ducnh1022/ser.py

## ser.py
from flask import Flask, url_for
from flask import request
from flask import json
from flask import Response
from flask import jsonify

from gevent.pywsgi import WSGIServer

import mysql.connector
from mysql.connector import Error
from mysql.connector import errorcode
import requests

# import dataset and deploying encoders

from numpy import array
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.preprocessing import quantile_transform
from xgboost import XGBClassifier

df = pd.read_csv("new1.txt",delimiter="|", names=["phone","arpu","home","arpu2","age","is_prepaid","id","cic_status","num_top_friend","salary","rank_id","action_avg","comment_avg","diff_day","freq_post","friend_education_level_0","friend_education_level_1","friend_education_level_2","friend_education_level_3","friend_pay_first","friend_pay_later","likes_count","mode_time_frame","post_count","reaction_avg","credit_score","active","age","end_date","package"]);
df.replace("empty", np.nan , inplace=True)
df.replace(-1, np.nan , inplace=True)
df.iloc[:,11:28].astype(float,inplace=True)
df.fillna(0, inplace=True)
df.drop(columns=["phone"], inplace=True)

# Normalize Home
def strip_it(s):
    return s.strip()

le_home = preprocessing.LabelEncoder()
df.home = df.home.astype(str).apply(strip_it)
le_home.fit(df.home)

df.home = le_home.transform(df.home)

# Normalize Package
le_p = preprocessing.LabelEncoder()
df.package = df.package.astype(str).apply(strip_it)
le_p.fit(df.package)

df.package = le_p.transform(df.package)

df.arpu = df.arpu.astype(float).rank(pct=True) * 10
df = df.astype(float)

def normalize_label(label):
    if(label > 0):
        return 1
    return 0

# Preparing Data For Training
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import VarianceThreshold

X = df.iloc[:,[0,1,14,15,16,17,18,24,26,27]].values
y = df.iloc[:, 9].apply(normalize_label).astype(int).values

print(df.iloc[:,[0,1,14,15,16,17,18,24,26,27]].columns)

sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
sel.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

regressor = XGBClassifier(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)
regressor.fit(X_train, y_train)

#  MODEL FOR SALARY

df = pd.read_csv("74k-sal.csv", encoding = "utf-8", sep = ",")
df.replace("empty", np.nan , inplace=True)
df.replace(-1, np.nan , inplace=True)
df.fillna(0, inplace=True)
df.drop(columns=["phone","user_id","dt"], inplace=True)

df_sal = pd.read_csv("all-predicted",delimiter="|", names=["phone","sal"])

# preprocessing Data

def retrieve_status(status):
    if(status == "0"):
        return 0
    else:
        return 1

df['status'] = df.avayStatus.apply(retrieve_status)

def retrieve_med_code(code):
    try:
        return code[:2]
    except:
        return "0"
def retrieve_med_in(code):
    try:
        return int(code[2:3])
    except:
        return 0

def retrieve_med_loc(code):
    try:
        return int(code[5:7])
    except:
        return 0

df['med_code'] = df.medicalInsuranceId.apply(retrieve_med_code)
df['med_in'] = df.medicalInsuranceId.apply(retrieve_med_in)
df['med_loc'] = df.medicalInsuranceId.apply(retrieve_med_loc)


def retrieve_cic(cic):
    if("cb" in cic):
        return 2
    if("cy" in cic):
        return 1
    else:
        return 0

df['cic'] = df.cic_status.apply(retrieve_cic)

feats = ["home","fbxPackage","fbxIsActive","med_code"]
le_l = preprocessing.LabelEncoder()
for feat in feats:
    le_x = preprocessing.LabelEncoder()
    df[feat] = df[feat].astype(str)
    le_x.fit(df[feat])
    df[feat] = le_x.transform(df[feat])
    le_l = le_x

df.drop(columns=["cic_status","avayStatus","medicalInsuranceId","family_info"],inplace=True)
df.astype(float,inplace=True)

df0 = df[(df.med_code > 0)]
df1 = df[(df.education_level > 0)]

from sklearn.model_selection import train_test_split
from sklearn.feature_selection import VarianceThreshold
from sklearn import metrics
from sklearn.metrics import mean_absolute_error
from xgboost import XGBRegressor

regressor0 = XGBRegressor(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)
regressor1 = XGBRegressor(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)

# MODEL 1

X = df0.drop(columns=["sal"]).loc[:,['med_code']].values
y = df0.sal.astype(float).values

print(len(X))

sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
sel.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
regressor0.fit(X_train, y_train)
y_pred = regressor0.predict(X_test)
print(mean_absolute_error(y_test, y_pred))

# MODEL 2

X = df1.drop(columns=["sal"]).loc[:,['comment_avg']].values
y = df1.sal.astype(float).values

print(len(X))

sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
sel.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

regressor1.fit(X_train, y_train)
y_pred = regressor1.predict(X_test)
print(mean_absolute_error(y_test, y_pred))

app = Flask(__name__)

connection = mysql.connector.connect(host='178.128.85.52',database='db_dcredit',user='dcre',password='dcredit@2019')

def rank_convert(rank):
    r = float(rank)
    rank_number = 0
    if(r > 0.18):
        rank_number = 9
    if(r > 0.16 and r < 0.18):
        rank_number = 8
    if(r > 0.14 and r < 0.16):
        rank_number = 7
    if(r > 0.13 and r < 0.14):
        rank_number = 6
    if(r > 0.11 and r < 0.13):
        rank_number = 5
    if(r > 0.09 and r < 0.11):
        rank_number = 4
    if(r > 0.07 and r < 0.09):
        rank_number = 3
    if(r > 0.06 and r < 0.07):
        rank_number = 2
    if(r > 0.04 and r < 0.06):
        rank_number = 1
    if(r > 0.01 and r < 0.04):
        rank_number = 0
    return rank_number
    # 0	 : 0.015222915448248386
    # 1	 : 0.048723580315709114
    # 2	 : 0.06371929943561555
    # 3	 : 0.07859582751989364
    # 4	 : 0.09529682248830795
    # 5	 : 0.11306775361299515
    # 6	 : 0.13043687045574187
    # 7	 : 0.14534296691417692
    # 8	 : 0.16037331521511078
    # 9	 : 0.1815473824739456

@app.route('/get_score/')
def api_get():
    global connection
    try:
        args_dict  = request.args.to_dict()
        print(args_dict)
        id = ""
        mob  = args_dict["mobile"]
        url = 'http://178.128.100.101:9010/profile_data/get?id='+id+'&mobile='+mob
        # url = 'http://178.128.100.101:9010/profile_data/get?id=13229814&mobile=841639401222'
        headers = {'content-type': 'application/json','token': 'kalapa'}
        r = requests.get(url,headers=headers,timeout=5)
        data = r.json()
        # print(data)

        #['arpu', 'home', 'friend_education_level_0', 'friend_education_level_1',
        #   'friend_education_level_2', 'friend_education_level_3',
        #   'friend_pay_first', 'credit_score', 'age.1', 'end_date']

        arpu = float(data["arp"])
        home = float(le_home.transform([data["home"]]))
        f0 = float(data["score"]["friend_education_level_0"])
        f1 = float(data["score"]["friend_education_level_1"])
        f2 = float(data["score"]["friend_education_level_2"])
        f3 = float(data["score"]["friend_education_level_3"])
        fp = float(data["score"]["friend_pay_first"])
        cs = float(data["score"]["credit_score"])
        age = float(data["age"])
        end_date = float(2019 - int(data["subInfo"]["register_date"][:4]) )

        X = array( [arpu,home,f0,f1,f2,f3,fp,cs,age,end_date] )
        pred = regressor.predict_proba([X])[0]

        # print(pred)

        #handle  data, encode it and pass to predictor regressor

        headers = request.headers
        auth = headers.get("token")

        data = {
            'score'  : str(pred[1]),
            'rank' : str(rank_convert(pred[1]))
        }

        js = json.dumps(data)

        if auth == 'kalapa':
            resp = jsonify(data)
            resp.status_code = 200
        else:
            resp = jsonify({"message": "ERROR: Unauthorized"})
            resp.status_code = 401

        try:
            sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
            cursor = connection.cursor()
            result  = cursor.execute(sql_insert_query % (id,pred[1]))
            connection.commit()
            print ("Record inserted successfully into table")
        except Exception as e:
            print(e)
            print ("RECONNECT MYSQL")
            connection = mysql.connector.connect(host='localhost',database='db_dcredit',user='dcre',password='dcredit@2019')
            sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
            cursor = connection.cursor()
            result  = cursor.execute(sql_insert_query % (id,pred[1]))
            connection.commit()
            print ("Record inserted successfully into table")

        return resp
    except Exception as e:
        data = {
            'score'  : str(-1),
            'rank' : str(-1)
        }
        resp = jsonify(data)
        resp.status_code = 200
        return resp


@app.route('/get_sal/')
def api_get_sal():
    global connection
    try:
        args_dict  = request.args.to_dict()
        print(args_dict)
        id = args_dict["id"]
        mob  = args_dict["mobile"]
        url = 'http://178.128.100.101:9010/profile_data/get?id='+id+'&mobile='+mob
        headers = {'content-type': 'application/json','token': 'kalapa'}
        r = requests.get(url,headers=headers,timeout=5)
        data = r.json()

        comment_avg = float(0)
        med_code = float(0)

        sal_main = 0
        sal_by_fb = 0
        sal_by_id = 0

        def phone_convert(phone):
            p = phone
            if(phone.startswith("9") or phone.startswith("3")):
                p = "84" + phone
            if(phone.startswith("03")):
                p = phone.replace("03","8416",1)
            if(phone.startswith("09")):
                p = phone.replace("09","849",1)
            return p

        try:
            sal_main = int(df_sal[df_sal["phone"] == int(phone_convert(mob))]["sal"])
            # sal_by_fb = sal_main
        except Exception as e:
            print(e)

        if(1 == 1):
            try:
                comment_avg = float(data["score"]["comment_avg"])
                X = array( [comment_avg] )
                sal_by_fb = regressor1.predict([X])[0]
            except Exception as e:
                print(e)

            try:
                med_code = float(le_l.transform([data["careerStatus"]["medicalInsurance"][:2]]))
                X = array( [med_code] )
                sal_by_id = regressor0.predict([X])[0]
            except Exception as e:
                print(e)


        headers = request.headers
        auth = headers.get("token")

        data = {
            'sal_main' : str(sal_main),
            'sal_by_id'  : str(sal_by_id),
            'sal_by_fb' : str(sal_by_fb)
        }

        js = json.dumps(data)

        if auth == 'kalapa':
            resp = jsonify(data)
            resp.status_code = 200
        else:
            resp = jsonify({"message": "ERROR: Unauthorized"})
            resp.status_code = 401
        #
        # try:
        #     sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
        #     cursor = connection.cursor()
        #     result  = cursor.execute(sql_insert_query % (id,pred[1]))
        #     connection.commit()
        #     print ("Record inserted successfully into table")
        # except Exception as e:
        #     print(e)
        #     print ("RECONNECT MYSQL")
        #     connection = mysql.connector.connect(host='localhost',database='db_dcredit',user='dcre',password='dcredit@2019')
        #     sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
        #     cursor = connection.cursor()
        #     result  = cursor.execute(sql_insert_query % (id,pred[1]))
        #     connection.commit()
        #     print ("Record inserted successfully into table")

        return resp
    except Exception as e:
        print(e)
        print("Something went wrong")


if __name__ == '__main__':
    http_server = WSGIServer(('', 5000), app)
    http_server.serve_forever()
	from flask import Flask, url_for
	from flask import request
	from flask import json
	from flask import Response
	from flask import jsonify

	from gevent.pywsgi import WSGIServer

	import mysql.connector
	from mysql.connector import Error
	from mysql.connector import errorcode
	import requests

	# import dataset and deploying encoders

	from numpy import array
	import pandas as pd
	import numpy as np
	from sklearn import preprocessing
	from sklearn.preprocessing import quantile_transform
	from xgboost import XGBClassifier

	df = pd.read_csv("new1.txt",delimiter="\|", names=["phone","arpu","home","arpu2","age","is_prepaid","id","cic_status","num_top_friend","salary","rank_id","action_avg","comment_avg","diff_day","freq_post","friend_education_level_0","friend_education_level_1","friend_education_level_2","friend_education_level_3","friend_pay_first","friend_pay_later","likes_count","mode_time_frame","post_count","reaction_avg","credit_score","active","age","end_date","package"]);
	df.replace("empty", np.nan , inplace=True)
	df.replace(-1, np.nan , inplace=True)
	df.iloc[:,11:28].astype(float,inplace=True)
	df.fillna(0, inplace=True)
	df.drop(columns=["phone"], inplace=True)

	# Normalize Home
	def strip_it(s):
	return s.strip()

	le_home = preprocessing.LabelEncoder()
	df.home = df.home.astype(str).apply(strip_it)
	le_home.fit(df.home)

	df.home = le_home.transform(df.home)

	# Normalize Package
	le_p = preprocessing.LabelEncoder()
	df.package = df.package.astype(str).apply(strip_it)
	le_p.fit(df.package)

	df.package = le_p.transform(df.package)

	df.arpu = df.arpu.astype(float).rank(pct=True) * 10
	df = df.astype(float)

	def normalize_label(label):
	if(label > 0):
	return 1
	return 0

	# Preparing Data For Training
	from sklearn.model_selection import train_test_split
	from sklearn.feature_selection import VarianceThreshold

	X = df.iloc[:,[0,1,14,15,16,17,18,24,26,27]].values
	y = df.iloc[:, 9].apply(normalize_label).astype(int).values

	print(df.iloc[:,[0,1,14,15,16,17,18,24,26,27]].columns)

	sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
	sel.fit_transform(X)

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

	regressor = XGBClassifier(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)
	regressor.fit(X_train, y_train)

	# MODEL FOR SALARY

	df = pd.read_csv("74k-sal.csv", encoding = "utf-8", sep = ",")
	df.replace("empty", np.nan , inplace=True)
	df.replace(-1, np.nan , inplace=True)
	df.fillna(0, inplace=True)
	df.drop(columns=["phone","user_id","dt"], inplace=True)

	df_sal = pd.read_csv("all-predicted",delimiter="\|", names=["phone","sal"])

	# preprocessing Data

	def retrieve_status(status):
	if(status == "0"):
	return 0
	else:
	return 1

	df['status'] = df.avayStatus.apply(retrieve_status)

	def retrieve_med_code(code):
	try:
	return code[:2]
	except:
	return "0"
	def retrieve_med_in(code):
	try:
	return int(code[2:3])
	except:
	return 0

	def retrieve_med_loc(code):
	try:
	return int(code[5:7])
	except:
	return 0

	df['med_code'] = df.medicalInsuranceId.apply(retrieve_med_code)
	df['med_in'] = df.medicalInsuranceId.apply(retrieve_med_in)
	df['med_loc'] = df.medicalInsuranceId.apply(retrieve_med_loc)


	def retrieve_cic(cic):
	if("cb" in cic):
	return 2
	if("cy" in cic):
	return 1
	else:
	return 0

	df['cic'] = df.cic_status.apply(retrieve_cic)

	feats = ["home","fbxPackage","fbxIsActive","med_code"]
	le_l = preprocessing.LabelEncoder()
	for feat in feats:
	le_x = preprocessing.LabelEncoder()
	df[feat] = df[feat].astype(str)
	le_x.fit(df[feat])
	df[feat] = le_x.transform(df[feat])
	le_l = le_x

	df.drop(columns=["cic_status","avayStatus","medicalInsuranceId","family_info"],inplace=True)
	df.astype(float,inplace=True)

	df0 = df[(df.med_code > 0)]
	df1 = df[(df.education_level > 0)]

	from sklearn.model_selection import train_test_split
	from sklearn.feature_selection import VarianceThreshold
	from sklearn import metrics
	from sklearn.metrics import mean_absolute_error
	from xgboost import XGBRegressor

	regressor0 = XGBRegressor(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)
	regressor1 = XGBRegressor(n_estimators=100, random_state=0,min_samples_leaf = 3,min_samples_split = 8)

	# MODEL 1

	X = df0.drop(columns=["sal"]).loc[:,['med_code']].values
	y = df0.sal.astype(float).values

	print(len(X))

	sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
	sel.fit_transform(X)

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
	regressor0.fit(X_train, y_train)
	y_pred = regressor0.predict(X_test)
	print(mean_absolute_error(y_test, y_pred))

	# MODEL 2

	X = df1.drop(columns=["sal"]).loc[:,['comment_avg']].values
	y = df1.sal.astype(float).values

	print(len(X))

	sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
	sel.fit_transform(X)

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

	regressor1.fit(X_train, y_train)
	y_pred = regressor1.predict(X_test)
	print(mean_absolute_error(y_test, y_pred))

	app = Flask(__name__)

	connection = mysql.connector.connect(host='178.128.85.52',database='db_dcredit',user='dcre',password='dcredit@2019')

	def rank_convert(rank):
	r = float(rank)
	rank_number = 0
	if(r > 0.18):
	rank_number = 9
	if(r > 0.16 and r < 0.18):
	rank_number = 8
	if(r > 0.14 and r < 0.16):
	rank_number = 7
	if(r > 0.13 and r < 0.14):
	rank_number = 6
	if(r > 0.11 and r < 0.13):
	rank_number = 5
	if(r > 0.09 and r < 0.11):
	rank_number = 4
	if(r > 0.07 and r < 0.09):
	rank_number = 3
	if(r > 0.06 and r < 0.07):
	rank_number = 2
	if(r > 0.04 and r < 0.06):
	rank_number = 1
	if(r > 0.01 and r < 0.04):
	rank_number = 0
	return rank_number
	# 0 : 0.015222915448248386
	# 1 : 0.048723580315709114
	# 2 : 0.06371929943561555
	# 3 : 0.07859582751989364
	# 4 : 0.09529682248830795
	# 5 : 0.11306775361299515
	# 6 : 0.13043687045574187
	# 7 : 0.14534296691417692
	# 8 : 0.16037331521511078
	# 9 : 0.1815473824739456

	@app.route('/get_score/')
	def api_get():
	global connection
	try:
	args_dict = request.args.to_dict()
	print(args_dict)
	id = ""
	mob = args_dict["mobile"]
	url = 'http://178.128.100.101:9010/profile_data/get?id='+id+'&mobile='+mob
	# url = 'http://178.128.100.101:9010/profile_data/get?id=13229814&mobile=841639401222'
	headers = {'content-type': 'application/json','token': 'kalapa'}
	r = requests.get(url,headers=headers,timeout=5)
	data = r.json()
	# print(data)

	#['arpu', 'home', 'friend_education_level_0', 'friend_education_level_1',
	# 'friend_education_level_2', 'friend_education_level_3',
	# 'friend_pay_first', 'credit_score', 'age.1', 'end_date']

	arpu = float(data["arp"])
	home = float(le_home.transform([data["home"]]))
	f0 = float(data["score"]["friend_education_level_0"])
	f1 = float(data["score"]["friend_education_level_1"])
	f2 = float(data["score"]["friend_education_level_2"])
	f3 = float(data["score"]["friend_education_level_3"])
	fp = float(data["score"]["friend_pay_first"])
	cs = float(data["score"]["credit_score"])
	age = float(data["age"])
	end_date = float(2019 - int(data["subInfo"]["register_date"][:4]) )

	X = array( [arpu,home,f0,f1,f2,f3,fp,cs,age,end_date] )
	pred = regressor.predict_proba([X])[0]

	# print(pred)

	#handle data, encode it and pass to predictor regressor

	headers = request.headers
	auth = headers.get("token")

	data = {
	'score' : str(pred[1]),
	'rank' : str(rank_convert(pred[1]))
	}

	js = json.dumps(data)

	if auth == 'kalapa':
	resp = jsonify(data)
	resp.status_code = 200
	else:
	resp = jsonify({"message": "ERROR: Unauthorized"})
	resp.status_code = 401

	try:
	sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
	cursor = connection.cursor()
	result = cursor.execute(sql_insert_query % (id,pred[1]))
	connection.commit()
	print ("Record inserted successfully into table")
	except Exception as e:
	print(e)
	print ("RECONNECT MYSQL")
	connection = mysql.connector.connect(host='localhost',database='db_dcredit',user='dcre',password='dcredit@2019')
	sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
	cursor = connection.cursor()
	result = cursor.execute(sql_insert_query % (id,pred[1]))
	connection.commit()
	print ("Record inserted successfully into table")

	return resp
	except Exception as e:
	data = {
	'score' : str(-1),
	'rank' : str(-1)
	}
	resp = jsonify(data)
	resp.status_code = 200
	return resp


	@app.route('/get_sal/')
	def api_get_sal():
	global connection
	try:
	args_dict = request.args.to_dict()
	print(args_dict)
	id = args_dict["id"]
	mob = args_dict["mobile"]
	url = 'http://178.128.100.101:9010/profile_data/get?id='+id+'&mobile='+mob
	headers = {'content-type': 'application/json','token': 'kalapa'}
	r = requests.get(url,headers=headers,timeout=5)
	data = r.json()

	comment_avg = float(0)
	med_code = float(0)

	sal_main = 0
	sal_by_fb = 0
	sal_by_id = 0

	def phone_convert(phone):
	p = phone
	if(phone.startswith("9") or phone.startswith("3")):
	p = "84" + phone
	if(phone.startswith("03")):
	p = phone.replace("03","8416",1)
	if(phone.startswith("09")):
	p = phone.replace("09","849",1)
	return p

	try:
	sal_main = int(df_sal[df_sal["phone"] == int(phone_convert(mob))]["sal"])
	# sal_by_fb = sal_main
	except Exception as e:
	print(e)

	if(1 == 1):
	try:
	comment_avg = float(data["score"]["comment_avg"])
	X = array( [comment_avg] )
	sal_by_fb = regressor1.predict([X])[0]
	except Exception as e:
	print(e)

	try:
	med_code = float(le_l.transform([data["careerStatus"]["medicalInsurance"][:2]]))
	X = array( [med_code] )
	sal_by_id = regressor0.predict([X])[0]
	except Exception as e:
	print(e)


	headers = request.headers
	auth = headers.get("token")

	data = {
	'sal_main' : str(sal_main),
	'sal_by_id' : str(sal_by_id),
	'sal_by_fb' : str(sal_by_fb)
	}

	js = json.dumps(data)

	if auth == 'kalapa':
	resp = jsonify(data)
	resp.status_code = 200
	else:
	resp = jsonify({"message": "ERROR: Unauthorized"})
	resp.status_code = 401
	#
	# try:
	# sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
	# cursor = connection.cursor()
	# result = cursor.execute(sql_insert_query % (id,pred[1]))
	# connection.commit()
	# print ("Record inserted successfully into table")
	# except Exception as e:
	# print(e)
	# print ("RECONNECT MYSQL")
	# connection = mysql.connector.connect(host='localhost',database='db_dcredit',user='dcre',password='dcredit@2019')
	# sql_insert_query = """ INSERT INTO `tasks` (`id`, `score`,`start_date`) VALUES ('%s','%s',CURDATE())"""
	# cursor = connection.cursor()
	# result = cursor.execute(sql_insert_query % (id,pred[1]))
	# connection.commit()
	# print ("Record inserted successfully into table")

	return resp
	except Exception as e:
	print(e)
	print("Something went wrong")


	if __name__ == '__main__':
	http_server = WSGIServer(('', 5000), app)
	http_server.serve_forever()