app.py

# Import necessary libraries
import numpy as np
import joblib  # For loading the serialized model
import pandas as pd  # For data manipulation
from flask import Flask, request, jsonify  # For creating the Flask API

# Initialize the Flask application
sales_predictor_api = Flask("Product Sales Predictor")

# Load the trained machine learning model
model = joblib.load("product_sales_prediction_model_rf_tuned_v2_0.joblib")

# Define a route for the home page (GET request)
@sales_predictor_api.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a simple welcome message.
    """
    return "Welcome to the Product Sales Prediction API!"

# Define an endpoint for single property prediction (POST request)
@sales_predictor_api.post('/v1/salespredict')
def predict_rental_price():
    """
    This function handles POST requests to the '/v1/salespredict' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """
    # Get the JSON data from the request body
    salespredict_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': salespredict_data['Product_Weight'],
        'Product_Sugar_Content': salespredict_data['Product_Sugar_Content'],
        'Product_Allocated_Area': salespredict_data['Product_Allocated_Area'],
        'Product_MRP': salespredict_data['Product_MRP'],
        'Store_Size': salespredict_data['Store_Size'],
        'Store_Location_City_Type': salespredict_data['Store_Location_City_Type'],
        'Store_Type': salespredict_data['Store_Type'],
        'Product_Id_Code': salespredict_data['Product_Id_Code'],
        'Store_Age_Years': salespredict_data['Store_Age_Years'],
        'Product_Type_Category': salespredict_data['Product_Type_Category']
    }

    # Convert the extracted data into a Pandas DataFrame
    input_data = pd.DataFrame([sample])

    # Make a churn prediction using the trained model
    prediction = model.predict(input_data).tolist()[0]

    # Return the prediction as a JSON response
    return jsonify({'Sales': prediction})

# Run the Flask app in debug mode
if __name__ == '__main__':
    sales_predictor_api.run(debug=True)