app.py

# -*- coding: utf-8 -*-
"""
Created on Thu Dec 19 15:13:01 2019

@author: murat
"""

import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle


#%%

app = Flask(__name__)
model = pickle.load(open("nb_model.pkl", "rb"))

#%%

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['POST'])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    int_features = [int(x) for x in request.form.values()]
    final_features = [np.array(int_features)]
    prediction = model.predict(final_features)

    output = round(prediction[0], 2)

    if output == 0:
        return render_template('index.html', prediction_text = 'Üzgünüm! Kredi başvurunuz olumsuz sonuçlanmıştır.')
    else:
        return render_template('index.html', prediction_text = 'Harika! Kredi başvurunuz olumlu sonuçlanmıştır.')

@app.route('/predict_api',methods=['POST'])
def predict_api():
    '''
    For direct API calls trought request
    '''
    data = request.get_json(force = True)
    prediction = model.predict([np.array(list(data.values()))])

    output = prediction[0]
    return jsonify(output)

if __name__ == "__main__":
    app.run(debug=True)

#%%

gistfile1.txt

# -*- coding: utf-8 -*-
"""
Created on Thu Dec 19 14:43:34 2019

@author: murat
"""
#%%

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle

#%%

df = pd.read_csv("kredi.csv", sep=";")

#%%

from pandas.api.types import CategoricalDtype
df["evDurumu"].replace("evsahibi",1, inplace = True)
df["evDurumu"].replace("kiraci",0, inplace= True)
df["telefonDurumu"].replace("var",1, inplace = True)
df["telefonDurumu"].replace("yok",0, inplace= True)
df["KrediDurumu"].replace("krediver",1, inplace = True)
df["KrediDurumu"].replace("verme",0, inplace= True)

#%%

y = df["KrediDurumu"]
X = df.drop(["KrediDurumu"], axis = 1)

#%%

from sklearn.naive_bayes import GaussianNB

nb = GaussianNB()
nb_model = nb.fit(X, y)

pickle.dump(nb_model, open("nb_model.pkl", "wb"))

#%%
model = pickle.load(open("nb_model.pkl", "rb"))
#print(model.predict([[12000, 70, 0,2,1]]))

gistfile2.txt

# -*- coding: utf-8 -*-
"""
Created on Thu Dec 19 15:21:23 2019

@author: murat
"""

import requests

url = "http://localhost:5000/predict_api"
r = request.post(url, json = {"krediMiktari" : 1200, "yas" : 70, "evDurumu" : 0, "aldigi_kredi_sayi" : 2, "telefonDurumu" : 1})

print(r.json())
 

model.py

# -*- coding: utf-8 -*-
"""
Created on Thu Dec 19 14:43:34 2019

@author: murat
"""
#%%

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle

#%%

df = pd.read_csv("kredi.csv", sep=";")

#%%

from pandas.api.types import CategoricalDtype
df["evDurumu"].replace("evsahibi",1, inplace = True)
df["evDurumu"].replace("kiraci",0, inplace= True)
df["telefonDurumu"].replace("var",1, inplace = True)
df["telefonDurumu"].replace("yok",0, inplace= True)
df["KrediDurumu"].replace("krediver",1, inplace = True)
df["KrediDurumu"].replace("verme",0, inplace= True)

#%%

y = df["KrediDurumu"]
X = df.drop(["KrediDurumu"], axis = 1)

#%%

from sklearn.naive_bayes import GaussianNB

nb = GaussianNB()
nb_model = nb.fit(X, y)

pickle.dump(nb_model, open("nb_model.pkl", "wb"))

#%%
model = pickle.load(open("nb_model.pkl", "rb"))
#print(model.predict([[12000, 70, 0,2,1]]))

request.py

# -*- coding: utf-8 -*-
"""
Created on Thu Dec 19 15:21:23 2019

@author: murat
"""

import requests

url = "http://localhost:5000/predict_api"
r = request.post(url, json = {"krediMiktari" : 1200, "yas" : 70, "evDurumu" : 0, "aldigi_kredi_sayi" : 2, "telefonDurumu" : 1})

print(r.json())