File size: 4,044 Bytes
3559120 4643a52 3559120 4643a52 3559120 |
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 99 100 101 102 103 104 |
import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
import os
# Common constants
HUGGINGFACE_USER_NAME = os.getenv('HUGGINGFACE_USER_NAME')
HUGGINGFACE_MODEL_NAME = os.getenv('HUGGINGFACE_MODEL_NAME')
# Download the model from the Model Hub
# Note: Ensure the filename matches what you uploaded in your training script
try:
model_path = hf_hub_download(
repo_id=f"{HUGGINGFACE_USER_NAME}/{HUGGINGFACE_MODEL_NAME}",
filename="model.joblib"
)
model = joblib.load(model_path)
except Exception as e:
st.error(f"Error loading model from Hugging Face: {e}")
st.stop()
# Streamlit UI Setup
st.set_page_config(page_title="Wellness Package Prediction", layout="centered")
st.title("Tourism Wellness Package Purchase Prediction")
st.write("""
This tool predicts whether a customer is likely to purchase a **Wellness Package** based on their demographic and interaction history.
""")
st.divider()
# Create two columns for a cleaner UI layout
col1, col2 = st.columns(2)
with col1:
st.subheader("Demographics")
Age = st.number_input("Age", min_value=18, max_value=100, value=30)
Gender = st.selectbox("Gender", ["Male", "Female"])
MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
Occupation = st.selectbox("Occupation", ['Salaried', 'Free Lancer', 'Small Business', 'Large Business'])
Designation = st.selectbox("Designation", ['Manager', 'Executive', 'Senior Manager', 'AVP', 'VP'])
MonthlyIncome = st.number_input("Monthly Income", min_value=0.0, value=25000.0)
CityTier = st.slider("City Tier", 1, 3, 1)
with col2:
st.subheader("Travel Behavior")
TypeofContact = st.selectbox("Type of Contact", ['Self Enquiry', 'Company Invited'])
ProductPitched = st.selectbox("Product Pitched", ['Deluxe', 'Basic', 'Standard', 'Super Deluxe', 'King'])
DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=15)
NumberOfFollowups = st.slider("Number of Follow-ups", 1, 10, 3)
NumberOfTrips = st.number_input("Number of Trips", min_value=0, value=2)
PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
PreferredPropertyStar = st.slider("Preferred Property Star", 3, 5, 3)
st.subheader("Additional Info")
c3, c4, c5 = st.columns(3)
with c3:
Passport = st.selectbox("Has Passport?", ["Yes", "No"])
with c4:
OwnCar = st.selectbox("Owns a Car?", ["Yes", "No"])
with c5:
NumberOfPersonVisiting = st.number_input("Adults Visiting", min_value=1, value=2)
NumberOfChildrenVisiting = st.number_input("Children Visiting", min_value=0, value=0)
# Prepare input data matching the exact training schema
input_dict = {
'Age': Age,
'CityTier': CityTier,
'DurationOfPitch': DurationOfPitch,
'NumberOfPersonVisiting': NumberOfPersonVisiting,
'NumberOfFollowups': NumberOfFollowups,
'PreferredPropertyStar': PreferredPropertyStar,
'NumberOfTrips': NumberOfTrips,
'Passport': 1 if Passport == "Yes" else 0,
'PitchSatisfactionScore': PitchSatisfactionScore,
'OwnCar': 1 if OwnCar == "Yes" else 0,
'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
'MonthlyIncome': MonthlyIncome,
'TypeofContact': TypeofContact,
'Occupation': Occupation,
'Gender': Gender,
'ProductPitched': ProductPitched,
'MaritalStatus': MaritalStatus,
'Designation': Designation
}
input_data = pd.DataFrame([input_dict])
# Prediction Logic
classification_threshold = 0.45
st.divider()
if st.button("Generate Prediction", type="primary"):
# Get probability from XGBoost
prediction_proba = model.predict_proba(input_data)[0, 1]
# Apply custom threshold
prediction = 1 if prediction_proba >= classification_threshold else 0
if prediction == 1:
st.success(f"High Potential: Customer is likely to **PURCHASE** (Prob: {prediction_proba:.2f})")
else:
st.warning(f"Low Potential: Customer is likely to **NOT PURCHASE** (Prob: {prediction_proba:.2f})")
|