Update app.py

app.py

@@ -18,6 +18,13 @@ nltk.download('stopwords')
 ps = PorterStemmer()
 scaler = StandardScaler()
 
+vectorizer = joblib.load('./vectorizer.pkl')
+nb_classifier = joblib.load('./nb_classifier.pkl')
+tfidf_vectorizer = joblib.load('./tfidf_vectorizer.pkl')
+random_forest = joblib.load('./random_forest.pkl')
+word2vec_model = joblib.load('./word2vec_model.pkl')
+svm_classifier = joblib.load('./svm_classifier.pkl')
+
 def preprocess_for_bow_and_tfidf(text):
     corpus = []
     text = re.sub('[^a-zA-Z0-9$£€¥%]',' ',text)
@@ -50,12 +57,7 @@ def average_word_vectors(words, model, vocabulary, num_features):
         
     return feature_vector
     
-vectorizer = joblib.load('./vectorizer.pkl')
-nb_classifier = joblib.load('./nb_classifier.pkl')
-tfidf_vectorizer = joblib.load('./tfidf_vectorizer.pkl')
-random_forest = joblib.load('./random_forest.pkl')
-word2vec_model = joblib.load('./word2vec_model.pkl')
-svm_classifier = joblib.load('./svm_classifier.pkl')
+
 
 def classify(text,choice):
   corpus=[text]