File size: 1,145 Bytes
3fa9533 49c1eb7 3fa9533 9146e82 8dc82de 3fa9533 e4273e0 1724a3d a752b67 1724a3d 8dc82de 1724a3d 69d2aec da64840 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Libraries different ensemble classifiers
from sklearn.ensemble import (
BaggingRegressor,
RandomForestRegressor,
AdaBoostRegressor,
GradientBoostingRegressor,
)
import xgboost as xgb
# Initialize Flask app
app = Flask("Supermarket Product Price Predictor")
loaded_model = joblib.load("predict_product_price_v1_0.joblib")
# Define a route for the home page
# @sapp.get('/')
# def home():
# return "Welcome to the Supermarket Product Price Predictor!"
# Define an endpoint
@app.post('/price')
def predict_price():
# Get JSON data from the request
input_json = request.get_json()
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([input_json])
# Make a churn prediction using the trained model
prediction = loaded_model.predict(input_data)
# Return the prediction as a JSON response
return jsonify({'Price': prediction})
##
if __name__ == '__main__':
# Run the app on all available interfaces on port 5000
app.run(debug=True, host='0.0.0.0', port=5000)
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