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a645397 | 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 70 71 72 73 74 75 76 77 78 | import joblib
import pandas as pd
from flask import Flask, request, jsonify
import numpy as np
# Initialize Flask app
sales_forecast_api = Flask("SuperKart Sales Forecast Predictor")
# Load the trained SuperKart sales model
model = joblib.load("superkart_sales_model_v1_0.joblib")
# Define a route for the home page
@sales_forecast_api.get('/')
def home():
return "Welcome to the SuperKart Sales Revenue Forecasting API!"
# Define an endpoint to predict sales for a single product-store combination
@sales_forecast_api.post('/v1/sales')
def predict_sales():
# Get JSON data from the request
sales_data = request.get_json()
# Extract relevant features from the input data
# Note: Store_Age will be calculated from Store_Establishment_Year
current_year = 2024
store_age = current_year - sales_data['Store_Establishment_Year']
sample = {
'Product_Weight': sales_data['Product_Weight'],
'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
'Product_Type': sales_data['Product_Type'],
'Product_MRP': sales_data['Product_MRP'],
'Store_Establishment_Year': sales_data['Store_Establishment_Year'],
'Store_Size': sales_data['Store_Size'],
'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
'Store_Type': sales_data['Store_Type'],
'Store_Age': store_age
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Predicted_Sales_Total': prediction})
# Define an endpoint to predict sales for a batch of product-store combinations
@sales_forecast_api.post('/v1/salesbatch')
def predict_sales_batch():
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the file into a DataFrame
input_data = pd.read_csv(file)
# Calculate Store_Age if not present
if 'Store_Age' not in input_data.columns:
current_year = 2024
input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']
# Make predictions for the batch data
predictions = model.predict(input_data).tolist()
# Add predictions to the DataFrame
input_data['Predicted_Sales_Total'] = predictions
# Convert results to dictionary
result = input_data.to_dict(orient="records")
return jsonify(result)
# Run the Flask app in debug mode
if __name__ == '__main__':
sales_forecast_api.run(debug=True)
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