import csv
import pickle
from typing import re

import numpy
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD

def predict():
    input_file = open("l_regression.pkl",'rb')
    l_regression = pickle.load(input_file)
    input_file = open("tfidf_model.pkl",'rb')
    tfidf = pickle.load(input_file)

    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["txt"])
    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["txt"])
    print(testA)
    devtxt = dev0["txt"]
    testAtxt = testA["txt"]

    print(testAtxt)

    dev0_vector = tfidf.fit_transform(devtxt)
    testA_vector = tfidf.fit_transform(testAtxt)

    #print(testA_vector)
    pca = TruncatedSVD(n_components=100)

    dev0_pca = pca.fit_transform(dev0_vector)
    testA_pca = pca.fit_transform(testA_vector)
    y_dev = l_regression.predict(dev0_pca)
    y_test = l_regression.predict(testA_pca)
    numpy.savetxt('dev-0/out.tsv', y_dev)
    numpy.savetxt('test-A/out.tsv', y_test)
    #y_dev.to_csv(r'dev-0/out.csv',index=False)
    #y_test.to_csv(r'test-A/out.csv',index=False)

predict()