GoogleNLPAPI.py

# from google.cloud import language_v1

import os
import spacy
# import pytextrank



# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/Users/gkukal/PycharmProjects/HackTheLib/googleNLPAPIcodes.json"
# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "googleNLPAPIcodes.json"



def sample_analyze_entities(text_content):
    nlp = spacy.load("en_core_web_sm")

    doc = nlp(text_content)
    peoplePlacesOrganOther = []

    for ent in doc.ents:
        peoplePlacesOrganOther.append(ent.text)

    return list(set(peoplePlacesOrganOther))

    # client = language_v1.LanguageServiceClient()
    # type_ = language_v1.Document.Type.PLAIN_TEXT
    # language = "en"
    # document = {"content": text_content, "type_": type_, "language": language}
    # encoding_type = language_v1.EncodingType.UTF8
    # response = client.analyze_entities(
    #     request={'document': document, 'encoding_type': encoding_type})
    #
    # entitiesDict = {}
    # for entity in response.entities:
    #     entitiesDict[entity.name] = entity.type_.name
    #
    # people = []
    # placesOrOrganizations = []
    # other = []
    #
    # for entity in entitiesDict.items():
    #     if entity[1] == "PERSON" or entity[1] == "PROPER":
    #         people.append(entity[0])
    #     elif entity[1] == "LOCATION" or entity[1] == "ORGANIZATION":
    #         placesOrOrganizations.append(entity[0])
    #     else:
    #         other.append(entity[0])
    #
    # print(len(people), len(placesOrOrganizations), len(other))
    # people5 = []
    # placesOrOrganizations5 = []
    # other5 = []
    # for x in people[:20]:
    #     print(x)
    #     people5.append(x)
    # for y in placesOrOrganizations[:20]:
    #     print(y)
    #     placesOrOrganizations5.append(y)
    # for z in other[:20]:
    #     print(z)
    #     other5.append(z)
    #
    # peoplePlacesOrganOther = {
    #     "people": people5, "placesOrOrganizations": placesOrOrganizations5, "other": other5}