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Dockerfile

@@ -1,18 +1,16 @@
-FROM python:3.9-slim
-# FROM python:3.13-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.13-slim
 
-
-# Set the working directory inside the container
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-# Copy all files from the current directory to the container's working directory
+# Copy all files from the current directory on the host to the container's /app 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
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -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"]
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -1,70 +1,57 @@
-import joblib
+import requests
+import streamlit as st
 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
-
-# Run the Flask app in debug mode
-if __name__ == '__main__':
-    app.run(debug=True)
+st.title("Customer Churn Prediction")
+
+# Batch Prediction
+st.subheader("Online Prediction")
+
+# Input fields for customer data
+CustomerID = st.number_input("Customer ID", min_value=10000000, max_value=99999999)
+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)
+
+customer_data = {
+    '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
+}
+
+username_ns = 'VrundavGamit-containerization_case_study'
+
+if st.button("Predict", type='primary'):
+    response = requests.post("https://{username_ns}.hf.space/v1/customer", json=customer_data)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        churn_prediction = result["Prediction"]  # Extract only the value
+        st.write(f"Based on the information provided, the customer with ID {CustomerID} is likely to {churn_prediction}.")
+    else:
+        st.error("Error in API request")
+
+# Batch Prediction
+st.subheader("Batch Prediction")
+
+file = st.file_uploader("Upload CSV file", type=["csv"])
+if file is not None:
+    if st.button("Predict for Batch", type='primary'):
+        response = requests.post("https://{username_ns}.hf.space/v1/customerbatch", files={"file": file})    # enter user name and space name before running the cell
+        if response.status_code == 200:
+            result = response.json()
+            st.header("Batch Prediction Results")
+            st.write(result)
+        else:
+            st.error("Error in API request")

requirements.txt

@@ -1,11 +1,3 @@
-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==1.43.2
+pandas
+requests
+streamlit