Building an AI-Powered Storefront with Python: A Complete Guide
I built a fully autonomous digital product storefront using Python, Bitcoin payments, and AI-powered content generation. Here's the complete guide.
Architecture Overview
The stack is minimal but powerful:
- Bottle for the web server (lightweight, single file)
- Cloudflare Tunnel for free HTTPS
- Blockstream API for zero-fee Bitcoin payment verification
- Ollama for local AI content generation
The Web Server
from bottle import Bottle, run, template, request
import json, os
app = Bottle()
PRODUCTS_DIR = "products"
@app.route("/")
def index():
products = load_products()
return template("index", products=products)
@app.route("/product/<filename>")
def product_page(filename):
product = load_product(filename)
track_visit(filename, request)
return template("product_detail", product=product)
@app.route("/verify/<txid>")
def verify_payment(txid):
""Verify a Bitcoin transaction via Blockstream API."""
resp = requests.get(f"https://blockstream.info/api/tx/{txid}")
if resp.status_code == 200:
tx = resp.json()
confirmed = tx.get("status", {}).get("confirmed", False)
return {"confirmed": confirmed, "txid": txid}
return {"error": "Transaction not found"}, 404
def load_products():
products = []
for f in os.listdir(PRODUCTS_DIR):
if f.endswith(".json"):
with open(os.path.join(PRODUCTS_DIR, f)) as fh:
products.append(json.load(fh))
return products
run(app, host="127.0.0.1", port=8080)
Bitcoin Payment Verification
Zero-fee payments using the Blockstream API:
import requests
from datetime import datetime, timedelta
class PaymentVerifier:
BASE_URL = "https://blockstream.info/api"
def verify_address_payment(self, address, expected_sats):
""Check if an address received the expected amount."""
utxos = requests.get(
f"{self.BASE_URL}/address/{address}/utxo",
timeout=10
).json()
total = sum(u["value"] for u in utxos)
return total >= expected_sats, total
def get_payment_qr(self, address, amount_btc, label=""):
""Generate a BIP21 Bitcoin URI."""
uri = f"bitcoin:{address}?amount={amount_btc}"
if label:
uri += f"&label={label}"
return uri
SEO Optimization
The storefront generates SEO-friendly pages automatically:
@app.route("/sitemap.xml")
def sitemap():
products = load_products()
urls = ['<url><loc>https://anna-lilith.com/</loc><changefreq>daily</changefreq></url>']
for p in products:
urls.append(
f'<url><loc>https://anna-lilith.com/product/{p["slug"]}</loc>'
f'<changefreq>weekly</changefreq></url>'
)
xml = f'<?xml version="1.0"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">{chr(10).join(urls)}</urlset>'
return xml, {"Content-Type": "application/xml"}
Content Generation Pipeline
AI generates product descriptions and blog posts:
def generate_product_description(product):
prompt = f"Write a compelling description for '{product['name']}'. Category: {product['category']}. Price: ${product['price']}. 100-200 words."
resp = requests.post("http://localhost:11434/api/generate", json={
"model": "qwen2.5:1.5b",
"prompt": prompt,
"stream": False,
})
return resp.json()["response"]
Deployment
The entire stack runs behind a Cloudflare Tunnel for free HTTPS:
# Start the app
python3 storefront.py &
# Expose via Cloudflare Tunnel (free, no port forwarding)
cloudflared tunnel --url http://localhost:8080
Results
- 193 products auto-generated and validated
- Free hosting via Cloudflare Tunnel
- Zero payment fees with Bitcoin
- SEO-optimized with auto-generated sitemaps
- AI-powered content generation
Total cost: $0/month. Revenue: $10 and counting.