import numpy as np
import torch
from gensim import downloader
import gensim


class NeuralNetworkModel(torch.nn.Module):

    def __init__(self):
        super(NeuralNetworkModel, self).__init__()
        self.fc1 = torch.nn.Linear(100, 500)
        self.fc2 = torch.nn.Linear(500, 1)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x


BATCH_SIZE = 5
PATH = "new_model_full.pt"
model = torch.load(PATH)
model.eval()

glove_vectors = downloader.load("glove-wiki-gigaword-100")

with open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\dev-0\in.tsv", "r", encoding="utf-8") as dev_in, \
        open(r"C:\Users\Kacper Dudzic\Desktop\paranormal-or-skeptic\test-A\in.tsv", "r", encoding="utf-8") as test_in:
    X_dev = [line for line in dev_in.readlines()]
    X_dev = [np.mean([glove_vectors[tk] for tk in gensim.utils.tokenize(text, lowercase=True) if tk in glove_vectors] or
                     [np.zeros(100)], axis=0) for text in X_dev]

    X_test = [line for line in test_in.readlines()]
    X_test = [np.mean([glove_vectors[tk] for tk in gensim.utils.tokenize(text, lowercase=True) if tk in glove_vectors] or
                [np.zeros(100)], axis=0) for text in X_test]

with open('dev_out.tsv', 'w', encoding='utf-8') as dev_out, open('test_out.tsv', 'w', encoding='utf-8') as test_out:
    dev_predictions = []
    test_predictions = []

    for i in range(0, len(X_dev), BATCH_SIZE):
        X = X_dev[i:i + BATCH_SIZE]
        X = torch.tensor(np.array(X).astype(np.float32))
        Y_predictions = (model(X) > 0.5)
        dev_predictions.extend(Y_predictions)

    for i in range(0, len(X_test), BATCH_SIZE):
        X = X_test[i:i + BATCH_SIZE]
        X = torch.tensor(np.array(X).astype(np.float32))
        Y_predictions = (model(X) > 0.5)
        test_predictions.extend(Y_predictions)

    for pred in dev_predictions:
        dev_out.write(str(pred.int()[0].item()) + '\n')

    for pred in test_predictions:
        test_out.write(str(pred.int()[0].item()) + '\n')