Update app.py

app.py

@@ -1,7 +1,75 @@
 import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
 
-st.title("Tourism Package Prediction App ")
+# -------------------------------------------------------
+# DOWNLOAD AND LOAD THE TOURISM MODEL FROM HUGGINGFACE
+# -------------------------------------------------------
+model_path = hf_hub_download(
+    repo_id="Amitgupta2982/Tourism-Package-Model",
+    filename="tourism_xgb_best_model_v1.joblib"
+)
+model = joblib.load(model_path)
 
-st.write("Your Streamlit Space is running successfully!")
+# -------------------------------------------------------
+# STREAMLIT USER INTERFACE
+# -------------------------------------------------------
+st.title("Tourism Package Purchase Prediction App")
 
-st.success("Model + Pipeline integrated ")
+st.write("""
+This interactive application predicts whether a customer is likely to purchase a tourism package.
+
+Please enter the customer's demographic, income, and interaction details below to generate the prediction.
+""")
+
+# -------------------------------------------------------
+# USER INPUT FIELDS
+# -------------------------------------------------------
+Age = st.number_input("Age", min_value=18, max_value=80, value=35)
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+DurationOfPitch = st.number_input("Duration of Pitch (Minutes)", min_value=0, max_value=50, value=10)
+Gender = st.selectbox("Gender", ["Male", "Female"])
+NumberOfPersonVisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2)
+NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=1)
+PreferredPropertyStar = st.selectbox("Preferred Hotel Star Rating", [3, 4, 5])
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+Passport = st.selectbox("Passport Available?", [0, 1])
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+OwnCar = st.selectbox("Own Car?", [0, 1])
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
+Designation = st.selectbox(
+    "Customer Designation",
+    ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
+)
+MonthlyIncome = st.number_input("Monthly Income (in local currency)", min_value=3000, max_value=300000, value=50000)
+
+# -------------------------------------------------------
+# ASSEMBLE INPUT FOR THE MODEL
+# -------------------------------------------------------
+input_data = pd.DataFrame([{
+    "Age": Age,
+    "CityTier": CityTier,
+    "DurationOfPitch": DurationOfPitch,
+    "Gender": Gender,
+    "NumberOfPersonVisiting": NumberOfPersonVisiting,
+    "NumberOfFollowups": NumberOfFollowups,
+    "PreferredPropertyStar": PreferredPropertyStar,
+    "MaritalStatus": MaritalStatus,
+    "Passport": Passport,
+    "PitchSatisfactionScore": PitchSatisfactionScore,
+    "OwnCar": OwnCar,
+    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+    "Designation": Designation,
+    "MonthlyIncome": MonthlyIncome
+}])
+
+# -------------------------------------------------------
+# PREDICTION BUTTON
+# -------------------------------------------------------
+if st.button("Predict Tourism Package Purchase"):
+    prediction = model.predict(input_data)[0]
+    result = "LIKELY to Purchase" if prediction == 1 else "NOT Likely to Purchase"
+
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")