import gradio as gr

# 🔗 Learn more about integrating Wishfinity's Add-to-Wishlist functionality:
# Visit: https://info.wishfinity.com/button

# Example product database
PRODUCTS = {
    "headphones": [
        {
            "name": "Sony WH-1000XM5",
            "description": "Industry-leading noise canceling wireless headphones.",
            "price": "$328.00",
            "image": "https://m.media-amazon.com/images/I/61eeHPRFQ9L._AC_SL1500_.jpg",
            "url": "https://www.amazon.com/Sony-WH-1000XM5-Headphones-Hands-Free-WH1000XM5/dp/B0BXYCS74H/"
        },
        {
            "name": "Bose QuietComfort 45",
            "description": "Premium comfort and superior noise canceling.",
            "price": "$166.00",
            "image": "https://m.media-amazon.com/images/I/51smLBpimbL._AC_SL1347_.jpg",
            "url": "https://www.amazon.com/Bose-QuietComfort-Bluetooth-Cancelling-Headphones/dp/B09HL9MQ64/"
        }
    ]
}

def recommend_product(query):
    """
    Simulated AI product recommendation function.
    Developers can replace this with their own AI-powered product discovery logic.
    """
    key = "headphones" if "headphone" in query.lower() else None
    if key and key in PRODUCTS:
        products = PRODUCTS[key]
        response = "<hr style='border: 1px solid lightgray; margin: 20px 0;'>"  # Divider above first product
        for product in products:
            # ----- Wishfinity +W Integration (Extract this for use in your own AI project) -----
            wishfinity_url = f"https://wishfinity.com/add?url={product['url']}"
            icon_url = "https://wishfinity.com/assets/imgs/Wishfinity-Button-Small.svg"
            save_to_wishlist = (
                f"<a href='{wishfinity_url}' target='_blank' style='display: inline-flex; align-items: center; text-decoration: none;'>"
                f"<img src='{icon_url}' width='20' height='20' style='margin-right:5px;'> Save to Wishlist</a>"
            )
            # -----------------------------------------------------------------------------------
            response += f"<div style='margin-bottom: 15px;'>"
            response += f"<strong>{product['name']}</strong><br><br>"
            response += f"📝 {product['description']}<br><br>"
            response += f"💲 {product['price']}<br><br>"
            response += f"<img src='{product['image']}' width='250' style='margin-top: 10px;'>  \n"
            response += f"<div style='display: flex; align-items: center; gap: 15px; margin-top: 10px;'>"
            response += save_to_wishlist
            response += f"<span style='color: lightgray; margin: 0 10px;'>|</span>"
            response += f"<a href='{product['url']}' target='_blank' style='display: inline-flex; align-items: center; text-decoration: none;'><span style='margin-right: 5px;'>🛒</span> Visit Product</a>"
            response += f"</div>"
            response += f"</div>"
            response += f"<hr style='border: 1px solid lightgray; margin: 20px 0;'>"  # Adjusted spacing for separator line
        return response
    return "Sorry, I couldn't find matching products. Try searching for 'headphones'.\n\n**Note:** This is an example setup. Developers can replace the recommendation logic with their own AI model and integrate the 'Save to Wishlist' button into their existing AI applications."

iface = gr.Interface(
    fn=recommend_product,
    inputs="text",
    outputs="markdown",
    title="Add Wishlist Integration to AI-Powered Product Discovery",
    description="In this demo, enter the example product-related query 'Best wireless headphones' to represent your AI-powered recommendations. Users click 'Save to Wishlist' to save products for later.\n\n**Note:** This is an example setup. Developers can modify the recommendation logic to fit their AI use cases."
)

iface.launch()