Update helper.py

helper.py

@@ -248,98 +248,6 @@ def identify_loc_embeddings(sentence, countries, cities):
     return embd_locations
 
 
-def identify_locations(sentence):
-    """
-    Identify all the possible Country and City references in the given sentence, using different approaches in a hybrid manner
-    """
-
-    locations = []  
-
-    try:
-
-      # ner
-      locations.append(identify_loc_ner(sentence))
-
-      # geoparse libs
-      geoparse_list, countries, cities = identify_loc_geoparselibs(sentence)
-      locations.append(geoparse_list)
-
-      # flatten the geoparse list
-      locations_flat_1 = list(flatten(locations))
-
-      # regex
-      locations_flat_1.append(identify_loc_regex(sentence))
-
-      # flatten the regex list
-      locations_flat_2 = list(flatten(locations))
-
-      # embeddings
-      locations_flat_2.append(identify_loc_embeddings(sentence, countries, cities))    
-
-      # flatten the embeddings list
-      locations_flat_3 = list(flatten(locations))
-
-      # remove duplicates while also taking under consideration capitalization (e.g. a reference of italy should be valid, while also a reference of Italy and italy)
-      # Lowercase the words and get their unique references using set()
-      loc_unique = set([loc.lower() for loc in locations_flat_3])
-
-      # Create a new list of locations with initial capitalization, removing duplicates
-      loc_capitalization = list(set([loc.capitalize() if loc.lower() in loc_unique else loc.lower() for loc in locations_flat_3]))
-      
-      # validate that indeed each one of the countries/cities are indeed countries/cities
-      validated_locations = validate_locations(loc_capitalization)
-
-      # create a proper dictionary with country/city tags and the relevant entries as a result
-      locations_dict = {}
-      for location, loc_type in validated_locations:
-          if loc_type not in locations_dict:
-              locations_dict[loc_type] = []
-          locations_dict[loc_type].append(location)
-      
-      # conditions for multiple references
-      # it is mandatory that a country will exist
-      if locations_dict['country']:
-
-          # if a city exists
-          if 'city' in locations_dict:
-
-              # we accept one country and one city
-              if len(locations_dict['country']) == 1 and len(locations_dict['city']) == 1:
-
-                  # capitalize because there may be cases that it will return 'italy'
-                  locations_dict['country'][0] = locations_dict['country'][0].capitalize()
-                  return locations_dict
-
-              # we can accept an absence of city but a country is always mandatory
-              elif len(locations_dict['country']) == 1 and len(locations_dict['city']) == 0:
-                  locations_dict['country'][0] = locations_dict['country'][0].capitalize()
-                  return locations_dict
-
-              # error if more than one country or city
-              else:
-                return (0, "LOCATION", "more_city_or_country")
-
-
-          # if a city does not exist
-          else:
-              # we only accept for one country
-              if len(locations_dict['country']) == 1:                  
-                  locations_dict['country'][0] = locations_dict['country'][0].capitalize()
-                  return locations_dict
-
-              # error if more than one country
-              else:
-                return (0, "LOCATION", "more_country")
-      
-      # error if no country is referred
-      else:
-         return (0, "LOCATION", "no_country")
-
-    except: 
-      # handle the exception if any errors occur while identifying a country/city
-      return (0, "LOCATION", "unknown_error")
-
-
 def identify_locations(sentence):
     """
     Identify all the possible Country and City references in the given sentence, using different approaches in a hybrid manner