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

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

# Load the trained churn prediction model
model = joblib.load("superkart_model_v1_0.joblib")

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

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

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

    # 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]

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

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