added app.py

app.py

@@ -0,0 +1,137 @@
+#Importing the libraries
+import gradio as gr
+import pickle
+import pandas as pd
+import numpy as np
+import joblib
+from PIL import Image
+
+#using joblib to load the model:
+num_imputer = joblib.load('assets\\num_imputer.joblib') # loading the imputer 
+cat_imputer = joblib.load('assets\\cat_imputer.joblib') # loading the imputer
+encoder = joblib.load('assets\\encoder.joblib') # loading the encoder
+scaler = joblib.load('assets\\scaler.joblib') # loading the scaler
+model = joblib.load('assets\\ml.joblib') # loading the model
+
+
+# Create a function that applies the ML pipeline and makes predictions
+def predict(gender,SeniorCitizen,Partner,Dependents, tenure, PhoneService,MultipleLines,
+                       InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,
+                       Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges):
+
+
+
+    # Create a dataframe with the input data
+     input_df = pd.DataFrame({
+        'gender': [gender],
+        'SeniorCitizen': [SeniorCitizen],
+        'Partner': [Partner],
+        'Dependents': [Dependents],
+        'tenure': [tenure],
+        'PhoneService': [PhoneService],
+        'MultipleLines': [MultipleLines],
+        'InternetService': [InternetService],
+        'OnlineSecurity': [OnlineSecurity],
+        'OnlineBackup': [OnlineBackup],
+        'DeviceProtection': [DeviceProtection],
+        'TechSupport': [TechSupport],
+        'StreamingTV': [StreamingTV],
+        'StreamingMovies': [StreamingMovies],
+        'Contract': [Contract],
+        'PaperlessBilling': [PaperlessBilling],
+        'PaymentMethod': [PaymentMethod],
+        'MonthlyCharges': [MonthlyCharges],
+        'TotalCharges': [TotalCharges]
+
+ })
+
+# Create a list with the categorical and numerical columns
+     cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
+     num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+    # Impute the missing values
+     input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns]) 
+     input_df_imputed_num = num_imputer.transform(input_df[num_columns]) 
+
+    # Encode the categorical columns
+     input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(),
+                                   columns=encoder.get_feature_names_out(cat_columns))
+
+    # Scale the numerical columns
+     input_df_scaled = scaler.transform(input_df_imputed_num)
+     input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
+
+
+    #joining the cat encoded and num scaled
+     final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+
+     final_df = final_df.reindex(columns=['SeniorCitizen','tenure','MonthlyCharges','TotalCharges',
+     'gender_Female','gender_Male','Partner_No','Partner_Yes','Dependents_No','Dependents_Yes','PhoneService_No',
+     'PhoneService_Yes','MultipleLines_No','MultipleLines_Yes','InternetService_DSL','InternetService_Fiber optic',
+     'InternetService_No','OnlineSecurity_No','OnlineSecurity_Yes','OnlineBackup_No','OnlineBackup_Yes','DeviceProtection_No',
+     'DeviceProtection_Yes','TechSupport_No','TechSupport_Yes','StreamingTV_No','StreamingTV_Yes','StreamingMovies_No',
+     'StreamingMovies_Yes','Contract_Month-to-month','Contract_One year','Contract_Two year','PaperlessBilling_No',
+     'PaperlessBilling_Yes','PaymentMethod_Bank transfer (automatic)','PaymentMethod_Credit card (automatic)','PaymentMethod_Electronic check',
+     'PaymentMethod_Mailed check'])
+
+    # Make predictions using the model
+     predict = model.predict(final_df)
+
+
+     prediction_label = "THIS CUSTOMER WILL CHURN" if predict.item() == "Yes" else "THIS CUSTOMER WILL NOT CHURN"
+
+
+     return prediction_label
+
+     #return predictions
+
+#define the input interface
+
+
+input_interface = []
+
+with gr.Blocks(css=".gradio-container {background-color:silver}") as app:
+    title = gr.Label('VODAFONE CUSTOMER CHURN PREDICTION')
+    img = gr.Image("assets\\VODA.png").style(height= 210 , width= 1250)
+
+ 
+    with gr.Row():
+        gr.Markdown("This application provides predictions on whether a customer will churn or remain with the Company. Please enter the customer's information below and click PREDICT to view the prediction outcome.")
+
+    with gr.Row():
+        with gr.Column(scale=3.5, min_width=500):
+            input_interface = [
+                gr.components.Radio(['male', 'female'], label='What is your Gender?'),
+                gr.components.Number(label="Are you a Seniorcitizen? (No=0 and Yes=1), 55years and above"),
+                gr.components.Radio(['Yes', 'No'], label='Do you have a Partner?'),
+                gr.components.Dropdown(['No', 'Yes'], label='Do you have any Dependents?'),
+                gr.components.Number(label='Length of Tenure (No. of months with Vodafone)'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use Phone Service?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use Multiple Lines?'),
+                gr.components.Radio(['DSL', 'Fiber optic', 'No'], label='Do you use Internet Service?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use Online Security?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use Online Backup?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use Device Protection?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you use the Tech Support?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you Streaming TV?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you Streaming Movies?'),
+                gr.components.Dropdown(['Month-to-month', 'One year', 'Two year'], label='Please what Contract Type do you Subscribe to?'),
+                gr.components.Radio(['Yes', 'No'], label='Do you use Paperless Billing?'),
+                gr.components.Dropdown(['Electronic check', 'Mailed check', 'Bank transfer (automatic)',
+                                        'Credit card (automatic)'], label='What type of Payment Method do you use please?'),
+                gr.components.Number(label="How much is you Monthly Charges?"),
+                gr.components.Number(label="How much is your Total Charges?")
+            ]
+
+    with gr.Row():
+        predict_btn = gr.Button('Predict') 
+        
+ 
+
+# Define the output interfaces
+    output_interface = gr.Label(label="churn", type="label", style="font-weight: bold; font-size: larger; color: red")
+
+    predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)
+
+
+app.launch(share=False)