add app files

app.py

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+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()