File size: 3,975 Bytes
416ce05 211aa18 a1626c0 fc55797 a1626c0 4067998 416ce05 211aa18 416ce05 a1626c0 416ce05 211aa18 416ce05 211aa18 416ce05 211aa18 389dccc 211aa18 a1626c0 416ce05 fc55797 a1626c0 fc55797 416ce05 211aa18 416ce05 211aa18 416ce05 211aa18 416ce05 4ca754d 416ce05 fc55797 416ce05 fc55797 416ce05 211aa18 416ce05 fc55797 211aa18 416ce05 211aa18 416ce05 211aa18 fc55797 211aa18 416ce05 fc55797 416ce05 fc55797 416ce05 |
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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
from flask import Flask, request, jsonify, send_from_directory
from flask_cors import CORS
import joblib
import numpy as np
import json
import math
import xgboost as xgb
import os
app = Flask(__name__, static_folder='.', static_url_path='/')
CORS(app)
# Load ML models
try:
rf = joblib.load("rf_model.pkl")
xgb_model = xgb.Booster()
xgb_model.load_model("xgb_model.json")
print("✅ Models loaded successfully.")
except Exception as e:
print(f"❌ Error loading models: {e}")
raise e
# Load tile data
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 index():
return send_from_directory(".", "index.html")
@app.route("/recommend", methods=["POST"])
def recommend():
try:
data = request.get_json()
tile_type = data.get("tile_type", "").lower()
coverage = float(data.get("coverage", 1))
area = float(data.get("area", 1))
price_range = data.get("price_range", [1, 100])
preferred_sizes = data.get("preferred_sizes", [])
features = prepare_features(tile_type, coverage, area, price_range)
xgb_pred = xgb_model.predict(xgb.DMatrix(features))[0]
rf_pred = rf.predict_proba(features)[0][1]
score = (xgb_pred + rf_pred) / 2
products = filter_products(tile_type, price_range, preferred_sizes)
return jsonify({
"recommendation_score": round(float(score), 3),
"recommended_products": products[:4],
"total_matches": len(products),
})
except Exception as e:
print("❌ Error in /recommend:", str(e))
return jsonify({"error": "Server error"}), 500
@app.route("/calculate", methods=["POST"])
def calculate():
try:
data = request.get_json()
tile_type = data.get("tile_type", "").lower()
area = float(data.get("area", 0))
tile_size = data.get("tile_size", "")
if tile_size not in tile_sizes:
return jsonify({"error": "Invalid tile size"}), 400
info = tile_sizes[tile_size]
per_tile_area = info["length"] * info["width"]
tiles_needed = math.ceil((area / per_tile_area) * 1.1)
boxes = math.ceil(tiles_needed / info.get("tiles_per_box", 10))
matches = [p for p in tile_catalog if p["type"].lower() == tile_type and p["size"] == tile_size]
return jsonify({
"tiles_needed": tiles_needed,
"boxes_needed": boxes,
"matching_products": matches[:3],
"total_matches": len(matches)
})
except Exception as e:
print("❌ Error in /calculate:", str(e))
return jsonify({"error": "Server error"}), 500
def prepare_features(tile_type, coverage, area, price_range):
tile_type_num = 0 if tile_type == "floor" else 1
min_price, max_price = price_range
price_per_sqft = max_price / coverage
efficiency = coverage / max_price
return np.array([[tile_type_num, area, coverage, min_price, max_price, price_per_sqft, efficiency]])
def filter_products(tile_type, price_range, preferred_sizes):
min_price, max_price = price_range
filtered = []
for product in tile_catalog:
if product["type"].lower() != tile_type:
continue
if not (min_price <= product["price"] <= max_price):
continue
if preferred_sizes and product["size"] not in preferred_sizes:
continue
price_score = 1 - (product["price"] - min_price) / (max_price - min_price + 1e-6)
size_score = 1 if product["size"] in preferred_sizes else 0.5
score = round((price_score + size_score) / 2, 2)
filtered.append({**product, "recommendation_score": score})
return sorted(filtered, key=lambda x: x["recommendation_score"], reverse=True)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
|