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feat: richer B2B assessment — legal page scraping, full contacts, summary

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beauty_ai.py:
- Add _scrape_legal_pages(): fetches /aviso-legal, /politica-de-privacidad,
  /privacidad, /quienes-somos, /legal in parallel — Spanish aviso legal pages
  legally contain razón social, CIF/NIF, address and a contact email; legal
  snippet passed to AI so it can identify the registered company name
- Rewrite _build_beauty_prompt(): full technical profile (SSL, analytics, CMS,
  load time, word count, GDPR, mobile), all contact channels merged from both
  site_analyzer and legal pages, updated assessment rules with clearer HOT/WARM
  criteria, 700-char search results, richer portfolio portfolio context
- New JSON schema fields: summary (executive description), pitch_angle (one
  Spanish hook sentence), all_contacts dict (emails/phones/whatsapp/social
  full lists), best_contact_channel, best_contact_value, partnership_signals,
  revenue_estimate; outreach_email is now a complete ready-to-send email
- max_output_tokens raised from 2000 → 4000
- Contact merge: all_contacts populated from both site_analyzer and legal pages;
  top-level contact_* fields filled from merged data as fallback
- Run DDG search and legal page scraping in parallel (no extra wall-clock cost)

index.html (Pipeline):
- Business Summary panel with pitch_angle as accent subtitle
- Full all_contacts display: all emails (mailto links), all phones, all
  WhatsApp (green links), all social profiles (shortened display)
- partnership_signals chips alongside brand detection
- outreach_notes shown in amber at bottom of contact panel
- best_contact_channel chip in contact header
- Table contact column now shows best_contact_value if available

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Malin
2026-05-13 08:33:14 +02:00
parent e7e39bed1f
commit e426922544
2 changed files with 336 additions and 106 deletions
Download Patch File Download Diff File Expand all files Collapse all files

346
app/beauty_ai.py
View File

@@ -19,6 +19,18 @@ REPLICATE_TOKEN = os.getenv("REPLICATE_API_TOKEN", "r8_7I7Feai78f9PzMOs20y5GVFKi
REPLICATE_MODEL = "https://api.replicate.com/v1/models/google/gemini-3-pro/predictions"
AI_CONCURRENCY = int(os.getenv("AI_CONCURRENCY", "3"))
# Contact extraction regexes (same patterns as site_analyzer)
_EMAIL_RE = re.compile(r"[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}")
_PHONE_RE = re.compile(r"(?:\+\d{1,3}[\s\-]?)?(?:6|7|8|9)\d{2}[\s\-]?\d{3}[\s\-]?\d{3}")
# Pages that often contain company registration info (CIF/NIF, registered address,
# legal email) — not fetched by site_analyzer, but rich sources for B2B contact data
_LEGAL_PATHS = [
"/aviso-legal", "/aviso_legal", "/legal",
"/politica-de-privacidad", "/politica_privacidad", "/privacidad",
"/quienes-somos", "/quienes_somos", "/nosotros",
]
_ai_sem: Optional[asyncio.Semaphore] = None
def _sem() -> asyncio.Semaphore:
@@ -182,91 +194,214 @@ async def _ddg_search(query: str) -> str:
return ""
# ── Legal / about page scraper ────────────────────────────────────────────────
async def _scrape_legal_pages(domain: str) -> dict:
"""Fetch legal and about pages not covered by site_analyzer.
Spanish Aviso Legal pages legally must contain: company name (razón social),
CIF/NIF, registered address, and a contact email — making them the richest
source of verified B2B contact data.
Returns:
emails: all unique emails found across all pages
phones: all unique phones found across all pages
legal_snippet: first 800 chars of the aviso legal page (company registration
info: razón social, CIF, domicilio, etc.)
"""
result: dict = {"emails": [], "phones": [], "legal_snippet": ""}
async def _fetch(path: str) -> tuple[str, str | None]:
try:
async with httpx.AsyncClient(
timeout=8, follow_redirects=True, verify=False,
headers={"User-Agent": "Mozilla/5.0"},
) as c:
r = await c.get(f"https://{domain}{path}")
if r.status_code == 200:
return path, r.text
except Exception:
pass
return path, None
pages = await asyncio.gather(*[_fetch(p) for p in _LEGAL_PATHS])
for path, html in pages:
if not html:
continue
try:
soup = BeautifulSoup(html, "html.parser")
# Extract from anchor tags
for a in soup.find_all("a", href=True):
href = a["href"]
if href.startswith("mailto:"):
em = href[7:].split("?")[0].strip().lower()
if em and em not in result["emails"]:
result["emails"].append(em)
elif href.startswith("tel:"):
ph = re.sub(r"[^\d+]", "", href[4:])
if ph and ph not in result["phones"]:
result["phones"].append(ph)
# Regex scan full HTML for emails
for em in _EMAIL_RE.findall(html[:60000]):
em = em.lower()
if em not in result["emails"] and not any(
em.endswith(x) for x in (".png", ".jpg", ".css", ".js", ".svg")
):
result["emails"].append(em)
# Regex scan visible text for phones
visible = soup.get_text(separator=" ", strip=True)
for ph in _PHONE_RE.findall(visible):
ph_c = re.sub(r"[\s\-]", "", ph)
if ph_c and ph_c not in result["phones"]:
result["phones"].append(ph_c)
# Capture legal snippet from the first legal page that resolves
if not result["legal_snippet"] and any(
k in path for k in ("aviso", "legal", "privacidad")
):
result["legal_snippet"] = " ".join(visible.split()[:150])
except Exception:
pass
result["emails"] = list(dict.fromkeys(result["emails"]))[:8]
result["phones"] = list(dict.fromkeys(result["phones"]))[:6]
return result
# ── Prompt builder ─────────────────────────────────────────────────────────────
def _build_beauty_prompt(a: dict, detected_brands: list, dist_matches: list,
search_results: str = "") -> str:
contacts_block = []
if a.get("emails"): contacts_block.append(f" Emails: {', '.join(a['emails'][:3])}")
if a.get("phones"): contacts_block.append(f" Phones: {', '.join(a['phones'][:3])}")
if a.get("social_links"): contacts_block.append(f" Social: {', '.join(a['social_links'][:4])}")
contacts_str = "\n".join(contacts_block) or " Not found"
search_results: str = "",
extra_contacts: dict | None = None) -> str:
"""Build the Gemini assessment prompt.
snippet = (a.get("visible_text_snippet") or "")[:1200]
title = a.get("page_title") or ""
meta = a.get("meta_description") or ""
country = a.get("ip_country") or "unknown"
cms = a.get("cms") or "unknown"
detected_str = ", ".join(detected_brands) if detected_brands else "none detected"
dist_str = ", ".join(dist_matches) if dist_matches else "none"
extra_contacts comes from _scrape_legal_pages() and adds emails/phones/company
info found in the aviso legal, privacy policy, and about pages.
"""
ec = extra_contacts or {}
return f"""You are a senior B2B sales analyst for a cosmetics distribution company operating in Europe.
Your task: evaluate whether this website is a viable B2B customer (retailer, multi-brand store,
e-commerce, distributor or chain that buys beauty products wholesale) and generate an outreach plan.
# Merge contact sources: site_analyzer (main page + contact pages) + legal pages
all_emails = list(dict.fromkeys((a.get("emails") or []) + (ec.get("emails") or [])))[:8]
all_phones = list(dict.fromkeys((a.get("phones") or []) + (ec.get("phones") or [])))[:6]
all_whatsapp = list(dict.fromkeys(a.get("whatsapp") or []))[:4]
all_social = list(dict.fromkeys(a.get("social_links") or []))[:6]
=== SITE DATA ===
Domain: {a.get("domain")}
Country (IP): {country}
Title: {title}
Meta desc: {meta}
CMS: {cms}
Contact info:
{contacts_str}
def _fmt(lst: list) -> str:
return ", ".join(lst) if lst else "—"
# Site technical signals
ssl_info = ("✓ valid" if a.get("ssl_valid") else "✗ invalid/missing")
analytics = ", ".join(a.get("analytics_present") or []) or "none detected"
word_count = a.get("word_count", 0)
load_ms = a.get("load_time_ms", 0)
copyright = a.get("copyright_year") or a.get("last_modified") or "unknown"
snippet = (a.get("visible_text_snippet") or "")[:1600]
legal_snippet = (ec.get("legal_snippet") or "")[:800]
detected_str = ", ".join(detected_brands) if detected_brands else "none detected"
dist_str = ", ".join(dist_matches) if dist_matches else "none"
return f"""You are a senior B2B sales analyst for a cosmetics distribution company
operating across Europe. Your task: thoroughly evaluate this website as a potential
wholesale B2B customer and produce a complete outreach dossier.
=== BUSINESS PROFILE ===
Domain: {a.get("domain")}
Country (IP): {a.get("ip_country") or "unknown"}
Region: {a.get("ip_region") or "unknown"}
Hosting (EU?): {a.get("eu_hosted")} | ISP/Org: {a.get("org") or a.get("isp") or "unknown"}
Page title: {a.get("page_title") or "—"}
H1: {a.get("h1_text") or "—"}
Meta desc: {(a.get("meta_description") or "—")[:200]}
CMS: {a.get("cms") or "unknown"}
Last updated: {copyright}
=== TECHNICAL SIGNALS ===
SSL: {ssl_info}
Load time: {load_ms}ms
Word count: {word_count}
Analytics: {analytics}
Mobile: {"yes" if a.get("has_mobile_viewport") else "no"}
Sitemap/Robots: sitemap={"yes" if a.get("has_sitemap") else "no"}, robots={"yes" if a.get("has_robots") else "no"}
GDPR/Privacy: cookie_tool={a.get("cookie_tool") or "none"}, privacy_policy={"yes" if a.get("has_privacy_policy") else "no"}
=== ALL CONTACT CHANNELS ===
Emails: {_fmt(all_emails)}
Phones: {_fmt(all_phones)}
WhatsApp: {_fmt(all_whatsapp)}
Social media: {_fmt(all_social)}
=== LEGAL / COMPANY REGISTRATION INFO ===
(extracted from aviso legal / política de privacidad — may contain razón social, CIF, address)
{legal_snippet or "Not found or page not accessible"}
=== PAGE CONTENT SAMPLE ===
{snippet}
=== BRANDS ALREADY DETECTED ON SITE ===
=== BRANDS DETECTED ON SITE ===
{detected_str}
=== OUR PORTFOLIO BRANDS FOUND ON THEIR SITE ===
(These brands we distribute — finding them means we're already in their market)
(brands we distribute that appear on their site — confirms shared market)
{dist_str}
=== WEB SEARCH RESULTS ===
{(search_results or "No results.")[:500]}
{(search_results or "No results available.")[:700]}
=== OUR DISTRIBUTION PORTFOLIO ===
=== OUR FULL DISTRIBUTION PORTFOLIO ===
{', '.join(OUR_BRANDS)}
=== BEAUTY CATEGORIES WE COVER ===
{', '.join(BEAUTY_CATEGORIES)}
=== ASSESSMENT RULES ===
1. Determine if this is a B2B prospect: retailer, pharmacy, parafarmacia,
perfumería, multi-brand beauty ecommerce, salon chain, supermarket beauty section,
or beauty products distributor based in Europe.
2. Identify which categories from our list they cover.
3. From the page content (even if brands list is empty), identify any beauty brands mentioned.
4. Match detected brands against our portfolio — this drives lead quality:
- HOT: 3+ of our portfolio brands detected, OR a large EU retailer clearly in our niche
- WARM: 1-2 portfolio brand matches, OR clear beauty retailer with good potential
- COLD: beauty-adjacent but weak match, OR can't confirm they buy wholesale
- NOT_RELEVANT: not a beauty business or not in Europe
5. Write all human text (proposal, email) in Spanish.
6. Keep JSON values concise (≤ 25 words each).
1. TARGET PROFILE: retailer, pharmacy, parafarmacia, perfumería, multi-brand beauty
ecommerce, salon chain, beauty distributor, or supermarket beauty section in Europe.
2. Identify ALL beauty brands mentioned anywhere on the page — go beyond the pre-detected
list above. Use product names, brand references in body text, alt text, etc.
3. Match brands against our portfolio. Lead quality is driven by portfolio overlap:
- HOT: 3+ portfolio brands detected, OR major EU beauty retailer clearly in our niche
- WARM: 1-2 portfolio brand matches, OR clear beauty multi-brand retailer with good reach
- COLD: beauty-adjacent but weak portfolio overlap, OR single-brand, OR unclear wholesale
- NOT_RELEVANT: not a beauty business, not in Europe, or clearly a consumer-only brand
4. Extract the BEST contact for outreach:
- Prefer business/commercial emails (info@, ventas@, compras@, admin@) over personal
- If WhatsApp exists, flag it — it's often the fastest channel in Spain/LatAm
- Check social media for direct messaging channels
5. Use the legal/company info to identify the official business name (razón social),
and if a CIF/NIF is visible, mention it in outreach_notes as it confirms legitimacy.
6. Write summary, pitch_angle, b2b_proposal, outreach_subject, and outreach_email in Spanish.
7. The outreach_email must be a complete ready-to-send email: greeting, 2-3 body sentences
(reference their specific range, 1-2 matching portfolio brands, add value), clear CTA.
Respond ONLY with valid JSON, no markdown, no text outside JSON:
Respond ONLY with valid JSON, no markdown fences, no text outside the JSON object:
{{
"is_relevant": true/false,
"is_relevant": true,
"lead_quality": "HOT|WARM|COLD|NOT_RELEVANT",
"lead_reasoning": "1-2 sentences why",
"business_type": "retailer|ecommerce|distributor|pharmacy|salon_chain|other",
"business_name": "name from title or domain",
"country_fiscal": "2-letter ISO or full name",
"countries_active": ["ES","FR"],
"summary": "2-3 sentence executive summary: what this business does, their product range, who their customers are, and their apparent scale",
"lead_reasoning": "2-3 sentences explaining the lead quality rating — reference specific brands found, categories covered, and portfolio overlap",
"business_type": "retailer|ecommerce|distributor|pharmacy|parafarmacia|salon_chain|perfumeria|other",
"business_name": "official business name from title, H1, or aviso legal",
"country_fiscal": "2-letter ISO",
"countries_active": ["ES"],
"categories": ["Hair Care","Makeup"],
"detected_brands": ["brand1","brand2"],
"dist_matches": ["OurBrand1","OurBrand2"],
"contact_email": "email or empty string",
"contact_phone": "phone or empty string",
"contact_whatsapp": "whatsapp link or empty string",
"contact_social": "primary social profile URL or empty string",
"b2b_proposal": "1-2 sentence value proposition in Spanish referencing their categories and our matching brands",
"outreach_subject": "short Spanish subject line referencing their business name",
"outreach_email": "3-4 sentence ready-to-send email in Spanish. Mention their business, 1-2 specific brands from our portfolio that match their range, and a clear call to action (catálogo, muestra, llamada).",
"revenue_estimate": "unknown",
"outreach_notes": "brief context for sales rep"
"detected_brands": ["all beauty brands found on site — be thorough"],
"dist_matches": ["our portfolio brands found on their site"],
"partnership_signals": ["carries multi-brand","has wholesale section","stockist page","B2B portal"],
"pitch_angle": "1 punchy sentence in Spanish: the specific angle for this business (reference their range, a gap you fill, or the portfolio brands that match)",
"b2b_proposal": "2-3 sentence value proposition in Spanish: what we offer, why it fits their range, what differentiates our brands",
"outreach_subject": "specific Spanish subject line mentioning their business name and 1 relevant brand",
"outreach_email": "complete ready-to-send Spanish email: greeting + 3-4 body sentences referencing their specific product range and 1-2 portfolio brands that match + clear CTA (catálogo, muestra, llamada, pedido mínimo) + valediction. Do not use placeholders.",
"best_contact_channel": "email|phone|whatsapp|social|web_form|unknown",
"best_contact_value": "the actual email/phone/URL to use — prefer commercial emails, then phone, then social",
"all_contacts": {{
"emails": {json.dumps(all_emails)},
"phones": {json.dumps(all_phones)},
"whatsapp": {json.dumps(all_whatsapp)},
"social": {json.dumps(all_social)}
}},
"revenue_estimate": "unknown|<100k€|100k-500k€|500k-2M€|>2M€",
"outreach_notes": "2-3 sentences for the sales rep: timing, approach, red flags, CIF if found, any urgency signals"
}}"""
@@ -309,21 +444,31 @@ async def assess_beauty_domain(analysis: dict) -> dict:
detected = detect_brands_in_text(text)
dist_match = get_dist_matches(detected)
# Also search for company context
# Run DDG search and legal page scraping in parallel
title = analysis.get("page_title") or ""
biz_name = title.split("|")[0].split("-")[0].strip() or domain
search_results = await _ddg_search(f'"{biz_name}" {domain} beauty cosmetics wholesale contact')
logger.info("Beauty assess %s: %d brands detected, %d portfolio matches",
domain, len(detected), len(dist_match))
search_results, extra_contacts = await asyncio.gather(
_ddg_search(f'"{biz_name}" {domain} cosmetics beauty wholesale B2B contacto'),
_scrape_legal_pages(domain),
)
logger.info(
"Beauty assess %s: %d brands, %d portfolio matches, "
"%d extra emails from legal pages",
domain, len(detected), len(dist_match),
len(extra_contacts.get("emails", [])),
)
payload = {
"input": {
"prompt": _build_beauty_prompt(analysis, detected, dist_match, search_results),
"prompt": _build_beauty_prompt(
analysis, detected, dist_match, search_results, extra_contacts
),
"images": [], "videos": [],
"top_p": 0.9,
"temperature": 0.15,
"temperature": 0.2,
"thinking_level": "low",
"max_output_tokens": 2000,
"max_output_tokens": 4000,
}
}
try:
@@ -351,17 +496,45 @@ async def assess_beauty_domain(analysis: dict) -> dict:
if not result.get("detected_brands") and detected:
result["detected_brands"] = detected
# Always merge contact data directly from site_analyzer — more reliable
# than AI extraction since it uses regex against raw HTML
phones = analysis.get("phones", [])
whatsapp = analysis.get("whatsapp", [])
social_links = analysis.get("social_links", [])
if phones and not result.get("contact_phone"):
result["contact_phone"] = phones[0]
if whatsapp:
result["contact_whatsapp"] = "; ".join(whatsapp[:2])
if social_links:
result["contact_social"] = "; ".join(social_links[:3])
# Merge contact data directly from site_analyzer + legal pages —
# more reliable than AI extraction since it's regex against raw HTML.
# The AI's all_contacts field may already have the right data if it
# followed the schema; fill gaps from our own extraction.
all_emails = list(dict.fromkeys(
(analysis.get("emails") or []) + (extra_contacts.get("emails") or [])
))[:8]
all_phones = list(dict.fromkeys(
(analysis.get("phones") or []) + (extra_contacts.get("phones") or [])
))[:6]
all_whatsapp = list(dict.fromkeys(analysis.get("whatsapp") or []))[:4]
all_social = list(dict.fromkeys(analysis.get("social_links") or []))[:6]
# Ensure all_contacts in result is always populated from our own data
if not result.get("all_contacts") or not isinstance(result.get("all_contacts"), dict):
result["all_contacts"] = {}
result["all_contacts"].setdefault("emails", [])
result["all_contacts"].setdefault("phones", [])
result["all_contacts"].setdefault("whatsapp", [])
result["all_contacts"].setdefault("social", [])
# Merge our extracted data into the AI's all_contacts
result["all_contacts"]["emails"] = list(dict.fromkeys(
result["all_contacts"]["emails"] + all_emails))[:8]
result["all_contacts"]["phones"] = list(dict.fromkeys(
result["all_contacts"]["phones"] + all_phones))[:6]
result["all_contacts"]["whatsapp"] = list(dict.fromkeys(
result["all_contacts"]["whatsapp"] + all_whatsapp))[:4]
result["all_contacts"]["social"] = list(dict.fromkeys(
result["all_contacts"]["social"] + all_social))[:6]
# Fill top-level contact fields from merged data if AI left them blank
if not result.get("contact_email") and all_emails:
result["contact_email"] = all_emails[0]
if not result.get("contact_phone") and all_phones:
result["contact_phone"] = all_phones[0]
if not result.get("contact_whatsapp") and all_whatsapp:
result["contact_whatsapp"] = all_whatsapp[0]
if not result.get("contact_social") and all_social:
result["contact_social"] = all_social[0]
logger.info("Beauty AI %s → quality=%s, dist_matches=%s",
domain, result.get("lead_quality"), result.get("dist_matches"))
@@ -369,17 +542,24 @@ async def assess_beauty_domain(analysis: dict) -> dict:
except Exception as e:
logger.error("Beauty AI error %s: %s", domain, e)
phones = analysis.get("phones", [])
whatsapp = analysis.get("whatsapp", [])
social = analysis.get("social_links", [])
all_emails = list(dict.fromkeys(
(analysis.get("emails") or []) + (extra_contacts.get("emails") or [])))[:8]
all_phones = list(dict.fromkeys(
(analysis.get("phones") or []) + (extra_contacts.get("phones") or [])))[:6]
all_whatsapp = list(dict.fromkeys(analysis.get("whatsapp") or []))[:4]
all_social = list(dict.fromkeys(analysis.get("social_links") or []))[:6]
return {
"error": str(e)[:300],
"is_relevant": False,
"lead_quality": "COLD",
"dist_matches": dist_match,
"detected_brands": detected,
"contact_email": "",
"contact_phone": phones[0] if phones else "",
"contact_whatsapp": "; ".join(whatsapp[:2]) if whatsapp else "",
"contact_social": "; ".join(social[:3]) if social else "",
"contact_email": all_emails[0] if all_emails else "",
"contact_phone": all_phones[0] if all_phones else "",
"contact_whatsapp": all_whatsapp[0] if all_whatsapp else "",
"contact_social": all_social[0] if all_social else "",
"all_contacts": {
"emails": all_emails, "phones": all_phones,
"whatsapp": all_whatsapp, "social": all_social,
},
}

96
app/static/beauty/index.html
View File

@@ -340,7 +340,7 @@ textarea{width:100%;resize:vertical;font-family:monospace;font-size:12px}
<span x-show="!((row._beauty||{}).dist_matches||[]).length" style="color:var(--muted)">—</span>
</td>
<td style="font-size:11px;max-width:160px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap"
x-text="(row._beauty||{}).contact_email||row.emails||'—'"></td>
x-text="(row._beauty||{}).best_contact_value||(row._beauty||{}).contact_email||row.emails||'—'"></td>
<td @click.stop style="white-space:nowrap;display:flex;gap:4px">
<button class="btn-secondary btn-sm" @click="copyOutreach(row)">Copy email</button>
<button class="btn-secondary btn-sm" @click="reassessOne(row.domain)" title="Re-run B2B assessment">↺</button>
@@ -350,14 +350,18 @@ textarea{width:100%;resize:vertical;font-family:monospace;font-size:12px}
<tr class="detail-row" x-show="expandedLead===row.domain" @click="expandedLead=null" style="cursor:pointer">
<td colspan="8">
<div class="detail-grid" @click.stop>
<div class="detail-box">
<h4>B2B Proposal</h4>
<p x-text="(row._beauty||{}).b2b_proposal||'—'"></p>
</div>
<div class="detail-box">
<h4>Lead Reasoning</h4>
<p x-text="(row._beauty||{}).lead_reasoning||'—'"></p>
<!-- Summary + pitch -->
<div class="detail-box" style="grid-column:1/-1;background:rgba(232,121,160,.06);border-color:rgba(232,121,160,.2)">
<h4 style="display:flex;align-items:center;gap:8px">
Business Summary
<span x-show="(row._beauty||{}).pitch_angle" style="color:var(--accent);font-size:11px;font-weight:400;font-style:italic" x-text="'→ '+((row._beauty||{}).pitch_angle||'')"></span>
</h4>
<p x-text="(row._beauty||{}).summary||(row._beauty||{}).b2b_proposal||'—'"></p>
<p x-show="(row._beauty||{}).lead_reasoning" style="margin-top:6px;font-size:11px;color:var(--muted)" x-text="(row._beauty||{}).lead_reasoning"></p>
</div>
<!-- Outreach email -->
<div class="detail-box" style="grid-column:1/-1">
<h4 style="display:flex;align-items:center;gap:8px">
Outreach Email
@@ -366,35 +370,81 @@ textarea{width:100%;resize:vertical;font-family:monospace;font-size:12px}
</h4>
<p style="white-space:pre-wrap;font-size:11px;color:var(--text);margin-top:6px;line-height:1.6" x-text="(row._beauty||{}).outreach_email||'—'"></p>
</div>
<!-- Brands detected -->
<div class="detail-box">
<h4>Brands Detected on Site</h4>
<p style="font-size:11px">
<template x-for="b in ((row._beauty||{}).detected_brands||[]).slice(0,30)" :key="b">
<template x-for="b in ((row._beauty||{}).detected_brands||[]).slice(0,40)" :key="b">
<span class="chip" x-text="b"></span>
</template>
<span x-show="!((row._beauty||{}).detected_brands||[]).length" style="color:var(--muted)">None detected in scraped text</span>
<span x-show="!((row._beauty||{}).detected_brands||[]).length" style="color:var(--muted)">None detected</span>
</p>
<template x-if="((row._beauty||{}).partnership_signals||[]).length>0">
<p style="margin-top:8px;font-size:11px">
<span style="color:var(--muted)">Signals: </span>
<template x-for="s in ((row._beauty||{}).partnership_signals||[])" :key="s">
<span class="chip chip-match" x-text="s"></span>
</template>
</p>
</template>
</div>
<!-- Full contact details -->
<div class="detail-box">
<h4>Contact Details</h4>
<p style="font-size:12px;line-height:1.8">
<template x-if="(row._beauty||{}).contact_email">
<span>Email: <a :href="'mailto:'+(row._beauty||{}).contact_email" x-text="(row._beauty||{}).contact_email"></a><br></span>
<h4 style="display:flex;align-items:center;gap:8px">
Contact Details
<template x-if="(row._beauty||{}).best_contact_channel">
<span class="chip chip-match" x-text="'best: '+((row._beauty||{}).best_contact_channel||'')"></span>
</template>
<template x-if="(row._beauty||{}).contact_phone">
<span>Phone: <span x-text="(row._beauty||{}).contact_phone"></span><br></span>
</h4>
<div style="font-size:12px;line-height:1.9">
<!-- All emails -->
<template x-if="((row._beauty||{}).all_contacts||{}).emails?.length">
<div>
<span style="color:var(--muted);font-size:10px;text-transform:uppercase;letter-spacing:.04em">Emails</span><br>
<template x-for="em in ((row._beauty||{}).all_contacts||{}).emails||[]" :key="em">
<span><a :href="'mailto:'+em" x-text="em" style="display:inline-block;margin-right:8px"></a></span>
</template>
</div>
</template>
<template x-if="(row._beauty||{}).contact_whatsapp">
<span>WhatsApp: <a :href="(row._beauty||{}).contact_whatsapp" target="_blank" x-text="(row._beauty||{}).contact_whatsapp"></a><br></span>
<!-- All phones -->
<template x-if="((row._beauty||{}).all_contacts||{}).phones?.length">
<div style="margin-top:4px">
<span style="color:var(--muted);font-size:10px;text-transform:uppercase;letter-spacing:.04em">Phones</span><br>
<template x-for="ph in ((row._beauty||{}).all_contacts||{}).phones||[]" :key="ph">
<span x-text="ph" style="display:inline-block;margin-right:8px"></span>
</template>
</div>
</template>
<template x-if="(row._beauty||{}).contact_social">
<span style="color:var(--muted)">Social: <span x-text="(row._beauty||{}).contact_social"></span><br></span>
<!-- WhatsApp -->
<template x-if="((row._beauty||{}).all_contacts||{}).whatsapp?.length">
<div style="margin-top:4px">
<span style="color:var(--muted);font-size:10px;text-transform:uppercase;letter-spacing:.04em">WhatsApp</span><br>
<template x-for="wa in ((row._beauty||{}).all_contacts||{}).whatsapp||[]" :key="wa">
<a :href="wa" target="_blank" x-text="wa" style="display:inline-block;margin-right:8px;color:var(--success)"></a>
</template>
</div>
</template>
<template x-if="row.emails && !(row._beauty||{}).contact_email">
<span style="color:var(--muted);font-size:11px">On-site: <span x-text="row.emails"></span></span>
<!-- Social -->
<template x-if="((row._beauty||{}).all_contacts||{}).social?.length">
<div style="margin-top:4px">
<span style="color:var(--muted);font-size:10px;text-transform:uppercase;letter-spacing:.04em">Social</span><br>
<template x-for="soc in ((row._beauty||{}).all_contacts||{}).social||[]" :key="soc">
<a :href="soc" target="_blank" x-text="soc.replace('https://','').replace('www.','').split('/').slice(0,2).join('/')" style="display:inline-block;margin-right:8px;color:var(--info)"></a>
</template>
</div>
</template>
</p>
<!-- Fallback if all_contacts not set (older assessments) -->
<template x-if="!(row._beauty||{}).all_contacts && (row._beauty||{}).contact_email">
<div>Email: <a :href="'mailto:'+(row._beauty||{}).contact_email" x-text="(row._beauty||{}).contact_email"></a></div>
</template>
</div>
<template x-if="(row._beauty||{}).outreach_notes">
<p style="margin-top:8px;font-size:11px;color:var(--warn);border-top:1px solid var(--border);padding-top:6px" x-text="(row._beauty||{}).outreach_notes"></p>
</template>
</div>
</div>
</td>
</tr>