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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("churn_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Customer Churn Prediction App")
+st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
+
+# Collect user input based on dataset columns
+Partner = st.selectbox("Does the customer have a partner?", ["Yes", "No"])
+Dependents = st.selectbox("Does the customer have dependents?", ["Yes", "No"])
+PhoneService = st.selectbox("Does the customer have phone service?", ["Yes", "No"])
+InternetService = st.selectbox("Type of Internet Service", ["DSL", "Fiber optic", "No"])
+Contract = st.selectbox("Type of Contract", ["Month-to-month", "One year", "Two year"])
+PaymentMethod = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
+Tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=12)
+MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
+TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Partner': 1 if Partner == "Yes" else 0,
+    'Dependents': 1 if Dependents == "Yes" else 0,
+    'PhoneService': 1 if PhoneService == "Yes" else 0,
+    'InternetService': InternetService,
+    'Contract': Contract,
+    'PaymentMethod': PaymentMethod,
+    'Tenure': Tenure,
+    'MonthlyCharges': MonthlyCharges,
+    'TotalCharges': TotalCharges
+}])
+
+# Set classification threshold
+classification_threshold = 0.5
+
+# 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"Prediction: The customer is likely to **{result}**.")
+    st.write(f"Churn Probability: {prediction_proba:.2f}")

churn_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1890379bd8a492620e028476e5e1c8f63cffabc25741b518f80a8c2ef11c919b
+size 340069

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