app.py

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

# Initialize the Flask app
app = Flask("Revenue Predictor")

# Load trained model
model = joblib.load("backend_files/Superkart_prediction_model_v1_0.joblib")

# Root endpoint
@app.route('/', methods=['GET'])
def home():
    return "Welcome to the Revenue Prediction API!"

# Single record prediction
@app.route('/v1/revenue', methods=['POST'])
def predict_revenue():
    customer_data = request.get_json()

    # Prepare input data
    sample = {
        "Product_Type": customer_data['Product_Type'],
        "Product_MRP": customer_data['Product_MRP'],
        "Product_Weight": customer_data['Product_Weight'],
        "Product_Sugar_Content": customer_data['Product_Sugar_Content'],
        "Store_Size": customer_data['Store_Size'],
        "Store_Location_City": customer_data['Store_Location_City'],
        "Store_Type": customer_data['Store_Type'],
        "Product_Allocated_Area": customer_data['Product_Allocated_Area'],
        "Store_Establishment_Year": customer_data['Store_Establishment_Year']
    }

    # Convert to DataFrame
    input_data = pd.DataFrame([sample])

    # Predict log revenue
    predicted_log_revenue = model.predict(input_data).tolist()[0]

    # Convert log to actual revenue
    predicted_revenue = np.exp(predicted_log_revenue)
    predicted_revenue = round(float(predicted_revenue), 2)

    return jsonify({'Predicted revenue (in dollars)': predicted_revenue})


# Batch prediction endpoint
@app.route('/v1/revenuebatch', methods=['POST'])
def predict_revenue_batch():
    file = request.files['file']
    input_data = pd.read_csv(file)

    # Predict log revenue
    predicted_log_revenue = model.predict(input_data.drop(["Product_Id", "Store_Id"], axis=1)).tolist()

    # Convert to actual revenue
    predicted_revenue = [round(float(np.exp(x)), 2) for x in predicted_log_revenue]

    # Create output dictionary
    product_ids = input_data["Product_Id"].astype(str) + "_" + input_data["Store_Id"].astype(str)
    output_dict = dict(zip(product_ids, predicted_revenue))

    return jsonify(output_dict)


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