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app.py

@@ -1,59 +1,48 @@
-import streamlit as st
-import pandas as pd
+import json
 import joblib
-import numpy as np
-
-# Load assets
-model = joblib.load("model.pkl")
-mappings = joblib.load(".pkl")
-features = joblib.load("features.pkl")
-
-st.set_page_config(page_title="Visit with Us - Predictor", layout="wide")
-
-st.title("🏝️ Wellness Tourism Package Predictor")
-st.markdown("Predict if a customer will purchase the new Wellness Package based on their profile.")
-
-with st.sidebar:
-    st.header("Customer Profile")
-    age = st.number_input("Age", 18, 100, 35)
-    gender = st.selectbox("Gender", list(mappings['Gender'].keys()))
-    income = st.number_input("Monthly Income", 0, 100000, 25000)
-    passport = st.selectbox("Has Passport? (1=Yes, 0=No)", [0, 1])
-
-col1, col2 = st.columns(2)
-with col1:
-    contact = st.selectbox("Type of Contact", list(mappings['TypeofContact'].keys()))
-    occupation = st.selectbox("Occupation", list(mappings['Occupation'].keys()))
-    marital = st.selectbox("Marital Status", list(mappings['MaritalStatus'].keys()))
-    designation = st.selectbox("Designation", list(mappings['Designation'].keys()))
-
-with col2:
-    pitch_dur = st.slider("Duration of Pitch (min)", 5, 60, 15)
-    pitch_sat = st.slider("Pitch Satisfaction Score", 1, 5, 3)
-    trips = st.number_input("Number of Trips per Year", 0, 20, 2)
-    property_star = st.selectbox("Preferred Property Star", [3, 4, 5])
-
-# Prepare input data
-input_dict = {
-    'Age': age, 'TypeofContact': mappings['TypeofContact'][contact],
-    'CityTier': 1, 'DurationOfPitch': pitch_dur,
-    'Occupation': mappings['Occupation'][occupation], 'Gender': mappings['Gender'][gender],
-    'NumberOfPersonVisiting': 2, 'NumberOfFollowups': 3,
-    'ProductPitched': mappings['ProductPitched']['Basic'], # Default for prediction
-    'PreferredPropertyStar': property_star, 'MaritalStatus': mappings['MaritalStatus'][marital],
-    'NumberOfTrips': trips, 'Passport': passport, 'PitchSatisfactionScore': pitch_sat,
-    'OwnCar': 1, 'NumberOfChildrenVisiting': 1,
-    'Designation': mappings['Designation'][designation], 'MonthlyIncome': income
-}
-
-# Ensure feature order matches training
-input_df = pd.DataFrame([input_dict])[features]
-
-if st.button("Calculate Purchase Probability"):
-    prediction = model.predict(input_df)[0]
-    prob = model.predict_proba(input_df)[0][1]
-
-    if prediction == 1:
-        st.success(f"✅ HIGH POTENTIAL: {prob*100:.1f}% chance of purchase.")
+import pandas as pd
+import streamlit as st
+from huggingface_hub import hf_hub_download
+
+# ========= CONFIG (YOUR EXACT DETAILS) =========
+MODEL_REPO = "Prashantbhat1607/wellness-tourism-model"  # <-- your HF model repo
+MODEL_FILE = "model.joblib"
+META_FILE  = "model_meta.json"
+# ==============================================
+
+st.set_page_config(page_title="Wellness Tourism Predictor", layout="centered")
+st.title("🏝️ Wellness Tourism Purchase Predictor")
+st.write("Predict whether a customer will purchase the Wellness Tourism Package (ProdTaken).")
+
+@st.cache_resource
+def load_artifacts():
+    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
+    meta_path  = hf_hub_download(repo_id=MODEL_REPO, filename=META_FILE,  repo_type="model")
+    model = joblib.load(model_path)
+    with open(meta_path, "r") as f:
+        meta = json.load(f)
+    return model, meta
+
+model, meta = load_artifacts()
+features = meta["features"]
+cat_cols = set(meta["categorical_cols"])
+num_cols = set(meta["numeric_cols"])
+
+st.subheader("Enter customer details")
+
+inputs = {}
+for col in features:
+    if col in cat_cols:
+        # free text input for categorical
+        inputs[col] = st.text_input(col, value="")
     else:
-        st.warning(f"❌ LOW POTENTIAL: {prob*100:.1f}% chance of purchase.")
+        # numeric input
+        inputs[col] = st.number_input(col, value=0.0)
+
+if st.button("Predict"):
+    X = pd.DataFrame([inputs], columns=features)
+    proba = model.predict_proba(X)[0][1]
+    pred = int(proba >= 0.5)
+
+    st.success(f"Prediction: {'✅ Will Purchase (Yes)' if pred==1 else '❌ Will NOT Purchase (No)'}")
+    st.write(f"Probability of purchase: **{proba:.3f}**")