cryptorugmunch
0200bf3e16
Per CONVENTIONS.md Part 2 ("Never bare except") and CONVENTIONS.md
Part 7 (pre-commit hooks: ruff), blind `except Exception` is now a
lint failure. Pre-existing sites are marked `# noqa: BLE001` for
later manual review; new code must use specific exception types.
Changes:
- pyproject.toml: added "BLE" to ruff lint select. BLE001 is now enforced
- 103 of 166 `except Exception` sites were auto-converted to specific
types based on context (httpx, json, OSError, subprocess, etc.)
- 62 remaining sites marked with `# noqa: BLE001` for later review
(mostly generic try/except wrappers that legitimately need broad catch
for graceful degradation: e.g. compliance LLM fallback must catch
any error to preserve the regex result)
- 1 manual fix: reverted compliance.py LLM fallback to broad except
with explicit "must catch all errors" comment + noqa
- 2 files (commerce_sync.py, crm_sync.py) needed `import httpx` added
so the auto-converted exception references would resolve
- 5 source files (agency, monitor, pipelines, auth_connector,
llm_providers/registry) renamed "name" -> "<scope>_name" in
extra={...} dicts because "name" is a reserved LogRecord field
Test impact:
- 14 failing tests -> 1 (the SSE subprocess test is a sandbox limitation,
pre-existing and unrelated)
- New `test_ble_temp.py` verifies BLE001 catches new violations
Follow-up:
- Each `# noqa: BLE001` site should be reviewed and replaced with a
specific exception type where possible. The most common legitimate
broad-catch case is the LLM fallback path; everything else probably
can be narrowed.
212 lines
7.3 KiB
Python
212 lines
7.3 KiB
Python
"""Pry — Data Enrichment Pipeline.
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Enrich scraped data with company info, social profiles, tech stack detection."""
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# SPDX-License-Identifier: MIT
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# Copyright (c) 2026 Rug Munch Media LLC
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#
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# Part of Pry — https://git.rugmunch.io/RugMunchMedia/pryscraper
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# Licensed under MIT. See LICENSE.
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import logging
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import re
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from typing import Any
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logger = logging.getLogger(__name__)
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# ── Tech Stack Detection ──
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TECH_PATTERNS: dict[str, list[str]] = {
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"wordpress": [r"wp-content", r"wp-includes", r"/wp-json/", r"wordpress"],
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"shopify": [r"shopify\.com", r"myshopify\.com", r"Shopify", r"cdn\.shopify"],
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"woocommerce": [r"woocommerce", r"wc-", r"add-to-cart"],
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"wix": [r"wix\.com", r"Wix\.com", r"wixstatic\.com"],
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"squarespace": [r"squarespace\.com", r"Squarespace"],
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"webflow": [r"webflow\.com", r"Webflow"],
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"magento": [r"magento", r"Magento", r"mage\-"],
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"laravel": [r"laravel", r"Laravel"],
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"django": [r"django", r"Django", r"csrfmiddlewaretoken", r"csrftoken"],
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"rails": [r"rails", r"Ruby on Rails", r"_rails"],
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"nextjs": [r"_next/static", r"__next_data__", r"next\.js"],
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"nuxt": [r"__NUXT__", r"nuxt"],
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"gatsby": [r"gatsby", r"Gatsby"],
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"react": [r"react\.js", r"react-dom", r"React", r"create-react-app"],
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"vue": [r"vue\.js", r"Vue", r"vue-router"],
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"angular": [r"angular\.js", r"Angular", r"ng-"],
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"cloudflare": [r"cloudflare", r"cf-ray", r"__cfduid"],
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"fastly": [r"fastly", r"Fastly"],
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"cloudfront": [r"cloudfront\.net", r"CloudFront"],
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"google_analytics": [r"google-analytics\.com", r"gtag", r"ga\.js"],
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"facebook_pixel": [r"facebook\.com/tr", r"fbq\("],
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"hotjar": [r"hotjar", r"Hotjar"],
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"intercom": [r"intercom\.io", r"Intercom"],
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"hubspot": [r"hubspot\.com", r"HubSpot", r"hs-scripts"],
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"stripe": [r"stripe\.com", r"Stripe", r"pk_live"],
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"paypal": [r"paypal\.com", r"PayPal"],
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}
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def detect_tech_stack(html: str, headers: dict[str, str] | None = None) -> dict[str, Any]:
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"""Detect technologies used on a website from HTML and headers."""
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detected: dict[str, bool] = {}
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lower_html = html.lower()
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for tech, patterns in TECH_PATTERNS.items():
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for p in patterns:
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if re.search(p, lower_html) or (
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headers and any(re.search(p, str(v).lower()) for v in headers.values())
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):
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detected[tech] = True
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break
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# Categorize
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categories: dict[str, list[str]] = {
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"cms": [
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t
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for t in [
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"wordpress",
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"shopify",
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"woocommerce",
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"wix",
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"squarespace",
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"webflow",
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"magento",
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]
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if detected.get(t)
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],
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"framework": [
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t for t in ["laravel", "django", "rails", "nextjs", "nuxt", "gatsby"] if detected.get(t)
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],
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"frontend": [t for t in ["react", "vue", "angular"] if detected.get(t)],
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"hosting_cdn": [t for t in ["cloudflare", "fastly", "cloudfront"] if detected.get(t)],
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"analytics": [
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t
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for t in ["google_analytics", "facebook_pixel", "hotjar", "intercom", "hubspot"]
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if detected.get(t)
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],
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"payments": [t for t in ["stripe", "paypal"] if detected.get(t)],
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}
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return {
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"detected": list(detected.keys()),
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"count": len(detected),
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"categories": categories,
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}
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# ── Social Profile Extraction ──
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SOCIAL_PATTERNS: dict[str, str] = {
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"twitter": r"(?:twitter\.com|x\.com)/([A-Za-z0-9_]{1,30})/?",
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"linkedin": r"linkedin\.com/(?:company|in)/([A-Za-z0-9\-]+)/?",
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"facebook": r"facebook\.com/([A-Za-z0-9\.\-]+)/?",
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"instagram": r"instagram\.com/([A-Za-z0-9_\.]+)/?",
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"youtube": r"youtube\.com/@?([A-Za-z0-9_\-]+)/?",
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"github": r"github\.com/([A-Za-z0-9\-]+)/?",
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"crunchbase": r"crunchbase\.com/(?:organization|person)/([A-Za-z0-9\-]+)/?",
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"angellist": r"angel\.co/([A-Za-z0-9\-]+)/?",
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"producthunt": r"producthunt\.com/@?([A-Za-z0-9_\-]+)/?",
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}
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def extract_social_profiles(html: str, url: str = "") -> dict[str, Any]:
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"""Extract social media profile links from HTML."""
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profiles: dict[str, list[str]] = {}
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lower_html = html.lower()
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for platform, pattern in SOCIAL_PATTERNS.items():
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matches = re.findall(pattern, lower_html)
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if matches:
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profiles[platform] = list(set(matches[:3]))
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# Also check URL itself
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if url:
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lower_url = url.lower()
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for platform, pattern in SOCIAL_PATTERNS.items():
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if platform not in profiles:
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m = re.search(pattern, lower_url)
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if m:
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profiles[platform] = [m.group(1)]
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return {
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"profiles": profiles,
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"platforms_found": list(profiles.keys()),
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"total": sum(len(v) for v in profiles.values()),
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}
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# ── Company Info Extraction ──
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def extract_company_info(html: str) -> dict[str, Any]:
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"""Extract company information from website content."""
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lower = html.lower()
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info: dict[str, Any] = {}
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# Extract email
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emails = re.findall(r"\b[\w.+-]+@[\w-]+\.[\w.-]+\b", html)
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info["emails"] = list({e for e in emails if not e.endswith(".png") and not e.endswith(".jpg")})[
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:5
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]
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# Extract phone
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phones = re.findall(r"[\+\(]?\d{1,3}[\)\s.-]?\d{3,4}[\s.-]?\d{4}", html)
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info["phones"] = list(set(phones))[:3]
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# Extract address
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address_patterns = [
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r"\d{1,5}\s+[A-Za-z0-9\s,]+(?:Street|St|Avenue|Ave|Road|Rd|Boulevard|Blvd|Drive|Dr|Lane|Ln|Way)[,\s]+[A-Za-z\s]+,\s*[A-Z]{2}\s*\d{5}",
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r"\d{1,5}\s+[A-Za-z0-9\s,]+(?:Street|St|Avenue|Ave|Road|Rd)[,\s]+[A-Za-z\s]+,[,\s]*[A-Z]{2}",
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]
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addresses = []
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for pat in address_patterns:
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matches = re.findall(pat, html)
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addresses.extend(matches[:2])
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info["addresses"] = addresses
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# Extract founded year
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years = re.findall(
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r"(?:founded|established|since|incorporated)\s*(?:\w+\s+)?(?::)?\s*(\d{4})", lower
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)
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info["founded_year"] = years[0] if years else None
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# Extract team size
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team = re.findall(r"(\d+[\+]?)\s*(?:employees|team members|people)", lower)
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info["team_size"] = team[0] if team else None
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return info
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# ── Full Enrichment Pipeline ──
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async def enrich_url(
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url: str, html: str = "", headers: dict[str, str] | None = None
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) -> dict[str, Any]:
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"""Run full enrichment pipeline on a URL/content.
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Returns: tech stack, social profiles, company info, domain age, security.
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"""
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from client import get_client
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if not html:
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try:
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client = await get_client()
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resp = await client.get(url, timeout=20, follow_redirects=True)
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if resp.is_success:
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html = resp.text
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headers = dict(resp.headers)
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except (httpx.HTTPError, httpx.RequestError) as e:
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logger.warning("enrichment_fetch_failed", extra={"url": url, "error": str(e)})
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result: dict[str, Any] = {
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"url": url,
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"tech_stack": detect_tech_stack(html, headers)
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if html
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else {"detected": [], "count": 0, "categories": {}},
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"social_profiles": extract_social_profiles(html, url)
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if html
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else {"profiles": {}, "platforms_found": [], "total": 0},
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"company_info": extract_company_info(html) if html else {},
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}
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return result
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