c737871c4c
This PR introduces TRIAL data — yield-performance results from real
field trials — as a SEPARATE data type alongside variety identity.
The two are complementary:
search_docs → "What's the disease resistance of DKC62-08RIB?"
(variety identity — what it IS)
search_trials → "Which corn hybrid won the IA 2024 trials?"
(performance data — how it PERFORMED)
scrape/sources/gh_plot_reports.py — Golden Harvest plot reports
- 4,618 expected (2024+2025; 2023 deferred to a backfill pass).
- URL: /<crop>/plot-report/<state>/<year>/<plot_id>
- Cross-vendor: each plot lists products from multiple brands
(NK / DEKALB / Golden Harvest / Enogen / Pioneer / Channel) side
by side at one cooperator's field — the kind of independent
comparison data Bayer doesn't publish itself.
- Generic per-column metrics dict (Yield/MST/Test Weight/$/Ac for
corn+soy, Ton/Acre + Milk + Beef columns for silage).
- Politeness: 1 req/sec, retries on 429/5xx, no redirect-follow.
scrape/sources/agripro_trials.py — AgriPro regional trial PDFs
- 14 unique PDFs (38 sitemap links deduped) at /trials-data
- pdfplumber text extraction, region/year detection from filename
- Verbatim PDF text preserved in chunk body so variety + yield
number adjacency drives retrieval (AP Iliad's Aberdeen ID yield
matches a query about "AP Iliad Idaho yield")
rag/chunk.py — chunks_from_trial() dispatching by source
- Plot reports: identity preamble + Top-5 by primary metric + full
ranking table. Metric labels chosen from the data (corn/soy use
"Yield", silage uses "Ton/Acre").
- AgriPro PDFs: identity preamble + verbatim trial body inline so
per-location yields surface for region+variety queries.
- Variety chunks get data_type="variety" metadata; trial chunks get
data_type="trial". Single Chroma collection; the tool router
filters by data_type rather than maintaining two collections.
rag/index.py — dispatch by sidecar's data_type field
rag/bm25.py — new filter columns (data_type, year, state)
docs_mcp/server.py — sixth MCP tool: search_trials(crop?, state?,
year?, product?, k=10)
- Filters trial chunks via where={"data_type": "trial", ...}
- Optional product substring post-filter for "DKC62-08RIB Iowa 2024"
style searches
- search_docs now defaults to data_type="variety" so trial chunks
don't bleed into variety identity queries
- Tool docstring routes the agent: "use lookup_variety to verify
identity details on any trial winner you surface"
NK trial endpoint (/NKSeeds/wsProxy.asmx/GetPlotResult) is documented
as deferred — the ASMX-SOAP shape returned empty XML on initial
probe. Bayer per-variety yield data is not publicly indexed at all
— documented in the trial-scope note (DEKALB/Asgrow trial data flows
through Channel reps, not the web). AgRevival research books exist
as 10 large annual PDFs but are deferred (low ROI per parse).
Initial corpus shipped in this PR: 14 AgriPro trial PDFs. The 4,618
Golden Harvest plot reports are scraping in background and will be
added in a follow-up corpus-snapshot PR (~70 min ETA).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
484 lines
16 KiB
Python
484 lines
16 KiB
Python
"""AgriPro trial-PDF scraper.
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Source: ``agriprowheat.com/trials-data`` — a single page listing
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~38 PDF links to regional wheat trial summary documents. Each PDF
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is a multi-year multi-location performance test comparing AgriPro
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varieties against competitors (LCS, Norwest, PNW, UI, etc.).
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Discovery: walk ``/trials-data``, collect every ``href="*.pdf"``.
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Per-PDF content (parsed via pdfplumber):
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- First line: usually the title (e.g.
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"2024 Pacific Northwest Combined Summary, Three-Year Data")
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- A multi-column table with one row per variety. Columns vary by
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PDF but typically include: 3-yr combined yield, 2-yr combined,
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most-recent-year yield, plus per-location yields with location
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names in the header.
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- Footer notes: locations covered, LSD/CV statistical caveats,
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copyright.
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Trial PDFs are stable text-extractable (no charts). We capture the
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full per-page text verbatim in the chunk body — preserving
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variety-name + yield-number adjacency for the embedder — plus
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metadata derived from the title (region, year, crop class). This is
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a deliberate trade-off: perfect table parsing across the PDF
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variants would be brittle; verbatim text preserves every data point
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and the embedder + BM25 between them can match queries like
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"AP Iliad yield Aberdeen Idaho" reliably.
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Output:
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corpus/agripro_trials/<source_key>.md
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corpus/agripro_trials/<source_key>.json
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source_key convention: ``agt-<slugified-filename-stem>`` lowercased,
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e.g. ``agt-2024-pnw-combined``.
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CLI:
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python -m scrape.sources.agripro_trials --limit 5
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python -m scrape.sources.agripro_trials --force
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"""
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from __future__ import annotations
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import argparse
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import io
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import json
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import logging
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import os
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import random
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import re
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import sys
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import time
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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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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from bs4 import BeautifulSoup
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import pdfplumber
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SCRAPER_VERSION = "0.1.0"
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USER_AGENT = "seed-mcp-scraper/0.1 (+https://drawbar.example/contact)"
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BASE = "https://agriprowheat.com"
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LIST_URL = f"{BASE}/trials-data"
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REPO_ROOT = Path(__file__).resolve().parents[2]
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CORPUS_ROOT = Path(os.environ.get("CORPUS_ROOT") or REPO_ROOT / "corpus")
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CORPUS_DIR = CORPUS_ROOT / "agripro_trials"
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REQ_INTERVAL_SEC = 1.0
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log = logging.getLogger("scrape.agripro_trials")
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# Region name patterns we recognize in PDF filenames / titles. The
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# value is a human-readable normalized region.
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REGION_PATTERNS = (
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(re.compile(r"\bPNW\b|Pacific Northwest", re.I), "Pacific Northwest"),
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(re.compile(r"\bNE Colorado\b|Northeast Colorado", re.I), "NE Colorado"),
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(re.compile(r"\bSC KS\b|South Central Kansas", re.I), "SC Kansas / N Central OK"),
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(re.compile(r"\bWestern Plains\b", re.I), "Western Plains"),
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(re.compile(r"\bCentral Plains\b", re.I), "Central Plains"),
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(re.compile(r"\bPlains Irrigated\b", re.I), "Plains Irrigated"),
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(re.compile(r"\bWashington[/:]?N? *Idaho\b", re.I), "WA / N. Idaho"),
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(re.compile(r"\bSouthern Idaho\b", re.I), "Southern Idaho"),
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(re.compile(r"\bMontana\b", re.I), "Montana"),
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(re.compile(r"\bNP Perf Data\b|Northern Plains", re.I), "Northern Plains"),
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(re.compile(r"\bWheat after Soy\b", re.I), "Wheat-after-Soy rotation"),
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)
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# --------------------------------------------------------------------- HTTP
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class RateLimitedSession:
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def __init__(self, interval: float = REQ_INTERVAL_SEC) -> None:
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self.s = requests.Session()
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self.s.headers["User-Agent"] = USER_AGENT
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self.interval = interval
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self._last = 0.0
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def _wait(self) -> None:
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delta = time.monotonic() - self._last
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if delta < self.interval:
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time.sleep(self.interval - delta)
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self._last = time.monotonic()
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def request(
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self,
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method: str,
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url: str,
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*,
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max_retries: int = 4,
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timeout: float = 60.0,
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**kw: Any,
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) -> requests.Response:
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last_exc: Exception | None = None
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for attempt in range(max_retries):
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self._wait()
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try:
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resp = self.s.request(method, url, timeout=timeout, **kw)
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except requests.RequestException as exc:
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last_exc = exc
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backoff = min(30.0, (2 ** attempt) + random.random())
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log.warning("network error on %s %s: %s — retry in %.1fs",
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method, url, exc, backoff)
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time.sleep(backoff)
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continue
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if resp.status_code == 429 or 500 <= resp.status_code < 600:
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ra = resp.headers.get("Retry-After")
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backoff = float(ra) if (ra and ra.isdigit()) else min(30.0, (2 ** attempt) + random.random())
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log.warning("HTTP %d on %s %s — retry in %.1fs",
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resp.status_code, method, url, backoff)
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time.sleep(backoff)
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continue
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return resp
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if last_exc:
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raise last_exc
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return resp # type: ignore[return-value]
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def get(self, url: str, **kw: Any) -> requests.Response:
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return self.request("GET", url, **kw)
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# --------------------------------------------------------------------- model
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@dataclass
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class TrialPDF:
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source_key: str
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source_url: str
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pdf_url: str
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filename: str
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title: str | None = None
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year: int | None = None
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years_covered: list[int] = field(default_factory=list)
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region: str | None = None
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wheat_class_section: str | None = None # e.g. "Soft White Winter Wheat" — derived from PDF text
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page_text: str = ""
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varieties_found: list[str] = field(default_factory=list)
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# --------------------------------------------------------------------- discovery
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def discover_pdfs(http: RateLimitedSession) -> list[tuple[str, str, str, str]]:
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"""Return ``[(pdf_url, filename, section_heading, section_anchor), ...]``
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for every PDF on /trials-data.
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De-duplicates by pdf_url — multiple section headings may link to
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the same PDF (e.g. a multi-state summary).
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"""
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log.info("fetching trials index %s", LIST_URL)
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r = http.get(LIST_URL)
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r.raise_for_status()
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soup = BeautifulSoup(r.text, "html.parser")
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seen: dict[str, tuple[str, str, str, str]] = {}
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for a in soup.find_all("a", href=re.compile(r"\.pdf(?:$|\?)", re.I)):
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href = a["href"]
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from urllib.parse import urljoin
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full = urljoin(LIST_URL, href)
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fn = href.rsplit("/", 1)[-1]
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# Section context — closest preceding h2/h3/h4
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section = ""
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parent = a.parent
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for _ in range(10):
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if parent is None:
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break
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head = parent.find_previous(["h2", "h3", "h4"])
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if head:
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section = head.get_text(strip=True)
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break
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parent = parent.parent
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if full not in seen:
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seen[full] = (full, fn, section, href)
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out = list(seen.values())
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log.info("trial PDFs found: %d (deduped from %d total links)",
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len(out),
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sum(1 for a in soup.find_all("a", href=re.compile(r"\.pdf", re.I))))
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return out
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# --------------------------------------------------------------------- helpers
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def source_key_for(filename: str) -> str:
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"""``2024 PNW Combined.pdf`` → ``agt-2024-pnw-combined``."""
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from urllib.parse import unquote
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stem = unquote(filename).rsplit(".", 1)[0]
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slug = re.sub(r"[^a-zA-Z0-9]+", "-", stem).strip("-").lower()
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return f"agt-{slug}"
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def _detect_region(text: str) -> str | None:
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for pat, label in REGION_PATTERNS:
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if pat.search(text):
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return label
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return None
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def _detect_years(text: str) -> list[int]:
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"""Return sorted years found in the PDF title / first lines.
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Filters to 2010-2030 to ignore page numbers / table values."""
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years = sorted({
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int(y) for y in re.findall(r"\b(20[1-3]\d)\b", text[:600])
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})
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return years
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def _detect_wheat_class_section(text: str) -> str | None:
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"""The trial PDFs typically have a class label line like
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'Soft White Winter Wheat' near the top of the table."""
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for label in (
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"Hard Red Winter Wheat", "Hard Red Spring Wheat",
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"Hard White Spring Wheat", "Hard White Winter Wheat",
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"Soft White Winter Wheat", "Soft White Spring Wheat",
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"Soft Red Winter Wheat", "Durum",
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):
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if re.search(r"\b" + re.escape(label) + r"\b", text[:1500], re.I):
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return label
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return None
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# Variety name patterns we expect to see in AgriPro trial PDFs.
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# AgriPro varieties = AP <name>, SY <name>; competitors include
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# LCS <name>, UI <name>, PNW <name>, Norwest <name>.
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_VARIETY_LINE_RE = re.compile(
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r"^(?:AP|SY|LCS|UI|PNW|Norwest|WB|Stine|Pioneer)\b[A-Za-z0-9 \-+]*",
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)
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def _detect_varieties(text: str) -> list[str]:
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out: list[str] = []
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seen: set[str] = set()
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for line in text.splitlines():
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line = line.strip()
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if not line:
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continue
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m = _VARIETY_LINE_RE.match(line)
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if m:
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# Up to first run of digits / spaces — variety name only
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name_match = re.match(r"^([A-Za-z][A-Za-z0-9 \-+]*?)\s+\d", line)
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name = name_match.group(1).strip() if name_match else m.group(0).strip()
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# Trim trailing single tokens that are clearly stats
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if name and name not in seen and len(name) <= 40:
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seen.add(name)
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out.append(name)
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return out
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# --------------------------------------------------------------------- detail
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def fetch_pdf_detail(
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http: RateLimitedSession,
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pdf_url: str,
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filename: str,
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) -> TrialPDF | None:
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"""Download + parse one trial PDF."""
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r = http.get(pdf_url)
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if r.status_code == 404:
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return None
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r.raise_for_status()
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try:
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with pdfplumber.open(io.BytesIO(r.content)) as pdf:
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pages_text = []
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for p in pdf.pages:
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t = p.extract_text() or ""
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pages_text.append(t)
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text = "\n\n".join(pages_text).strip()
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except Exception as exc: # noqa: BLE001
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log.warning("PDF parse failed for %s: %s", pdf_url, exc)
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return None
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title = ""
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if text:
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# First non-empty line is usually the title.
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for line in text.splitlines():
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line = line.strip()
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if line:
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title = line
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break
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region = _detect_region(filename) or _detect_region(title or "")
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years = _detect_years(title + "\n" + filename)
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wheat_class_section = _detect_wheat_class_section(text)
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varieties = _detect_varieties(text)
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return TrialPDF(
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source_key=source_key_for(filename),
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source_url=LIST_URL,
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pdf_url=pdf_url,
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filename=filename,
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title=title or None,
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year=years[-1] if years else None,
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years_covered=years,
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region=region,
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wheat_class_section=wheat_class_section,
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page_text=text,
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varieties_found=varieties,
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)
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# --------------------------------------------------------------------- render
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def render_markdown(p: TrialPDF) -> str:
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head: list[str] = [
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f"# {p.title or p.filename}",
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"",
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"- **Source:** AgriPro (Syngenta) regional trial PDF",
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"- **Vendor:** Syngenta",
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"- **Brand:** AgriPro",
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"- **Crop:** Wheat",
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"- **Data type:** trial",
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]
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if p.region:
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head.append(f"- **Region:** {p.region}")
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if p.wheat_class_section:
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head.append(f"- **Wheat class:** {p.wheat_class_section}")
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if p.year:
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head.append(f"- **Year:** {p.year}")
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if p.years_covered and len(p.years_covered) > 1:
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head.append(f"- **Years covered:** {p.years_covered[0]}–{p.years_covered[-1]}")
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head.append(f"- **PDF:** {p.pdf_url}")
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head.append(f"- **Index page:** {p.source_url}")
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if p.varieties_found:
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head.append(
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f"- **Varieties listed:** {', '.join(p.varieties_found[:30])}"
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+ ("…" if len(p.varieties_found) > 30 else "")
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)
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head.append("")
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head.append("---")
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head.append("")
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head.append("## Trial data (verbatim from PDF)")
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head.append("")
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head.append("```")
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head.append(p.page_text)
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head.append("```")
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return "\n".join(head)
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# --------------------------------------------------------------------- write
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def write_pdf(prod: TrialPDF, body_md: str) -> None:
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CORPUS_DIR.mkdir(parents=True, exist_ok=True)
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md_path = CORPUS_DIR / f"{prod.source_key}.md"
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json_path = CORPUS_DIR / f"{prod.source_key}.json"
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md_path.write_text(body_md, encoding="utf-8")
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sidecar = {
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"source": "agripro_trials",
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"source_key": prod.source_key,
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"data_type": "trial",
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"vendor": "Syngenta",
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"brand": "AgriPro",
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"crop": "wheat",
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"title": prod.title,
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"filename": prod.filename,
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"region": prod.region,
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"wheat_class_section": prod.wheat_class_section,
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"year": prod.year,
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"years_covered": prod.years_covered,
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"varieties_found": prod.varieties_found,
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"pdf_url": prod.pdf_url,
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"source_urls": [prod.source_url, prod.pdf_url],
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"page_text_chars": len(prod.page_text),
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"fetched_at": datetime.now(timezone.utc).isoformat(),
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"scraper_version": SCRAPER_VERSION,
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}
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json_path.write_text(
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json.dumps(sidecar, indent=2, ensure_ascii=False) + "\n",
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encoding="utf-8",
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)
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# --------------------------------------------------------------------- pipeline
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def process_pdf(
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http: RateLimitedSession,
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*,
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pdf_url: str,
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filename: str,
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force: bool,
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) -> tuple[str, TrialPDF | None]:
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sk = source_key_for(filename)
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md_path = CORPUS_DIR / f"{sk}.md"
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if md_path.exists() and not force:
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return "skipped", None
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try:
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prod = fetch_pdf_detail(http, pdf_url, filename)
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except Exception as exc: # noqa: BLE001
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log.error("PDF fetch/parse failed for %s: %s", pdf_url, exc)
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return "failed", None
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if prod is None:
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return "missing", None
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body = render_markdown(prod)
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write_pdf(prod, body)
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return "written", prod
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def run(*, limit: int | None, force: bool) -> int:
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CORPUS_DIR.mkdir(parents=True, exist_ok=True)
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http = RateLimitedSession()
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targets = discover_pdfs(http)
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counts = {"written": 0, "skipped": 0, "missing": 0, "failed": 0}
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processed = 0
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for pdf_url, filename, _section, _href in targets:
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if limit is not None and processed >= limit:
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break
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processed += 1
|
||
status, prod = process_pdf(
|
||
http, pdf_url=pdf_url, filename=filename, force=force,
|
||
)
|
||
counts[status] = counts.get(status, 0) + 1
|
||
log.info(
|
||
"[%d/%d] %s %s | region=%s year=%s varieties=%d chars=%d",
|
||
processed, len(targets),
|
||
source_key_for(filename), status,
|
||
(prod.region if prod else "-") or "-",
|
||
prod.year if prod else "-",
|
||
len(prod.varieties_found) if prod else 0,
|
||
len(prod.page_text) if prod else 0,
|
||
)
|
||
|
||
log.info(
|
||
"done: processed=%d written=%d skipped=%d missing=%d failed=%d (of %d PDFs)",
|
||
processed, counts["written"], counts["skipped"],
|
||
counts["missing"], counts["failed"], len(targets),
|
||
)
|
||
return 0 if counts["failed"] == 0 else 1
|
||
|
||
|
||
# --------------------------------------------------------------------- CLI
|
||
|
||
|
||
def _build_argparser() -> argparse.ArgumentParser:
|
||
p = argparse.ArgumentParser(
|
||
prog="scrape.sources.agripro_trials",
|
||
description="Scrape AgriPro regional trial PDFs.",
|
||
)
|
||
p.add_argument("--limit", type=int, default=None,
|
||
help="Stop after processing N PDFs (default: all).")
|
||
p.add_argument("--force", action="store_true",
|
||
help="Re-fetch even if the markdown file already exists.")
|
||
p.add_argument("--log-level", default=os.environ.get("LOG_LEVEL", "INFO"))
|
||
return p
|
||
|
||
|
||
def main(argv: list[str] | None = None) -> int:
|
||
args = _build_argparser().parse_args(argv)
|
||
logging.basicConfig(
|
||
level=args.log_level.upper(),
|
||
format="%(asctime)s %(levelname)s %(name)s %(message)s",
|
||
stream=sys.stderr,
|
||
)
|
||
return run(limit=args.limit, force=args.force)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|