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# Create API of ML model using flask | |
''' | |
This code takes the JSON data while POST request an performs the prediction using loaded model and returns | |
the results in JSON format. | |
''' | |
# Import libraries | |
import numpy as np | |
from flask import Flask, request, jsonify | |
import pickle | |
app = Flask(__name__) | |
# Load the model | |
model = pickle.load(open('model.pkl','rb')) | |
def predict(): | |
# Get the data from the POST request. | |
data = request.get_json(force=True) | |
# Make prediction using model loaded from disk as per the data. | |
prediction = model.predict([[np.array(data['exp'])]]) | |
# Take the first value of prediction | |
output = prediction[0] | |
return jsonify(output) | |
if __name__ == '__main__': | |
try: | |
app.run(port=5000, debug=True) | |
except: | |
print("Server is exited unexpectedly. Please contact server admin.") | |