Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,76 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import pandas as pd
+
+def load_model():
+    model_path = "model/model.joblib"
+    model_dict = joblib.load(model_path)
+    return model_dict['model']  
+
+def main():
+    st.set_page_config(page_title="Customer Churn Prediction App",layout="wide")
+
+    st.title(" Customer Churn Prediction")
+    st.write("Predict whether a customer is likely to **churn or stay**, "
+        "based on behavioral and transaction-level features."
+        )
+    st.header("Input Customer Features")
+
+    Frequency = st.number_input("Frequency", min_value=0.0, value=10.0)
+    Monetary = st.number_input("Monetary", min_value=0.0, value=10000.0)
+    Total_Products_Purchased = st.number_input("Total Products Purchased", min_value=0, value=20)
+    Unique_Products_Purchased = st.number_input("Unique Products Purchased", min_value=0, value=10)
+    Avg_Transaction_Value = st.number_input("Average Transaction Value", min_value=0.0, value=50.0)
+    Customer_Tenure_Days = st.number_input("Customer Tenure (Days)", min_value=0, value=365)
+    Revenue_Per_Product = st.number_input("Revenue Per Product", min_value=0.0, value=25.0)
+    Avg_Days_Between_Purchases = st.number_input("Avg Days Between Purchases", min_value=0.0, value=15.0)
+    Purchase_Regularity = st.number_input("Purchase Regularity", min_value=0.0, value=0.5)
+    Top_Product_Concentration = st.number_input("Top Product Concentration", min_value=0.0, max_value=1.0, value=0.4)
+    Category_Diversity = st.number_input("Category Diversity", min_value=0.0, value=3.0)
+    Quarterly_Spending_Trend = st.number_input("Quarterly Spending Trend", value=0.1)
+    Price_Sensitivity = st.number_input("Price Sensitivity", min_value=0.0, value=0.3)
+    Spending_Trend = st.number_input("Spending Trend", value=0.05)
+    Cancellation_Rate = st.number_input("Cancellation Rate", min_value=0.0, max_value=1.0, value=0.1)
+    Is_UK = st.selectbox("Is UK Customer?", [0, 1])
+
+    input_data = pd.DataFrame(
+        [{
+            "Frequency": Frequency,
+            "Monetary": Monetary,
+            "Total_Products_Purchased": Total_Products_Purchased,
+            "Unique_Products_Purchased": Unique_Products_Purchased,
+            "Avg_Transaction_Value": Avg_Transaction_Value,
+            "Customer_Tenure_Days": Customer_Tenure_Days,
+            "Revenue_Per_Product": Revenue_Per_Product,
+            "Avg_Days_Between_Purchases": Avg_Days_Between_Purchases,
+            "Purchase_Regularity": Purchase_Regularity,
+            "Top_Product_Concentration": Top_Product_Concentration,
+            "Category_Diversity": Category_Diversity,
+            "Quarterly_Spending_Trend": Quarterly_Spending_Trend,
+            "Price_Sensitivity": Price_Sensitivity,
+            "Spending_Trend": Spending_Trend,
+            "Cancellation_Rate": Cancellation_Rate,
+            "Is_UK": Is_UK
+        }]
+    )
+
+
+    if st.button("Predict Churn"):
+        model = load_model()
+
+        prediction = model.predict(input_data)[0]
+
+        churn_label = "Churn" if prediction == 1 else "Not Churn"
+
+        st.subheader("Prediction Result")
+        st.success(f"**Customer Status:** {churn_label}")
+
+        if hasattr(model, "predict_proba"):
+            prob = model.predict_proba(input_data)[0][1]
+            st.info(f"Churn Probability: **{prob:.2%}**")
+
+    else:
+        st.info("Enter feature values and click **Predict Churn**")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()