Matt Van Horn
e03046bd49
Root cause of empty TikTok results: Apify required monthly subscription.
ScrapeCreators is PAYG with 100 free credits and no subscription.
Key fix: ScrapeCreators nests items under aweme_info wrapper
(search_item_list[].aweme_info.{fields}), which the previous
implementation missed, causing all fields to be empty.
Changes:
- Rewrite tiktok.py to use ScrapeCreators REST API
- Add aweme_info unwrapping for correct field extraction
- Add transcript fetching via /video/transcript endpoint
- Add SCRAPECREATORS_API_KEY to env.py config
- Update last30days.py to use env.get_tiktok_token()
- Delete apify_client_wrapper.py (no longer needed)
- Update tests for new date field format (create_time)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
422 lines
13 KiB
Python
422 lines
13 KiB
Python
"""TikTok search via ScrapeCreators API for /last30days.
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Uses ScrapeCreators REST API to search TikTok by keyword, extract engagement
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metrics (views, likes, comments, shares), and fetch video transcripts.
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Requires SCRAPECREATORS_API_KEY in config. 100 free credits, then PAYG.
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API docs: https://scrapecreators.com/docs
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"""
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import re
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import sys
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional, Set
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try:
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import requests as _requests
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except ImportError:
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_requests = None
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SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/tiktok"
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# Depth configurations: how many results to fetch / captions to extract
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DEPTH_CONFIG = {
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"quick": {"results_per_page": 10, "max_captions": 3},
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"default": {"results_per_page": 20, "max_captions": 5},
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"deep": {"results_per_page": 40, "max_captions": 8},
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}
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# Max words to keep from each caption
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CAPTION_MAX_WORDS = 500
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# Stopwords for relevance computation (shared with youtube_yt.py pattern)
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STOPWORDS = frozenset({
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'the', 'a', 'an', 'to', 'for', 'how', 'is', 'in', 'of', 'on',
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'and', 'with', 'from', 'by', 'at', 'this', 'that', 'it', 'my',
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'your', 'i', 'me', 'we', 'you', 'what', 'are', 'do', 'can',
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'its', 'be', 'or', 'not', 'no', 'so', 'if', 'but', 'about',
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'all', 'just', 'get', 'has', 'have', 'was', 'will',
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})
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# Synonym groups for relevance scoring
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SYNONYMS = {
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'hip': {'rap', 'hiphop'},
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'hop': {'rap', 'hiphop'},
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'rap': {'hip', 'hop', 'hiphop'},
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'hiphop': {'rap', 'hip', 'hop'},
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'js': {'javascript'},
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'javascript': {'js'},
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'ts': {'typescript'},
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'typescript': {'ts'},
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'ai': {'artificial', 'intelligence'},
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'ml': {'machine', 'learning'},
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'react': {'reactjs'},
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'reactjs': {'react'},
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}
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def _tokenize(text: str) -> Set[str]:
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"""Lowercase, strip punctuation, remove stopwords, drop single-char tokens."""
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words = re.sub(r'[^\w\s]', ' ', text.lower()).split()
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tokens = {w for w in words if w not in STOPWORDS and len(w) > 1}
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expanded = set(tokens)
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for t in tokens:
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if t in SYNONYMS:
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expanded.update(SYNONYMS[t])
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return expanded
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def _compute_relevance(query: str, text: str, hashtags: List[str] = None) -> float:
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"""Compute relevance as ratio of query tokens found in text + hashtags.
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Uses ratio overlap (intersection / query_length). Hashtags provide
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a TikTok-specific relevance boost. Floors at 0.1.
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"""
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q_tokens = _tokenize(query)
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# Combine text and hashtags for matching
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combined = text
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if hashtags:
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combined = f"{text} {' '.join(hashtags)}"
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t_tokens = _tokenize(combined)
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# Split concatenated hashtags (e.g., "claudecode" -> "claude", "code")
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if hashtags:
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for tag in hashtags:
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tag_lower = tag.lower()
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for qt in q_tokens:
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if qt in tag_lower and qt != tag_lower:
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t_tokens.add(qt)
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if not q_tokens:
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return 0.5 # Neutral fallback
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overlap = len(q_tokens & t_tokens)
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ratio = overlap / len(q_tokens)
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return max(0.1, min(1.0, ratio))
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def _extract_core_subject(topic: str) -> str:
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"""Extract core subject from verbose query for TikTok search.
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Strips meta/research words to keep only the core product/concept name.
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"""
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text = topic.lower().strip()
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# Strip multi-word prefixes
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prefixes = [
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'what are the best', 'what is the best', 'what are the latest',
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'what are people saying about', 'what do people think about',
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'how do i use', 'how to use', 'how to',
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'what are', 'what is', 'tips for', 'best practices for',
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]
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for p in prefixes:
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if text.startswith(p + ' '):
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text = text[len(p):].strip()
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# Strip individual noise words
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noise = {
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'best', 'top', 'good', 'great', 'awesome', 'killer',
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'latest', 'new', 'news', 'update', 'updates',
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'trending', 'hottest', 'popular', 'viral',
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'practices', 'features',
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'recommendations', 'advice',
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'prompt', 'prompts', 'prompting',
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'methods', 'strategies', 'approaches',
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}
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words = text.split()
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filtered = [w for w in words if w not in noise]
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result = ' '.join(filtered) if filtered else text
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return result.rstrip('?!.')
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def _log(msg: str):
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"""Log to stderr (only in interactive terminals; spinner handles non-TTY)."""
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if sys.stderr.isatty():
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sys.stderr.write(f"[TikTok] {msg}\n")
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sys.stderr.flush()
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def _sc_headers(token: str) -> Dict[str, str]:
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"""Build ScrapeCreators request headers."""
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return {
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"x-api-key": token,
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"Content-Type": "application/json",
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}
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def _parse_date(item: Dict[str, Any]) -> Optional[str]:
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"""Parse date from ScrapeCreators TikTok item to YYYY-MM-DD.
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Handles create_time (unix timestamp).
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"""
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ts = item.get("create_time")
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if ts:
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try:
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dt = datetime.fromtimestamp(int(ts), tz=timezone.utc)
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return dt.strftime("%Y-%m-%d")
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except (ValueError, TypeError, OSError):
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pass
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return None
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def _clean_webvtt(text: str) -> str:
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"""Strip WebVTT timestamps and headers from transcript text."""
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if not text:
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return ""
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lines = text.split('\n')
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cleaned = []
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for line in lines:
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line = line.strip()
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if not line:
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continue
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if line.startswith('WEBVTT'):
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continue
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if re.match(r'^\d{2}:\d{2}', line):
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continue
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if '-->' in line:
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continue
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cleaned.append(line)
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return ' '.join(cleaned)
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def search_tiktok(
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topic: str,
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from_date: str,
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to_date: str,
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depth: str = "default",
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token: str = None,
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) -> Dict[str, Any]:
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"""Search TikTok via ScrapeCreators API.
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Args:
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topic: Search topic
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from_date: Start date (YYYY-MM-DD)
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to_date: End date (YYYY-MM-DD)
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depth: 'quick', 'default', or 'deep'
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token: ScrapeCreators API key
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Returns:
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Dict with 'items' list and optional 'error'.
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"""
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if not token:
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return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
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if not _requests:
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return {"items": [], "error": "requests library not installed"}
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config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
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core_topic = _extract_core_subject(topic)
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_log(f"Searching TikTok for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/search/keyword",
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params={"query": core_topic, "sort_by": "relevance"},
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headers=_sc_headers(token),
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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except Exception as e:
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_log(f"ScrapeCreators error: {e}")
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return {"items": [], "error": f"{type(e).__name__}: {e}"}
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# Items are nested under aweme_info
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raw_entries = data.get("search_item_list") or data.get("data") or []
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raw_items = []
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for entry in raw_entries:
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if isinstance(entry, dict):
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info = entry.get("aweme_info", entry)
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raw_items.append(info)
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# Limit to configured count
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raw_items = raw_items[:config["results_per_page"]]
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# Parse items
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items = []
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for raw in raw_items:
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video_id = str(raw.get("aweme_id", ""))
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text = raw.get("desc", "")
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stats = raw.get("statistics") or {}
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play_count = stats.get("play_count") or 0
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digg_count = stats.get("digg_count") or 0
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comment_count = stats.get("comment_count") or 0
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share_count = stats.get("share_count") or 0
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author = raw.get("author") or {}
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author_name = author.get("unique_id", "")
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share_url = raw.get("share_url", "")
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text_extra = raw.get("text_extra") or []
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hashtag_names = [t.get("hashtag_name", "") for t in text_extra
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if isinstance(t, dict) and t.get("hashtag_name")]
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duration = (raw.get("video") or {}).get("duration")
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date_str = _parse_date(raw)
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# Compute relevance with hashtag boost
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relevance = _compute_relevance(core_topic, text, hashtag_names)
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# Build URL: prefer share_url, fallback to constructed URL
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url = share_url.split("?")[0] if share_url else ""
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if not url and author_name and video_id:
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url = f"https://www.tiktok.com/@{author_name}/video/{video_id}"
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items.append({
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"video_id": video_id,
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"text": text,
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"url": url,
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"author_name": author_name,
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"date": date_str,
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"engagement": {
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"views": play_count,
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"likes": digg_count,
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"comments": comment_count,
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"shares": share_count,
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},
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"hashtags": hashtag_names,
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"duration": duration,
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"relevance": relevance,
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"why_relevant": f"TikTok: {text[:60]}" if text else f"TikTok: {core_topic}",
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"caption_snippet": "", # populated by fetch_captions
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})
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# Hard date filter
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in_range = [i for i in items if i["date"] and from_date <= i["date"] <= to_date]
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out_of_range = len(items) - len(in_range)
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if in_range:
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items = in_range
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if out_of_range:
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_log(f"Filtered {out_of_range} videos outside date range")
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else:
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_log(f"No videos within date range, keeping all {len(items)}")
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# Sort by views descending
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items.sort(key=lambda x: x["engagement"]["views"], reverse=True)
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_log(f"Found {len(items)} TikTok videos")
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return {"items": items}
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def fetch_captions(
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video_items: List[Dict[str, Any]],
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token: str,
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depth: str = "default",
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) -> Dict[str, str]:
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"""Fetch transcripts for top N TikTok videos via ScrapeCreators.
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Strategy:
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1. Use the 'text' field (video description) as baseline caption
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2. For top N, call /video/transcript for spoken-word captions
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Args:
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video_items: Items from search_tiktok()
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token: ScrapeCreators API key
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depth: Depth level for caption limit
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Returns:
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Dict mapping video_id -> caption text (truncated to 500 words)
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"""
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config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
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max_captions = config["max_captions"]
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if not video_items or not token or not _requests:
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return {}
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top_items = video_items[:max_captions]
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_log(f"Enriching captions for {len(top_items)} videos")
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captions = {}
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# First pass: use text field as caption (always available, free)
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for item in top_items:
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vid = item["video_id"]
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text = item.get("text", "")
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if text:
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words = text.split()
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if len(words) > CAPTION_MAX_WORDS:
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text = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
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captions[vid] = text
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# Second pass: try to get spoken-word transcripts (1 credit each)
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for item in top_items:
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vid = item["video_id"]
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url = item.get("url", "")
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if not url:
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continue
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/video/transcript",
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params={"url": url},
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headers=_sc_headers(token),
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timeout=15,
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)
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if resp.status_code == 200:
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data = resp.json()
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transcript = data.get("transcript")
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if transcript:
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if isinstance(transcript, list):
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transcript = " ".join(str(s) for s in transcript)
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transcript = _clean_webvtt(transcript)
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if transcript:
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words = transcript.split()
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if len(words) > CAPTION_MAX_WORDS:
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transcript = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
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captions[vid] = transcript
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except Exception as e:
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_log(f"Transcript fetch failed for {vid}: {e}")
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got = sum(1 for v in captions.values() if v)
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_log(f"Got captions for {got}/{len(top_items)} videos")
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return captions
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def search_and_enrich(
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topic: str,
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from_date: str,
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to_date: str,
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depth: str = "default",
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token: str = None,
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) -> Dict[str, Any]:
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"""Full TikTok search: find videos, then fetch captions for top results.
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Args:
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topic: Search topic
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from_date: Start date (YYYY-MM-DD)
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to_date: End date (YYYY-MM-DD)
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depth: 'quick', 'default', or 'deep'
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token: ScrapeCreators API key
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Returns:
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Dict with 'items' list. Each item has a 'caption_snippet' field.
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"""
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# Step 1: Search
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search_result = search_tiktok(topic, from_date, to_date, depth, token)
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items = search_result.get("items", [])
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if not items:
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return search_result
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# Step 2: Fetch captions for top N
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captions = fetch_captions(items, token, depth)
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# Step 3: Attach captions to items
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for item in items:
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vid = item["video_id"]
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caption = captions.get(vid)
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if caption:
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item["caption_snippet"] = caption
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return {"items": items, "error": search_result.get("error")}
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def parse_tiktok_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Parse TikTok search response to normalized format.
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Returns:
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List of item dicts ready for normalization.
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"""
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return response.get("items", [])
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