ClassifyData.py

import tensorflow as tf
from tensorflow import keras
import numpy as np

data = keras.datasets.imdb

(train_data, train_labels), (test_data, test_labels) = data.load_data(num_words=100000)

word_index = data.get_word_index()

word_index = {k:(v+3) for k, v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding="post", maxlen=250)
test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding="post", maxlen=250)

def decode_review(text):
    return " ".join([reverse_word_index.get(i, "?") for i in text])

def review_encode(s):
    encoded = [1]
    
    for word in s:
        if word.lower() in word_index:
            encoded.append(word_index[word.lower()])
        else:
            encoded.append(2)
    
    return encoded

# model must first be saved with GenerateModel.py
model = keras.models.load_model("model.h5")

# reads file named test.txt
with open("test.txt", encoding="utf-8") as f:
    line = f.read()
    nline = line.replace(",", "").replace(".", "").replace("(.)", "").replace(")", "").replace(":", "").replace("\"", "").strip().split(" ")
    encode = review_encode(nline)
    encode = keras.preprocessing.sequence.pad_sequences([encode], value=word_index["<PAD>"], padding="post", maxlen=250)
    predict = model.predict(encode)
    print(line)
    print(encode)
    print(predict[0])