Update extractor.py

extractor.py

@@ -1,7 +1,6 @@
 from transformers import RobertaTokenizerFast, AutoModelForTokenClassification
 import re
 import torch
-from itertools import cycle
 
 tokenizer = RobertaTokenizerFast.from_pretrained("mrfirdauss/robert-base-finetuned-cv")
 model = AutoModelForTokenClassification.from_pretrained("mrfirdauss/robert-base-finetuned-cv")
@@ -138,8 +137,7 @@ def predict(text):
     for links in process_tokens(data, 'URL'):
       profile['links'].append(links['text'])
     # Process experiences and education
-    print(process_tokens(data, 'EXPERIENCES DESC'))
-    for designation, company, experience_desc in zip(cycle(process_tokens(data, 'DESIGNATION')),cycle(process_tokens(data, 'COMPANY')),cycle(process_tokens(data, 'EXPERIENCES DESC'))):
+    for designation, company, experience_desc in zip((process_tokens(data, 'DESIGNATION')),(process_tokens(data, 'COMPANY')),(process_tokens(data, 'EXPERIENCES DESC'))):
         profile['experiences'].append({
             "start": None,
             "end": None,