services/product_service.py

import random
from bson import ObjectId
# Constants for mock data generation
BRANDS = ["Nike", "Adidas", "H&M", "Zara", "Uniqlo", "Gap", "Levi's"]
COLORS = ["Black", "White", "Blue", "Red", "Green", "Yellow", "Purple", "Pink", "Orange", "Grey"]
GENDERS = ["Male", "Female"]
SIZE_OPTIONS = ["XS", "S", "M", "L", "XL"]

def generate_mock_product_data():
    """Generate realistic product data if missing."""
    sample_products = [
        {"product_name": "Stylish Shirt", "brand_name": "Zara", "color": "Blue", "price": "$29.99"},
        {"product_name": "Casual Sneakers", "brand_name": "Nike", "color": "White", "price": "$79.99"},
        {"product_name": "Leather Jacket", "brand_name": "H&M", "color": "Black", "price": "$99.99"},
    ]

    selected_product = random.choice(sample_products)  # Pick a random sample

    mock_data = {
        "product_name": selected_product["product_name"],
        "brand_name": selected_product["brand_name"],
        "price": selected_product["price"],
        "description": f"A trendy {selected_product['color']} {selected_product['product_name']} perfect for all occasions.",
        "gender": "Female",
        "color": selected_product["color"],
        "stock_level": 50,
        "manufacturing_price": round(random.uniform(10.0, 50.0), 2),
        "category": "T-shirts",
        "size_options": SIZE_OPTIONS,
        "features": [random.random() for _ in range(2048)],  # Simulated embeddings
        
        
    }

    print("Generated mock product data:", mock_data)  # Debugging print
    return mock_data



def create_product(product_image_path, model_image_path, product_data, db, cloudinary_service, feature_service):
    """Create a new product with all required data"""
    
    print("Received product data:", product_data)  # Debugging print

    # Generate mock data for missing fields
    mock_data = generate_mock_product_data()
    complete_product_data = {key: product_data.get(key, mock_data[key]) for key in mock_data}

    print("Product data after merging with mock data:", complete_product_data)  # Debugging print

    # Upload images to Cloudinary
    image_urls = cloudinary_service.upload_multiple_to_cloudinary([product_image_path, model_image_path])
    
    # Extract features and generate auto description
    analysis_result = feature_service.analyze_product_image(product_image_path)

    # Merge product data with generated details
    complete_product = {
        **complete_product_data,
        "image_urls": image_urls,
        "features": analysis_result["features"],
        "auto_description": analysis_result["auto_description"]
    }

    print("Final product data before saving:", complete_product)  # Debugging print

    # Save to database
    product_id = db.save_product_data(complete_product)
    
    return product_id, complete_product