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app_demo.py

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+# Streamlit app supporting RF and XGB models
+import streamlit as st
+import pandas as pd, os, joblib
+
+st.title('Tourism Package Prediction - Demo (RF & XGB)')
+
+MODEL_RF = '/data/output/best_model.joblib'
+MODEL_XGB = '/data/output/best_xgb.joblib'
+
+models = {}
+if os.path.exists(MODEL_RF):
+    models['RandomForest'] = joblib.load(MODEL_RF)
+if os.path.exists(MODEL_XGB):
+    models['XGBoost'] = joblib.load(MODEL_XGB)
+
+if not models:
+    st.error('No model artifacts found. Place best_model.joblib or best_xgb.joblib in /mnt/data/output/')
+    st.stop()
+
+choice = st.selectbox('Choose model', list(models.keys()))
+model = models[choice]
+
+st.write('Selected model:', choice)
+st.write('Upload a CSV (features only) to predict:')
+uploaded = st.file_uploader('Input CSV', type=['csv'])
+if uploaded:
+    df = pd.read_csv(uploaded)
+    st.write('Preview:', df.head())
+    preds = model.predict(df)
+    st.write('Predictions:', preds)