import gradio as gr
import pickle
import pandas as pd
import numpy as np

# load the saved model and DictVectorizer
with open('dict_vectorizer.bin', 'rb') as f_in:
    loaded_dict_vec = pickle.load(f_in)

with open('model_lr.bin', 'rb') as f_in:
    loaded_model = pickle.load(f_in)

# Function to make predictions
def predict_churn(gender, seniorcitizen, partner, dependents, phoneservice,
                  multiplelines, internetservice, onlinesecurity, onlinebackup,
                  deviceprotection, techsupport, streamingtv, streamingmovies,
                  contract, paperlessbilling, paymentmethod, tenure,
                  monthlycharges, totalcharges):

    customer = {
        'gender': gender,
        'seniorcitizen': seniorcitizen,
        'partner': partner,
        'dependents': dependents,
        'phoneservice': phoneservice,
        'multiplelines': multiplelines,
        'internetservice': internetservice,
        'onlinesecurity': onlinesecurity,
        'onlinebackup': onlinebackup,
        'deviceprotection': deviceprotection,
        'techsupport': techsupport,
        'streamingtv': streamingtv,
        'streamingmovies': streamingmovies,
        'contract': contract,
        'paperlessbilling': paperlessbilling,
        'paymentmethod': paymentmethod,
        'tenure': tenure,
        'monthlycharges': monthlycharges,
        'totalcharges': totalcharges
    }

    labels = ['No Churn', 'Churn']

    X = loaded_dict_vec.transform([customer])
    y_pred = loaded_model.predict_proba(X)[0,:]
    churn_probability = y_pred
    
    return {labels[i]: float(y_pred[i]) for i in range(2)}


with gr.Blocks() as demo:
    gr.Markdown("<h1 style='text-align: center;'>Telecom Customer Churn Prediction</h1>")
    gr.Markdown("<h3 style='text-align: center;'>Predict the probability of a customer churning based on their demographics and service usage.</h3>")
    
    with gr.Row():
        gender = gr.components.Dropdown(['male', 'female'], value='male', label="Gender")
        senior_citizen = gr.components.Radio([0, 1], value=0, label="Senior Citizen")
        partner = gr.components.Radio(['yes', 'no'], value='yes', label="Partner")
        dependents = gr.components.Radio(['yes', 'no'], value='yes', label="Dependents")

    with gr.Row():
        phone_service = gr.components.Radio(['yes', 'no'], value='yes', label="Phone Service")
        multiple_lines = gr.components.Dropdown(['no_phone_service', 'no', 'yes'], label="Multiple Lines")
        internet_service = gr.components.Dropdown(['dsl', 'fiber_optic', 'no'], label="Internet Service")
        online_security = gr.components.Dropdown(['no', 'yes', 'no_internet_service'], label="Online Security")
        online_backup = gr.components.Dropdown(['yes', 'no', 'no_internet_service'], label="Online Backup")
        device_protection = gr.components.Dropdown(['no', 'yes', 'no_internet_service'], label="Device Protection")
        tech_support = gr.components.Dropdown(['no', 'yes', 'no_internet_service'], label="Tech Support")

    with gr.Row():
        streaming_tv = gr.Dropdown(label="Streaming TV", choices=['no', 'yes', 'no_internet_service'], value="no")
        streaming_movies = gr.Dropdown(label="Streaming Movies", choices=['no', 'yes', 'no_internet_service'], value="no")

    with gr.Row():
        contract = gr.components.Dropdown(['month-to-month', 'one_year', 'two_year'], value='month-to-month', label="Contract")
        paperless_billing = gr.components.Radio(['yes', 'no'], value='no', label="Paperless Billing")
        payment_method = gr.components.Dropdown(['electronic_check', 'mailed_check', 'bank_transfer_(automatic)', 'credit_card_(automatic)'], value='electronic_check', label="Payment Method")
        tenure = gr.components.Number(label="Tenure (Months)", minimum=0, maximum=1000)
        monthly_charges = gr.components.Number(label="Monthly Charges (₹)", minimum=0, maximum=10000)
        total_charges = gr.components.Number(label="Total Charges (₹)", minimum=0, maximum=1000000)
    
    submit_btn = gr.Button("Submit")
    
    output_label = gr.Label(label="Churn Probability")
    
    submit_btn.click(predict_churn, inputs=[gender, senior_citizen, partner, dependents,
                                             phone_service, multiple_lines,
                                             internet_service, online_security, online_backup, device_protection, tech_support,
                                             streaming_tv, streaming_movies, contract, paperless_billing, payment_method, tenure,
                                             monthly_charges, total_charges
                                             ], outputs=output_label)

demo.launch()