Restructure as Codex plugin

2026-04-23 20:14:23 -04:00
parent 1f7e85a03f
commit 72495c1c14
156 changed files with 218 additions and 140 deletions
@@ -0,0 +1,182 @@
+"""Pinterest search via ScrapeCreators API for /last30days.
+
+Uses ScrapeCreators REST API to search Pinterest by keyword, extract
+engagement metrics (saves, comments), and return pin descriptions.
+
+Requires SCRAPECREATORS_API_KEY in config. 100 free API calls, then PAYG.
+API docs: https://scrapecreators.com/docs
+"""
+
+import re
+import sys
+from typing import Any, Dict, List, Optional, Set
+
+try:
+    import requests as _requests
+except ImportError:
+    _requests = None
+
+from . import dates, http, log
+
+SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/pinterest"
+
+# Depth configurations: how many results to fetch
+DEPTH_CONFIG = {
+    "quick":   {"results_per_page": 10},
+    "default": {"results_per_page": 20},
+    "deep":    {"results_per_page": 40},
+}
+
+from .relevance import token_overlap_relevance as _compute_relevance
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query for Pinterest search."""
+    from .query import extract_core_subject
+    _PINTEREST_NOISE = frozenset({
+        'best', 'top', 'good', 'great', 'awesome', 'killer',
+        'latest', 'new', 'news', 'update', 'updates',
+        'trending', 'hottest', 'popular', 'viral',
+        'practices', 'features',
+        'recommendations', 'advice',
+        'prompt', 'prompts', 'prompting',
+        'methods', 'strategies', 'approaches',
+    })
+    return extract_core_subject(topic, noise=_PINTEREST_NOISE)
+
+
+def _log(msg: str):
+    log.source_log("Pinterest", msg)
+
+
+def _parse_items(raw_items: List[Dict[str, Any]], core_topic: str) -> List[Dict[str, Any]]:
+    """Parse raw Pinterest items into normalized dicts.
+
+    Pinterest pins are visual content with descriptions. Saves are the
+    primary engagement signal (analogous to upvotes/likes on other platforms).
+    """
+    items = []
+    for raw in raw_items:
+        if not isinstance(raw, dict):
+            continue
+
+        pin_id = str(raw.get("id", raw.get("pin_id", "")))
+        description = str(raw.get("description") or raw.get("title") or "")
+
+        # Engagement metrics - saves are the primary signal
+        save_count = raw.get("save_count") or raw.get("saves") or raw.get("repin_count") or 0
+        comment_count = raw.get("comment_count") or raw.get("comments") or 0
+
+        # Author info
+        pinner = raw.get("pinner") or raw.get("creator") or raw.get("user") or {}
+        if isinstance(pinner, dict):
+            author_name = pinner.get("username") or pinner.get("full_name") or ""
+        elif isinstance(pinner, str):
+            author_name = pinner
+        else:
+            author_name = ""
+
+        # URL
+        url = raw.get("link") or raw.get("url") or ""
+        if not url and pin_id:
+            url = f"https://www.pinterest.com/pin/{pin_id}/"
+
+        # Board info (container for pins)
+        board = raw.get("board") or {}
+        board_name = board.get("name", "") if isinstance(board, dict) else ""
+
+        # Compute relevance
+        relevance = _compute_relevance(core_topic, description, [])
+
+        items.append({
+            "pin_id": pin_id,
+            "description": description,
+            "url": url,
+            "author": author_name,
+            "board": board_name,
+            "engagement": {
+                "saves": save_count,
+                "comments": comment_count,
+            },
+            "relevance": relevance,
+            "why_relevant": f"Pinterest: {description[:60]}" if description else f"Pinterest: {core_topic}",
+        })
+    return items
+
+
+def parse_pinterest_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse Pinterest search response to normalized format.
+
+    Returns:
+        List of item dicts ready for normalization.
+    """
+    return response.get("items", [])
+
+
+def search_pinterest(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+) -> Dict[str, Any]:
+    """Search Pinterest via ScrapeCreators API.
+
+    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
+
+    Returns:
+        Dict with 'items' list and optional 'error'.
+    """
+    if not token:
+        return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
+
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    core_topic = _extract_core_subject(topic)
+
+    _log(f"Searching Pinterest for '{core_topic}' (depth={depth}, count={config['results_per_page']})")
+
+    if not _requests:
+        _log("requests library not installed, falling back to urllib")
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"keyword": core_topic})
+            url = f"{SCRAPECREATORS_BASE}/search?{params}"
+            headers = http.scrapecreators_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+        except Exception as e:
+            _log(f"ScrapeCreators error (urllib): {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+    else:
+        try:
+            resp = _requests.get(
+                f"{SCRAPECREATORS_BASE}/search",
+                params={"keyword": core_topic},
+                headers=http.scrapecreators_headers(token),
+                timeout=30,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+        except Exception as e:
+            _log(f"ScrapeCreators error: {e}")
+            return {"items": [], "error": f"{type(e).__name__}: {e}"}
+
+    # Extract items from response - try common SC response shapes
+    raw_items = data.get("pins") or data.get("results") or data.get("data") or data.get("items") or []
+
+    # Limit to configured count
+    raw_items = raw_items[:config["results_per_page"]]
+
+    # Parse items
+    items = _parse_items(raw_items, core_topic)
+
+    # Sort by saves descending (primary engagement signal)
+    items.sort(key=lambda x: x["engagement"]["saves"], reverse=True)
+
+    _log(f"Found {len(items)} Pinterest pins")
+    return {"items": items}