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edabbbe b36cf5b edabbbe 28cf885 | 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 | import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the model
model_path = hf_hub_download(repo_id="KishoreKT/tourism_study_model", filename="best_tourism_study_model_v1.joblib")
model = joblib.load(model_path)
# Streamlit UI for Machine Failure Prediction
st.title("Tourism Study App")
st.write("""
This application predicts the likelihood of a user opting to choose a tourism package.
Please enter the relevant data below to get a prediction.
""")
# User input
age = st.number_input("Age", min_value=18, max_value=100, value=35)
type_of_contact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
city_tier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Freelancer"])
gender = st.selectbox("Gender", ["Male", "Female"])
number_of_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
preferred_property_star = st.selectbox("Preferred Property Star", [3, 4, 5])
marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
number_of_trips = st.number_input("Number of Trips (Annual)", min_value=0, max_value=20, value=3)
passport = st.selectbox("Passport", ["Yes", "No"])
own_car = st.selectbox("Own Car", ["Yes", "No"])
number_of_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
monthly_income = st.number_input("Monthly Income", min_value=0, max_value=200000, value=25000, step=1000)
# Customer Interaction Data
pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
number_of_followups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=3)
duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=5, max_value=120, value=30)
# Assemble input into DataFrame
input_data = pd.DataFrame([{
'Age': age,
'TypeofContact': type_of_contact,
'CityTier': city_tier,
'Occupation': occupation,
'Gender': gender,
'NumberOfPersonVisiting': number_of_person_visiting,
'PreferredPropertyStar': preferred_property_star,
'MaritalStatus': marital_status,
'NumberOfTrips': number_of_trips,
'Passport': 1 if passport == "Yes" else 0,
'OwnCar': 1 if own_car == "Yes" else 0,
'NumberOfChildrenVisiting': number_of_children_visiting,
'Designation': designation,
'MonthlyIncome': monthly_income,
'PitchSatisfactionScore': pitch_satisfaction_score,
'ProductPitched': product_pitched,
'NumberOfFollowups': number_of_followups,
'DurationOfPitch': duration_of_pitch
}])
if st.button("Predict User Choice for Opting Package"):
prediction = model.predict(input_data)[0]
result = "User Opt is YES" if prediction == 1 else "User Opt is NO"
st.subheader("Prediction Result:")
st.success(f"The model predicts: **{result}**")
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