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server.py
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server.py
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from flask import Flask, request
from flask_cors import CORS, cross_origin
from flask_restful import Resource, Api
from json import dumps
from flask_jsonpify import jsonify
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns
from matplotlib.pylab import rcParams
from datetime import datetime
app = Flask(__name__)
api = Api(app)
from sklearn.externals import joblib
CORS(app)
@app.route("/get_",methods=["POST"])
def get_():
#date_=request.form['date_']
#tons=request.form['tons']
#return json.dumps({'status':'OK'});
data = request.get_json(force=True)
date_= datetime.strptime(data['date_'], '%Y-%m-%d').toordinal()
qty=float(data["tons"])
lin_reg = joblib.load("regression_model.pkl")
dat= lin_reg.predict(np.array([[qty,date_]]))
dat=np.round(dat,2)
dat=dat.tolist()
return jsonify(dat)
##api.add_resource(Employees_Name, '/employees/<employee_id>') # Route_3
@app.route("/get1_",methods=["POST"])
def get1_():
data1=request.get_json(force=True)
date_=datetime.strptime(data1['date_'],'%Y-%m-%d').toordinal()
qty=float(data1["tons"])
lin_reg1 = joblib.load("regression_model1.pkl")
dat1= lin_reg1.predict(np.array([[date_,qty]]))
dat1=dat1.tolist()
return jsonify(dat1)
#@app.route("/get2_",methods=["POST"])
#def get2_():
# data2=request.get_json(force=True)
# date_=datetime.strptime(data2['date_'],'%Y-%m-%d').toordinal()
# qty=float(data2["tons"])
#print(date_,qty)
#lin_reg2 = joblib.load("regression_model2.pkl")
#dat2= lin_reg2.predict(np.array([[date_,qty]]))
#dat2=dat2.tolist()
#return jsonify(dat2)
if __name__ == '__main__':
app.run(port=8080)