app.py

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


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

# Load the trained superkart sales forecast model
model = joblib.load("tuned_rf_model.pkl")

# Define a route for the home page
@superkart_api.get('/')
def home():
    return "Welcome to the Superkart Sales Forecasting API!" 

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

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

    # Converting the data into a DataFrame
    input_df = pd.DataFrame([sample])

    # Making a prediction using the trained model
    prediction = model.predict(input_df).tolist()[0]

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


@superkart_api.post('/v1/salesbatch')
def predict_sales_batch():
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the file into a dataframe
    input_df = pd.read_csv(file)

    # Make predictions for the batch data and convert raw predictions into a readable format
    predictions = model.predict(input_df).tolist()
    input_df['Predicted_Sales'] = predictions

    return input_df[['Predicted_Sales']].to_json(orient='records')


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