%%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)
app.run(host='0.0.0.0', debug=True, port=7878)