Upload folder using huggingface_hub

app.py

@@ -1,84 +1,94 @@
 import streamlit as st
 import pandas as pd
 import joblib
-import os
 from huggingface_hub import hf_hub_download
 
-# 1. Configuration - Matching your train.py setup
-REPO_ID = "SagarAtHf/tourismpackagepredict-model"
+# 1. Load Model from Hub
+REPO_ID = "SagarAtHf/wellness-tourism-model-hub"
 FILENAME = "productionmodel.joblib"
 
-@st.cache_resource # This ensures the model only downloads once, not on every click
+@st.cache_resource
 def load_model():
-    try:
-        # Pulling the model from the Model Hub
-        model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
-        return joblib.load(model_path)
-    except Exception as e:
-        st.error(f"Error loading model from Hub: {e}")
-        return None
+    model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+    return joblib.load(model_path)
 
-# Load the model
 model = load_model()
 
-# 2. UI Header
-st.title("🌴 Wellness Tourism Package Predictor")
-st.markdown("Enter customer details below to predict the likelihood of a package purchase.")
+st.set_page_config(page_title="Tourism Predictor", layout="wide")
+st.title("🌴 Full Feature Wellness Tourism Predictor")
+st.write("Please fill in all 19 parameters to get an accurate prediction.")
 
-# 3. User Input Form
+# 2. Complete Form with All 19 Features
 with st.form("prediction_form"):
-    col1, col2 = st.columns(2)
+    # Using 4 columns to fit all 19 features neatly
+    c1, c2, c3, c4 = st.columns(4)
     
-    with col1:
-        age = st.number_input("Age", min_value=18, max_value=100, value=30)
+    with c1:
+        age = st.number_input("Age", 18, 100, 30)
+        type_of_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
         city_tier = st.selectbox("City Tier", [1, 2, 3])
+        duration_pitch = st.number_input("Duration of Pitch (mins)", 0, 120, 15)
         occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+
+    with c2:
         gender = st.selectbox("Gender", ["Male", "Female"])
-        duration = st.number_input("Duration of Pitch", value=15)
-        
-    with col2:
+        num_person = st.number_input("Number of Persons Visiting", 1, 10, 2)
+        num_followups = st.number_input("Number of Follow-ups", 1, 10, 3)
+        product_pitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+        prop_stars = st.slider("Preferred Property Star", 3, 5, 3)
+
+    with c3:
         marital_status = st.selectbox("Marital Status", ["Married", "Unmarried", "Divorced"])
-        designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
-        product_pitched = st.selectbox("Product Pitched", ["Deluxe", "Basic", "Standard", "Super Deluxe", "King"])
-        monthly_income = st.number_input("Monthly Income", value=20000)
+        num_trips = st.number_input("Number of Trips", 1, 20, 1)
         passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x==1 else "No")
+        pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+        own_car = st.selectbox("Owns a Car?", [0, 1], format_func=lambda x: "Yes" if x==1 else "No")
 
-    # Additional features required by the model (using averages/defaults)
-    submit = st.form_submit_button("Predict Probability")
+    with c4:
+        num_children = st.number_input("Number of Children", 0, 5, 0)
+        designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+        monthly_income = st.number_input("Monthly Income", value=25000)
 
-# 4. Prediction Logic
-if submit and model:
-    # Prepare input dataframe with exact column names from training
-    input_data = pd.DataFrame({
-        'Age': [age],
-        'CityTier': [city_tier],
-        'DurationOfPitch': [duration],
-        'Occupation': [occupation],
-        'Gender': [gender],
-        'NumberOfPersonVisiting': [2], # Defaulting common values
-        'NumberOfFollowups': [3],
-        'ProductPitched': [product_pitched],
-        'PreferredPropertyStar': [3],
-        'MaritalStatus': [marital_status],
-        'NumberOfTrips': [1],
-        'Passport': [passport],
-        'PitchSatisfactionScore': [3],
-        'OwnCar': [1],
-        'NumberOfChildrenVisiting': [0],
-        'Designation': [designation],
-        'MonthlyIncome': [monthly_income],
-        'TypeofContact': ["Self Enquiry"]
-    })
+    submit = st.form_submit_button("Generate Prediction")
 
-    # Get probability from the model
-    # Note: We use the threshold 0.45 you defined in your local tests
-    prob = model.predict_proba(input_data)[0][1]
-    prediction = 1 if prob >= 0.45 else 0
+# 3. Prediction Logic
+if submit:
+    # IMPORTANT: Dictionary keys must match the EXACT column names used during training
+    data = {
+        "Age": age,
+        "TypeofContact": type_of_contact,
+        "CityTier": city_tier,
+        "DurationOfPitch": duration_pitch,
+        "Occupation": occupation,
+        "Gender": gender,
+        "NumberOfPersonVisiting": num_person,
+        "NumberOfFollowups": num_followups,
+        "ProductPitched": product_pitched,
+        "PreferredPropertyStar": prop_stars,
+        "MaritalStatus": marital_status,
+        "NumberOfTrips": num_trips,
+        "Passport": passport,
+        "PitchSatisfactionScore": pitch_satisfaction,
+        "OwnCar": own_car,
+        "NumberOfChildrenVisiting": num_children,
+        "Designation": designation,
+        "MonthlyIncome": monthly_income
+    }
+    
+    input_df = pd.DataFrame([data])
 
-    # 5. Display Results
-    st.divider()
-    if prediction == 1:
-        st.success(f"🎯 High Potential Customer! (Probability: {prob:.2%})")
-        st.balloons()
-    else:
-        st.warning(f"⏳ Low Likelihood of purchase. (Probability: {prob:.2%})")
+    # Get the probability
+    try:
+        # Note: Pipeline applies ColumnTransformer automatically
+        prob = model.predict_proba(input_df)[0][1]
+        
+        st.divider()
+        if prob >= 0.45:
+            st.success(f"### Result: 🎯 High Potential (Prob: {prob:.2%})")
+            st.balloons()
+        else:
+            st.warning(f"### Result: ⏳ Low Likelihood (Prob: {prob:.2%})")
+            
+    except Exception as e:
+        st.error(f"Prediction Error: {e}")
+        st.info("Ensure the column names in app.py match your training data exactly.")