pyr0ball
56f942b3fd
scrape_recipes.py: - Switch CDX to HTTPS (avoids HTTP 503 rate-limit bucket) - Restrict product API CDX to 2019–2021 window (pre-HelloFresh instruction stripping) - Replace inline CDX requests with _cdx_get() helper: retries on 429/503 with exponential backoff (15s, 30s, 60s, 120s) - Increase HTML fallback CDX limit from 5 to 10 timestamps - Bump CDX_DELAY 0.5s → 3.0s and REPLAY_DELAY 1.2s → 2.0s (polite scraping) - Fix KeyError: 0 on hero_images dict (normalise dict to list before indexing) discover_wayback.py: - Switch CDX to HTTPS scripts/pipeline/log_utils.py (new): - attach_pipeline_log(script_name): adds a JSON FileHandler to the root logger writing to /Library/Assets/logs/pipeline/<script>_<ts>.jsonl for Avocet Turnstone training data ingestion (kiwi#141 / avocet#67)
538 lines
21 KiB
Python
538 lines
21 KiB
Python
"""
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scrape_recipes.py — fetch full recipe data for slugs in pc_slugs.jsonl.
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For each slug:
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1. Try Wayback /api/v1/products/<slug> — oldest capture first (pre-HelloFresh
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acquisition data is more complete).
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2. If instructions are empty, try the recipe HTML page via Wayback and parse
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inline JSON state or structured markup.
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3. Merge with metadata already in the manifest (title, tags, cook_time, etc.)
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4. Emit one row per recipe to recipes_purplecarrot.parquet in food.com columnar
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format so build_recipe_index.py can import it unchanged.
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Output columns (food.com schema + PC extras ignored by the indexer):
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RecipeId, Name, Subtitle, RecipeIngredientParts, RecipeInstructions,
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RecipeCategory, Keywords, Calories, FatContent, ProteinContent,
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SodiumContent, SugarContent, CarbohydrateContent, FiberContent,
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RecipeServings, Description, ImageURL, CookTime, Slug, Source
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Usage:
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conda run -n cf python -m scripts.pipeline.purple_carrot.scrape_recipes
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conda run -n cf python -m scripts.pipeline.purple_carrot.scrape_recipes \\
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--slugs /Library/Assets/kiwi/pipeline/pc_slugs.jsonl \\
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--out /Library/Assets/kiwi/pipeline/recipes_purplecarrot.parquet \\
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--resume
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"""
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from __future__ import annotations
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import argparse
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import json
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import logging
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import re
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import time
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from pathlib import Path
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from typing import Any
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import requests
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logger = logging.getLogger(__name__)
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CDX_BASE = "https://web.archive.org/cdx/search/cdx"
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WB_BASE = "https://web.archive.org/web"
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PC_HOST = "www.purplecarrot.com"
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REPLAY_DELAY = 2.0
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CDX_DELAY = 3.0 # archive.org CDX rate-limits aggressively; be polite
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DEFAULT_SLUGS = Path("/Library/Assets/kiwi/pipeline/pc_slugs.jsonl")
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DEFAULT_OUT = Path("/Library/Assets/kiwi/pipeline/recipes_purplecarrot.parquet")
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# Inline JSON state embedded by the SSR renderer — used as fallback HTML parser
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_NEXT_DATA_RE = re.compile(r'<script id="__NEXT_DATA__"[^>]*>(.*?)</script>', re.DOTALL)
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_REDUX_STATE_RE = re.compile(r'window\.__INITIAL_STATE__\s*=\s*(\{.*?\});\s*\n', re.DOTALL)
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# ── Wayback helpers ───────────────────────────────────────────────────────────
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def _cdx_get(params: dict) -> list:
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"""CDX request with retry on 429/503 (archive.org rate-limits aggressively)."""
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for attempt in range(4):
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try:
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resp = requests.get(CDX_BASE, params=params, timeout=25)
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if resp.status_code in (429, 503):
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wait = 15 * (2 ** attempt)
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logger.debug("CDX %s — backing off %ds", resp.status_code, wait)
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time.sleep(wait)
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continue
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resp.raise_for_status()
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rows = resp.json()
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return rows if rows else []
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except Exception as exc:
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logger.debug("CDX attempt %d failed: %s", attempt + 1, exc)
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time.sleep(5 * (attempt + 1))
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return []
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def _cdx_timestamps(slug: str) -> list[str]:
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"""Return captured timestamps for a product slug, oldest first (pre-2022 window)."""
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rows = _cdx_get({
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"url": f"{PC_HOST}/api/v1/products/{slug}",
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"output": "json",
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"fl": "timestamp,statuscode",
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"filter": "statuscode:200",
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"limit": "20",
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# Pre-HelloFresh-acquisition captures (2019-2021) are most likely
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# to have full instructions — API stripped them post-acquisition.
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"from": "20190101",
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"to": "20211231",
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})
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if len(rows) < 2:
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return []
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return [row[0] for row in rows[1:]] # timestamps only, oldest first
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def _wayback_json(url: str, timestamp: str) -> Any | None:
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replay = f"{WB_BASE}/{timestamp}/{url}"
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for attempt in range(3):
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try:
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resp = requests.get(replay, timeout=30)
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if resp.status_code == 200:
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return resp.json()
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if resp.status_code in (404, 410):
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return None
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except Exception as exc:
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logger.debug("Wayback JSON attempt %d failed (%s): %s", attempt + 1, url, exc)
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time.sleep(2 ** attempt)
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return None
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def _wayback_html(url: str, timestamp: str) -> str | None:
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replay = f"{WB_BASE}/{timestamp}/{url}"
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for attempt in range(3):
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try:
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resp = requests.get(replay, timeout=30)
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if resp.status_code == 200:
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return resp.text
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if resp.status_code in (404, 410):
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return None
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except Exception as exc:
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logger.debug("Wayback HTML attempt %d failed (%s): %s", attempt + 1, url, exc)
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time.sleep(2 ** attempt)
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return None
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# ── Recipe extraction from API JSON ──────────────────────────────────────────
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def _extract_from_api(data: dict) -> dict | None:
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"""Parse a /api/v1/products/<slug> response into our recipe dict.
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Returns None if the response has no usable content (empty title, etc.).
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Returns a partial dict if only some fields are populated — caller merges
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with manifest metadata.
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"""
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if not data or not isinstance(data, dict):
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return None
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title = data.get("title", "").strip()
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subtitle = data.get("subtitle", "").strip()
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slug = data.get("slug", "")
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skus = data.get("skus") or []
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sku = skus[0] if skus else {}
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# Instructions: list of {step_number, title, description}
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raw_instructions = sku.get("instructions") or []
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steps: list[str] = []
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for step in sorted(raw_instructions, key=lambda s: s.get("step_number", 0)):
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parts = []
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if step.get("title"):
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parts.append(step["title"])
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if step.get("description"):
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parts.append(step["description"])
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if parts:
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steps.append(". ".join(parts))
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# Ingredients: may be in ingredients_quantity or ingredients
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raw_ingr = sku.get("ingredients_quantity") or sku.get("ingredients") or []
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ingredients: list[str] = []
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for item in raw_ingr:
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if isinstance(item, dict):
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qty = item.get("quantity") or item.get("qty") or ""
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unit = item.get("unit") or ""
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name = item.get("name") or item.get("ingredient", {}).get("name", "") if isinstance(item.get("ingredient"), dict) else item.get("ingredient_name", "")
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raw = item.get("raw") or item.get("display_name") or ""
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line = raw or " ".join(filter(None, [str(qty), str(unit), str(name)])).strip()
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if line:
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ingredients.append(line)
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elif isinstance(item, str) and item.strip():
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ingredients.append(item.strip())
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nutrition = sku.get("nutrition_label") or {}
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calories = _num(nutrition.get("calories") or sku.get("calories"))
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fat = _num(nutrition.get("total_fat") or sku.get("fat"))
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protein = _num(nutrition.get("protein") or sku.get("protein"))
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sodium = _num(nutrition.get("sodium") or sku.get("sodium"))
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sugar = _num(nutrition.get("sugar") or nutrition.get("total_sugars"))
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carbs = _num(nutrition.get("total_carbohydrate") or sku.get("carbs"))
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fiber = _num(nutrition.get("dietary_fiber") or sku.get("fiber"))
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tags = sku.get("tags") or data.get("tags") or []
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category = sku.get("meal_type") or sku.get("product_type") or ""
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servings = _num(sku.get("servings"))
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cook_time = sku.get("prep_and_cook_time") or ""
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description = sku.get("description") or ""
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images = sku.get("hero_images") or sku.get("image_versions") or []
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# hero_images can be a list OR a dict keyed by size string — normalise to list
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if isinstance(images, dict):
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images = list(images.values())
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image_url = ""
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if images and isinstance(images[0], dict):
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image_url = images[0].get("image_url") or images[0].get("url") or ""
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if not image_url and data.get("square_image"):
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sq = data["square_image"]
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image_url = sq.get("url") if isinstance(sq, dict) else ""
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return {
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"slug": slug,
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"title": title,
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"subtitle": subtitle,
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"steps": steps,
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"ingredients": ingredients,
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"category": category,
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"tags": tags,
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"calories": calories,
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"fat": fat,
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"protein": protein,
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"sodium": sodium,
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"sugar": sugar,
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"carbs": carbs,
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"fiber": fiber,
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"servings": servings,
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"cook_time": cook_time,
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"description": description,
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"image_url": image_url,
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"has_full_recipe": bool(steps and ingredients),
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}
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def _num(val: Any) -> float | None:
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if val is None:
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return None
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try:
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v = float(str(val).replace("g", "").replace("mg", "").split()[0])
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return v if v > 0 else None
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except Exception:
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return None
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# ── Fallback: HTML inline state parsing ──────────────────────────────────────
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def _extract_from_html(html: str, slug: str) -> dict | None:
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"""Try to pull recipe data from inline JS state in older SSR pages."""
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# Attempt 1: Next.js __NEXT_DATA__
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m = _NEXT_DATA_RE.search(html)
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if m:
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try:
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state = json.loads(m.group(1))
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# Walk the Next.js page props tree looking for recipe data
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props = state.get("props", {}).get("pageProps", {})
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recipe = props.get("recipe") or props.get("product")
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if recipe and isinstance(recipe, dict) and recipe.get("title"):
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return _extract_from_api(recipe)
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except Exception:
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pass
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# Attempt 2: Redux __INITIAL_STATE__
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m = _REDUX_STATE_RE.search(html)
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if m:
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try:
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state = json.loads(m.group(1))
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# Try common Redux state shapes
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for key in ("recipe", "product", "currentRecipe", "currentProduct"):
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recipe = state.get(key)
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if recipe and isinstance(recipe, dict) and recipe.get("title"):
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return _extract_from_api(recipe)
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except Exception:
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pass
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# Attempt 3: JSON-LD structured data
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ld_matches = re.findall(
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r'<script[^>]+type=["\']application/ld\+json["\'][^>]*>(.*?)</script>',
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html, re.DOTALL
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)
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for raw in ld_matches:
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try:
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ld = json.loads(raw)
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if isinstance(ld, list):
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ld = next((x for x in ld if x.get("@type") == "Recipe"), None)
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if not ld or ld.get("@type") != "Recipe":
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continue
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steps = []
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for inst in (ld.get("recipeInstructions") or []):
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if isinstance(inst, dict):
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steps.append(inst.get("text", ""))
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elif isinstance(inst, str):
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steps.append(inst)
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ingredients = ld.get("recipeIngredient") or []
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return {
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"slug": slug,
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"title": ld.get("name", ""),
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"subtitle": "",
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"steps": [s for s in steps if s],
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"ingredients": [i for i in ingredients if i],
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"category": ld.get("recipeCategory", ""),
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"tags": ld.get("keywords", "").split(",") if isinstance(ld.get("keywords"), str) else [],
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"calories": _num((ld.get("nutrition") or {}).get("calories")),
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"fat": None, "protein": None, "sodium": None,
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"sugar": None, "carbs": None, "fiber": None,
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"servings": _num(ld.get("recipeYield")),
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"cook_time": str(ld.get("totalTime") or ld.get("cookTime") or ""),
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"description": ld.get("description", ""),
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"image_url": (ld["image"][0] if isinstance(ld.get("image"), list) else ld.get("image", "")) or "",
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"has_full_recipe": True,
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}
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except Exception:
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pass
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return None
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# ── Per-slug fetch ─────────────────────────────────────────────────────────────
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def fetch_recipe(slug: str, manifest_meta: dict) -> dict | None:
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"""Fetch the fullest available recipe data for a slug from Wayback.
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Returns a merged dict of manifest metadata + API/HTML-extracted content.
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"""
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api_url = f"https://{PC_HOST}/api/v1/products/{slug}"
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html_url = f"https://{PC_HOST}/recipe/{slug}"
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recipe: dict | None = None
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# Try product API — oldest captures are most likely to have full data
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timestamps = _cdx_timestamps(slug)
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time.sleep(CDX_DELAY)
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if not timestamps and manifest_meta.get("wayback_ts"):
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timestamps = [manifest_meta["wayback_ts"]]
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for ts in timestamps:
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data = _wayback_json(api_url, ts)
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time.sleep(REPLAY_DELAY)
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if not data:
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continue
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candidate = _extract_from_api(data)
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if not candidate:
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continue
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recipe = candidate
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if recipe.get("has_full_recipe"):
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logger.debug("[%s] Full recipe from API (ts=%s)", slug, ts)
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break
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logger.debug("[%s] Partial API data (ts=%s) — trying HTML fallback", slug, ts)
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# HTML fallback when API has no steps/ingredients
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if not recipe or not recipe.get("has_full_recipe"):
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html_ts_rows = _cdx_get({
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"url": f"{PC_HOST}/recipe/{slug}",
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"output": "json",
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"fl": "timestamp,statuscode",
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"filter": "statuscode:200",
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"limit": "10",
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})
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html_timestamps = [row[0] for row in html_ts_rows[1:]] if len(html_ts_rows) > 1 else []
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time.sleep(CDX_DELAY)
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for ts in html_timestamps:
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html = _wayback_html(html_url, ts)
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time.sleep(REPLAY_DELAY)
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if not html:
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continue
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html_recipe = _extract_from_html(html, slug)
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if html_recipe and html_recipe.get("has_full_recipe"):
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logger.debug("[%s] Full recipe from HTML (ts=%s)", slug, ts)
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recipe = html_recipe
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break
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# Build merged record: manifest metadata fills any gaps from API/HTML
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merged: dict = {
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"slug": slug,
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"title": manifest_meta.get("title", ""),
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"subtitle": manifest_meta.get("subtitle", ""),
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"steps": [],
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"ingredients": [],
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"category": "",
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"tags": manifest_meta.get("tags") or [],
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"calories": None,
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"fat": None,
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"protein": None,
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"sodium": None,
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"sugar": None,
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"carbs": None,
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"fiber": None,
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"servings": manifest_meta.get("serving_size"),
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"cook_time": manifest_meta.get("cook_time", ""),
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"description": manifest_meta.get("description", ""),
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"image_url": manifest_meta.get("image_url", ""),
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"source": "purple_carrot",
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"wayback_ts": manifest_meta.get("wayback_ts", ""),
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"has_full_recipe": False,
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}
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if recipe:
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for key in recipe:
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# Prefer API/HTML data; keep manifest value only when API field is empty
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val = recipe[key]
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if val or key not in merged or not merged[key]:
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merged[key] = val
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if not merged["title"]:
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logger.warning("[%s] No title — skipping", slug)
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return None
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return merged
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# ── Output formatting ─────────────────────────────────────────────────────────
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def _to_dataframe_row(r: dict) -> dict:
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"""Convert merged recipe dict to food.com-compatible parquet row."""
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# Build plain-text input for allrecipes-style corpus compatibility
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lines = [r["title"]]
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if r.get("subtitle"):
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lines.append(r["subtitle"])
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if r.get("description"):
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lines.append("")
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lines.append(r["description"])
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if r.get("ingredients"):
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lines += ["", "Ingredients:"] + [f"- {i}" for i in r["ingredients"]]
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if r.get("steps"):
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lines += ["", "Directions:"] + [f"- {s}" for s in r["steps"]]
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plain_text = "\n".join(lines)
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source_url = f"https://www.purplecarrot.com/recipe/{r['slug']}"
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return {
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# food.com schema columns (used by build_recipe_index.py)
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"RecipeId": f"pc_{r['slug']}",
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"Name": r["title"],
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"RecipeIngredientParts": r.get("ingredients") or [],
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"RecipeInstructions": r.get("steps") or [],
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"RecipeCategory": r.get("category", ""),
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"Keywords": r.get("tags") or [],
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"Calories": r.get("calories"),
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"FatContent": r.get("fat"),
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"ProteinContent": r.get("protein"),
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"SodiumContent": r.get("sodium"),
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"SugarContent": r.get("sugar"),
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"CarbohydrateContent": r.get("carbs"),
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"FiberContent": r.get("fiber"),
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"RecipeServings": r.get("servings"),
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# PC-specific extras (ignored by indexer, used by training pipeline)
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"Subtitle": r.get("subtitle", ""),
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"Description": r.get("description", ""),
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"ImageURL": r.get("image_url", ""),
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"CookTime": r.get("cook_time", ""),
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"Slug": r["slug"],
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"Source": "purple_carrot",
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"SourceURL": source_url, # canonical attribution link shown in recipe UI
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"HasFullRecipe": r.get("has_full_recipe", False),
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"WaybackTs": r.get("wayback_ts", ""),
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# Also emit plain-text input for allrecipes-compatible corpus search
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"input": plain_text,
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}
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# ── Main ──────────────────────────────────────────────────────────────────────
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def scrape(slugs_file: Path, out_file: Path, resume: bool = True) -> None:
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import pandas as pd
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# Load manifest
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if not slugs_file.exists():
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logger.error("Slugs manifest not found: %s", slugs_file)
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return
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manifest: dict[str, dict] = {}
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with open(slugs_file) as f:
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for line in f:
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line = line.strip()
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if line:
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rec = json.loads(line)
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slug = rec["slug"]
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# Keep the richest metadata if slug appears from multiple sources
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if slug not in manifest or rec.get("source") == "menu":
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manifest[slug] = rec
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logger.info("Manifest: %d unique slugs", len(manifest))
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# Load already-scraped slugs for resume
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done_slugs: set[str] = set()
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existing_rows: list[dict] = []
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if resume and out_file.exists():
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try:
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existing_df = pd.read_parquet(out_file)
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done_slugs = set(existing_df["Slug"].tolist())
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existing_rows = existing_df.to_dict("records")
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logger.info("Resume: %d already scraped", len(done_slugs))
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except Exception as exc:
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logger.warning("Could not load existing parquet for resume: %s", exc)
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todo = [s for s in manifest if s not in done_slugs]
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logger.info("%d slugs to fetch", len(todo))
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rows = list(existing_rows)
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for i, slug in enumerate(todo, 1):
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logger.info("[%d/%d] %s", i, len(todo), slug)
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recipe = fetch_recipe(slug, manifest[slug])
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if recipe:
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rows.append(_to_dataframe_row(recipe))
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status = "full" if recipe.get("has_full_recipe") else "partial"
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logger.info(" -> %s (%s)", recipe.get("title", "?"), status)
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else:
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logger.warning(" -> skipped (no title)")
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# Write checkpoint every 50 recipes
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if i % 50 == 0:
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_write_parquet(rows, out_file)
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logger.info("Checkpoint: %d recipes written", len(rows))
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_write_parquet(rows, out_file)
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full = sum(1 for r in rows if r.get("HasFullRecipe"))
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logger.info(
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"Done. %d recipes written to %s (%d full, %d partial).",
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len(rows), out_file, full, len(rows) - full,
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)
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def _write_parquet(rows: list[dict], out_file: Path) -> None:
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import pandas as pd
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out_file.parent.mkdir(parents=True, exist_ok=True)
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pd.DataFrame(rows).to_parquet(out_file, index=False)
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def main() -> None:
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parser = argparse.ArgumentParser(description="Scrape Purple Carrot recipes from Wayback")
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parser.add_argument("--slugs", type=Path, default=DEFAULT_SLUGS)
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parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
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parser.add_argument(
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"--no-resume", dest="resume", action="store_false",
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help="Start fresh (ignore existing parquet)",
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)
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parser.add_argument("--debug", action="store_true")
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args = parser.parse_args()
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logging.basicConfig(
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level=logging.DEBUG if args.debug else logging.INFO,
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format="%(asctime)s %(levelname)s %(name)s: %(message)s",
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)
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from scripts.pipeline.log_utils import attach_pipeline_log
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attach_pipeline_log("scrape_recipes")
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scrape(args.slugs, args.out, resume=args.resume)
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if __name__ == "__main__":
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main()
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