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Dockerfile

@@ -1,5 +1,5 @@
 # Use a minimal base image with Python 3.9 installed
-FROM python:3.9
+FROM python:3.9-slim
 
 # Set the working directory inside the container to /app
 WORKDIR /app

app.py

@@ -9,12 +9,12 @@ import joblib
 st.set_page_config(page_title="Tourism Package Prediction", page_icon="🌍", layout="centered")
 
 st.title("🌍 Tourism Package Prediction App")
-st.write(
+st.markdown(
     """
-    This application predicts whether a customer is likely to **opt for a tourism package**
-    based on their profile and preferences.
-    Please provide the customer details below:
-    """)
+    Provide customer details below to predict whether they are likely to
+    **opt for a tourism package**.
+    """
+)
 
 # ================================
 # Load Model from Hugging Face Hub
@@ -29,41 +29,42 @@ def load_model():
 
 model = load_model()
 
-# Mapping for City Tier
 city_tier_map = {"Tier 1": 1, "Tier 2": 2, "Tier 3": 3}
 
 # ================================
-# Sidebar Input Form (Improved Layout)
+# Tabs for Input Sections
 # ================================
-st.sidebar.header("📝 Enter Customer Details")
-
-# --------- 1. Personal Information ---------
-with st.sidebar.expander("👤 Personal Information", expanded=True):
+tabs = st.tabs([
+    "👤 Personal Information",
+    "💰 Lifestyle & Financial",
+    "✈️ Travel Preferences",
+    "👨‍👩‍👧 Family & Trips",
+    "📞 Sales Interaction"
+])
+
+with tabs[0]:
     age = st.number_input("Age of Customer", min_value=18, max_value=100, value=30)
     gender = st.selectbox("Gender", ["Female", "Male"])
     marital_status = st.selectbox("Marital Status", ["Single", "Divorced", "Married", "Unmarried"])
     occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
     designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
     city_tier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
-# --------- 2. Lifestyle & Financial ---------
-with st.sidebar.expander("💰 Lifestyle & Financial", expanded=True):
+
+with tabs[1]:
     monthly_income = st.number_input("Monthly Income", min_value=100, max_value=200000, value=10000)
-    own_car = st.radio("Owns a Car?", ["Yes", "No"])
-    passport = st.radio("Has Passport?", ["Yes", "No"])
+    own_car = st.radio("Owns a Car?", ["Yes", "No"], horizontal=True)
+    passport = st.radio("Has Passport?", ["Yes", "No"], horizontal=True)
 
-# --------- 3. Travel Preferences ---------
-with st.sidebar.expander("✈️ Travel Preferences", expanded=False):
+with tabs[2]:
     product_pitched = st.selectbox("Product Pitched", ["Deluxe", "Basic", "Standard", "Super Deluxe", "King"])
     preferred_property_star = st.selectbox("Preferred Property Star", [3, 4, 5])
 
-# --------- 4. Trip & Family Details ---------
-with st.sidebar.expander("👨‍👩‍👧 Family & Trips", expanded=False):
+with tabs[3]:
     num_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=5, value=1)
     num_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=3, value=0)
     num_trips = st.number_input("Number of Trips", min_value=1, max_value=22, value=3)
 
-# --------- 5. Sales Interaction ---------
-with st.sidebar.expander("📞 Sales Interaction", expanded=False):
+with tabs[4]:
     type_of_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
     duration_of_pitch = st.number_input("Pitch Duration (minutes)", min_value=0, max_value=150, value=30)
     num_followups = st.number_input("Number of Followups", min_value=1, max_value=6, value=1)
@@ -96,18 +97,16 @@ input_data = pd.DataFrame([{
 # ================================
 # Prediction
 # ================================
-
-# Classification threshold used during training
 CLASSIFICATION_THRESHOLD = 0.45
 
-if st.button("🔮 Predict"):
-    # Get probability of "Product Taken" (class = 1)
-    proba = model.predict_proba(input_data)[0][1]
+if st.button("🔮 Predict", use_container_width=True):
+    proba = float(model.predict_proba(input_data)[0][1])
     prediction = 1 if proba >= CLASSIFICATION_THRESHOLD else 0
 
     result = "✅ Package Opted" if prediction == 1 else "❌ Package Not Opted"
-    confidence = round(proba * 100, 2)
+    confidence = f"{proba * 100:.2f}"
 
+    st.markdown("---")
     st.subheader("📊 Prediction Result")
     st.success(f"**{result}** with {confidence}% confidence")