app.py

# 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
extraalearn_api = Flask("ExtraaLearn")

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

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

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

    # Extract relevant lead 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 churn prediction using the trained model
    prediction = model.predict(input_data)[0]

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


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