App2.py

app.py

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+# Importing required Libraries
+from IPython.utils.py3compat import encode
+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+
+
+# Loading Machine Learning Objects
+def load_saved_objets(filepath='ML_items'):
+    "Function to load saved objects"
+
+    with open(filepath, 'rb') as file:
+        loaded_object = pickle.load(file)
+    
+    return loaded_object
+
+# Instantiating ML_items
+loaded_object = load_saved_objets()
+pipeline_of_my_app = loaded_object["pipeline"]
+num_cols = loaded_object['numeric_columns']
+cat_cols = loaded_object['categorical_columns']
+encoder_categories = loaded_object["encoder_categories"]
+
+# Main function to collect the inputs process them and outpuT the predicition
+def predict_churn(
+    TotalCharges,
+    MonthlyCharges,
+    tenure, 
+    StreamingTV,
+    PaperlessBilling,
+    DeviceProtection,
+    TechSupport,
+    InternetService,
+    OnlineSecurity,
+    StreamingMovies,
+    PaymentMethod,
+    Dependents,
+    Partner,
+    OnlineBackup,
+    gender,
+    SeniorCitizen,
+    MultipleLines,
+    Contract,
+    PhoneService,
+):
+    
+    df = pd.DataFrame(
+        [
+            [
+                TotalCharges,
+                MonthlyCharges,
+                tenure, 
+                StreamingTV,
+                PaperlessBilling,
+                DeviceProtection,
+                TechSupport,
+                InternetService,
+                OnlineSecurity,
+                StreamingMovies,
+                PaymentMethod,
+                Dependents,
+                Partner
+                OnlineBackup,
+                gender,
+                SeniorCitizen,
+                MultipleLines,
+                Contract,
+                PhoneService,
+            ]
+        ],  
+        columns= num_cols + cat_cols,
+    ).replace("", np.nan)
+    
+    df[cat_cols] = df[cat_cols].astype("object")
+    
+    # Passing data to pipeline to make prediction
+    output = pipeline_of_my_app.predict(df)
+    
+    # Labelling Model output
+    if output == 0:
+        model_output = "No"
+    else:
+        model_output = "Yes"
+
+    return model_output
+ 
+
+# Setting up app interface and data inputs
+inputs = []
+
+with gr.Blocks() as demo:
+    
+    # Setting Titles for App
+    gr.Markdown("<h2 style='text-align: center;'> Customer Churn Prediction App </h2> ", unsafe_allow_html=True)
+    gr.Markdown("<h6 style='text-align: center;'> (Fill in the details below and click on PREDICT button to make a prediction for Customer Churn) </h6> ", unsafe_allow_html=True)   
+    
+    with gr.Column(): #main frame 
+        
+        with gr.Row(): #col 1 : for num features
+
+            for i in num_cols:
+                inputs.append(gr.Number(label=f"Input {i} "))
+        
+        with gr.Row(): #col 2 : for cat features
+
+            for (lab, choices) in zip(cat_cols, encoder_categories):
+                inputs.append(gr.inputs.Dropdown(
+                choices=choices.tolist(),
+                type="value",
+                label=f"Select {lab}",
+                default=choices.tolist()[0],))
+    # Setting up preediction Button
+    with gr.Row():
+        make_prediction = gr.Button("Predict")
+    
+    # Setting up prediction output row
+    with gr.Row():
+        output_prediction = gr.Text(label="Will Customer Churn?")
+    make_prediction.click(predict_churn, inputs, output_prediction)
+
+# Launching app
+demo.launch(
+    share=True,
+    # debug=True
+)