-
Notifications
You must be signed in to change notification settings - Fork 0
/
SOL_app_model.py
56 lines (41 loc) · 1.7 KB
/
SOL_app_model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
from flask import Flask, jsonify, request
import os
import pickle
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
import numpy as np
os.chdir(os.path.dirname(__file__))
app = Flask(__name__)
app.config['DEBUG'] = True
@app.route("/", methods=['GET'])
def hello():
return "Bienvenido a mi API del modelo advertising"
@app.route('/api/v1/predict', methods=['GET'])
def predict():
model = pickle.load(open('ad_model.pkl','rb'))
tv = request.args.get('tv', None)
radio = request.args.get('radio', None)
newspaper = request.args.get('newspaper', None)
print(tv,radio,newspaper)
print(type(tv))
if tv is None or radio is None or newspaper is None:
return "Args empty, the data are not enough to predict"
else:
prediction = model.predict([[float(tv),float(radio),float(newspaper)]])
return jsonify({'predictions': prediction[0]})
@app.route('/api/v1/retrain', methods=['GET'])
def retrain():
data = pd.read_csv('data/Advertising.csv', index_col=0)
X_train, X_test, y_train, y_test = train_test_split(data.drop(columns=['sales']),
data['sales'],
test_size = 0.20,
random_state=42)
model = Lasso(alpha=6000)
model.fit(X_train, y_train)
rmse = np.sqrt(mean_squared_error(y_test, model.predict(X_test)))
model.fit(data.drop(columns=['sales']), data['sales'])
pickle.dump(model, open('ad_model.pkl', 'wb'))
return "Model retrained. New evaluation metric RMSE: " + str(rmse)
app.run()