app.py

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

# Initialize Flask app with a name
app = Flask("Store Sales Predictor")

# Load the trained churn prediction model
model = joblib.load("superkart_prediction_model_v1_0.joblib") 

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

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

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

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

    # Make a sales prediction using the trained model
    prediction = model.predict(input_data)
    #return jsonify({'predicted_sales': prediction})
    return jsonify({'predicted_sales': float(prediction[0])})

# Define an endpoint to predict churn for a batch of customers
@app.post('/v1/customerbatch')
def predict_churn_batch():
    try:
        # Get the uploaded CSV file from the request
        file = request.files['file']
        if not file:
            return jsonify({'error': 'No file uploaded'}), 400

        # Read the file into a DataFrame
        input_data = pd.read_csv(file)

        # Make predictions using the model
        predictions = model.predict(input_data.drop(columns=["Product_Id", "Store_Id","Product_Store_Sales_Total"], errors='ignore'))

        # Pair predictions with Store_Id or Product_Id
        output = {
            str(store_id): float(pred)
            for store_id, pred in zip(input_data["Store_Id"], predictions.round(2))
        }

        return jsonify(output)

    except Exception as e:
        return jsonify({'error': str(e)}), 500

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