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Dockerfile

@@ -1,14 +1,16 @@
-# Use a minimal base image with Python 3.9 installed
 FROM python:3.9-slim
 
-# Set the working directory inside the container to /app
+# Set the working directory inside the container
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from the current directory to the container's working directory
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
-RUN pip3 install -r requirements.txt
+# 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 run the Streamlit app on port 8501 and make it accessible externally
-CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+# 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:churn_predictor_api"]

app.py

@@ -1,49 +1,70 @@
-
-import streamlit as st
-import pandas as pd
 import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+churn_predictor_api = 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
+@churn_predictor_api.get('/')
+def home():
+    return "Welcome to the Customer Churn Prediction API!"
+
+# Define an endpoint to predict churn for a single customer
+@churn_predictor_api.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({'Prediction': prediction_label})
+
+# Define an endpoint to predict churn for a batch of customers
+@churn_predictor_api.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 and convert raw predictions into a readable format
+    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
 
-# Load the trained model
-def load_model():
-    return joblib.load("churn_prediction_model_v1_0.joblib")
-
-model = load_model()
-
-# Streamlit UI for Customer Churn Prediction
-st.title("Customer Churn Prediction App")
-st.write("The Customer Churn Prediction App is an internal tool for bank staff that predicts whether customers are at risk of churning based on their details.")
-st.write("Kindly enter the customer details to check whether they are likely to churn.")
-
-# Collect user input
-CreditScore = st.number_input("Credit Score (customer's credit score)", min_value=300, max_value=900, value=650)
-Geography = st.selectbox("Geography (country where the customer resides)", ["France", "Germany", "Spain"])
-Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
-Tenure = st.number_input("Tenure (number of years the customer has been with the bank)", value=12)
-Balance = st.number_input("Account Balance (customer’s account balance)", min_value=0.0, value=10000.0)
-NumOfProducts = st.number_input("Number of Products (number of products the customer has with the bank)", min_value=1, value=1)
-HasCrCard = st.selectbox("Has Credit Card?", ["Yes", "No"])
-IsActiveMember = st.selectbox("Is Active Member?", ["Yes", "No"])
-EstimatedSalary = st.number_input("Estimated Salary (customer’s estimated salary)", min_value=0.0, value=50000.0)
-
-# Convert categorical inputs to match model training
-input_data = pd.DataFrame([{
-    'CreditScore': CreditScore,
-    'Geography': Geography,
-    'Age': Age,
-    'Tenure': Tenure,
-    'Balance': Balance,
-    'NumOfProducts': NumOfProducts,
-    'HasCrCard': 1 if HasCrCard == "Yes" else 0,
-    'IsActiveMember': 1 if IsActiveMember == "Yes" else 0,
-    'EstimatedSalary': EstimatedSalary
-}])
-
-# Set the classification threshold
-classification_threshold = 0.45
-
-# Predict button
-if st.button("Predict"):
-    prediction_proba = model.predict_proba(input_data)[0, 1]
-    prediction = (prediction_proba >= classification_threshold).astype(int)
-    result = "churn" if prediction == 1 else "not churn"
-    st.write(f"Based on the information provided, the customer is likely to {result}.")
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -3,4 +3,9 @@ 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==1.43.2