Flask with machine learning
1. flask
$ touch dss.py
$ mkdir models
$ mkdir static
$ mkdir templates
$ cd templates
$ touch index.html
$ cd ..
- flask with dss.py
from flask import Flask, render_template, request, jsonify
import pickle
app = Flask(__name__)
app.config.update(
TEMPLATE_AUTO_RELOAD = True,
)
# load model
models = {}
def init():
with open('./models/classification.plk', 'rb') as f:
models['classification'] = pickle.load(f)
init()
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict/', methods = ['POST'])
def predict():
classification_list = ['정치', '경제', '사회', '생활/문화', '세계', 'IT/과학']
model = model['classification']
sentence = request.values.get('sentence')
predict_data = model.predict_proba([sentence])[0]
result = {'status':200, 'result':list(predict_data), 'category':classification_list}
return jsonify(result)
if __name__ == '__main__':
app.run()
#run
#$ python3 dss.py
- index.html
at ATOM
html ==> enter
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>my title</title>
</head>
<body>
test body
</body>
</html>
- templates via CSS
bootstrap - documentation - alert, buttons
highcharts - highcart demo - pie chart - html 3 scripts
Reference