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app.py
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app.py
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from flask import Flask, request, jsonify
from flask.logging import create_logger
import logging
import pandas as pd
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
LOG = create_logger(app)
LOG.setLevel(logging.INFO)
def scale(payload):
"""Scales Payload"""
LOG.info("Scaling Payload: \n%s", payload)
scaler = StandardScaler().fit(payload.astype(float))
scaled_adhoc_predict = scaler.transform(payload.astype(float))
return scaled_adhoc_predict
@app.route("/")
def home():
html = f"<h3>Sklearn Prediction Home</h3>"
return html.format(format)
@app.route("/predict", methods=['POST'])
def predict():
"""Performs an sklearn prediction
input looks like:
{
"CHAS":{
"0":0
},
"RM":{
"0":6.575
},
"TAX":{
"0":296.0
},
"PTRATIO":{
"0":15.3
},
"B":{
"0":396.9
},
"LSTAT":{
"0":4.98
}
result looks like:
{ "prediction": [ <val> ] }
"""
# Logging the input payload
json_payload = request.json
LOG.info("JSON payload: \n%s", json_payload)
inference_payload = pd.DataFrame(json_payload)
LOG.info("Inference payload DataFrame: \n%s", inference_payload)
# scale the input
scaled_payload = scale(inference_payload)
# get an output prediction from the pretrained model, clf
prediction = list(clf.predict(scaled_payload))
LOG.info("Predictions for the given input [%s] : %s ", json_payload, prediction)
# TO DO: Log the output prediction value
return jsonify({'prediction': prediction})
if __name__ == "__main__":
# load pretrained model as clf
clf = joblib.load("./model_data/boston_housing_prediction.joblib")
app.run(host='0.0.0.0', port=80, debug=True) # specify port=80