diff --git a/application/functions/errors.py b/application/functions/errors.py
index 77f5ace..62e2548 100644
--- a/application/functions/errors.py
+++ b/application/functions/errors.py
@@ -1,10 +1,6 @@
-from transformers import AutoTokenizer
 from transformers import pipeline
-#from transformers import BartForConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("szymonj/polish-simple-error-correction")
-#model = BartForConditionalGeneration.from_pretrained("szymonj/polish-simple-error-correction")
-pipe = pipeline("text2text-generation",model="szymonj/polish-simple-error-correction",tokenizer=tokenizer,max_length=2000)
+pipe = pipeline("text2text-generation",model="szymonj/polish-simple-error-correction", tokenizer="szymonj/polish-simple-error-correction", max_length=2000)
 
 def errors_correction(data):
     result = pipe(data)
diff --git a/application/functions/sentiment.py b/application/functions/sentiment.py
index dc1f42b..d2534d1 100644
--- a/application/functions/sentiment.py
+++ b/application/functions/sentiment.py
@@ -1,17 +1,12 @@
-from transformers import AutoTokenizer
-from transformers import pipeline, GPT2ForSequenceClassification
+from transformers import pipeline
 import re
 from facebook_scraper import get_posts
 
-# model = 'application/models/sentiment_model'
-# tokenizer = AutoTokenizer.from_pretrained('application/tokenizers/sentiment_tokenizer')
-
-#model = GPT2ForSequenceClassification.from_pretrained("Scigi/sentiment-analysis-model", num_labels=3)
-tokenizer = AutoTokenizer.from_pretrained("Scigi/sentiment-analysis-model")
-pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = tokenizer)
+pipe = pipeline('text-classification', model="Scigi/sentiment-analysis-model", tokenizer = "Scigi/sentiment-analysis-model")
 
 def sentiment_prediction(data):
     result = pipe(data)
+    
     return result
 
 def clear_data(data):
@@ -19,6 +14,7 @@ def clear_data(data):
     data = [x for x in data if x != '']
     data = [i.strip() for i in data]
     data = [i.lower() for i in data]
+
     return data
 
 def count_predictions(predictions):
@@ -50,4 +46,5 @@ def scrapp_comments(url):
             comments.append(comment['comment_text'])
     all['post'] = text_post
     all['sentences'] = comments
+
     return all