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app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+
+# Load the model and encoder and scaler
+model = pickle.load(open("model.pkl", "rb"))
+encoder = pickle.load(open("encoder.pkl", "rb"))
+scaler = pickle.load(open("scaler.pkl", "rb"))
+
+# Load the data
+data = pd.read_csv('data.csv')
+
+# Define the input and output interfaces for the Gradio app
+def create_gradio_inputs(data):
+    input_components = []
+    for column in data.columns:
+        if data[column].dtype == 'object' and len(data[column].unique()) > 3:
+            input_components.append(gr.Dropdown(choices=list(data[column].unique()), label=column))
+        elif data[column].dtype == 'object' and len(data[column].unique()) <= 3:
+            input_components.append(gr.Radio(choices=list(data[column].unique()), label=column))
+        elif data[column].dtype in ['int64', 'float64']:
+            if data[column].min() == 1:
+                input_components.append(gr.Slider(minimum=1, maximum=data[column].max(), step=1, label=column))
+            else:
+                input_components.append(gr.Slider(maximum=data[column].max(), step=0.5, label=column))
+    return input_components
+
+input_components = create_gradio_inputs(data)
+
+output_components = [
+    gr.Label(label="Churn Prediction"),
+]
+
+# Convert the input values to a pandas DataFrame with the appropriate column names
+def input_df_creator(gender, SeniorCitizen, Partner, Dependents, tenure,
+       PhoneService, InternetService, OnlineBackup, TechSupport,
+       Contract, PaperlessBilling, PaymentMethod, MonthlyCharges,
+       TotalCharges, StreamingService, SecurityService):
+    input_data = pd.DataFrame({
+        "gender": [gender],
+        "SeniorCitizen": [SeniorCitizen],
+        "Partner": [Partner],
+        "Dependents": [Dependents],
+        "tenure": [int(tenure)],
+        "PhoneService": [PhoneService],
+        "InternetService": [InternetService],
+        "OnlineBackup": [OnlineBackup],
+        "TechSupport": [TechSupport],
+        "Contract": [Contract],
+        "PaperlessBilling": [PaperlessBilling],
+        "PaymentMethod": [PaymentMethod],
+        "StreamingService": [StreamingService],
+        "SecurityService": [SecurityService],
+        "MonthlyCharges": [float(MonthlyCharges)],
+        "TotalCharges": [float(TotalCharges)],
+    }) 
+    return input_data
+
+# Define the function to be called when the Gradio app is run
+def predict_churn(gender, SeniorCitizen, Partner, Dependents, tenure,
+       PhoneService, InternetService, OnlineBackup, TechSupport,
+       Contract, PaperlessBilling, PaymentMethod, MonthlyCharges,
+       TotalCharges, StreamingService, SecurityService):
+    input_df = input_df_creator(gender, SeniorCitizen, Partner, Dependents, tenure,
+       PhoneService, InternetService, OnlineBackup, TechSupport,
+       Contract, PaperlessBilling, PaymentMethod, MonthlyCharges,
+       TotalCharges, StreamingService, SecurityService)
+    
+    # Encode categorical variables
+    cat_cols = data.select_dtypes(include=['object']).columns
+    cat_encoded = encoder.transform(input_df[cat_cols])
+
+    # Scale numerical variables
+    num_cols = data.select_dtypes(include=['int64', 'float64']).columns
+    num_scaled = scaler.transform(input_df[num_cols])
+
+    # joining encoded and scaled columns back together
+    processed_df = pd.concat([num_scaled, cat_encoded], axis=1)
+
+    # Make prediction
+    prediction = model.predict(processed_df)
+    return "Churn" if prediction[0] == 1 else "No Churn"
+
+# Launch the Gradio app
+iface = gr.Interface(predict_churn, inputs=input_components, outputs=output_components)
+iface.launch(inbrowser= True, show_error= True)

encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:425975f1f8685775681e0b1cdd172192f118bf5738245a93e559c3ab3ce5816e
+size 1614

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d69efd028f0febbb979a392353c8481ca60facf77d8123488f9d3be6c12549c
+size 4740256

requirements.txt

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+gradio
+scikit-learn==1.2.1

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82685d10226631ab8fa9d4e3745af304f12a8f8a1333d8931bcd30b412f12fb9
+size 698