Flask框架创建模型API接口并部署上线

模型训练后如何将模型打包上线，下面用Flask框架实现模型的部署和实时预测。

直接上干货,文件名称为flask_model.py

import numpy as np
from flask import Flask
from flask import request
from flask import jsonify
from sklearn.externals import joblib
#导入模型
model = joblib.load('model.pickle')

#temp =  [5.1,3.5,1.4,0.2]
#temp = np.array(temp).reshape((1, -1))
#ouputdata = model.predict(temp)	
##获取预测分类结果
#print('分类结果是：',ouputdata[0])

app = Flask(__name__)

@app.route('/',methods=['POST','GET'])
def output_data():
    text=request.args.get('inputdata')
    if text:
        temp =  [float(x) for x in text.split(',')]
        temp = np.array(temp).reshape((1, -1))
        ouputdata = model.predict(temp)	
        return jsonify(str(ouputdata[0]))
if __name__ == '__main__':
    app.config['JSON_AS_ASCII'] = False
    app.run(host='127.0.0.1',port=5003)  # 127.0.0.1 #指的是本地ip
    
print('运行结束')

在cmd命令行中执行命令

>>> python flask_model

代码实时预测

# 调用API接口
import requests
base = 'http://127.0.0.1:5002/?inputdata=5.1,3.5,1.4,0.2'
response = requests.get(base)
answer = response.json()
print('预测结果',answer)