app.py

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

app = Flask(__name__)
CORS(app)  # Allow cross-origin requests (important for frontend integration)

# Setup logging
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 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('/recommend', methods=['POST'])
def recommend():
    """
    Endpoint for product recommendations
    Expected JSON payload:
    {
        "tile_type": "floor"|"wall",
        "coverage": float,
        "area": float,
        "price_range": [min, max],
        "preferred_sizes": [size1, size2] (optional)
    }
    """
    try:
        data = request.get_json()

        required_fields = ['tile_type', 'coverage', 'area', 'price_range']
        if not all(field in data for field in required_fields):
            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. Use 'floor' or 'wall'"}), 400

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

        features = prepare_features(data)

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

        recommended_products = filter_products(
            tile_type=tile_type,
            min_price=data['price_range'][0],
            max_price=data['price_range'][1],
            preferred_sizes=data.get('preferred_sizes', []),
            min_score=0.5
        )

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

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

@app.route('/calculate', methods=['POST'])
def calculate():
    """
    Endpoint for tile calculation
    Expected JSON payload:
    {
        "tile_type": "floor"|"wall",
        "area": float,
        "tile_size": "12x12"|etc
    }
    """
    try:
        data = request.get_json()

        if 'tile_type' not in data or 'area' not in data or 'tile_size' not in data:
            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

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

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

        tile_info = tile_sizes[data['tile_size']]
        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_products = [
            p for p in tile_catalog
            if p['type'].lower() == tile_type and p['size'] == data['tile_size']
        ]

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

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

def prepare_features(data):
    """Prepare feature vector for ML prediction"""
    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):
    """Filter and score products"""
    filtered = []
    for product in tile_catalog:
        if (product['type'].lower() == tile_type and
            min_price <= product['price'] <= max_price and
            (not preferred_sizes or product['size'] in preferred_sizes)):

            price_score = 1 - ((product['price'] - min_price) / (max_price - min_price + 1e-6))
            size_score = 1 if not preferred_sizes or product['size'] in preferred_sizes else 0.5
            product_score = (price_score + size_score) / 2

            if product_score >= min_score:
                filtered.append({
                    **product,
                    "recommendation_score": round(product_score, 2)
                })

    return sorted(filtered, key=lambda x: x['recommendation_score'], reverse=True)

def calculate_requirements(area, coverage):
    """Calculate tile quantities and estimated costs"""
    min_tiles = math.ceil(area / coverage)
    suggested_tiles = math.ceil(min_tiles * 1.1)
    return {
        "minimum_tiles": min_tiles,
        "suggested_tiles": suggested_tiles,
        "estimated_cost_range": [
            round(area * 3, 2),  # example: ₹3 per sqft
            round(area * 10, 2)  # example: ₹10 per sqft
        ]
    }

def validate_positive_number(value, field):
    """Raise ValueError if value is not a positive number"""
    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=False)