Ilia Alshanetsky
9dd3f21476
Six source modules each defined an identical 8-line _sc_headers(token)
function returning {"x-api-key": token, "Content-Type": "application/json"}.
Moved it to http.scrapecreators_headers() and migrated all 33 call sites.
Affected files: reddit.py, threads.py, tiktok.py, instagram.py, pinterest.py,
youtube_yt.py. Zero per-source variation, zero behavior change.
Net: -40 lines. 1022 tests pass (15 pre-existing failures unchanged).
Live smoke test: reddit search returns 12 threads with full engagement.
542 lines
18 KiB
Python
542 lines
18 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 API calls, 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 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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from . import dates, http, log
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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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from .relevance import token_overlap_relevance as _compute_relevance
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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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from .query import extract_core_subject
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_TIKTOK_NOISE = frozenset({
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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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return extract_core_subject(topic, noise=_TIKTOK_NOISE)
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def _infer_query_intent(topic: str) -> str:
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"""Tiny local intent classifier for TikTok query expansion."""
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text = topic.lower().strip()
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if re.search(r"\b(vs|versus|compare|difference between)\b", text):
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return "comparison"
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if re.search(r"\b(how to|tutorial|guide|setup|step by step|deploy|install)\b", text):
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return "how_to"
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if re.search(r"\b(thoughts on|worth it|should i|opinion|review)\b", text):
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return "opinion"
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if re.search(r"\b(pricing|feature|features|best .* for)\b", text):
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return "product"
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return "breaking_news"
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def expand_tiktok_queries(topic: str, depth: str) -> List[str]:
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"""Generate multiple TikTok search queries from a topic.
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Mirrors reddit.py's expand_reddit_queries() pattern:
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1. Extract core subject (strip noise words)
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2. Include original topic if different from core
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3. Add intent-specific OR-joined content-type variants
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4. Cap by depth: 1 for quick, 2 for default, 3 for deep
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Returns 1-3 query strings depending on depth.
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"""
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core = _extract_core_subject(topic)
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queries = [core]
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# Include cleaned original topic as variant if different from core
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original_clean = topic.strip().rstrip('?!.')
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if core.lower() != original_clean.lower() and len(original_clean.split()) <= 8:
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queries.append(original_clean)
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qtype = _infer_query_intent(topic)
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# Intent-specific TikTok content-type variants
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if qtype in ("breaking_news", "opinion"):
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queries.append(f"{core} edit OR reaction OR trend")
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elif qtype == "product":
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queries.append(f"{core} review OR haul OR unboxing")
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elif qtype == "comparison":
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queries.append(f"{core} vs OR compared OR which is better")
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elif qtype == "how_to":
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queries.append(f"{core} tutorial OR hack OR tip")
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else:
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queries.append(f"{core} edit OR reaction OR trend")
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# Deep depth: add viral content variant
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if depth == "deep":
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queries.append(f"{core} viral OR fyp OR trending")
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# Cap by depth budget
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caps = {"quick": 1, "default": 2, "deep": 3}
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cap = caps.get(depth, 2)
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return queries[:cap]
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def _log(msg: str):
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log.source_log("TikTok", msg)
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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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ts = item.get("create_time")
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if ts:
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try:
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return dates.timestamp_to_date(int(ts))
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except (ValueError, TypeError):
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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 _parse_items(raw_items: List[Dict[str, Any]], core_topic: str) -> List[Dict[str, Any]]:
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"""Parse raw TikTok items into normalized dicts."""
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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") if isinstance(raw.get("statistics"), dict) else {}
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play_count = stats.get("play_count") if stats.get("play_count") is not None else 0
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digg_count = stats.get("digg_count") if stats.get("digg_count") is not None else 0
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comment_count = stats.get("comment_count") if stats.get("comment_count") is not None else 0
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share_count = stats.get("share_count") if stats.get("share_count") is not None else 0
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author_raw = raw.get("author")
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if isinstance(author_raw, dict):
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author_name = author_raw.get("unique_id", "")
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elif isinstance(author_raw, str):
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author_name = author_raw
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else:
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author_name = ""
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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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video_raw = raw.get("video")
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duration = video_raw.get("duration") if isinstance(video_raw, dict) else None
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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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return items
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def _hashtag_search(
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hashtag: str,
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token: str,
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) -> List[Dict[str, Any]]:
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"""Search TikTok by hashtag via ScrapeCreators.
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Args:
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hashtag: Hashtag name (without #)
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token: ScrapeCreators API key
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Returns:
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List of raw TikTok item dicts (aweme_info format).
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"""
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_log(f"Hashtag search: #{hashtag}")
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if not _requests:
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try:
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from urllib.parse import urlencode
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params = urlencode({"hashtag": hashtag})
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url = f"{SCRAPECREATORS_BASE}/search/hashtag?{params}"
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headers = http.scrapecreators_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(url, headers=headers, timeout=30, retries=2)
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except Exception as e:
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_log(f"Hashtag search error (urllib) for #{hashtag}: {e}")
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return []
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else:
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try:
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resp = _requests.get(
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f"{SCRAPECREATORS_BASE}/search/hashtag",
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params={"hashtag": hashtag},
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headers=http.scrapecreators_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"Hashtag search error for #{hashtag}: {e}")
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return []
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raw_items = data.get("aweme_list") or data.get("data") or []
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_log(f" -> {len(raw_items)} results for #{hashtag}")
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return raw_items
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def _profile_videos(
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handle: str,
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token: str,
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count: int = 10,
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) -> List[Dict[str, Any]]:
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"""Fetch a TikTok creator's recent videos via ScrapeCreators.
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Args:
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handle: TikTok username (without @)
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token: ScrapeCreators API key
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count: Max videos to return
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Returns:
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List of raw TikTok item dicts (aweme_info format).
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"""
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_log(f"Profile videos: @{handle}")
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profile_url = "https://api.scrapecreators.com/v3/tiktok/profile/videos"
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if not _requests:
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try:
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from urllib.parse import urlencode
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params = urlencode({"handle": handle, "sort_by": "latest"})
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url = f"{profile_url}?{params}"
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headers = http.scrapecreators_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(url, headers=headers, timeout=30, retries=2)
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except Exception as e:
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_log(f"Profile videos error (urllib) for @{handle}: {e}")
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return []
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else:
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try:
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resp = _requests.get(
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profile_url,
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params={"handle": handle, "sort_by": "latest"},
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headers=http.scrapecreators_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"Profile videos error for @{handle}: {e}")
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return []
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raw_items = data.get("aweme_list") or data.get("data") or []
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_log(f" -> {len(raw_items)} videos from @{handle}")
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return raw_items[:count]
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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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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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if not _requests:
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_log("requests library not installed, falling back to urllib")
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try:
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from urllib.parse import urlencode
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params = urlencode({"query": core_topic, "sort_by": "relevance"})
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url = f"{SCRAPECREATORS_BASE}/search/keyword?{params}"
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headers = http.scrapecreators_headers(token)
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headers["User-Agent"] = http.USER_AGENT
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data = http.get(url, headers=headers, timeout=30, retries=2)
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except Exception as e:
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_log(f"ScrapeCreators error (urllib): {e}")
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return {"items": [], "error": f"{type(e).__name__}: {e}"}
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else:
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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=http.scrapecreators_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 = _parse_items(raw_items, core_topic)
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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=http.scrapecreators_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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hashtags: List[str] | None = None,
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creators: List[str] | None = 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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Uses expand_tiktok_queries() to generate multiple search queries,
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runs ScrapeCreators for each, and merges/deduplicates results by video ID.
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Args:
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topic: Search topic (raw topic, not planner's narrowed query)
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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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hashtags: Optional list of TikTok hashtags to search (without #)
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creators: Optional list of TikTok creator handles to fetch videos from
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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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core_topic = _extract_core_subject(topic)
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seen_ids: Set[str] = set()
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items: List[Dict[str, Any]] = []
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last_error = None
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# Step 0a: Hashtag search (high-signal, runs first)
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if hashtags and token:
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for hashtag in hashtags:
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|
raw_items = _hashtag_search(hashtag, token)
|
|
parsed = _parse_items(raw_items, core_topic)
|
|
for item in parsed:
|
|
vid = item.get("video_id", "")
|
|
if vid and vid not in seen_ids:
|
|
seen_ids.add(vid)
|
|
items.append(item)
|
|
|
|
# Step 0b: Creator profile videos (high-signal)
|
|
if creators and token:
|
|
for creator in creators:
|
|
raw_items = _profile_videos(creator, token)
|
|
parsed = _parse_items(raw_items, core_topic)
|
|
for item in parsed:
|
|
vid = item.get("video_id", "")
|
|
if vid and vid not in seen_ids:
|
|
seen_ids.add(vid)
|
|
items.append(item)
|
|
|
|
# Step 1: Multi-query keyword search — run ScrapeCreators for each expanded query
|
|
queries = expand_tiktok_queries(topic, depth)
|
|
for q in queries:
|
|
search_result = search_tiktok(q, from_date, to_date, depth, token)
|
|
if search_result.get("error"):
|
|
last_error = search_result["error"]
|
|
for item in search_result.get("items", []):
|
|
vid = item.get("video_id", "")
|
|
if vid and vid not in seen_ids:
|
|
seen_ids.add(vid)
|
|
items.append(item)
|
|
|
|
# Sort merged results by views descending
|
|
items.sort(key=lambda x: x.get("engagement", {}).get("views", 0), reverse=True)
|
|
|
|
if not items:
|
|
return {"items": [], "error": last_error}
|
|
|
|
# Step 2: Fetch captions for top N
|
|
captions = fetch_captions(items, token, depth)
|
|
|
|
# Step 3: Attach captions to items
|
|
for item in items:
|
|
vid = item["video_id"]
|
|
caption = captions.get(vid)
|
|
if caption:
|
|
item["caption_snippet"] = caption
|
|
|
|
return {"items": items, "error": last_error}
|
|
|
|
|
|
def parse_tiktok_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Parse TikTok search response to normalized format.
|
|
|
|
Returns:
|
|
List of item dicts ready for normalization.
|
|
"""
|
|
return response.get("items", [])
|