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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app with a name
sales_revenue_predictor_api = Flask("Product Sales Revenue Predictor")
# Load the trained churn prediction model
model = joblib.load("product_sales_revenue_prediction_model_v1_0.joblib")
# Define a route for the home page
@sales_revenue_predictor_api.get('/')
def home():
return "Welcome to the Product Sales Revenue Prediction API"
# Define an endpoint to predict churn for a single customer
@sales_revenue_predictor_api.post('/v1/product_sales_revenue')
def predict_churn():
# Get JSON data from the request
product_data = request.get_json()
# Extract relevant customer features from the input data
sample = {
'Product_Weight': product_data.get('Product_Weight'),
'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
'Product_Type': product_data.get('Product_Type'),
'Product_MRP': product_data.get('Product_MRP'),
'Store_Id': product_data.get('Store_Id'),
'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
'Store_Size': product_data.get('Store_Size'),
'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
'Store_Type': product_data.get('Store_Type')
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a churn prediction using the trained model
predicted_sales = model.predict(input_data).tolist()[0]
# Convert predicted_price to Python float
predicted_sales = round(float(predicted_sales), 2)
# Return the prediction as a JSON response
return jsonify({'Prediction': predicted_sales})
# Run the Flask app in debug mode
if __name__ == '__main__':
app.run(debug=True)
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