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main.py
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main.py
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import pickle
import pandas as pd
from flask import Flask, request, Response
from classes.electrical_consumption import Electrical_Consumption
import os
# loading model trained from pickle file
with open('models/rfr_final.pkl', 'rb') as file:
model = pickle.load(file)
# initializing API
app = Flask(__name__)
@app.route('/predict', methods=['POST'])
def electrical_consumption_prediction():
test_json = request.get_json()
if test_json:
# unique observation
if isinstance(test_json, dict):
test_raw = pd.DataFrame(test_json, index=[0])
# multiple observations
else: # multiple Example
test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())
# instantiating EC class
pipeline = Electrical_Consumption()
# manipulating features
test_raw_features = pipeline.features_engineering(test_raw)
# rescaling and encoding features to predict
test_raw_prepared = pipeline.data_preparation(test_raw_features)
# predicting
df_response = pipeline.get_predict(model, test_raw_prepared)
return df_response
else:
return Response("{}", status=200, mimetype='application/json')
if __name__ == '__main__':
port = os.environ.get('PORT', 5000)
app.run(host='0.0.0.0', port=port)