diff --git a/azure/function_app.py b/azure/function_app.py
index a8aa85a..518bdd4 100644
--- a/azure/function_app.py
+++ b/azure/function_app.py
@@ -12,16 +12,16 @@
 
 flask_app = Flask(__name__)
 
-with open('models/logistic_regression.pkl', 'rb') as f:
-    logistic_regression_model = pickle.load(f)
-with open('models/naive_bayes.pkl', 'rb') as f:
-    naive_bayes_model = pickle.load(f)
-with open('models/sklearn_vectorizer.pkl', 'rb') as f:
-    sklearn_vectorizer = pickle.load(f)
-lstm_model = tf.keras.models.load_model('models/lstm.h5', compile=False)
-lstm_vectorizer = tf.keras.models.load_model('models/tf_vectorizer')
-transformers_vectorizer = tf.keras.models.load_model('models/tf_vectorizer_t')
-transformers_model = tf.keras.models.load_model('models/transformers', compile=False)
+# with open('./models/logistic_regression.pkl', 'rb') as f:
+#     logistic_regression_model = pickle.load(f)
+# with open('./models/naive_bayes.pkl', 'rb') as f:
+#     naive_bayes_model = pickle.load(f)
+# with open('./models/sklearn_vectorizer.pkl', 'rb') as f:
+#     sklearn_vectorizer = pickle.load(f)
+# lstm_model = tf.keras.models.load_model('./models/lstm.h5', compile=False)
+# lstm_vectorizer = tf.keras.models.load_model('./models/tf_vectorizer')
+# transformers_vectorizer = tf.keras.models.load_model('./models/tf_vectorizer_t')
+# transformers_model = tf.keras.models.load_model('./models/transformers', compile=False)
 
 batch_size = 32
 
@@ -44,52 +44,52 @@ def tokenize_text(x):
 def basic_test():
     return [1], 200
 
-@flask_app.post("/predict")
-def run_ai():
-    if request.headers['aikey'] == os.environ['AIKEY']:
-        if request.is_json:
-            try:
-                json_body = request.get_json()
-                data = json_body['json_data']
-                model_choice = json_body['model_choice']
-                # logging.info('successfully retrieved data')
-                data = pd.DataFrame.from_dict(json.loads(data))
-                # data was reordered by string indexing as it was serialized and sent
-                data = data.loc[list(map(str, range(len(data.index))))]
-                # logging.info('successfully converted data to data frame')
-                if model_choice != 'Vote':
-                    predictions = {model_choice: None}
-                else:
-                    predictions = {'Transformers': None, 'LSTM': None, 'Logistic Regression': None, 'Naive Bayes': None}
-                data['0'] = data['0'].apply(filter_thai)
-                if 'Logistic Regression' in predictions.keys():
-                    predictions['Logistic Regression'] = logistic_regression_model.predict_proba(
-                        sklearn_vectorizer.transform(data['0']).toarray()
-                    ).tolist()
-                if 'Naive Bayes' in predictions.keys():
-                    predictions['Naive Bayes'] = naive_bayes_model.predict_proba(
-                        sklearn_vectorizer.transform(data['0']).toarray()
-                    ).tolist()
-                data['0'] = data['0'].apply(tokenize_text)
-                if 'LSTM' in predictions.keys():
-                    predictions['LSTM'] = np.concatenate([lstm_model(lstm_vectorizer(
-                        data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
-                                                          range(1 + len(data.index) // batch_size)], axis=0).tolist()
-                if 'Transformers' in predictions.keys():
-                    predictions['Transformers'] = np.concatenate([transformers_model(transformers_vectorizer(
-                        data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
-                                                                  range(1 + len(data.index) // batch_size)],
-                                                                 axis=0).tolist()
-                # logging.info('successfully ran prediction models')
-                return {'predictions': predictions,
-                        'message': tf.config.list_physical_devices('GPU')
-                        }, 201
-            except Exception as e:
-                return {"error": str(e)}, 400
-        else:
-            return {"error": "Request must be JSON"}, 415
-    else:
-        return {'error': 'Wrong key'}, 401
+# @flask_app.post("/predict")
+# def run_ai():
+#     if request.headers['aikey'] == os.environ['AIKEY']:
+#         if request.is_json:
+#             try:
+#                 json_body = request.get_json()
+#                 data = json_body['json_data']
+#                 model_choice = json_body['model_choice']
+#                 # logging.info('successfully retrieved data')
+#                 data = pd.DataFrame.from_dict(json.loads(data))
+#                 # data was reordered by string indexing as it was serialized and sent
+#                 data = data.loc[list(map(str, range(len(data.index))))]
+#                 # logging.info('successfully converted data to data frame')
+#                 if model_choice != 'Vote':
+#                     predictions = {model_choice: None}
+#                 else:
+#                     predictions = {'Transformers': None, 'LSTM': None, 'Logistic Regression': None, 'Naive Bayes': None}
+#                 data['0'] = data['0'].apply(filter_thai)
+#                 if 'Logistic Regression' in predictions.keys():
+#                     predictions['Logistic Regression'] = logistic_regression_model.predict_proba(
+#                         sklearn_vectorizer.transform(data['0']).toarray()
+#                     ).tolist()
+#                 if 'Naive Bayes' in predictions.keys():
+#                     predictions['Naive Bayes'] = naive_bayes_model.predict_proba(
+#                         sklearn_vectorizer.transform(data['0']).toarray()
+#                     ).tolist()
+#                 data['0'] = data['0'].apply(tokenize_text)
+#                 if 'LSTM' in predictions.keys():
+#                     predictions['LSTM'] = np.concatenate([lstm_model(lstm_vectorizer(
+#                         data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
+#                                                           range(1 + len(data.index) // batch_size)], axis=0).tolist()
+#                 if 'Transformers' in predictions.keys():
+#                     predictions['Transformers'] = np.concatenate([transformers_model(transformers_vectorizer(
+#                         data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
+#                                                                   range(1 + len(data.index) // batch_size)],
+#                                                                  axis=0).tolist()
+#                 # logging.info('successfully ran prediction models')
+#                 return {'predictions': predictions,
+#                         'message': tf.config.list_physical_devices('GPU')
+#                         }, 201
+#             except Exception as e:
+#                 return {"error": str(e)}, 400
+#         else:
+#             return {"error": "Request must be JSON"}, 415
+#     else:
+#         return {'error': 'Wrong key'}, 401
 
 
 app = func.WsgiFunctionApp(app=flask_app.wsgi_app,