TikTok Product Trend Detection Before Amazon

import requests, os

def search_tiktok_trends(keyword, num_results=20):
    """Search TikTok for trending product content."""
    resp = requests.post(
        "https://api.scavio.dev/api/v1/tiktok/search",
        headers={"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"},
        json={"query": keyword, "num_results": num_results},
    )
    return resp.json()

def analyze_trend_strength(keyword):
    """Measure how strongly a product is trending on TikTok."""
    data = search_tiktok_trends(keyword)
    videos = data.get("results", [])

    if not videos:
        return {"keyword": keyword, "trend_score": 0, "signal": "no_data"}

    total_views = sum(v.get("views", 0) for v in videos)
    total_likes = sum(v.get("likes", 0) for v in videos)
    recent_videos = len(videos)

    return {
        "keyword": keyword,
        "total_views": total_views,
        "total_likes": total_likes,
        "video_count": recent_videos,
        "avg_views": total_views // max(recent_videos, 1),
        "engagement_rate": total_likes / max(total_views, 1),
    }

# Example: check if a product is trending
trend = analyze_trend_strength("sunset lamp")
print(f"Product: {trend['keyword']}")
print(f"Videos found: {trend['video_count']}")
print(f"Total views: {trend['total_views']:,}")
print(f"Avg views: {trend['avg_views']:,}")

import requests, os

def cross_reference_demand(product_name):
    """Cross-reference TikTok trend with Google and Amazon signals."""
    headers = {"x-api-key": os.environ["SCAVIO_API_KEY"]}
    base = "https://api.scavio.dev/api/v1/search"

    signals = {}

    # 1. TikTok trend strength
    tiktok_data = analyze_trend_strength(product_name)
    signals["tiktok"] = tiktok_data

    # 2. Google purchase intent
    resp = requests.post(
        base, headers=headers,
        json={
            "query": f"buy {product_name} 2026",
            "num_results": 10,
        },
    )
    google_results = resp.json().get("organic_results", [])
    shopping_results = resp.json().get("shopping_results", [])
    signals["google"] = {
        "organic_results": len(google_results),
        "shopping_results": len(shopping_results or []),
        "amazon_in_results": sum(
            1 for r in google_results if "amazon.com" in r.get("link", "")
        ),
    }

    # 3. Amazon competition check
    resp = requests.post(
        base, headers=headers,
        json={
            "query": f"{product_name} site:amazon.com",
            "num_results": 10,
        },
    )
    amazon_results = resp.json().get("organic_results", [])
    signals["amazon"] = {
        "existing_listings": len(amazon_results),
        "top_listings": [
            {"title": r.get("title", ""), "url": r.get("link", "")}
            for r in amazon_results[:3]
        ],
    }

    # 4. Trend timing assessment
    signals["assessment"] = {
        "tiktok_trending": tiktok_data.get("total_views", 0) > 100000,
        "google_confirms": signals["google"]["shopping_results"] > 0,
        "amazon_saturated": signals["amazon"]["existing_listings"] > 7,
    }

    return signals

# Example
demand = cross_reference_demand("cloud light LED")
assessment = demand["assessment"]
print(f"TikTok trending: {assessment['tiktok_trending']}")
print(f"Google confirms demand: {assessment['google_confirms']}")
print(f"Amazon already saturated: {assessment['amazon_saturated']}")

import requests, os, json
from datetime import datetime

def daily_trend_scan(product_categories):
    """Daily scan of TikTok trends across product categories."""
    results = []

    for category in product_categories:
        demand = cross_reference_demand(category)
        assessment = demand.get("assessment", {})

        if assessment.get("tiktok_trending") and not assessment.get("amazon_saturated"):
            results.append({
                "date": datetime.now().isoformat(),
                "product": category,
                "tiktok_views": demand["tiktok"].get("total_views", 0),
                "google_shopping": demand["google"].get("shopping_results", 0),
                "amazon_listings": demand["amazon"].get("existing_listings", 0),
                "opportunity": "HIGH" if not assessment.get("google_confirms") else "MEDIUM",
            })

    return results

# Run daily on target categories
categories = [
    "LED cloud light",
    "portable blender",
    "mini projector",
    "aesthetic desk organizer",
    "sunset lamp",
    "book nook kit",
]
opportunities = daily_trend_scan(categories)
for opp in opportunities:
    print(f"[{opp['opportunity']}] {opp['product']}: "
          f"{opp['tiktok_views']:,} TikTok views, "
          f"{opp['amazon_listings']} Amazon listings")