Matt Van Horn
db75f9e341
Add Instagram Reels as the 8th research source via ScrapeCreators API. One API key (SCRAPECREATORS_API_KEY) now covers both TikTok and Instagram. - Add scripts/lib/instagram.py: keyword search, transcript extraction, relevance scoring, engagement metrics (views, likes, comments) - Add InstagramItem to schema, normalization, scoring, dedup, rendering - Add Instagram to orchestrator pipeline, watchlist, and UI spinners - Update SKILL.md: stats template, citation priority, item format, URL-to-name extraction rules, anti-Sources instruction - Update README and CHANGELOG for v2.8 - Fix: Instagram/TikTok not running in --search= web-only path - Fix: web stats line showing full URLs instead of domain names - Replace APIFY_API_TOKEN with SCRAPECREATORS_API_KEY throughout Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
438 lines
14 KiB
Python
438 lines
14 KiB
Python
"""Instagram Reels search via ScrapeCreators API for /last30days.
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Uses ScrapeCreators REST API to search Instagram Reels by keyword, extract
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engagement metrics (views, likes, comments), 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"
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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 tiktok.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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an Instagram-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 Instagram 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"[Instagram] {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 Instagram item to YYYY-MM-DD.
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Handles taken_at as ISO string (e.g. "2026-02-26T16:00:00.000Z")
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or unix timestamp.
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"""
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ts = item.get("taken_at")
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if not ts:
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return None
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# Try ISO string first (ScrapeCreators reels/search returns this)
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if isinstance(ts, str):
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try:
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# Handle "2026-02-26T16:00:00.000Z" format
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dt = datetime.fromisoformat(ts.replace("Z", "+00:00"))
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return dt.strftime("%Y-%m-%d")
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except (ValueError, TypeError):
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pass
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# Try just the date portion
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if len(ts) >= 10:
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return ts[:10]
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# Fall back to unix timestamp
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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 _extract_hashtags(caption_text: str) -> List[str]:
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"""Extract hashtags from Instagram caption text."""
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if not caption_text:
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return []
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return re.findall(r'#(\w+)', caption_text)
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def search_instagram(
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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 Instagram Reels 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 Instagram 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}/v1/instagram/reels/search",
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params={"query": core_topic},
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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 in the 'reels' array (ScrapeCreators v1 response)
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raw_items = data.get("reels") or data.get("items") or data.get("data") or []
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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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if not isinstance(raw, dict):
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continue
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# Extract reel ID and shortcode
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reel_pk = str(raw.get("id", raw.get("pk", "")))
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shortcode = raw.get("shortcode", raw.get("code", ""))
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# Caption text — can be a string or dict depending on endpoint
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caption_obj = raw.get("caption", "")
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if isinstance(caption_obj, dict):
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text = caption_obj.get("text", "")
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elif isinstance(caption_obj, str):
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text = caption_obj
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else:
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text = raw.get("desc", raw.get("text", ""))
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# Engagement metrics
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play_count = raw.get("video_play_count") or raw.get("video_view_count") or raw.get("play_count") or 0
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like_count = raw.get("like_count") or 0
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comment_count = raw.get("comment_count") or 0
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# Author info — 'owner' in reels/search, 'user' in user/reels
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owner = raw.get("owner") or raw.get("user") or {}
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author_name = owner.get("username", "")
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# Duration
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duration = raw.get("video_duration")
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# Date
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date_str = _parse_date(raw)
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# Hashtags from caption text
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hashtags = _extract_hashtags(text)
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# Compute relevance with hashtag boost
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relevance = _compute_relevance(core_topic, text, hashtags)
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# Build URL — prefer API-provided url, fallback to shortcode
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url = raw.get("url", "")
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if not url and shortcode:
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url = f"https://www.instagram.com/reel/{shortcode}"
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items.append({
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"video_id": reel_pk,
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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": like_count,
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"comments": comment_count,
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},
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"hashtags": hashtags,
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"duration": duration,
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"relevance": relevance,
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"why_relevant": f"Instagram: {text[:60]}" if text else f"Instagram: {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} reels outside date range")
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else:
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_log(f"No reels 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)} Instagram reels")
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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 Instagram reels via ScrapeCreators.
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Strategy:
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1. Use the 'text' field (caption) as baseline
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2. For top N, call /v2/instagram/media/transcript for spoken-word captions
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Args:
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video_items: Items from search_instagram()
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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)} reels")
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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}/v2/instagram/media/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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transcripts = data.get("transcripts") or []
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if transcripts and isinstance(transcripts, list):
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# Combine all transcript segments
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transcript_text = " ".join(
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t.get("text", "") for t in transcripts
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if isinstance(t, dict) and t.get("text")
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)
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if transcript_text:
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words = transcript_text.split()
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if len(words) > CAPTION_MAX_WORDS:
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transcript_text = ' '.join(words[:CAPTION_MAX_WORDS]) + '...'
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captions[vid] = transcript_text
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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)} reels")
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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 Instagram search: find reels, 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_instagram(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_instagram_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Parse Instagram 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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