예제1
%%writefile app.py
#app.py
from flask import Flask
app = Flask(__name__)
# 한글 깨짐 방지
app.config['JSON_AS_ASCII'] = False
print('Flask running~')
@app.route('/')
def index():
return '<h1>Hello World</h1>'
@app.route('/json')
def json():
import json
data = {}
data['empno'] = 31
data['ename'] = 'Smith'
data['phone'] = '010-2547-8459'
js_str = json.dumps(data)
return js_str
@app.route('/login',methods=['GET','POST'])
def login():
from flask import request
if request.method == 'GET':
userid = request.args.get('userid')
pwd = request.args.get('pwd')
import json
result = {}
if (userid == 'smith' and pwd == '1234'):
result['result'] = '로그인 성공'
else:
result['result'] = '로그인 실패'
return json.dumps(result, ensure_ascii=False).encode('utf8')
elif request.method == 'POST':
userid = request.form['userid']
pwd = request.form['pwd']
@app.route('/calc/<int:num>/<int:num2>') # int, float, str
def calc(num,num2):
return '%s + %s = %s'%(num,num2,num+num2)
app.run(host='0.0.0.0',debug=True,port=7878)
예제2
%%writefile app2.py
from flask import Flask
app = Flask(__name__)
import json
import pickle
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
X_train = [[1],[2],[3],[4]]
y_train = [1,2,3,4]
clf.fit(X_train,y_train)
with open('app2.pkl','wb') as fin:
pickle.dump(clf, fin)
with open('app2.pkl','rb') as fout:
clf_loaded = pickle.load(fout)
@app.route('/predict/<int:num>')
def predict(num):
X_test = [[num]]
y_pred = clf_loaded.predict(X_test)
return str(y_pred)
app.run(host='0.0.0.0',debug=True,port=7878)
예제3
%%writefile app3.py
from flask import Flask
app = Flask(__name__)
####################################################
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pd.read_csv(url, names=names)
array = dataframe.values # array([[],...])
X = array[:,0:8] # 0 ~ 7번 열
y = array[:,8] # 8번 열
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create logistic regression object
clf = LogisticRegression(max_iter=1000)
# Train the model using the training sets
clf.fit(X_train, y_train)
# Predict the labels of test set
y_pred = clf.predict(X_test)
# Evaluate the accuracy of the model
acc = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(acc*100))
# save the model to a file using pickle
import pickle
filename = 'logistic_regression_model.pkl'
with open(filename, 'wb') as file:
pickle.dump(clf, file)
#########################
@app.route('/model/logreg/')
def load_model():
# load the model from the file
with open(filename, 'rb') as file:
clf_loaded = pickle.load(file)
pred = clf_loaded.predict([[6.,98.,58.,33.,190.,34.,0.43,43.]])
return str(pred)
from flask import request
from flask import jsonify
@app.route('/add/json',methods=['POST'])
def add_json():
json_data = request.get_json()
print('서버에서 받은 데이터:',json_data)
return jsonify(json_data)
app.run(host='0.0.0.0', debug=True, port=7878)