Upload recognition.py

recognition.py

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+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)])