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import joblib
import pandas as pd
from flask import Flask, request, jsonify
import numpy as np
# Initialize Flask app with a name
app = Flask("SuperKart sales prediction app backend")
# Load the trained churn prediction model
model = joblib.load("SuperKart_model_deployment_model_v1_0.joblib")
# Define a route for the home page
@app.get('/')
def home():
return "Welcome to the SuperKart Sales Prediction API"
# Define an endpoint to predict sales of the single product in a store
@app.post('/v1/sales')
def predict_sales():
# Get JSON data from the request
store_data = request.get_json()
# Extract relevant store features from the input data
requestData = {
'Product_Weight': store_data['Product_Weight'],
'Product_Sugar_Content': store_data['Product_Sugar_Content'],
'Product_Allocated_Area': store_data['Product_Allocated_Area'],
'Product_Type': store_data['Product_Type'],
'Product_MRP': store_data['Product_MRP'],
'Store_Id': store_data['Store_Id'],
'Store_Establishment_Year': store_data['Store_Establishment_Year'],
'Store_Size': store_data['Store_Size'],
'Store_Location_City_Type': store_data['Store_Location_City_Type'],
'Store_Type': store_data['Store_Type']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([requestData])
# Make a Sales prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
#Calculate the actual price
predicted_sales = np.exp(prediction)
# Convert predicted_price to Python float
predicted_sales = round(float(predicted_sales), 2)
# Return the prediction as a JSON response
return jsonify({'Predicted_Sale': prediction})
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