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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])
# create encoder with OneHotEncoder for encoding the selected values to match the training data
# encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False) # Important for handling unseen categories
# You MUST use the *trained* encoder to transform the new data
# encoded_new_data = encoder.transform(input_data[['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type']])
#encoded_new_data = pd.get_dummies(
# input_data,
# columns=['Product_Sugar_Content','Product_Type','Store_Id','Store_Size','Store_Location_City_Type','Store_Type'],
# drop_first=True,
#);
#print("The data entered are below")
#print(encoded_new_data)
# 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': predicted_sales})
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