import streamlit as st import pandas as pd from huggingface_hub import hf_hub_download import joblib # import re # Removed, as snake_case conversion is unnecessary # ============================= # Set Streamlit Page Configuration (for a clean look and wide mode) # ============================= st.set_page_config( page_title="Tourism Package Predictor", page_icon="✈️", layout="wide", initial_sidebar_state="expanded" # Keep sidebar for instructions/info ) # ============================= # Streamlit Custom Styling (Dark Theme & Colors) # ============================= # This CSS block is injected to customize the appearance, # enhancing the dark theme with vibrant prediction colors. st.markdown(""" """, unsafe_allow_html=True) # ============================= # Load the trained model # ============================= # Use a spinner while loading with st.spinner('Loading model...'): try: # Replace with your repo id where model is uploaded model_path = hf_hub_download( repo_id="ShaksML/tourism", filename="top_tourism_model_v1.joblib", repo_type="model" ) model = joblib.load(model_path) st.sidebar.success("Model loaded successfully! 🎉") except Exception as e: st.error(f"Error loading model: {e}") st.stop() # ============================= # Streamlit UI - Title and Description # ============================= st.title("✈️ Tourism Package Prediction App") # Add a concise description to the sidebar st.sidebar.header("About the App") st.sidebar.markdown(""" This application uses a pre-trained machine learning model to predict whether a customer is likely to **purchase a tourism package** based on their personal and behavioral data. --- **Instructions:** 1. Enter the customer details using the input fields below. 2. Click the **'Predict Purchase'** button. 3. The result will appear at the bottom. """) st.markdown("### Enter Customer Details") st.markdown(""" Please enter the customer's personal, financial, and interaction details across the two sections below to get a prediction. """) # ----------------------------- # User Inputs - Layout with st.columns # ----------------------------- # Use st.container for a nice grouping of inputs with st.container(border=True): col1, col2 = st.columns(2) # --- Column 1: Personal & General Details --- with col1: st.markdown("#### 👤 Personal & General") age = st.number_input("Age", min_value=18, max_value=100, value=30, key='age') gender = st.selectbox("Gender", ["Male", "Female"], key='gender') marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"], key='marital_status') occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"], key='occupation') designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"], key='designation') passport = st.selectbox("Has Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No", key='passport') own_car = st.selectbox("Owns a Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No", key='own_car') # --- Column 2: Package & Financial Details --- with col2: st.markdown("#### 💰 Financial & Package") monthly_income = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=20000, key='monthly_income') city_tier = st.selectbox("City Tier", [1, 2, 3], key='city_tier') product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"], key='product_pitched') preferred_star = st.selectbox("Preferred Property Star Rating", [1, 2, 3, 4, 5], key='preferred_star') duration_pitch = st.number_input("Duration of Pitch (mins)", min_value=0, max_value=60, value=10, key='duration_pitch') type_of_contact = st.selectbox("Type of Contact", ["Company Invited", "Self Enquiry"], key='typeof_contact') pitch_score = st.slider("Pitch Satisfaction Score (1=Low, 5=High)", 1, 5, 3, key='pitch_score') # --- Column for counts (Spans across full width) --- st.markdown("#### 👨‍👩‍👧‍👦 Travel Details") col3, col4, col5 = st.columns(3) with col3: num_persons = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2, key='num_persons') with col4: num_children = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0, key='num_children') with col5: num_trips = st.number_input("Number of Previous Trips", min_value=0, max_value=50, value=5, key='num_trips') st.markdown("#### 📞 Follow-up Details") num_followups = st.number_input("Number of Follow-ups Made", min_value=0, max_value=10, value=2, key='num_followups') # Assemble input data into DataFrame (using PascalCase names as required by the expected features list) input_data = pd.DataFrame([{ "Age": age, "CityTier": city_tier, "DurationOfPitch": duration_pitch, "NumberOfPersonVisiting": num_persons, "NumberOfFollowups": num_followups, "PreferredPropertyStar": preferred_star, "NumberOfTrips": num_trips, "Passport": passport, "PitchSatisfactionScore": pitch_score, "OwnCar": own_car, "NumberOfChildrenVisiting": num_children, "MonthlyIncome": monthly_income, "TypeofContact": type_of_contact, # Note: using type_of_contact from st.selectbox "Occupation": occupation, "Gender": gender, "ProductPitched": product_pitched, "MaritalStatus": marital_status, "Designation": designation }]) # ----------------------------- # Prediction Button and Logic # ----------------------------- # Center the button using columns st.markdown("---") pred_col1, pred_col2, pred_col3 = st.columns([1, 1, 1]) with pred_col2: if st.button("Predict Purchase", use_container_width=True): try: # --- FIX: Apply One-Hot Encoding and Column Alignment (UNCHANGED FUNCTIONALITY) --- # 1. Apply One-Hot Encoding and ensure original categorical columns are kept. input_dummies = pd.get_dummies(input_data, drop_first=False) # Start with the dummified features (which include the numeric features) input_data_processed = input_dummies.copy() # Manually add the original categorical columns back from input_data categorical_cols_to_restore = [ 'Designation', 'ProductPitched', 'MaritalStatus', 'TypeofContact', 'Gender', 'Occupation' ] for col in categorical_cols_to_restore: # Add the original column to the processed data input_data_processed[col] = input_data[col] # 2. Define the full list of features the model was trained on expected_features = [ # Numeric/Ordinal Features (12) 'Age', 'CityTier', 'DurationOfPitch', 'NumberOfPersonVisiting', 'NumberOfFollowups', 'PreferredPropertyStar', 'NumberOfTrips', 'Passport', 'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'MonthlyIncome', # Original Categorical Features (6, which the model is demanding) 'Designation', 'ProductPitched', 'MaritalStatus', 'TypeofContact', 'Gender', 'Occupation', # Categorical Features (21 dummified columns) 'TypeofContact_Company Invited', 'TypeofContact_Self Enquiry', 'Occupation_Salaried', 'Occupation_Small Business', 'Occupation_Large Business', 'Occupation_Free Lancer', 'Gender_Male', 'Gender_Female', 'ProductPitched_Basic', 'ProductPitched_Standard', 'ProductPitched_Deluxe', 'ProductPitched_Super Deluxe', 'ProductPitched_King', 'MaritalStatus_Single', 'MaritalStatus_Married', 'MaritalStatus_Divorced', 'Designation_Executive', 'Designation_Manager', 'Designation_Senior Manager', 'Designation_AVP', 'Designation_VP' ] # 3. Add any missing dummified columns (set to 0) to ensure feature completeness for col in expected_features: if col not in input_data_processed.columns: input_data_processed[col] = 0 # 4. Reorder columns to match the training order exactly input_data_final = input_data_processed[expected_features] # Predict using the properly structured DataFrame prediction = model.predict(input_data_final)[0] st.markdown("---") st.subheader("Prediction Result:") if prediction == 1: st.balloons() st.success(f"🎉 The model predicts: **Will Purchase Package** (Prediction: {prediction})") else: st.error(f"😔 The model predicts: **Will Not Purchase Package** (Prediction: {prediction})") # except Exception as e: st.exception(f"An error occurred during prediction: {e}")