Update backup/backup.py

backup/backup.py

@@ -1,75 +1,75 @@
-from .model import GLiNER
-
-# Initialize GLiNER with the base model
-model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
-
-# Sample text for entity prediction
-text = """
- lenskart m: (0)9428002330 Lenskart Store,Surat m: (0)9723817060) e:lenskartsurat@gmail.com 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"]
-
-    # Perform entity prediction
-    entities = model.predict_entities(text, labels, threshold=0.5)
-    
-    # Initialize the processed data dictionary
-    processed_data = {            
-            "Name": [],
-            "Contact": [],
-            "Designation": [],
-            "Address": [],
-            "Link": [],
-            "Company": [],
-            "Email": [],
-            "extracted_text": "",
-            }
-    
-    for entity in entities:
-
-        print(entity["text"], "=>", entity["label"])
-
-        #loading the data into json 
-        if entity["label"]==labels[0]:
-            processed_data['Name'].extend([entity["text"]])
-
-        if entity["label"]==labels[1]:
-            processed_data['Email'].extend([entity["text"]])
-        
-        if entity["label"]==labels[2]:
-            processed_data['Contact'].extend([entity["text"]])
-
-        if entity["label"]==labels[3]:
-            processed_data['Address'].extend([entity["text"]])
-
-        if entity["label"]==labels[4]:
-            processed_data['Company'].extend([entity["text"]])
-
-        if entity["label"]==labels[5]:
-            processed_data['Designation'].extend([entity["text"]])
-
-        if entity["label"]==labels[6]:
-            processed_data['Link'].extend([entity["text"]])
-
-
-    processed_data['Address']=[', '.join(processed_data['Address'])]
-    processed_data['extracted_text']=[text]
-
-    return processed_data
-
-# result=NER_Model(text)
-# print(result)
-
-
-
-
-
+from model import GLiNER
+
+# Initialize GLiNER with the base model
+model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
+
+# Sample text for entity prediction
+text = """
+ lenskart m: (0)9428002330 Lenskart Store,Surat m: (0)9723817060) e:lenskartsurat@gmail.com 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"]
+
+    # Perform entity prediction
+    entities = model.predict_entities(text, labels, threshold=0.5)
+    
+    # Initialize the processed data dictionary
+    processed_data = {            
+            "Name": [],
+            "Contact": [],
+            "Designation": [],
+            "Address": [],
+            "Link": [],
+            "Company": [],
+            "Email": [],
+            "extracted_text": "",
+            }
+    
+    for entity in entities:
+
+        print(entity["text"], "=>", entity["label"])
+
+        #loading the data into json 
+        if entity["label"]==labels[0]:
+            processed_data['Name'].extend([entity["text"]])
+
+        if entity["label"]==labels[1]:
+            processed_data['Email'].extend([entity["text"]])
+        
+        if entity["label"]==labels[2]:
+            processed_data['Contact'].extend([entity["text"]])
+
+        if entity["label"]==labels[3]:
+            processed_data['Address'].extend([entity["text"]])
+
+        if entity["label"]==labels[4]:
+            processed_data['Company'].extend([entity["text"]])
+
+        if entity["label"]==labels[5]:
+            processed_data['Designation'].extend([entity["text"]])
+
+        if entity["label"]==labels[6]:
+            processed_data['Link'].extend([entity["text"]])
+
+
+    processed_data['Address']=[', '.join(processed_data['Address'])]
+    processed_data['extracted_text']=[text]
+
+    return processed_data
+
+# result=NER_Model(text)
+# print(result)
+
+
+
+
+