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

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

# Load the trained churn prediction model
model = joblib.load("churn_prediction_model_v1_0.joblib")

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

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

    # 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
@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

# ### Debugger function
# @churn_predictor_api.post('/v1/customer')
# def predict_churn():
#     try:
#         customer_data = request.get_json()
#         sample = {
            
#             'Partner': customer_data['Partner'],
#             'Dependents': customer_data['Dependents'],
#             'PhoneService': customer_data['PhoneService'],
#             'InternetService': customer_data['InternetService'],
#             'Contract': customer_data['Contract'],
#             'PaymentMethod': customer_data['PaymentMethod'],
#             'tenure': customer_data['tenure'],
#             'MonthlyCharges': customer_data['MonthlyCharges'],
#             'TotalCharges': customer_data['TotalCharges'],
#             'SeniorCitizen':customer_data['SeniorCitizen']
#             }
      

#         input_data = pd.DataFrame([sample])
#         prediction = model.predict(input_data).tolist()[0]
#         prediction_label = "churn" if prediction == 1 else "not churn"
#         return jsonify({'Prediction': prediction_label})
#     except Exception as e:
#         return jsonify({"error": str(e)}), 500
### debugger Ends

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