| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing.text import one_hot | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import numpy as np | |
| import pandas as pd | |
| test_title = ["spark an inner revolution"] | |
| labels = ["Reliable", "Unreliable"] | |
| vocab_size = 5000 | |
| paddingLen = 20 | |
| oneHotRep = [one_hot(words, vocab_size) for words in test_title] | |
| padded = pad_sequences(oneHotRep, truncating="post", padding="post", maxlen=paddingLen) | |
| x = np.array(padded) | |
| model = load_model("fake_news.h5") | |
| pred = model.predict_classes(x)[0] | |
| print(labels[int(pred)]) |