index.html

from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import joblib
import xgboost as xgb
import numpy as np
import json
import math
import os
import logging

app = Flask(__name__)
CORS(app)
logging.basicConfig(level=logging.INFO)

# Load models
try:
    rf = joblib.load("rf_model.pkl")
    xgb_model = xgb.Booster()
    xgb_model.load_model("xgb_model.json")
    app.logger.info("✅ Models loaded successfully.")
except Exception as e:
    app.logger.error(f"❌ Error loading models: {e}")
    raise e

# Load tile metadata
with open("tile_catalog.json", "r", encoding="utf-8") as f:
    tile_catalog = json.load(f)

with open("tile_sizes.json", "r", encoding="utf-8") as f:
    tile_sizes = json.load(f)

@app.route("/")
def home():
    return send_file("index.html")

@app.route('/recommend', methods=['POST'])
def recommend():
    try:
        data = request.get_json()

        required = ['tile_type', 'coverage', 'area', 'price_range']
        if not all(k in data for k in required):
            return jsonify({"error": "Missing required fields"}), 400

        tile_type = data['tile_type'].lower()
        if tile_type not in ['floor', 'wall']:
            return jsonify({"error": "Invalid tile type"}), 400

        validate_positive_number(data['coverage'], "coverage")
        validate_positive_number(data['area'], "area")

        pr = data['price_range']
        if (not isinstance(pr, list) or len(pr) != 2 or pr[0] < 0 or pr[1] <= 0 or pr[0] >= pr[1]):
            return jsonify({"error": "Invalid price range"}), 400

        features = prepare_features(data)
        xgb_pred = float(xgb_model.predict(xgb.DMatrix(features))[0])
        rf_pred = float(rf.predict_proba(features)[0][1])
        combined_score = (xgb_pred + rf_pred) / 2

        recommended = filter_products(
            tile_type=tile_type,
            min_price=pr[0],
            max_price=pr[1],
            preferred_sizes=data.get("preferred_sizes", []),
            min_score=0.5
        )

        return jsonify({
            "recommendation_score": round(combined_score, 3),
            "total_matches": len(recommended),
            "recommended_products": recommended[:5],
            "calculation": calculate_requirements(data['area'], data['coverage'])
        })

    except Exception as e:
        app.logger.error(f"Error in /recommend: {e}")
        return jsonify({"error": "Internal server error"}), 500

@app.route('/calculate', methods=['POST'])
def calculate():
    try:
        data = request.get_json()

        for k in ['tile_type', 'area', 'tile_size']:
            if k not in data:
                return jsonify({"error": f"Missing field: {k}"}), 400

        tile_type = data['tile_type'].lower()
        if tile_type not in ['floor', 'wall']:
            return jsonify({"error": "Invalid tile type"}), 400

        tile_size_key = data['tile_size']
        if tile_size_key not in tile_sizes:
            return jsonify({"error": "Invalid tile size"}), 400

        validate_positive_number(data['area'], "area")

        tile_info = tile_sizes[tile_size_key]
        area_per_tile = tile_info['length'] * tile_info['width']
        tiles_needed = math.ceil((data['area'] / area_per_tile) * 1.1)
        tiles_per_box = tile_info.get('tiles_per_box', 10)
        boxes_needed = math.ceil(tiles_needed / tiles_per_box)

        matching = [
            p for p in tile_catalog
            if p['type'].lower() == tile_type and p['size'] == tile_size_key
        ]

        return jsonify({
            "tile_type": tile_type,
            "area": data['area'],
            "tile_size": tile_size_key,
            "tiles_needed": tiles_needed,
            "boxes_needed": boxes_needed,
            "matching_products": matching[:3],
            "total_matches": len(matching)
        })

    except Exception as e:
        app.logger.error(f"Error in /calculate: {e}")
        return jsonify({"error": "Internal server error"}), 500

def prepare_features(data):
    tile_type_num = 0 if data['tile_type'] == 'floor' else 1
    price_per_sqft = data['price_range'][1] / data['coverage']
    budget_efficiency = data['coverage'] / data['price_range'][1]

    return np.array([[
        tile_type_num,
        data['area'],
        data['coverage'],
        data['price_range'][0],
        data['price_range'][1],
        price_per_sqft,
        budget_efficiency
    ]])

def filter_products(tile_type, min_price, max_price, preferred_sizes, min_score=0.5):
    results = []
    for p in tile_catalog:
        if (
            p['type'].lower() == tile_type and
            min_price <= p['price'] <= max_price and
            (not preferred_sizes or p['size'] in preferred_sizes)
        ):
            price_score = 1 - ((p['price'] - min_price) / (max_price - min_price + 1e-6))
            size_score = 1 if not preferred_sizes or p['size'] in preferred_sizes else 0.5
            score = (price_score + size_score) / 2
            if score >= min_score:
                results.append({**p, "recommendation_score": round(score, 2)})
    return sorted(results, key=lambda x: x['recommendation_score'], reverse=True)

def calculate_requirements(area, coverage):
    min_tiles = math.ceil(area / coverage)
    suggested = math.ceil(min_tiles * 1.1)
    return {
        "minimum_tiles": min_tiles,
        "suggested_tiles": suggested,
        "estimated_cost_range": [round(area * 3, 2), round(area * 10, 2)]
    }

def validate_positive_number(value, field):
    if not isinstance(value, (int, float)) or value <= 0:
        raise ValueError(f"{field} must be a positive number")

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