with

app_utils.py

@@ -33,9 +33,9 @@ from wordcloud import WordCloud
 import base64
 import time
 
-stanford_ner_jar = open('stanford-ner.jar','rb')
-# Path to the pre-trained NER model file
-stanford_ner_model =open('english.all.3class.distsim.crf.ser.gz','rb')
+# stanford_ner_jar = open('stanform-ner.jar','rb')
+# # Path to the pre-trained NER model file
+# stanford_ner_model =open('english.all.3class.distsim.crf.ser.gz','rb')
 
 timestr = time.strftime("%Y%m%d-%H%M%S")
 
@@ -113,7 +113,11 @@ def nlp_analysis(text):
 
 
 def find_entities(text):
-    stan = StanfordNERTagger(stanford_ner_model, stanford_ner_jar)
+    stanford_ner_jar_path = 'stanford-ner.jar'
+    stanford_ner_model_path = 'english.all.3class.distsim.crf.ser.gz'
+    with open(stanford_ner_jar_path, 'rb') as stanford_ner_jar:
+        with open(stanford_ner_model_path, 'rb') as stanford_ner_model:
+            stan = StanfordNERTagger(stanford_ner_model_path, stanford_ner_jar_path)
     text=text.replace("\n\n","\n")
     tokens = nltk.word_tokenize(text)
     tagged_tokens = stan.tag(tokens)