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Dockerfile

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+FROM python:3.10-slim
+
+# Create non-root user (required by Hugging Face)
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY --chown=user . .
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import pandas as pd
+import os
+from joblib import load
+
+# --- Model Loading ---
+@st.cache_resource
+def load_model():
+    model_path = "customer_churn_pipeline.joblib"  # same folder
+    if not os.path.exists(model_path):
+        st.error(f"Model file not found: {model_path}")
+        st.stop()
+    try:
+        pipeline = load(model_path)
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        st.stop()
+    return pipeline
+
+pipeline = load_model()
+
+# --- Streamlit UI ---
+st.title("Credit Card Customer Churn Prediction")
+st.write("Adjust the input values below to predict whether a customer will churn:")
+
+# Numeric inputs
+customer_age = st.slider("Customer Age", 18, 100, 30)
+credit_limit = st.slider("Credit Limit", 0, 100000, 5000, step=100)
+
+# Categorical input
+gender = st.selectbox("Gender", ["Female", "Male"])
+
+# Build input dataframe
+input_data = pd.DataFrame({
+    'Customer_Age': [customer_age],
+    'Credit_Limit': [credit_limit],
+    'Gender': [gender]
+})
+
+# Predict button
+if st.button("Predict Churn"):
+    prediction = pipeline.predict(input_data)[0]
+    probability = pipeline.predict_proba(input_data)[0][1]
+
+    if prediction == 1:
+        st.warning(f"⚠️ Customer is likely to churn! Probability: {probability:.2%}")
+    else:
+        st.success(f"✅ Customer is not likely to churn. Probability: {probability:.2%}")

customer_churn_pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f04518cc0cd086023ea8fab1d2b8663c50293cfa6db17a201e68cb79cd34220f
+size 14835074

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn==1.6.1
+matplotlib
+seaborn
+joblib==1.4.2