Upload app.py

app.py

@@ -5,25 +5,13 @@ import tensorflow as tf
 import numpy as np
 from tensorflow.keras.layers import TextVectorization
 
-df = pd.read_csv(os.path.join('train.csv'))
-X = df['comment_text']
+# Load.
+filepath = "tmp-model"
+loaded_model = tf.keras.models.load_model(filepath)
+vectorizer = loaded_model.layers[0]
 
 columns = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
 
-# # load numpy array from npy file
-# from numpy import load
-# # load array
-# xValues = np.load('forVectorizer.npy', allow_pickle=True)
-
-# SET THE VECTORIZER    
-#set max features
-MAX_FEATURES = 200000
-vectorizer = TextVectorization(max_tokens=MAX_FEATURES,
-                              output_sequence_length=1800,
-                              output_mode='int')
-
-vectorizer.adapt(X.values)
-
 model = tf.keras.models.load_model('toxicity.h5')
 
 def score_comment(comment):