codechrl/test_space

1import pandas as pd2import pickle3import numpy as np4import streamlit as st5import gdown6import os7 8# File IDs9model_id = "1HSQTjJ_hvBBmVJmYUmrkq5T7ubpfDwzF"10top_country_id = "1aLkaAqfrs3GcrMvZcuyQ0NjFhAhrdIlR"11 12model_url = f"https://drive.google.com/uc?id={model_id}"13top_country_url = f"https://drive.google.com/uc?id={top_country_id}"14 15 16@st.cache_resource17def load_model():18    model_path = "best_rf_model.pkl"19    if not os.path.exists(model_path):20        gdown.download(model_url, model_path, quiet=False)21    with open(model_path, "rb") as f:22        return pickle.load(f)23 24 25@st.cache_resource26def load_top_country():27    country_path = "top_country.pkl"28    if not os.path.exists(country_path):29        gdown.download(top_country_url, country_path, quiet=False)30    with open(country_path, "rb") as f:31        return pickle.load(f)32 33 34model = load_model()35top_country = load_top_country()36 37st.set_page_config(page_title="Hotel Booking Prediction", layout="wide")38 39st.markdown(40    """41<div style="42    background-color: white;43    padding: 50px;44    border-radius: 20px;45    box-shadow: 0 4px 20px rgba(0,0,0,0.1);46    max-width: 800px;47    margin: auto;48    text-align: center;49">50    <h1 style="font-size:60px; font-weight:bold; color:black; margin-bottom:20px;">51        Hotel Booking Prediction52    </h1>53    <p style="font-size:20px; color:gray; margin-bottom:30px;">54        Welcome to Hotel Booking Prediction System55    </p>56    <p style="font-size:15px; color:black;">57        Fill in the form below to predict hotel booking!58    </p>59</div>60""",61    unsafe_allow_html=True,62)63 64st.write("")65st.write("")66 67with st.form(key="hotel_bookings"):68    col1, col2 = st.columns(2)69 70    with col1:71        name = st.selectbox("Hotel Type", ("city_hotel", "resort_hotel"), index=0)72        lead = st.number_input(73            "Lead Time",74            min_value=0,75            max_value=600,76            value=0,77            step=1,78            help="jarak antar waktu booking dan check-in",79        )80        arrival_year = st.selectbox("Arrival Year", ("2015", "2016", "2017"), index=0)81        arrival_month = st.selectbox(82            "Arrival Months",83            (84                "January",85                "February",86                "March",87                "April",88                "May",89                "June",90                "July",91                "August",92                "September",93                "October",94                "November",95                "December",96            ),97            index=0,98        )99 100    with col2:101        arrival_week = st.number_input(102            "Arrival Weeks",103            min_value=1,104            max_value=52,105            value=1,106            step=1,107            help="minggu kedatangan",108        )109        arrival_day = st.number_input(110            "Arrival Days",111            min_value=1,112            max_value=31,113            value=1,114            step=1,115            help="tanggal kedatangan",116        )117 118    submitted = st.form_submit_button("Predict", use_container_width=True)119 120    if submitted:121        # Prepare data for prediction122        data = {123            "hotel": name,124            "lead_time": lead,125            "arrival_date_year": int(arrival_year),126            "arrival_date_month": arrival_month,127            "arrival_date_week_number": arrival_week,128            "arrival_date_day_of_month": arrival_day,129        }130 131        df = pd.DataFrame([data])132 133        try:134            prediction = model.predict(df)135 136            st.success("Prediction Complete!")137 138            if prediction[0] == 1:139                st.error("⚠️ This booking is likely to be CANCELLED")140            else:141                st.success("✅ This booking is likely to be CONFIRMED")142 143        except Exception as e:144            st.error(f"Error making prediction: {str(e)}")145