([x for x in n if x.isdigit() or x=='.' or x=='x' or x=='x*'])


def make_eq(q,a,VERBOSE,TRAIN):
    bigtexamples = {x:([],[]) for x in ["+","*",'/','-','=']}
    #wps = open(q).readlines()
    #answs = open(a).readlines()
    #VERBOSE=True
    wps = q


    


    for k in range(len(wps)):
        if VERBOSE:
            for i in range(len(wps)):
                print(i,wps[i])
            k = int(input())
        print(k)
        problem = wps[k]
        #First preprocessing, tokenize slightly
        problem = problem.strip().split(" ")
        for i,x in enumerate(problem):
            if len(x)==0:continue
            if x[-1] in [',','.','?']:
                problem[i] = x[:-1]+" "+x[-1]
        problem = ' '.join(problem)
        problem = " " + problem + " "
        
        #Percentage putting
        Res = problem
        problem=""
        for i in range(len(Res)):
            if Res[i]=='%':
                problem += " percent"
            else:
                problem += Res[i]
        
        ##Change Percentage to times
        
        problem = problem.strip().split(" ")
        for i in range(len(problem)-1):
            if (problem[i+1]=='percent') or (problem[i+1]=='percentage') or (problem[i+1]=='Percentage') or (problem[i+1]=='Percent'): #
                strval = problem[i]
                val=''
                if strval[0]=='$':
                    val=strval[1:]
                    val=float(val)/100.0
                    problem[i]=str(val)
                elif strval[0] in ['0','1','2','3','4','5','6','7','8','9']:
                    val=strval
                    val=float(val)/100.0
                    problem[i]=str(val)
                problem[i+1]="times"
        
        
        problem = ' '.join(problem)
        problem = " " + problem + " "
        
        print(problem)

        story = nlp.parse(problem)
        sets = makesets.makesets(story['sentences']) # Imported Makesets
        pickle.dump(sets, open('madesets/'+str(k)+'.pickle','wb'))
        EF.main(sets,k,a[k],sys.argv[1])
        sets = [x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x']
        print(sets)
        for z in sets:
            z[1].details()

def parse_inp(inp):
    q=[]
    a=[]
    e=[]
    with open(inp) as f:
        f = f.readlines()
        i=0
        while i<len(f):
            q.append(f[i])
            i+=1
            e.append(f[i])
            i+=1
            a.append(f[i])
            i+=1
    return (q,a,e)



def parse_json(inp):
    q = []
    a = []
    with open(inp) as df:
        data = json.load(df)
        for i in data:
            q.append(i["sQuestion"])
            a.append(i["lSolutions"][0])
    return (q,a)
        
if __name__=="__main__":
    #q, a = sys.argv[1:3]
    inp = sys.argv[1]
    #q,a,e = parse_inp(inp)
    q,a = parse_json(inp)
    VERBOSE=False
    TRAIN=False
    '''
    if len(sys.argv)>3:
        if sys.argv[3]=='v':
            VERBOSE=True
        elif sys.argv[3]=='t':
            TRAIN = True
            OUT = sys.argv[4]
    '''
    make_eq(q,a,VERBOSE,TRAIN)