app.py

import joblib
import pandas as pd
from flask import Flask, request, jsonify

# Initialize Flask app with a name
app = Flask("SuperKart Sales Forecast")

# Load the trained churn prediction model
model = joblib.load("deployment_files/SuperKart_Sales_prediction_model_v1_0.joblib")

# Define a route for the home page
@app.get('/')
def home():
    return "Welcome to SuperKart Sales Forecast Prediction API"

# Define an endpoint to predict forecast for a single store
@app.post('/v1/Store')
def predict_churn():
    # Get JSON data from the request
    Store_data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
        'Store_Id': customer_data['Store_Id'],
        'Store_Size': customer_data['Store_Size'],
        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
        'Store_Type': customer_data['Store_Type'],
        'Store_Age_Years': customer_data['Store_Age_Years'],
        'Product_Type_Category': customer_data['Product_Type_Category'],
        'Product_Weight': customer_data['Product_Weight'],
        'Product_Allocated_Area': customStore_Age_Yearser_data['Product_Allocated_Area'],
        'Product_MRP': customer_data['Product_MRP'],
        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
    }

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

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

    # Map prediction result to a human-readable label
    # prediction_label = "churn" if prediction == 1 else "not churn"

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

# Define an endpoint to predict churn for a batch of customers
@app.post('/v1/Store_Id')
def predict_churn_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)

    # Make predictions for the batch data and convert raw predictions into a readable format
    predictions = [
        for x in model.predict(input_data.drop("Store_Id",axis=1)).tolist()
    ]

    store_id_list = input_data.Store_Id.values.tolist()
    output_dict = dict(zip(store_id_list, predictions))

    return output_dict

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