#!/usr/bin/env python
from flask_application import app
if __name__ == '__main__':
if app.debug:
app.run(debug=True)
else:
app.run(host='0.0.0.0')
#!env python
import sys
from pprint import pprint
def show(obj):
"""Show the dump of the properties of the object."""
pprint(vars(obj))
if sys.flags.interactive:
from flask_application import *
print "Loading Flask App in console mode. Use show(<obj)> to introspect."
elif __name__ == "__main__":
from flask_application import app
app.run(host="127.0.0.1", port=8080)
#!env python
import sys
from pprint import pprint
def show(obj):
'''Show the dump of the properties of the object.'''
pprint(vars(obj))
if sys.flags.interactive:
from flask_application import *
#from flask_application.models import *
print 'Loading Flask App in console mode. Use show(<obj)> to introspect.'
elif __name__ == '__main__':
from flask_application import app
app.debug = True
app.run(host = "0.0.0.0", port = 8080)
from flask_application import app
import os
if __name__ == "__main__":
try:
# If Flask is running on Docker,
# use host and port specified in Docker image
if int(os.environ["DOCKER"]) == 1:
app.run(host=os.environ["HOST"],
port=int(os.environ["PORT"]),
debug=True,
threaded=True)
except KeyError:
# default config
app.run(host="0.0.0.0", port="8080", debug=False, threaded=True)
form = PredictForm()
# form = YesNoQuestionForm()
if form.validate_on_submit():
flash("making prediction for the data given...")
dictionary = make_dictionary(form)
output = make_prediction(dictionary)
text = "Predicted price is {}".format(output)
return render_template("home.html", form=form, prediction_text=text)
return render_template("home.html", form=form)
@app.route("/predict", methods=["POST"])
@login_required
def results():
if not request.json:
abort(400)
data = request.get_json(force=True)
if not validate_json(data):
abort(422)
prediction = make_prediction(data)
return jsonify(prediction)
if __name__ == "__main__":
app.run()
# curl -u 'eyJhbGciOiJIUzUxMiJ9.InByYXRlZWsi.6pTTZjSXEyyqq_RMPaM53H9B-GMaT7sBZyPucNm-agpuSh4YY6573lUGwMTsTiGHsyuqN9MOKS9F6xWFK_kDYg':'eyJhbGciOiJIUzUxMiJ9.ImZsYXNrIg._s8ubXhQqH_s3RfO4CrPL5keU_s04k-1ZefmdIxtSS3m_aJsY9asSNpZDISjp_hVpvJLEnkislqe42enl8qtnQ' -i -H "Content-Type: application/json" -X POST -d '{"longitude":2, "latitude":2, "housing_median_age":1000, "total_rooms":2, "total_bedrooms":3, "population":5, "households":4, "median_income":10000, "ocean_proximity":"NEAR BAY"}' http://localhost:5000/predict
