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import os
import json
import pickle
import flask
import pandas as pd
prefix = "dir/"
model_path = os.path.join(prefix, 'model')
app = flask.Flask(__name__)
debug = True
app.debug = debug
class ScoringService:
"""Class that loads the model in memory."""
model = None # the model itself
@classmethod
def get_model(cls):
"""Loads the model"""
if cls.model is None:
with open(os.path.join(model_path, 'great_model.pkl'), 'rb') as inp:
cls.model = pickle.load(inp)
return cls.model
@classmethod
def predict(cls, input):
"""Predicts for the passed data"""
# load model
model = cls.get_model()
# get the data
data = pd.DataFrame(input)
# now we can do the inference
return model.predict(data)
@app.route('/ping', methods=['GET'])
def ping():
health = ScoringService.get_model() is not None
status = 200 if health else 404
return flask.Response(response='\n', status=status, mimetype='application/json')
@app.route('/', methods=['GET'])
def home():
return '''<h1>Bienvenido a la API de detección de anomalías.</h1>
<p>Algoritmo: Isolation Forest.</p>'''
@app.route('/ad', methods=['POST'])
def api_predict():
data = None
if flask.request.method == 'POST':
data = json.loads(flask.request.data)
if data is None:
return flask.jsonify("The transformation didn't succeed.")
# Do the prediction
predictions = ScoringService.predict(data)
# transform to json
predictions = flask.jsonify(predictions.tolist())
return predictions
if __name__ == '__main__':
app.run(host='0.0.0.0', debug=debug)
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