(old code) X (new code)

app.py

@@ -4,6 +4,21 @@ import tensorflow as tf
 # Load the saved model
 model = tf.keras.models.load_model("sentimentality.h5")
 
+def preprocess(text):
+      # Tokenize the text into a list of words
+      words = text.strip().lower().split()
+      # Load the vocabulary
+      with open('vocabulary.txt', 'r') as f:
+          vocab = f.read().splitlines()
+      # Convert the words to indices in the vocabulary
+      word_indices = [vocab.index(word) if word in vocab else 0 for word in words]
+      # Pad the sequence with zeros to a fixed length of 500
+      padded_indices = np.zeros(500, dtype=np.int32)
+      padded_indices[:len(word_indices)] = word_indices
+      # Convert the sequence to a tensor
+      tensor = np.expand_dims(padded_indices, axis=0)
+      return tensor
+
 def predict_sentiment(text):
     # preprocess input text
     processed_text = preprocess(text)