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4fd7ca0 0856265 4fd7ca0 0856265 66a887c 4fd7ca0 d6e0139 c6874ad 0856265 4fd7ca0 0856265 4fd7ca0 0856265 195cdab 0856265 c6874ad 0856265 4fd7ca0 0856265 | 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 60 61 62 63 64 65 66 67 68 69 | import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app
sales_predictor_api = Flask("Product_Store_Total_Sales_Predictor")
# Load the trained model
model = joblib.load("product_sales_total_prediction_v1_0.joblib")
#Label encoding Store Size as this is ordinal
size_map = {'Small': 1, 'Medium': 2, 'High': 3}
feasibility_map = {
'Fruits and Vegetables': 'Perishable',
'Hard Drinks': 'Non Perishable',
'Snack Foods': 'Perishable',
'Dairy': 'Perishable',
'Canned': 'Perishable',
'Baking Goods': 'Perishable',
'Breads': 'Perishable',
'Breakfast': 'Perishable',
'Frozen Foods': 'Perishable',
'Health and Hygiene': 'Perishable',
'Household': 'Perishable',
'Seafood': 'Perishable',
'Starchy Foods': 'Perishable',
'Others': 'Non Perishable',
'Meat': 'Perishable',
'Soft Drinks': 'Non Perishable',
}
# Home endpoint
@sales_predictor_api.get('/')
def home():
return "Welcome to the Product Store Total Sales Prediction API!"
# Prediction endpoint
@sales_predictor_api.post('/v1/product')
def predict_product_sales():
try:
product_data = request.get_json()
# Construct input DataFrame
input_data = pd.DataFrame([{
'Product_Weight': product_data['Product_Weight'],
'Product_Sugar_Content': product_data['Product_Sugar_Content'],
'Product_Allocated_Area': product_data['Product_Allocated_Area'],
'Product_Type': product_data['Product_Type'],
'Product_MRP': product_data['Product_MRP'],
'Store_Establishment_Year': product_data['Store_Establishment_Year'],
'Store_Size': size_map[product_data['Store_Size']],
'Store_Location_City_Type': product_data['Store_Location_City_Type'],
'Store_Type': product_data['Store_Type'],
'Store_Age': 2025 - product_data['Store_Establishment_Year'],
'Product_Perishability': feasibility_map[product_data['Product_Type']]
}])
# Predict
prediction = model.predict(input_data).tolist()[0]
return jsonify({'Product Store Sales Total': prediction})
except Exception as e:
return jsonify({'error': str(e)}), 500
# Run the app
if __name__ == '__main__':
sales_predictor_api.run(debug=True)
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