application.py

import joblib
import numpy as np
from config.paths_config import MODEL_OUTPUT_PATH
from flask import Flask, render_template, request

app = Flask(__name__)

loaded_model = joblib.load(MODEL_OUTPUT_PATH)

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':

        lead_time = int(request.form.get('lead_time'))
        no_of_special_request = int(request.form.get('no_of_special_request'))
        avg_price_per_room = float(request.form.get('avg_price_per_room'))
        arrival_month = int(request.form.get('arrival_month'))
        arrival_date = int(request.form.get('arrival_date'))
        market_segment_type = int(request.form.get('market_segment_type'))  # Convert to int
        no_of_week_nights = int(request.form.get('no_of_week_nights'))
        no_of_weekend_nights = int(request.form.get('no_of_weekend_nights'))
        type_of_meal_plan = int(request.form.get('type_of_meal_plan'))  # Convert to int
        room_type_reserved = int(request.form.get('room_type_reserved'))  # Convert to int

        features = np.array([[
            lead_time, 
            no_of_special_request, 
            avg_price_per_room, 
            arrival_month, 
            arrival_date, 
            market_segment_type, 
            no_of_week_nights, 
            no_of_weekend_nights, 
            room_type_reserved, 
            type_of_meal_plan
        ]], dtype=float)  # Ensure all values are numeric

        prediction = loaded_model.predict(features)

        return render_template('index.html', prediction=prediction[0])
    
    return render_template('index.html', prediction=None)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)