How to Build TikTok to Amazon Product Trend Pipeline

import os, requests, json
from datetime import datetime
from collections import Counter

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'}

NICHES = ['skincare routine', 'desk setup', 'kitchen gadgets', 'gym accessories']

def find_tiktok_trends(niche):
    data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
        headers=TH, json={'query': f'{niche} must have 2026'}).json()
    videos = data.get('videos', data.get('data', {}).get('videos', []))
    products = []
    for v in videos:
        desc = v.get('desc', '')
        plays = v.get('stats', {}).get('playCount', 0)
        likes = v.get('stats', {}).get('diggCount', 0)
        if plays > 10000:  # Only viral content
            products.append({
                'desc': desc[:100],
                'plays': plays,
                'likes': likes,
                'engagement': likes / plays if plays else 0,
                'author': v.get('author', {}).get('uniqueId', ''),
            })
    return sorted(products, key=lambda x: x['plays'], reverse=True)

trends = {}
for niche in NICHES:
    trending = find_tiktok_trends(niche)
    trends[niche] = trending
    print(f'\n{niche}: {len(trending)} trending videos')
    for t in trending[:3]:
        print(f'  {t["plays"]:>10,} plays | @{t["author"]:15} | {t["desc"][:45]}')
print(f'\nCost: ${len(NICHES) * 0.005:.3f}')

def check_amazon_availability(product_desc, niche):
    # Extract product keywords from TikTok description
    query = f'{niche} {product_desc[:30]}'
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'platform': 'amazon', 'country_code': 'us'}, timeout=10).json()
    results = data.get('organic_results', [])
    if not results:
        return None
    top = results[0]
    return {
        'title': top.get('title', '')[:60],
        'price': top.get('price', top.get('extracted_price', '')),
        'rating': top.get('rating', ''),
        'link': top.get('link', ''),
        'amazon_position': 1,
    }

print(f'\n=== TikTok -> Amazon Cross-Reference ===')
cross_ref = []
for niche, trending in trends.items():
    for trend in trending[:2]:  # Top 2 per niche
        amazon = check_amazon_availability(trend['desc'], niche)
        if amazon:
            cross_ref.append({
                'niche': niche,
                'tiktok_plays': trend['plays'],
                'tiktok_engagement': trend['engagement'],
                'amazon_title': amazon['title'],
                'amazon_price': amazon['price'],
            })
            print(f'  TikTok: {trend["plays"]:>10,} plays | Amazon: {amazon["price"]} - {amazon["title"][:40]}')
        else:
            print(f'  TikTok: {trend["plays"]:>10,} plays | Amazon: NOT FOUND (opportunity?)')
print(f'\nCross-ref cost: ${len(NICHES) * 2 * 0.005:.3f}')

def score_opportunities(cross_ref):
    print(f'\n{"=" * 60}')
    print(f'  TIKTOK -> AMAZON TREND REPORT')
    print(f'  Date: {datetime.now().strftime("%Y-%m-%d")}')
    print(f'{"=" * 60}')
    for item in cross_ref:
        # Virality score (0-50)
        virality = min(50, item['tiktok_plays'] / 20000)
        # Engagement quality (0-30)
        engagement = min(30, item['tiktok_engagement'] * 300)
        # Total opportunity score
        score = virality + engagement
        item['opportunity_score'] = score
    cross_ref.sort(key=lambda x: x['opportunity_score'], reverse=True)
    print(f'\n  Ranked Opportunities:')
    for i, item in enumerate(cross_ref, 1):
        print(f'  {i}. [{item["opportunity_score"]:5.1f}] {item["niche"]}')
        print(f'     TikTok: {item["tiktok_plays"]:,} plays | {item["tiktok_engagement"]*100:.1f}% engagement')
        print(f'     Amazon: {item["amazon_price"]} - {item["amazon_title"][:45]}')
    total_cost = (len(NICHES) + len(NICHES) * 2) * 0.005
    print(f'\n  Total cost: ${total_cost:.3f}')
    print(f'  Monthly (daily scans): ${total_cost * 30:.2f}')
    print(f'  Spot trends 2-4 weeks before Amazon sales spike.')

score_opportunities(cross_ref)