File size: 2,296 Bytes
1f3e093 01af802 1f3e093 | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | # Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize the Flask application
sales_predictor_api = Flask("Product Sales Predictor")
# Load the trained machine learning model
model = joblib.load("product_sales_prediction_model_rf_tuned_v2_0.joblib")
# Define a route for the home page (GET request)
@sales_predictor_api.get('/')
def home():
"""
This function handles GET requests to the root URL ('/') of the API.
It returns a simple welcome message.
"""
return "Welcome to the Product Sales Prediction API!"
# Define an endpoint for single property prediction (POST request)
@sales_predictor_api.post('/v1/salespredict')
def predict_rental_price():
"""
This function handles POST requests to the '/v1/salespredict' endpoint.
It expects a JSON payload containing property details and returns
the predicted rental price as a JSON response.
"""
# Get the JSON data from the request body
salespredict_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': salespredict_data['Product_Weight'],
'Product_Sugar_Content': salespredict_data['Product_Sugar_Content'],
'Product_Allocated_Area': salespredict_data['Product_Allocated_Area'],
'Product_MRP': salespredict_data['Product_MRP'],
'Store_Size': salespredict_data['Store_Size'],
'Store_Location_City_Type': salespredict_data['Store_Location_City_Type'],
'Store_Type': salespredict_data['Store_Type'],
'Product_Id_Code': salespredict_data['Product_Id_Code'],
'Store_Age_Years': salespredict_data['Store_Age_Years'],
'Product_Type_Category': salespredict_data['Product_Type_Category']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make a churn prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Sales': prediction})
# Run the Flask app in debug mode
if __name__ == '__main__':
sales_predictor_api.run(debug=True)
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