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Dockerfile

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+# Use the official slim Python 3.9 image as the base image
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

churn_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f56605d63fc054fec447e537348fa931718e3618c33668d869a920924a28a036
+size 124373

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]
+streamlit

server.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+app = Flask("Customer Churn Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("churn_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@app.get('/')
+def home():
+    return "Welcome to the Customer Churn Prediction API!"
+
+# Define an endpoint to predict churn for a single customer
+@app.post('/v1/customer')
+def predict_churn():
+    # Get JSON data from the request
+    customer_data = request.get_json()
+    
+    # Extract relevant customer features from the input data
+    sample = {
+        'CreditScore': customer_data['CreditScore'],
+        'Geography': customer_data['Geography'],
+        'Age': customer_data['Age'],
+        'Tenure': customer_data['Tenure'],
+        'Balance': customer_data['Balance'],
+        'NumOfProducts': customer_data['NumOfProducts'],
+        'HasCrCard': customer_data['HasCrCard'],
+        'IsActiveMember': customer_data['IsActiveMember'],
+        'EstimatedSalary': customer_data['EstimatedSalary']
+    }
+    
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a churn prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Map prediction result to a human-readable label
+    prediction_label = "churn" if prediction == 1 else "not churn"
+    
+    # Return the prediction as a JSON response
+    return jsonify({'Churn expected?': prediction_label})
+
+# Define an endpoint to predict churn for a batch of customers
+@app.post('/v1/customerbatch')
+def predict_churn_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for the batch data
+    predictions = ['Churn' if x == 1 else "Not Churn" for x in model.predict(input_data.drop("CustomerId",axis=1)).tolist()]
+
+    cust_id_list = input_data.CustomerId.values.tolist()
+    output_dict = dict(zip(cust_id_list, predictions))
+
+    return output_dict
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)