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from flask import Flask, request, jsonify, send_from_directory |
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from flask_cors import CORS |
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import joblib |
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import numpy as np |
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import xgboost as xgb |
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import math |
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app = Flask(__name__, static_folder='.', static_url_path='/') |
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CORS(app) |
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try: |
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rf = joblib.load("rf_model.pkl") |
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xgb_model = xgb.Booster() |
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xgb_model.load_model("xgb_model.json") |
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print("✅ Models loaded successfully.") |
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except Exception as e: |
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print(f"❌ Error loading models: {e}") |
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raise e |
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tile_catalog = [ |
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{"name": "Travertino Light Beige", "type": "Floor", "price": 780, "coverage": 15.5, "size": "1200x1200 MM", "url": "https://arqonz.ae/products/6775"}, |
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{"name": "Marquina Glossy Black", "type": "Wall", "price": 620, "coverage": 12.0, "size": "600x600 MM", "url": "https://arqonz.ae/products/6776"}, |
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{"name": "Carrara White Polished", "type": "Floor", "price": 1100, "coverage": 23.23, "size": "1800x1200 MM", "url": "https://arqonz.ae/products/103"}, |
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{"name": "Noir Marble Effect", "type": "Wall", "price": 550, "coverage": 6.89, "size": "800x800 MM", "url": "https://arqonz.ae/products/104"}, |
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{"name": "Sandstone Beige Matte", "type": "Floor", "price": 670, "coverage": 13.78, "size": "1600x800 MM", "url": "https://arqonz.ae/products/6644"}, |
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{"name": "Onyx Mist Polished", "type": "Wall", "price": 890, "coverage": 15.5, "size": "1200x1200 MM", "url": "https://arqonz.ae/products/6880"}, |
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{"name": "Terrazzo Pearl", "type": "Floor", "price": 720, "coverage": 7.75, "size": "1200x600 MM", "url": "https://arqonz.ae/products/6878"}, |
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{"name": "Statuario Silver", "type": "Wall", "price": 660, "coverage": 3.87, "size": "600x600 MM", "url": "https://arqonz.ae/products/6883"}, |
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{"name": "Grigio Urban", "type": "Floor", "price": 500, "coverage": 2.52, "size": "1200x195 MM", "url": "https://arqonz.ae/products/6653"}, |
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{"name": "Calacatta Nero", "type": "Wall", "price": 740, "coverage": 10.33, "size": "800x1200 MM", "url": "https://arqonz.ae/products/6885"}, |
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{"name": "Ivory Rock Slate", "type": "Floor", "price": 690, "coverage": 13.0, "size": "1200x600 MM", "url": "https://arqonz.ae/products/6777"}, |
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{"name": "Classic White Glossy", "type": "Wall", "price": 580, "coverage": 12.0, "size": "600x600 MM", "url": "https://arqonz.ae/products/89"}, |
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{"name": "Dark Stone Matte", "type": "Floor", "price": 830, "coverage": 15.0, "size": "1200x600 MM", "url": "https://arqonz.ae/products/107"}, |
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{"name": "Polished Statuario", "type": "Wall", "price": 810, "coverage": 10.5, "size": "600x1200 MM", "url": "https://arqonz.ae/products/116"} |
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] |
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@app.route("/") |
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def index(): |
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return send_from_directory(".", "index.html") |
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@app.route("/recommend", methods=["POST"]) |
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def recommend(): |
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try: |
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data = request.get_json() |
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tile_type = data.get("tile_type", "").strip().lower() |
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coverage = float(data.get("coverage", 1)) |
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area = float(data.get("area", 1)) |
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price_range = data.get("price_range", [3, 10000]) |
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preferred_sizes = data.get("preferred_sizes", []) |
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if area <= 0 or coverage <= 0: |
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return jsonify({"error": "Invalid area or coverage"}), 400 |
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features = prepare_features(tile_type, coverage, area, price_range) |
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xgb_pred = xgb_model.predict(xgb.DMatrix(features))[0] |
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rf_pred = rf.predict_proba(features)[0][1] |
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score = (xgb_pred + rf_pred) / 2 |
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matches = filter_products(tile_type, price_range, preferred_sizes) |
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return jsonify({ |
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"recommendation_score": round(float(score), 3), |
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"recommended_products": matches[:4], |
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"total_matches": len(matches) |
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}) |
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except Exception as e: |
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print("❌ /recommend error:", e) |
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return jsonify({"error": "Server error"}), 500 |
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def prepare_features(tile_type, coverage, area, price_range): |
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tile_type_num = 0 if tile_type == "floor" else 1 |
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min_price, max_price = price_range |
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price_per_sqft = max_price / coverage |
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efficiency = coverage / max_price |
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return np.array([[tile_type_num, area, coverage, min_price, max_price, price_per_sqft, efficiency]]) |
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def filter_products(tile_type, price_range, preferred_sizes): |
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min_price, max_price = price_range |
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filtered = [] |
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for p in tile_catalog: |
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if p["type"].lower() != tile_type: |
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continue |
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if not (min_price <= p["price"] <= max_price): |
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continue |
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size_match = False |
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for size in preferred_sizes: |
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if similar_size(p["size"], size): |
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size_match = True |
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break |
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if not size_match: |
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continue |
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price_score = 1 - (p["price"] - min_price) / (max_price - min_price + 1e-6) |
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filtered.append({**p, "recommendation_score": round(price_score, 2)}) |
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return sorted(filtered, key=lambda x: x["recommendation_score"], reverse=True) |
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def similar_size(size_a, size_b, tolerance=10): |
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try: |
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w1, h1 = map(int, size_a.lower().replace("mm", "").split("x")) |
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w2, h2 = map(int, size_b.lower().replace("mm", "").split("x")) |
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return abs(w1 - w2) <= tolerance and abs(h1 - h2) <= tolerance |
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except: |
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return False |
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if __name__ == "__main__": |
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app.run(host="0.0.0.0", port=7860) |
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