flask classification poc service

gistfile1.txt

"""
sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0rc0-cp34-cp34m-linux_x86_64.whl
"""



from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import shutil

from sklearn import datasets, metrics, cross_validation
from tensorflow.contrib import skflow


import numpy as np
from flask import Flask, abort, jsonify, request
#import cPickle as pickle

# basic toy nn pickled loader
#pkl_file = open('batch_model_2016_04_23.pkl', 'rb')
#latest_neural_network = pickle.load(pkl_file)
#pkl_file.close()

from sklearn import datasets, metrics, cross_validation
from tensorflow.contrib import skflow

iris = datasets.load_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
    test_size=0.2, random_state=42)

# trained batch offline saved skflow model (latest parameters and learned variables)
#classifier.save('/home/ubuntu/scratch/skflow_batch/batch_model_2016_04_23')

# restore skflow model from batch run 2016-04-23
new_classifier = skflow.TensorFlowEstimator.restore('/home/ubuntu/scratch/skflow_batch/batch_model_2016_04_23')

# check model load with test data
score = metrics.accuracy_score(y_test, new_classifier.predict(X_test))
print('Accuracy: {0:f}'.format(score))


app = Flask(__name__)

@app.route('/api', methods=['POST'])
def make_predict():

    # incoming data converted from json
    data = request.get_json(force=True)

    # shove into array
    predict_request = [data['sl'],data['sw'],data['pl'], data['pw']]
    predict_request = np.array([predict_request])

    # np array passed to toy neural network
    # https://gist.github.com/bigsnarfdude/57ff7d6095f7ee83d4195d1fed26388b
    y_hat = new_classifier.predict(predict_request)
    output = str(y_hat[0])

    # convert output to json
    return jsonify(results=output)

if __name__ == '__main__':
    app.run(port = 11111, debug = True)