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Dockerfile

@@ -1,20 +1,14 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -0,0 +1,49 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load the trained model
+def load_model():
+    return joblib.load("/content/drive/MyDrive/Deployment/deployment_files/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}.")

churn_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbfe0e0f47d3294eab34fe663bdf36d11f18930601e6258872ce4edf0b881ac4
+size 302538

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2