generate_data.py

"""
generate_data.py
-----------------
Generates a synthetic e-commerce product catalog with 2000+ unique entries
and saves it as products.csv. This CSV is the "custom database" the RAG
chatbot retrieves from.

Run:
    python generate_data.py
"""

import csv
import random

random.seed(42)

# ---------------------------------------------------------------------------
# Catalog taxonomy: category -> subcategories -> base product name templates
# ---------------------------------------------------------------------------
CATALOG = {
    "Electronics": {
        "subcats": ["Smartphones", "Laptops", "Headphones", "Smartwatches", "Cameras",
                    "Tablets", "Speakers", "Gaming Consoles", "Monitors", "Power Banks"],
        "price_range": (25, 2200),
        "brands": ["Voltek", "Nimbus", "Zentro", "Quantex", "Pulsar", "Orbitron",
                   "Skyline", "Nexura", "Vantek", "Corevolt"],
    },
    "Clothing": {
        "subcats": ["T-Shirts", "Jeans", "Jackets", "Dresses", "Sweaters",
                    "Activewear", "Shorts", "Formal Shirts", "Hoodies", "Coats"],
        "price_range": (8, 250),
        "brands": ["Urban Thread", "Loomcraft", "Northgate", "Willow & Ash",
                   "StrideWear", "Fablink", "Merino Co.", "CasualEdge", "DrapeLine", "Threadfox"],
    },
    "Home & Kitchen": {
        "subcats": ["Cookware", "Blenders", "Coffee Makers", "Bedding", "Vacuum Cleaners",
                    "Storage Bins", "Cutlery Sets", "Lamps", "Air Purifiers", "Dinnerware"],
        "price_range": (10, 600),
        "brands": ["HearthPro", "Domesta", "Kitchcraft", "CozyNest", "PureAir",
                   "Lumina Home", "ChefMate", "TidyBox", "SilverEdge", "WarmHearth"],
    },
    "Beauty & Personal Care": {
        "subcats": ["Moisturizers", "Shampoos", "Perfumes", "Makeup Kits", "Electric Razors",
                    "Hair Dryers", "Skincare Serums", "Sunscreens", "Lip Balms", "Face Masks"],
        "price_range": (5, 150),
        "brands": ["Glowvia", "PureSkin", "Belle Aura", "Dermalux", "Velvetone",
                   "NatureGlow", "SilkRoute", "LumiCare", "EssenceLab", "RadiantHue"],
    },
    "Sports & Outdoors": {
        "subcats": ["Yoga Mats", "Dumbbells", "Tents", "Bicycles", "Running Shoes",
                    "Backpacks", "Water Bottles", "Fitness Trackers", "Camping Stoves", "Skateboards"],
        "price_range": (10, 900),
        "brands": ["TrailBlaze", "PeakForm", "IronGrit", "SummitGear", "Velocore",
                   "RidgeLine", "ActivFit", "TerraTrek", "FlexCore", "AltitudePro"],
    },
    "Books": {
        "subcats": ["Fiction", "Non-Fiction", "Science Fiction", "Biography", "Self-Help",
                    "Children's Books", "Mystery & Thriller", "Cookbooks", "History", "Poetry"],
        "price_range": (4, 45),
        "brands": ["Penfield Press", "Storyhouse", "Chapter & Verse", "Inkwell Editions",
                   "Lanternlight Books", "Northpage", "Quillmark", "Bright Leaf Publishing",
                   "Cobblestone Press", "Willow Bind"],
    },
    "Toys & Games": {
        "subcats": ["Building Blocks", "Board Games", "Action Figures", "Puzzles", "Dolls",
                    "Remote Control Cars", "Educational Toys", "Card Games", "Plush Toys", "Outdoor Play Sets"],
        "price_range": (5, 180),
        "brands": ["Funkidoo", "Brightblox", "PlayNest", "Wonderrific", "Tinker Toys Co.",
                   "GigglePatch", "Kudo Kids", "Puzzlewise", "Playscape", "JoyForge"],
    },
    "Grocery & Gourmet": {
        "subcats": ["Coffee & Tea", "Snacks", "Spices", "Cereal", "Olive Oils",
                    "Pasta & Grains", "Chocolates", "Honey & Preserves", "Nuts & Seeds", "Sauces"],
        "price_range": (3, 60),
        "brands": ["Harvest Table", "Golden Pantry", "Rustic Roots", "PureField",
                   "Meadowbrook", "SpiceHaven", "Orchard Gold", "GrainWorks", "Savory Bay", "Farmstead Co."],
    },
    "Office & Stationery": {
        "subcats": ["Notebooks", "Pens", "Desk Organizers", "Backpacks", "Printers",
                    "Office Chairs", "Whiteboards", "Planners", "Sticky Notes", "Desk Lamps"],
        "price_range": (2, 400),
        "brands": ["Deskly", "Penmark", "Officia", "Craftline", "NotaBene",
                   "GridWorks", "ClearDesk", "InkPoint", "OrganizeIt", "Brightfold"],
    },
    "Pet Supplies": {
        "subcats": ["Dog Food", "Cat Toys", "Pet Beds", "Leashes & Collars", "Aquarium Kits",
                    "Grooming Kits", "Bird Cages", "Pet Carriers", "Litter Boxes", "Chew Toys"],
        "price_range": (4, 220),
        "brands": ["Pawsome", "Furry Friend Co.", "WhiskerWorks", "TailWag", "NestleNook",
                   "Barkline", "PetHaven", "ScratchCraft", "Critter Corner", "Loyal Paws"],
    },
}

ADJECTIVES = ["Premium", "Classic", "Compact", "Deluxe", "Ergonomic", "Portable",
              "Wireless", "Eco-Friendly", "Heavy-Duty", "Ultra-Light", "All-Season",
              "Professional", "Everyday", "Signature", "Modern", "Vintage-Style",
              "Advanced", "Essential", "Rugged", "Smart"]

COLORS = ["Black", "White", "Navy Blue", "Charcoal Grey", "Forest Green", "Sunset Orange",
          "Crimson Red", "Sand Beige", "Slate Blue", "Blush Pink", "Graphite", "Ivory"]

MATERIALS = ["stainless steel", "brushed aluminum", "organic cotton", "recycled polyester",
             "genuine leather", "BPA-free plastic", "tempered glass", "bamboo",
             "high-density foam", "merino wool", "silicone", "anodized alloy"]

FEATURES = [
    "long battery life", "a sleek minimalist design", "industry-leading durability",
    "fast and reliable performance", "an intuitive user experience", "excellent value for money",
    "top-rated customer reviews", "easy maintenance", "a comfortable fit for all-day use",
    "energy-efficient operation", "quick setup with no tools required", "a lightweight build",
    "water-resistant construction", "adjustable settings for personalized use",
    "a timeless design that fits any space", "reinforced stitching for extra durability",
    "noise isolation for an immersive experience", "a non-slip grip for added safety",
]

TAG_POOL = ["bestseller", "new arrival", "eco-friendly", "limited edition", "on sale",
            "top rated", "staff pick", "gift idea", "trending", "budget friendly",
            "premium choice", "family favorite"]


def make_description(name, category, subcat, brand, color, material, adj):
    feat1, feat2 = random.sample(FEATURES, 2)
    templates = [
        f"The {name} from {brand} combines {material} construction with {feat1}. "
        f"Designed for fans of {subcat.lower()}, this {adj.lower()} pick offers {feat2}, "
        f"making it a standout choice in our {category} collection.",

        f"Meet the {name} — a {adj.lower()} {subcat.lower()[:-1] if subcat.endswith('s') else subcat.lower()} "
        f"built from {material}. It delivers {feat1} and {feat2}, "
        f"perfect for anyone shopping our {category} lineup.",

        f"{brand} presents the {name}, finished in {color.lower()} with premium {material}. "
        f"Customers love its {feat1}, and it also offers {feat2}.",
    ]
    return random.choice(templates)


def generate_products(min_count=2000):
    rows = []
    product_id = 1

    for category, meta in CATALOG.items():
        subcats = meta["subcats"]
        brands = meta["brands"]
        low, high = meta["price_range"]

        # ~ enough combos per category to comfortably exceed 2000 total across 10 categories
        per_category_target = 210
        for _ in range(per_category_target):
            subcat = random.choice(subcats)
            brand = random.choice(brands)
            adj = random.choice(ADJECTIVES)
            color = random.choice(COLORS)
            material = random.choice(MATERIALS)

            # Singular-ish name from subcat
            base_noun = subcat[:-1] if subcat.endswith("s") and not subcat.endswith("ss") else subcat
            name = f"{brand} {adj} {base_noun}"

            price = round(random.uniform(low, high), 2)
            rating = round(random.uniform(3.3, 5.0), 1)
            num_reviews = random.randint(3, 4800)
            stock = random.choice(["In Stock"] * 8 + ["Low Stock"] * 2 + ["Out of Stock"])
            tags = ", ".join(random.sample(TAG_POOL, k=random.randint(1, 3)))

            description = make_description(name, category, subcat, brand, color, material, adj)

            rows.append({
                "product_id": f"P{product_id:05d}",
                "name": name,
                "category": category,
                "subcategory": subcat,
                "brand": brand,
                "price_usd": price,
                "rating": rating,
                "num_reviews": num_reviews,
                "stock_status": stock,
                "color": color,
                "material": material,
                "tags": tags,
                "description": description,
            })
            product_id += 1

    random.shuffle(rows)

    # De-duplicate exact name collisions by appending a variant suffix
    seen = {}
    for r in rows:
        key = r["name"]
        if key in seen:
            seen[key] += 1
            r["name"] = f'{r["name"]} (Style {seen[key]})'
        else:
            seen[key] = 1

    if len(rows) < min_count:
        raise RuntimeError(f"Only generated {len(rows)} rows, need {min_count}+")

    return rows


def main():
    rows = generate_products(2000)
    fieldnames = ["product_id", "name", "category", "subcategory", "brand", "price_usd",
                  "rating", "num_reviews", "stock_status", "color", "material", "tags",
                  "description"]

    with open("products.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(rows)

    print(f"Generated {len(rows)} products -> products.csv")


if __name__ == "__main__":
    main()