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# tourism_project/deployment/app.py
import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
import os
st.set_page_config(page_title="Visa With Us - Prediction App", layout="centered")
# --------------------------
# CONFIG
# --------------------------
MODEL_REPO = "Dewasheesh/test-mlops"
MODEL_FILENAME = "best_test-mlops_v1.joblib"
@st.cache_resource
def load_model(repo_id: str, filename: str):
"""Download and load joblib model from Hugging Face Hub (cached)."""
try:
#st.info("Loading model...")
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
model = joblib.load(model_path)
return model
except Exception as e:
st.error(f"Failed to load model: {e}")
return None
model = load_model(MODEL_REPO, MODEL_FILENAME)
st.title("Visa With Us - Prediction App")
st.write(
"This app predicts whether a customer will purchase the Wellness Tourism Package."
)
st.markdown("---")
st.header("Features")
# Numeric Inputs
Age = st.number_input("Age", min_value=0, max_value=120, value=35)
CityTier = st.selectbox("City Tier", [1, 2, 3], index=1)
DurationOfPitch = st.number_input("Duration Of Pitch (minutes)", 0, 600, 10)
NumberOfPersonVisiting = st.number_input("Number Of Persons Visiting", 1, 20, 2)
NumberOfFollowups = st.number_input("Number Of Followups", 0, 50, 1)
PreferredPropertyStar = st.number_input("Preferred Property Star", 1, 7, 4)
NumberOfTrips = st.number_input("Number Of Trips (past)", 0, 50, 2)
Passport = st.selectbox("Passport", [1, 0], index=1)
PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 0, 10, 7)
OwnCar = st.selectbox("Own Car", [1, 0], index=1)
NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", 0, 10, 0)
MonthlyIncome = st.number_input("Monthly Income", 0, 10_000_000, 50000, step=1000)
# --------------------------
# CATEGORICAL VALUES
# --------------------------
TYPEOFCONTACT = ["Self Enquiry", "Company Invited"]
OCCUPATION = ["Salaried", "Small Business", "Large Business", "Free Lancer"]
GENDER = ["Male", "Female"]
PRODUCTPITCHED = ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"]
MARITALSTATUS = ["Married", "Divorced", "Unmarried"]
DESIGNATION = ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
# Selectboxes for categories
TypeofContact = st.selectbox("Type of Contact", TYPEOFCONTACT)
Occupation = st.selectbox("Occupation", OCCUPATION)
Gender = st.selectbox("Gender", GENDER)
ProductPitched = st.selectbox("Product Pitched", PRODUCTPITCHED)
MaritalStatus = st.selectbox("Marital Status", MARITALSTATUS)
Designation = st.selectbox("Designation", DESIGNATION)
# Assemble input
input_data = pd.DataFrame([{
"Age": Age,
"CityTier": CityTier,
"DurationOfPitch": DurationOfPitch,
"NumberOfPersonVisiting": NumberOfPersonVisiting,
"NumberOfFollowups": NumberOfFollowups,
"PreferredPropertyStar": PreferredPropertyStar,
"NumberOfTrips": NumberOfTrips,
"Passport": Passport,
"PitchSatisfactionScore": PitchSatisfactionScore,
"OwnCar": OwnCar,
"NumberOfChildrenVisiting": NumberOfChildrenVisiting,
"MonthlyIncome": MonthlyIncome,
"TypeofContact": TypeofContact,
"Occupation": Occupation,
"Gender": Gender,
"ProductPitched": ProductPitched,
"MaritalStatus": MaritalStatus,
"Designation": Designation,
}])
st.markdown("### Preview Input")
st.dataframe(input_data)
# --------------------------
# PREDICT
# --------------------------
if st.button("Predict"):
if model is None:
st.error("Model not loaded.")
else:
try:
pred = model.predict(input_data)[0]
# probability
proba_text = ""
if hasattr(model, "predict_proba"):
proba = model.predict_proba(input_data)
if proba.shape[1] == 2:
proba_text = f" (Probability: {proba[0,1]:.3f})"
result = "Purchase" if int(pred) == 1 else "No Purchase"
st.success(f"Prediction: **{result}**{proba_text}")
except Exception as e:
st.error(f"Prediction failed: {e}")
st.markdown("---")
st.caption("All categorical fields are restricted to valid training values to prevent model mismatch.")
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