import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib

# Download and load the model from Hugging Face Hub
model_path = hf_hub_download(
    repo_id="Sandhya777/tourism_package_prediction_model1",
    filename="best_tourism_package_prediction_v2.joblib"
)
model = joblib.load(model_path)

# Streamlit UI for Insurance Charges Prediction
st.title("🌴Tourism Package Prediction App🌴")
st.write("Fill in the customer information below and click **Predict**.")

# User input

col1, col2 = st.columns(2)

with col1:
  age= st.number_input("Age", min_value=18, max_value=100, value=30, step=1)
  typeofcontact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
  citytier = st.selectbox("City Tier", [1, 2, 3])
  durationofpitch = st.number_input("Duration of Pitch", min_value=1, max_value=100, value=10, step=1)
  occupation= st.selectbox("Occupation", ["Salaried", "Free Lancer","Small Business","Large Business"])
  gender = st.selectbox("Gender", ["Male", "Female"])
  numberofpersonvisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2, step=1)
  numberoffollowups = st.number_input("Number of Follow-ups", min_value=1, max_value=10, value=2, step=1)
  productpitched= st.selectbox("Product Pitched", ["Basic", "Deluxe","Standard","King","Super Deluxe"])

with col2:
  preferredpropertystar= st.number_input("Preferred Property Star", min_value=1, max_value=5, value=3, step=1)
  maritalstatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced","Unmarried"])
  numberoftrips = st.number_input("Number of Trips", min_value=1, max_value=10, value=2, step=1)
  passport = st.selectbox("Passport", [0, 1])
  pitchsatisfactionscore = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, step=1)
  owncar = st.selectbox("Own Car", [0, 1])
  numberofchildrenvisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0, step=1)
  designation = st.selectbox("Designation", ["Executive", "Manager", "VP", "AVP","Senior Manager"])
  monthlyincome = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=5000, step=100)

# Assemble input into DataFrame
input_data = pd.DataFrame([{
    'age': age,
    'typeofcontact': typeofcontact,
    'citytier': citytier,
    'durationofpitch': durationofpitch,
    'occupation': occupation,
    'gender': gender,
    'numberofpersonvisiting': numberofpersonvisiting,
    'numberoffollowups': numberoffollowups,
    'productpitched': productpitched,
    'preferredpropertystar': preferredpropertystar,
    'maritalstatus': maritalstatus,
    'numberoftrips': numberoftrips,
    'passport': passport,
    'pitchsatisfactionscore': pitchsatisfactionscore,
    'owncar': owncar,
    'numberofchildrenvisiting': numberofchildrenvisiting,
    'designation': designation,
    'monthlyincome': monthlyincome
}])

# Set the classification threshold
classification_threshold = 0.45

# Predict button
if st.button("Predict"):
    prediction_proba = model.predict_proba(input_data)[0, 1]
    prediction = (prediction_proba >= classification_threshold).astype(int)
    result = "Take the Tourism Package" if prediction == 1 else "Not to Take the Tourism Package"
    #st.write(f"Based on the information provided, the customer is likely to {result}.")
    color = "#16a34a" if prediction == 1 else "#dc2626"   # nice green/red

st.markdown(
    f"""
    Based on the information provided, the customer is likely to
    <b><span style='color:{color}; font-size:18px;'>{result}</span></b>.
    """,
    unsafe_allow_html=True
)