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a814fbb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# -----------------------------
# Load model from Hugging Face Model Hub
# -----------------------------
MODEL_REPO_ID = "avatar2102/tourism-package-model"
MODEL_FILENAME = "best_tourism_model_v1.joblib"
model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
model = joblib.load(model_path)
# -----------------------------
# Streamlit UI
# -----------------------------
st.title("Tourism Package Purchase Prediction App")
st.write(
"This app predicts whether a customer is likely to purchase the newly introduced "
"Wellness Tourism Package based on customer details and interaction attributes."
)
st.write("Enter the customer details below and click **Predict**.")
# -----------------------------
# Collect user inputs (based on dataset dictionary)
# -----------------------------
Age = st.number_input("Age", min_value=18, max_value=100, value=35)
TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
CityTier = st.selectbox("City Tier", [1, 2, 3], index=1)
Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
Gender = st.selectbox("Gender", ["Male", "Female"])
NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
PreferredPropertyStar = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5], index=3)
MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
NumberOfTrips = st.number_input("Number of Trips (Annual)", min_value=0, max_value=50, value=2)
Passport = st.selectbox("Passport", ["Yes", "No"])
OwnCar = st.selectbox("Own Car", ["Yes", "No"])
NumberOfChildrenVisiting = st.number_input("Number of Children Visiting (<5 years)", min_value=0, max_value=10, value=0)
Designation = st.text_input("Designation", value="Executive")
MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=30000)
PitchSatisfactionScore = st.selectbox("Pitch Satisfaction Score", [1, 2, 3, 4, 5], index=3)
ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
NumberOfFollowups = st.number_input("Number of Followups", min_value=0, max_value=10, value=2)
DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, max_value=500, value=30)
# -----------------------------
# Create input dataframe (match training features)
# -----------------------------
input_data = pd.DataFrame([{
"Age": Age,
"TypeofContact": TypeofContact,
"CityTier": CityTier,
"Occupation": Occupation,
"Gender": Gender,
"NumberOfPersonVisiting": NumberOfPersonVisiting,
"PreferredPropertyStar": PreferredPropertyStar,
"MaritalStatus": MaritalStatus,
"NumberOfTrips": NumberOfTrips,
"Passport": 1 if Passport == "Yes" else 0,
"OwnCar": 1 if OwnCar == "Yes" else 0,
"NumberOfChildrenVisiting": NumberOfChildrenVisiting,
"Designation": Designation,
"MonthlyIncome": MonthlyIncome,
"PitchSatisfactionScore": PitchSatisfactionScore,
"ProductPitched": ProductPitched,
"NumberOfFollowups": NumberOfFollowups,
"DurationOfPitch": DurationOfPitch
}])
# Classification threshold (same style as your training logic)
classification_threshold = 0.45
# -----------------------------
# Predict
# -----------------------------
if st.button("Predict"):
pred_proba = model.predict_proba(input_data)[0, 1]
pred = int(pred_proba >= classification_threshold)
st.subheader("Prediction Result")
st.write(f"Purchase probability: **{pred_proba:.3f}**")
if pred == 1:
st.success("✅ Likely to purchase the Wellness Tourism Package (ProdTaken = 1)")
else:
st.warning("❌ Not likely to purchase the Wellness Tourism Package (ProdTaken = 0)")
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