Update backup/backup.py

backup/backup.py

@@ -7,14 +7,6 @@ model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
 text = """
  lenskart m: (0)9428002330 Lenskart Store,Surat m: (0)9723817060) e:[email protected] Store Address UG-4.Ascon City.Opp.Maheshwari Bhavan,Citylight,Surat-395007"""
 
-# Labels for entity prediction
-# # Most GLiNER models should work best when entity types are in lower case or title case
-# labels = ["Person", "Mail", "Number", "Address", "Organization","Designation"]
-
-# # Perform entity prediction
-# entities = model.predict_entities(text, labels, threshold=0.5)
-
-
 def NER_Model(text):
 
     labels = ["Person", "Mail", "Number", "Address", "Organization","Designation","Link"]
@@ -64,12 +56,4 @@ def NER_Model(text):
     processed_data['Address']=[', '.join(processed_data['Address'])]
     processed_data['extracted_text']=[text]
 
-    return processed_data
-
-# result=NER_Model(text)
-# print(result)
-
-
-
-
-
+    return processed_data