How to Build TikTok-to-Amazon Product Trend Pipeline

import os, requests, json
from datetime import datetime

API_KEY = os.environ['SCAVIO_API_KEY']
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

PRODUCT_QUERIES = [
    'tiktok made me buy it 2026',
    'viral amazon find',
    'amazon must haves trending',
    'best amazon products tiktok',
]

def search_tiktok_trends(query):
    data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
        headers=TH, json={'query': query}).json()
    videos = data.get('videos', data.get('data', {}).get('videos', []))
    return [{'desc': v.get('desc', '')[:100], 'plays': v.get('stats', {}).get('playCount', 0),
             'likes': v.get('stats', {}).get('diggCount', 0),
             'author': v.get('author', {}).get('uniqueId', '')} for v in videos]

all_trends = []
for q in PRODUCT_QUERIES:
    videos = search_tiktok_trends(q)
    all_trends.extend(videos)
    print(f'"{q}": {len(videos)} videos')
print(f'Total: {len(all_trends)} product videos. Cost: ${len(PRODUCT_QUERIES) * 0.005:.3f}')

import re

def extract_products(videos):
    """Extract likely product names from TikTok video descriptions."""
    products = []
    for v in videos:
        desc = v['desc']
        # Look for product-like patterns
        words = desc.split()
        # Filter for multi-word product names (capitalized sequences)
        current = []
        for w in words:
            clean = re.sub(r'[^a-zA-Z0-9]', '', w)
            if clean and (clean[0].isupper() or clean.lower() in ['led', 'usb', 'mini']):
                current.append(clean)
            else:
                if len(current) >= 2:
                    products.append(' '.join(current))
                current = []
        if len(current) >= 2:
            products.append(' '.join(current))
    # Count frequency
    from collections import Counter
    freq = Counter(products)
    top = freq.most_common(10)
    print(f'\nTop trending products from TikTok:')
    for product, count in top:
        print(f'  {product}: mentioned in {count} videos')
    return [p for p, _ in top]

product_names = extract_products(all_trends)
print(f'\nProducts to check on Amazon: {len(product_names)}')

def check_amazon(product):
    """Search Amazon for a product and return pricing data."""
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': product, 'platform': 'amazon', 'country_code': 'us'}).json()
    results = data.get('organic_results', [])
    if not results:
        return {'product': product, 'available': False}
    top = results[0]
    return {
        'product': product,
        'available': True,
        'title': top.get('title', '')[:60],
        'price': top.get('price', 'N/A'),
        'rating': top.get('rating', 'N/A'),
        'reviews': top.get('reviews', 0),
        'link': top.get('link', '')
    }

def trend_report(product_names):
    print(f'\n=== TikTok -> Amazon Trend Report - {datetime.now().strftime("%Y-%m-%d")} ===')
    for product in product_names[:8]:
        info = check_amazon(product)
        if info['available']:
            print(f'  FOUND: {product}')
            print(f'    Amazon: {info["title"]}')
            print(f'    Price: {info["price"]} | Rating: {info["rating"]} | Reviews: {info["reviews"]}')
        else:
            print(f'  GAP: {product} - not yet on Amazon (opportunity)')
    cost = len(product_names[:8]) * 0.005 + len(PRODUCT_QUERIES) * 0.005
    print(f'\nTotal cost: ${cost:.3f}')

trend_report(product_names)