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

# Initialize Flask app with a name
cust_churn_predictor_api = Flask ("Customer Churn Predictor Week1")

# Load the trained churn prediction model
model = joblib.load ("churn_prediction_model_v2_0.joblib")

# Define a route for the home page
@cust_churn_predictor_api.get ('/')
def home ():
    return "Welcome to the Customer Churn Prediction Week1 API!"

# Define an endpoint to predict churn for a single customer
@cust_churn_predictor_api.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 = {
        'customerID'      : customer_data ['customerID'],
        'SeniorCitizen'   : customer_data ['SeniorCitizen'],
        'tenure'          : customer_data ['tenure'],
        'MonthlyCharges'  : customer_data ['MonthlyCharges'],
        'TotalCharges'    : customer_data ['TotalCharges'],
        'Partner'         : customer_data ['Partner'],
        'Dependents'      : customer_data ['Dependents'],
        'PhoneService'    : customer_data ['PhoneService'],
        'InternetService' : customer_data ['InternetService'],
        'Contract'        : customer_data ['Contract'],
        'PaymentMethod'   : customer_data ['PaymentMethod']
    }

    # 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
@cust_churn_predictor_api.post ('/v1/customerbatch')
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 = [
        'Churn' if x == 1
        else "Not Churn"
        for x in model.predict (input_data.drop ("customerID",axis=1)).tolist ()
    ]

    cust_id_list = input_data.customerID.values.tolist ()
    output_dict = dict(zip (cust_id_list, predictions))

    return output_dict

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