【NLP】pyhanlp flask

# coding=utf-8
from flask import Flask
from flask import request
from flask import jsonify
from flask import make_response,Response
from flask_cors import CORS
from pyhanlp import *

port = 9999
app = Flask(__name__)
CORS(app, supports_credentials=True)

@app.route('/')
def index():
    str = "Hello, AI Server!"
    print(str)
    return str

# 成功请求
def successResp(data):
    return jsonify(code=0, message="success", data=data)

# 错误请求
def errorResp(msg):
    return jsonify(code=-1, message=msg)


# 分词服务
@app.route('/NLPWordSplit', methods=['POST'])
def NLPWordSplit():
    Article = request.form.get('Article')
    print(Article)

    if Article == None or Article == "" :
        rst = make_response(errorResp("请输入正确的数据！"))
        rst.headers['Access-Control-Allow-Origin'] = '*'
        return rst

    word = ""
    for term in HanLP.segment(Article):
        curWord = str(term.word)
        if len(curWord) != 0:
            word = word + " " + curWord
    print(word)

    return getResult(word)

# 词向量服务
@app.route('/NLPWordNearest', methods=['POST'])
def NLPWordNearest():
    Word = request.form.get('Word')
    print(Word)

    if Word == None or Word == "" :
        rst = make_response(errorResp("请输入正确的数据！"))
        rst.headers['Access-Control-Allow-Origin'] = '*'
        return rst
    try:
        # WordVectorModel = LazyLoadingJClass('com.hankcs.hanlp.mining.word2vec.WordVectorModel')
        # print(WordVectorModel)
        # MODEL_FILE_NAME = r"E:\Model.txt"
        # # 初始化模型
        # word2vec = WordVectorModel(MODEL_FILE_NAME)
        # print(word2vec)
        if word2vec != None:
            print("相关词分析")
            word = word2vec.nearest(Word)
            print(word)
            data = ""
            for x in word:
                if data == "":
                    data = str(x).replace("=",":")
                else:
                    data = data + "," + str(x).replace("=",":")
            print(data)

            word = "{" + data + "}"
            # word = json.dumps(word)
            # word = ','.join(word)
            print(word)
            return getResult(word)
        else:
            rst = make_response(errorResp("单词分析错误！"))
            rst.headers['Access-Control-Allow-Origin'] = '*'
            return rst
    except Exception as e:
        print("Exception:",e)
    finally:
        print("OK")

def getResult(word):
    if word != "":
        # 如果成功返回预测结果
        rst = make_response(successResp(word))
        rst.headers['Access-Control-Allow-Origin'] = '*'
        return rst
    else:
        # 如果失败返回错误信息
        rst = make_response(errorResp(word))
        rst.headers['Access-Control-Allow-Origin'] = '*'
        return rst

word2vec = None
if __name__ == '__main__':
    # if not jpype.isThreadAttachedToJVM():
    #     jpype.attachThreadToJVM()
    # WordVectorModel = jpype.JClass("com.hankcs.hanlp.mining.word2vec.WordVectorModel")
    # print(WordVectorModel)
    MODEL_FILE_NAME = r"E:\Model.txt"
    # 初始化模型
    word2vec = WordVectorModel(MODEL_FILE_NAME)
    print("port:" + str(port))
    app.run(debug = True,host = '0.0.0.0',port = port, threaded = True)