upload app.py

app.py.py

@@ -0,0 +1,153 @@
+import gradio as gr
+import pickle
+import pandas as pd
+import numpy as np
+import joblib
+from PIL import Image
+
+
+num_imputer = joblib.load('numerical_imputer.joblib')
+cat_imputer = joblib.load('categorical_imputer.joblib')
+encoder = joblib.load('encoder.joblib')
+scaler = joblib.load('scaler.joblib')
+model = joblib.load('Final_model.joblib')
+
+
+# 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]
+
+ })
+
+# Selecting categorical and numerical columns separately
+     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']
+
+    # Apply the imputers on the input data
+     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
+     predictions = model.predict(final_df)
+
+     # Make predictions using the model
+     #predictions = model.predict(final_df)
+
+     # Convert the numpy array to an integer
+     #prediction_label = int(predictions.item())
+
+     prediction_label = "Beware!!! This customer is likely to Churn" if predictions.item() == "Yes" else "This customer is Not likely churn"
+
+
+     return prediction_label
+
+     #return predictions
+
+input_interface=[]
+with gr.Blocks(css=".gradio-container {background-color: powderblue}") as app:
+    img = gr.Image("C:/Users/user/Documents/AZUBI PROGRAM/CAREER ACELERATOR/LP4-buiding an app/Gradio/lp4_part2-1/telecom churn.png").style(height='13')
+
+    Title=gr.Label('CUSTOMER CHURN PREDICTION APP')
+
+    with gr.Row():
+        Title
+    with gr.Row():
+        img
+
+#with gr.Blocks() as app:
+#    with gr.Blocks(css=".gradio-interface-container {background-color: powderblue}"):
+        #with gr.Row():
+        #    gr.Label('Customer Churn Prediction Model')
+    with gr.Row():
+        gr.Markdown("This app predicts whether a customer will leave your company or not. Enter the details of the customer below to see the result")
+
+    #with gr.Row():
+        #gr.Label('This app predicts whether a customer will leave your company or not. Enter the details of the customer below to see the result')
+
+
+    with gr.Row():
+        with gr.Column(scale=3, min_width=600):
+
+            input_interface = [
+                gr.components.Radio(['male', 'female'], label='Select your gender'),
+                gr.components.Number(label="Are you a Seniorcitizen; No=0 and Yes=1"),
+                gr.components.Radio(['Yes', 'No'], label='Do you have Partner'),
+                gr.components.Dropdown(['No', 'Yes'], label='Do you have any Dependents? '),
+                gr.components.Number(label='Lenght of tenure (no. of months with Telco)'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have PhoneService? '),
+                gr.components.Radio(['No', 'Yes'], label='Do you have MultipleLines'),
+                gr.components.Radio(['DSL', 'Fiber optic', 'No'], label='Do you have InternetService'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have OnlineSecurity?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have OnlineBackup?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have DeviceProtection?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have TechSupport?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have StreamingTV?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have StreamingMovies?'),
+                gr.components.Dropdown(['Month-to-month', 'One year', 'Two year'], label='which Contract do you use?'),
+                gr.components.Radio(['Yes', 'No'], label='Do you prefer PaperlessBilling?'),
+                gr.components.Dropdown(['Electronic check', 'Mailed check', 'Bank transfer (automatic)',
+                                        'Credit card (automatic)'], label='Which PaymentMethod do you prefer?'),
+                gr.components.Number(label="Enter monthly charges"),
+                gr.components.Number(label="Enter total charges")
+            ]
+
+    with gr.Row():
+        submit_btn = gr.Button('Submit')
+
+        predict_btn = gr.Button('Predict')
+
+# Define the output interfaces
+    output_interface = gr.Label(label="churn")
+
+    predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)
+
+
+
+
+app.launch(share=True)