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Spring-Python 예제


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)

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