Claire Novotny
704 lines
24 KiB
Python
704 lines
24 KiB
Python
"""Reddit search via ScrapeCreators API for the v3 pipeline.
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Uses ScrapeCreators REST API to search Reddit globally, discover relevant
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subreddits, run targeted subreddit searches, and fetch comment trees.
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Requires SCRAPECREATORS_API_KEY in config (same key as TikTok + Instagram).
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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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import time
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from collections import Counter
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from concurrent.futures import ThreadPoolExecutor, as_completed, wait as futures_wait
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from typing import Any, Dict, List, Optional, Set
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def _first_of(*values, default=None):
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"""Return first value that is not None."""
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for v in values:
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if v is not None:
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return v
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return default
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from . import dates, http, log
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SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/reddit"
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# Depth configurations: how many API calls per phase
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DEPTH_CONFIG = {
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"quick": {
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"global_searches": 1,
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"subreddit_searches": 2,
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"comment_enrichments": 3,
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"timeframe": "week",
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},
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"default": {
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"global_searches": 2,
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"subreddit_searches": 3,
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"comment_enrichments": 5,
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"timeframe": "month",
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},
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"deep": {
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"global_searches": 3,
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"subreddit_searches": 5,
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"comment_enrichments": 8,
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"timeframe": "month",
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},
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}
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from .query import extract_core_subject as _query_extract
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from .relevance import token_overlap_relevance
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# Reddit-specific noise words (preserves original smaller set)
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NOISE_WORDS = 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',
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'practices', 'features', 'tips',
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'recommendations', 'advice',
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'prompt', 'prompts', 'prompting',
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'methods', 'strategies', 'approaches',
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'how', 'to', 'the', 'a', 'an', 'for', 'with',
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'of', 'in', 'on', 'is', 'are', 'what', 'which',
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'guide', 'tutorial', 'using',
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})
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def _log(msg: str):
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log.source_log("Reddit", msg, tty_only=False)
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def _extract_core_subject(topic: str) -> str:
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"""Extract core subject from verbose query.
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Strips meta/research words to keep only the core product/concept name.
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"""
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return _query_extract(topic, noise=NOISE_WORDS)
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def expand_reddit_queries(topic: str, depth: str) -> List[str]:
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"""Generate multiple Reddit search queries from a topic.
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Uses local logic (no LLM call needed):
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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. For default/deep: add casual/review variant
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4. For deep: add problem/issues variant
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Returns 1-4 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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# Broader variant: include more context from original topic
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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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# Product queries: always include review-oriented variant to bias toward
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# review communities instead of keyword-matching unrelated subreddits.
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if qtype == "product":
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queries.append(f"{core} review OR recommendation OR best")
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# Comparison queries: include head-to-head discussion variant.
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if qtype == "comparison":
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queries.append(f"{core} worth it OR vs OR compared")
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# Opinion/review variants for default/deep depth.
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if depth in ("default", "deep") and qtype in ("product", "opinion"):
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queries.append(f"{core} worth it OR thoughts OR review")
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# Problem/bug variants are useful for tool workflows, not generic news.
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if depth == "deep" and qtype in ("product", "opinion", "how_to"):
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queries.append(f"{core} issues OR problems OR bug OR broken")
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return queries
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def _infer_query_intent(topic: str) -> str:
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"""Tiny local fallback for Reddit query expansion only."""
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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|configuration|configure|troubleshoot|troubleshooting|error|errors|fix|debug)\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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if re.search(r"\b(predict|prediction|odds|forecast|chance)\b", text):
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return "prediction"
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return "breaking_news"
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# Known utility/meta subreddits that match queries but aren't discussion subs.
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# These get a 0.3x penalty (not banned) in subreddit discovery scoring.
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UTILITY_SUBS = frozenset({
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'namethatsong', 'findthatsong', 'tipofmytongue',
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'whatisthissong', 'helpmefind', 'whatisthisthing',
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'whatsthissong', 'findareddit', 'subredditdrama',
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})
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def discover_subreddits(
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results: List[Dict[str, Any]],
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topic: str = "",
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max_subs: int = 5,
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) -> List[str]:
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"""Extract top subreddits from global search results with relevance weighting.
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Uses frequency + topic-word matching + utility-sub penalties + engagement
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bonus to find discussion subs rather than utility/meta subs.
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Args:
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results: List of post dicts from global search
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topic: Original search topic (for relevance matching)
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max_subs: Maximum subreddits to return
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Returns:
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Top subreddit names sorted by weighted score
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"""
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core = _extract_core_subject(topic) if topic else ""
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core_words = set(core.lower().split()) if core else set()
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scores = Counter()
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for post in results:
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sub = _extract_subreddit_name(post.get("subreddit", ""))
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if not sub:
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continue
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# Base: frequency count
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base = 1.0
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# Bonus: subreddit name contains a core topic word
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sub_lower = sub.lower()
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if core_words and any(w in sub_lower for w in core_words if len(w) > 2):
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base += 2.0
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# Penalty: known utility/meta subreddits
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if sub_lower in UTILITY_SUBS:
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base *= 0.3
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# Bonus: post engagement (high-engagement posts = better sub)
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ups = _first_of(post.get("ups"), post.get("score"), post.get("votes"), default=0)
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if ups and ups > 100:
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base += 0.5
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scores[sub] += base
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return [sub for sub, _ in scores.most_common(max_subs)]
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def _parse_date(value) -> Optional[str]:
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"""Convert Unix timestamp or ISO-8601 string to YYYY-MM-DD.
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Global search returns ``created_at`` as an ISO string
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(e.g. "2018-05-03T01:09:17.620000+0000"); subreddit search returns
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``created_utc`` as a Unix timestamp. dates.parse_date() handles both,
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plus edge cases like Z suffix and +0000 (no colon) offset.
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Falsy inputs (None, "", 0) return None, matching the original behavior
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where a Unix timestamp of 0 meant "no date" rather than epoch 0.
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"""
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if not value:
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return None
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dt = dates.parse_date(str(value))
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return dt.strftime("%Y-%m-%d") if dt else None
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def _extract_subreddit_name(value: Any) -> str:
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"""Extract subreddit name from string or API object dict."""
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if isinstance(value, dict):
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return str(value.get("name") or value.get("display_name") or "").strip()
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return str(value).strip()
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def _extract_score(post: Dict[str, Any]) -> int:
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"""Extract post score from either API schema.
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Global search uses ``votes``; subreddit search uses ``ups``/``score``.
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"""
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return _first_of(post.get("ups"), post.get("score"), post.get("votes"), default=0)
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def _extract_date(post: Dict[str, Any]) -> Optional[str]:
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"""Extract date from either API schema.
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Global search uses ``created_at`` (ISO); subreddit search uses ``created_utc`` (Unix).
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"""
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return _parse_date(
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post.get("created_utc") or post.get("created_at") or post.get("created_at_iso")
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)
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def _normalize_reddit_id(raw_id: str) -> str:
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"""Strip Reddit fullname prefix (t3_) for consistent dedup."""
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s = str(raw_id or "")
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return s[3:] if s.startswith("t3_") else s
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def _total_engagement(item: Dict[str, Any]) -> int:
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"""Combined engagement score: upvotes + comment count.
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Used for selecting which threads to enrich with comments.
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Threads with lots of comments are high-value even if upvote score is low.
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"""
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eng = item.get("engagement", {})
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score = eng.get("score", 0) or 0
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num_comments = eng.get("num_comments", 0) or 0
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return score + num_comments
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def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global", query: str = "") -> Dict[str, Any]:
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"""Normalize a ScrapeCreators Reddit post to our internal format.
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Handles both the global-search schema (``votes``, ``created_at``,
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``subreddit`` as dict) and the subreddit-search schema (``ups``/``score``,
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``created_utc``, ``subreddit`` as string).
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"""
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permalink = post.get("permalink", "")
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url = f"https://www.reddit.com{permalink}" if permalink else post.get("url", "")
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# Ensure URL looks like a Reddit thread
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if url and "reddit.com" not in url:
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url = ""
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title = str(post.get("title", "")).strip()
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selftext = str(post.get("selftext", ""))
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# Score the title first, then let the body provide limited support.
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# This keeps long selftexts from overpowering the visible topic signal.
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relevance = _compute_post_relevance(query, title, selftext) if query else 0.7
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return {
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"id": f"R{idx}",
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"reddit_id": _normalize_reddit_id(post.get("id", "")),
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"title": title,
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"url": url,
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"subreddit": _extract_subreddit_name(post.get("subreddit", "")),
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"date": _extract_date(post),
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"engagement": {
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"score": _extract_score(post),
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"num_comments": post.get("num_comments", 0),
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"upvote_ratio": post.get("upvote_ratio"),
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},
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"relevance": relevance,
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"why_relevant": f"Reddit {source_label} search",
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"selftext": str(post.get("selftext", ""))[:500],
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}
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def _compute_post_relevance(query: str, title: str, selftext: str) -> float:
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"""Compute Reddit relevance with title-first weighting.
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Title should carry most of the weight because it is the visible summary the
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user sees. Selftext can lift a marginal match, but it should not rescue a
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weak or ambiguous title into the top ranks.
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"""
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title_score = token_overlap_relevance(query, title)
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if not selftext.strip():
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return title_score
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body_score = token_overlap_relevance(query, selftext)
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support_score = max(title_score, body_score)
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return round(0.75 * title_score + 0.25 * support_score, 2)
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def _global_search(
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query: str,
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token: str,
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sort: str = "relevance",
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timeframe: str = "month",
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) -> List[Dict[str, Any]]:
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"""Search across all of Reddit via ScrapeCreators global search.
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Args:
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query: Search query
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token: ScrapeCreators API key
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sort: Sort order (relevance, hot, top, new)
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timeframe: Time filter (hour, day, week, month, year, all)
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Returns:
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List of post dicts
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"""
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try:
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data = http.get(
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f"{SCRAPECREATORS_BASE}/search",
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headers=http.scrapecreators_headers(token),
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params={"query": query, "sort": sort, "timeframe": timeframe},
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timeout=30,
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retries=2,
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)
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return data.get("posts", data.get("data", []))
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except http.HTTPError as e:
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if e.status_code in (401, 403):
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raise
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_log(f"Global search error: {e}")
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return []
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except Exception as e:
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_log(f"Global search error: {e}")
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return []
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def _subreddit_search(
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subreddit: str,
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query: str,
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token: str,
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sort: str = "relevance",
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timeframe: str = "month",
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) -> List[Dict[str, Any]]:
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"""Search within a specific subreddit via ScrapeCreators.
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Args:
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subreddit: Subreddit name (without r/)
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query: Search query
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token: ScrapeCreators API key
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sort: Sort order
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timeframe: Time filter
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Returns:
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List of post dicts
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"""
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try:
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data = http.get(
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f"{SCRAPECREATORS_BASE}/subreddit/search",
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headers=http.scrapecreators_headers(token),
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params={
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"subreddit": subreddit,
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"query": query,
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"sort": sort,
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"timeframe": timeframe,
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},
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timeout=30,
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retries=2,
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)
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return data.get("posts", data.get("data", []))
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except Exception as e:
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_log(f"Subreddit search error for r/{subreddit}: {e}")
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return []
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def fetch_post_comments(
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url: str,
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token: str,
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) -> List[Dict[str, Any]]:
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"""Fetch comments for a Reddit post via ScrapeCreators.
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Args:
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url: Reddit post URL or permalink
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token: ScrapeCreators API key
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Returns:
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List of comment dicts with score, author, body, etc.
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"""
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try:
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data = http.get(
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f"{SCRAPECREATORS_BASE}/post/comments",
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headers=http.scrapecreators_headers(token),
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params={"url": url},
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timeout=30,
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retries=2,
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)
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return data.get("comments", data.get("data", []))
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except Exception as e:
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_log(f"Comment fetch error: {e}")
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return []
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def _dedupe_posts(posts: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Deduplicate posts by reddit_id, keeping first occurrence."""
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seen_ids = set()
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seen_urls = set()
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unique = []
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for post in posts:
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rid = post.get("reddit_id", "")
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url = post.get("url", "")
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if rid and rid in seen_ids:
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continue
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if url and url in seen_urls:
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continue
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if rid:
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seen_ids.add(rid)
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if url:
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seen_urls.add(url)
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unique.append(post)
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return unique
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def search_reddit(
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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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subreddits: List[str] | None = None,
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) -> Dict[str, Any]:
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"""Full Reddit search: multi-query global discovery + subreddit drill-down.
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This is the main v3 Reddit entry point.
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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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subreddits: Optional list of subreddit names to search first (pre-resolved)
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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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timeframe = config["timeframe"]
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intent = _infer_query_intent(topic)
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# === Phase 1: Query Expansion ===
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queries = expand_reddit_queries(topic, depth)
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_log(f"Expanded '{topic}' into {len(queries)} queries: {queries}")
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core = _extract_core_subject(topic)
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# === Phase 1.5: Pre-resolved subreddit search (high-signal) ===
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all_raw_posts = []
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all_items: List[Dict[str, Any]] = []
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if subreddits:
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_log(f"Searching pre-resolved subreddits: {subreddits}")
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with ThreadPoolExecutor(max_workers=min(5, len(subreddits))) as executor:
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futures = {}
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for sub in subreddits:
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futures[executor.submit(_subreddit_search, sub, core, token, "relevance", timeframe)] = sub
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for future in as_completed(futures):
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sub = futures[future]
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sub_posts = future.result()
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_log(f" -> {len(sub_posts)} results from pre-resolved r/{sub}")
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for j, post in enumerate(sub_posts):
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item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}", query=core)
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all_items.append(item)
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# === Phase 2: Global Discovery ===
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max_global = config["global_searches"]
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with ThreadPoolExecutor(max_workers=max_global or 1) as executor:
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futures = {}
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for i, query in enumerate(queries[:max_global]):
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# Product/comparison queries: sort=top surfaces high-engagement posts
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# from relevant communities instead of keyword-matched noise.
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sort = "top" if intent in ("product", "comparison") else ("relevance" if i == 0 else "top")
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_log(f"Global search {i+1}/{max_global}: '{query}' (sort={sort})")
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futures[executor.submit(_global_search, query, token, sort, timeframe)] = query
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for future in as_completed(futures):
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query = futures[future]
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posts = future.result()
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_log(f" -> {len(posts)} results for '{query}'")
|
|
all_raw_posts.extend(posts)
|
|
|
|
# Normalize all posts (with query for relevance scoring)
|
|
for i, post in enumerate(all_raw_posts):
|
|
item = _normalize_post(post, i + 1, "global", query=core)
|
|
all_items.append(item)
|
|
|
|
# === Phase 3: Subreddit Discovery + Targeted Search ===
|
|
subreddit_budget = 0 if intent == "how_to" else config["subreddit_searches"]
|
|
discovered_subs = discover_subreddits(all_raw_posts, topic=topic, max_subs=subreddit_budget)
|
|
_log(f"Discovered subreddits: {discovered_subs}")
|
|
|
|
subreddit_limit = subreddit_budget
|
|
if subreddit_limit > 0:
|
|
with ThreadPoolExecutor(max_workers=subreddit_limit) as executor:
|
|
futures = {}
|
|
for sub in discovered_subs[:subreddit_limit]:
|
|
_log(f"Subreddit search: r/{sub} for '{core}'")
|
|
futures[executor.submit(_subreddit_search, sub, core, token, "relevance", timeframe)] = sub
|
|
for future in as_completed(futures):
|
|
sub = futures[future]
|
|
sub_posts = future.result()
|
|
_log(f" -> {len(sub_posts)} results from r/{sub}")
|
|
for j, post in enumerate(sub_posts):
|
|
item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}", query=core)
|
|
all_items.append(item)
|
|
|
|
# === Phase 4: Deduplicate ===
|
|
all_items = _dedupe_posts(all_items)
|
|
_log(f"After dedup: {len(all_items)} unique posts")
|
|
|
|
# === Phase 5: Date filter ===
|
|
in_range = []
|
|
out_of_range = 0
|
|
for item in all_items:
|
|
if item["date"] and from_date <= item["date"] <= to_date:
|
|
in_range.append(item)
|
|
elif item["date"] is None:
|
|
in_range.append(item) # Keep unknown dates
|
|
else:
|
|
out_of_range += 1
|
|
|
|
if in_range:
|
|
all_items = in_range
|
|
if out_of_range:
|
|
_log(f"Filtered {out_of_range} posts outside date range")
|
|
else:
|
|
_log(f"No posts within date range, keeping all {len(all_items)}")
|
|
|
|
# === Phase 6: Sort by engagement (upvotes + comment count) ===
|
|
all_items.sort(
|
|
key=lambda x: _total_engagement(x),
|
|
reverse=True,
|
|
)
|
|
|
|
# Re-index IDs
|
|
for i, item in enumerate(all_items):
|
|
item["id"] = f"R{i+1}"
|
|
|
|
_log(f"Final: {len(all_items)} Reddit posts")
|
|
return {"items": all_items}
|
|
|
|
|
|
def enrich_with_comments(
|
|
items: List[Dict[str, Any]],
|
|
token: str,
|
|
depth: str = "default",
|
|
budget_seconds: int = 60,
|
|
) -> List[Dict[str, Any]]:
|
|
"""Enrich top items with comment data from ScrapeCreators.
|
|
|
|
Args:
|
|
items: Reddit items from search_reddit()
|
|
token: ScrapeCreators API key
|
|
depth: Depth for comment limit
|
|
budget_seconds: Maximum total time for enrichment. If exceeded,
|
|
returns items with whatever enrichment completed. Never discards items.
|
|
|
|
Returns:
|
|
Items with top_comments and comment_insights added.
|
|
"""
|
|
config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
|
|
max_comments = config["comment_enrichments"]
|
|
|
|
if not items or not token or max_comments <= 0:
|
|
return items
|
|
|
|
# Select the top threads by total engagement (upvotes + comment count),
|
|
# not by list position. This ensures high-comment threads like [FRESH ALBUM]
|
|
# always get enriched even if their upvote score is low.
|
|
ranked = sorted(items, key=_total_engagement, reverse=True)
|
|
top_items = ranked[:max_comments]
|
|
_log(f"Enriching comments for {len(top_items)} posts (by total engagement)")
|
|
|
|
start = time.monotonic()
|
|
|
|
with ThreadPoolExecutor(max_workers=min(4, len(top_items))) as executor:
|
|
futures = {
|
|
executor.submit(fetch_post_comments, item.get("url", ""), token): item
|
|
for item in top_items
|
|
if item.get("url")
|
|
}
|
|
|
|
# Wait with budget instead of unbounded as_completed
|
|
remaining = max(0, budget_seconds - (time.monotonic() - start))
|
|
done, not_done = futures_wait(futures, timeout=remaining)
|
|
|
|
enriched_count = 0
|
|
for future in done:
|
|
item = futures[future]
|
|
try:
|
|
raw_comments = future.result(timeout=0)
|
|
except Exception:
|
|
continue
|
|
if not raw_comments:
|
|
continue
|
|
|
|
top_comments = []
|
|
insights = []
|
|
|
|
for ci, c in enumerate(raw_comments[:10]):
|
|
body = c.get("body", "")
|
|
if not body or body in ("[deleted]", "[removed]"):
|
|
continue
|
|
|
|
score = c.get("ups") or c.get("score", 0)
|
|
author = c.get("author", "[deleted]")
|
|
permalink = c.get("permalink", "")
|
|
comment_url = f"https://reddit.com{permalink}" if permalink else ""
|
|
|
|
max_excerpt = 400 if ci == 0 else 300
|
|
top_comments.append({
|
|
"score": score,
|
|
"date": _parse_date(c.get("created_utc")),
|
|
"author": author,
|
|
"excerpt": body[:max_excerpt],
|
|
"url": comment_url,
|
|
})
|
|
|
|
if len(body) >= 30 and author not in ("[deleted]", "[removed]", "AutoModerator"):
|
|
insight = body[:150]
|
|
if len(body) > 150:
|
|
for i, char in enumerate(insight):
|
|
if char in '.!?' and i > 50:
|
|
insight = insight[:i+1]
|
|
break
|
|
else:
|
|
insight = insight.rstrip() + "..."
|
|
insights.append(insight)
|
|
|
|
top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
|
|
item["top_comments"] = top_comments[:10]
|
|
item["comment_insights"] = insights[:10]
|
|
enriched_count += 1
|
|
|
|
if not_done:
|
|
_log(f"Enrichment budget hit ({budget_seconds}s): {enriched_count}/{len(futures)} posts enriched, {len(not_done)} skipped")
|
|
for future in not_done:
|
|
future.cancel()
|
|
else:
|
|
elapsed = time.monotonic() - start
|
|
_log(f"Enriched {enriched_count}/{len(futures)} posts in {elapsed:.1f}s")
|
|
|
|
return items
|
|
|
|
|
|
def search_and_enrich(
|
|
topic: str,
|
|
from_date: str,
|
|
to_date: str,
|
|
depth: str = "default",
|
|
token: str = None,
|
|
subreddits: List[str] | None = None,
|
|
) -> Dict[str, Any]:
|
|
"""Full Reddit pipeline: search + comment enrichment.
|
|
|
|
This is the convenience function that does everything.
|
|
|
|
Args:
|
|
topic: Search topic
|
|
from_date: Start date (YYYY-MM-DD)
|
|
to_date: End date (YYYY-MM-DD)
|
|
depth: 'quick', 'default', or 'deep'
|
|
token: ScrapeCreators API key
|
|
subreddits: Optional list of subreddit names to search first (pre-resolved)
|
|
|
|
Returns:
|
|
Dict with 'items' list. Items include top_comments and comment_insights.
|
|
"""
|
|
result = search_reddit(topic, from_date, to_date, depth, token, subreddits=subreddits)
|
|
items = result.get("items", [])
|
|
|
|
if items and token:
|
|
items = enrich_with_comments(items, token, depth)
|
|
result["items"] = items
|
|
|
|
return result
|
|
|
|
|
|
def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Parse ScrapeCreators response to item list.
|
|
|
|
Parse raw Reddit search output into the generic item shape.
|
|
"""
|
|
return response.get("items", [])
|