Created
January 18, 2019 19:20
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import flask | |
from flask import Flask, Response | |
import pandas as pd | |
from io import StringIO | |
app = Flask(__name__) | |
from keras.models import load_model | |
clf = load_model('clf.h5') | |
clf._make_predict_function() # GH#6462 @ keras-team/keras | |
def run_model(input_arr): | |
"""Predictor function.""" | |
# reshape the flat [0, 255]-entry list into a [0, 1]-entry grid, as desired by the CNN. | |
input_arr = input_arr.reshape(input_arr.shape[0], 28, 28, 1).astype('float') / 255 | |
return clf.predict(input_arr).argmax(axis=1) | |
@app.route('/ping', methods=['GET']) | |
def ping(): | |
""" | |
Determine if the container is healthy by running a sample through the algorithm. | |
""" | |
# we will return status ok if the model doesn't barf | |
# but you can also insert slightly more sophisticated tests here | |
health_check_arr = pd.read_csv("health-check-data.csv").values | |
try: | |
result = run_model(health_check_arr) | |
return Response(response='{"status": "ok"}', status=200, mimetype='application/json') | |
except: | |
return Response(response='{"status": "error"}', status=500, mimetype='application/json') | |
@app.route('/invocations', methods=['POST']) | |
def predict(): | |
""" | |
Do an inference on a single batch of data. | |
""" | |
if flask.request.content_type == 'text/csv': | |
X_train = flask.request.data.decode('utf-8') | |
X_train = pd.read_csv(StringIO(X_train), header=None).values | |
else: | |
return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain') | |
results = run_model(X_train) | |
# format into a csv | |
results_str = ",\n".join(results.astype('str')) | |
# return | |
return Response(response=results_str, status=200, mimetype='text/csv') |
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