import pickle
import sys
import math
import fileinput

model = pickle.load(open("model.pkl", "rb"))
word_index, vocabulary, weights, words_count = model

def predict():
    output = []
    for line in fileinput.input(openhook=fileinput.hook_encoded("utf-8")):
        line = line.rstrip()
        fields = line.split('\t')
        label = fields[0].strip()
        document = fields[0]
        terms = document.split(' ')
        for term in terms:
            if term in words_count:
                words_count[term] += 1
            else:
                words_count[term] = 1
        expected = weights[0]
        for t in terms:
            if t in vocabulary:
                expected +=(words_count[t]/len(words_count)*(weights[word_index[t]]))
        if expected > 0.9:
            output.append(1)
        else:
            output.append(0)

    with open("out.tsv", "w") as out:
        for val in output:
            out.write(str(val)+"\n")

predict()