# Import necessary libraries
import numpy as np
import joblib  # For loading the serialized model
import pandas as pd  # For data manipulation
from flask import Flask, request, jsonify  # For creating the Flask API

# Initialize Flask app with a name
learn_api = Flask("ExtraaLearn")

# Load the trained churn prediction model
model = joblib.load("backend_files/learn_model.joblib")

# Define a route for the home page
@superkart_api.get('/')
def home():
    return "Welcome to the Lead Prediction System"

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

    # Extract relevant customer features from the input data
    sample = {
    'age': data['age'],
    'current_occupation': data['current_occupation'],
    'first_interaction': data['first_interaction'],
    'profile_completed': data['profile_completed'],
    'website_visits': data['website_visits'],
    'time_spent_on_website': data['time_spent_on_website'],
    'page_views_per_visit': data['page_views_per_visit'],
    'last_activity': data['last_activity'],
    'print_media_type1': data['print_media_type1'],
    'print_media_type2': data['print_media_type2'],
    'digital_media': data['digital_media'],
    'educational_channels': data['educational_channels'],
    'referral': data['referral']
}


    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

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

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


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