[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_154":155,"_156":157},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":25,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",23,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","98187ff6-a7e3-11f1-ace8-1607ee4eb77e","name","fileType","interval","content","// process-b2b-orders.ts\n// Val Town interval val — polls orders@sodafountain.uk for genuine B2B/wholesale\n// POs, matches customer + products in Breww, creates the order at book price,\n// and accepts it automatically when confident. Falls back to a Draft + email\n// flag when the customer or product match is ambiguous. See SKILL.md\n// (lifeblood-order-processing) for the full business logic this implements.\n\nimport { blob } from \"https://esm.town/v/std/blob\";\n\n// ---------- Secrets (set these as Val Town secrets on this val) ----------\nconst TENANT_ID = \"2ff21b32-fd4b-4dc0-b43b-8d66bbe9d9fa\";\nconst CLIENT_ID = \"865646e4-96c0-4b95-aa0d-4520883e005f\";\nconst GRAPH_CLIENT_SECRET = Deno.env.get(\"GRAPH_CLIENT_SECRET\")!;\nconst BREWW_API_KEY = Deno.env.get(\"BREWW_API_KEY\")!;\nconst MAILBOX = \"orders@sodafountain.uk\";\nconst NOTIFY_TO = \"chris@sodafountain.uk\";\nconst NOTIFY_FROM = \"lucy@drinklifeblood.com\";\n\nconst BREWW_BASE = \"https://breww.com/api/\";\nconst BREWW_HEADERS = {\n Authorization: `Bearer ${BREWW_API_KEY}`,\n \"User-Agent\":\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 Chrome/124.0.0.0 Safari/537.36\",\n Accept: \"application/json\",\n \"Content-Type\": \"application/json\",\n};\n\n// ---------- Dynamic customer matching ----------\n// No hardcoded account table. Every run pulls the live Breww customer list\n// and matches purely on whether a customer's own name/address text appears\n// in the incoming PO — since a customer's PO will always contain their own\n// business name somewhere (letterhead, delivery address, stock location).\n// This means brand-new customers work automatically the moment they exist\n// as a Breww record; nothing needs to be added here by hand.\n//\n// A learned cache (sender email domain -> customer ID) is kept in a Val\n// Town blob so repeat orders from the same sender skip the fuzzy scan\n// entirely after the first successful match — faster, and immune to PO\n// wording changing between orders.\nconst MIN_MATCH_LENGTH = 6; // ignore customer names/fragments shorter than this to avoid false positives\n\ninterface BrewwCustomer {\n id: number;\n name: string;\n price_book_in_use?: { id: number; name: string } | null;\n billing_address?: { first_line?: string };\n delivery_address?: { first_line?: string };\n entity_type_display?: string;\n}\n\n// Price book names that mean \"this is a DTC/Shopify consumer, not a B2B\n// trade account\" — excluded from the matching pool entirely. This is what\n// cuts ~900 total customer records down to the ~100 that are actually\n// wholesale/trade accounts, both for speed and to stop a DTC customer's\n// personal name ever being mistaken for a trade account.\nconst NON_B2B_PRICE_BOOKS = [\"D2C\", \"tester\"];\n\nasync function getAllBrewwCustomers(): Promise {\n let url = BREWW_BASE + \"customers-suppliers/?page_size=200\";\n const all: BrewwCustomer[] = [];\n while (url) {\n const res = await fetch(url, { headers: BREWW_HEADERS });\n const data = await res.json();\n all.push(...(data.results ?? []));\n url = data.next ?? null;\n }\n // Only match against real, B2B trade customers — not leads, suppliers,\n // or DTC/Shopify consumers (identified by price_book_in_use.name).\n return all.filter(\n (c) =>\n c.entity_type_display === \"Customer\" &&\n c.price_book_in_use?.name &&\n !NON_B2B_PRICE_BOOKS.includes(c.price_book_in_use.name),\n );\n}\n\nfunction matchableStrings(c: BrewwCustomer): string[] {\n return [c.name, c.billing_address?.first_line, c.delivery_address?.first_line]\n .filter((s): s is string => !!s && s.length >= MIN_MATCH_LENGTH);\n}\n\n// Finds the customer whose name/address text appears in the PO, preferring\n// the longest (most specific) match. Returns null if zero or multiple\n// distinct customers match, so ambiguous cases fall to manual review\n// instead of guessing.\nfunction findCustomerInText(customers: BrewwCustomer[], text: string) {\n const lowerText = text.toLowerCase();\n let best: { customer: BrewwCustomer; matchLength: number } | null = null;\n const distinctMatches = new Set();\n\n for (const c of customers) {\n for (const s of matchableStrings(c)) {\n if (lowerText.includes(s.toLowerCase())) {\n distinctMatches.add(c.id);\n if (!best || s.length > best.matchLength) {\n best = { customer: c, matchLength: s.length };\n }\n }\n }\n }\n\n if (distinctMatches.size !== 1 || !best) return null; // none, or ambiguous\n return best.customer;\n}\n\n// Flavour keyword -> product name fragment (case-insensitive substring match\n// against Breww's live product list, so this survives product ID changes).\nconst FLAVOUR_KEYWORDS: { keyword: RegExp; nameContains: string }[] = [\n { keyword: /original/i, nameContains: \"The Original\" },\n { keyword: /hopical/i, nameContains: \"Hopical Punch\" },\n { keyword: /mangosaurus/i, nameContains: \"Mangosaurus\" },\n { keyword: /lemondrop/i, nameContains: \"Lemondrop\" },\n { keyword: /ginger/i, nameContains: \"Ginger Beer\" },\n];\n\n// Ignore these — DTC Shopify order emails, never touch.\nconst IGNORE_SENDER_DOMAINS = [\"drinklifeblood.com\"];\nconst IGNORE_SUBJECT_PATTERNS = [/^\\[Raw Culture\\] Order/i];\n\ninterface ReviewItem {\n subject: string;\n reason: string;\n messageId: string;\n}\ninterface ProcessedItem {\n subject: string;\n orderNumber: string;\n orderId: number;\n total: number;\n breww_url: string;\n}\n\nasync function getGraphToken(): Promise {\n const res = await fetch(\n `https://login.microsoftonline.com/${TENANT_ID}/oauth2/v2.0/token`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/x-www-form-urlencoded\" },\n body: new URLSearchParams({\n grant_type: \"client_credentials\",\n client_id: CLIENT_ID,\n client_secret: GRAPH_CLIENT_SECRET,\n scope: \"https://graph.microsoft.com/.default\",\n }),\n },\n );\n const data = await res.json();\n return data.access_token;\n}\n\nasync function getUnreadOrderEmails(token: string) {\n const url =\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/mailFolders/inbox/messages` +\n `?$filter=isRead eq false and hasAttachments eq true` +\n `&$top=25&$orderby=receivedDateTime desc` +\n `&$select=id,subject,from,receivedDateTime,hasAttachments`;\n const res = await fetch(url, {\n headers: { Authorization: `Bearer ${token}` },\n });\n const data = await res.json();\n return data.value ?? [];\n}\n\nasync function getAttachmentText(\n token: string,\n messageId: string,\n): Promise {\n const url =\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/messages/${messageId}/attachments`;\n const res = await fetch(url, {\n headers: { Authorization: `Bearer ${token}` },\n });\n const data = await res.json();\n const pdfAttachment = (data.value ?? []).find((a: any) =>\n a.name?.toLowerCase().endsWith(\".pdf\")\n );\n if (!pdfAttachment?.contentBytes) return null;\n\n // Extract text via a PDF-to-text endpoint. Val Town doesn't ship a native\n // PDF parser, so this uses a small npm import — pdf-parse works fine here.\n const pdfParse = (await import(\"npm:pdf-parse\")).default;\n const buffer = Uint8Array.from(\n atob(pdfAttachment.contentBytes),\n (c) => c.charCodeAt(0),\n );\n const parsed = await pdfParse(buffer);\n return parsed.text as string;\n}\n\nasync function markRead(token: string, messageId: string) {\n await fetch(\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/messages/${messageId}`,\n {\n method: \"PATCH\",\n headers: {\n Authorization: `Bearer ${token}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ isRead: true }),\n },\n );\n}\n\nasync function getBrewwProducts() {\n const res = await fetch(BREWW_BASE + \"products/?page_size=200\", {\n headers: BREWW_HEADERS,\n });\n const data = await res.json();\n return data.results ?? data;\n}\n\n// Very rough line-item extraction: looks for \" Case \"\n// patterns per flavour keyword. This is deliberately conservative — if it\n// can't parse a line confidently, it should surface a review item rather\n// than guess.\nfunction extractLines(poText: string) {\n const lines: { flavour: string; quantity: number }[] = [];\n for (const { keyword, nameContains } of FLAVOUR_KEYWORDS) {\n const lineMatch = poText.match(\n new RegExp(\n `(\\\\d+(?:\\\\.\\\\d+)?)\\\\s*Case[\\\\s\\\\S]{0,40}${keyword.source}`,\n \"i\",\n ),\n );\n if (lineMatch) {\n lines.push({\n flavour: nameContains,\n quantity: Math.round(parseFloat(lineMatch[1])),\n });\n }\n }\n return lines;\n}\n\nasync function createOrder(\n customerId: number,\n priceBook: number,\n poNumber: string,\n orderLines: { product: number; quantity: number }[],\n) {\n const payload = {\n customer: customerId,\n price_book: priceBook,\n issue_date: new Date().toISOString().slice(0, 10),\n po_number: poNumber,\n order_status: 1, // draft first, promote after sanity check\n fulfillment_required: true,\n order_lines: orderLines,\n };\n const res = await fetch(BREWW_BASE + \"orders/\", {\n method: \"POST\",\n headers: BREWW_HEADERS,\n body: JSON.stringify(payload),\n });\n return res.json();\n}\n\nasync function acceptOrder(orderId: number) {\n await fetch(`${BREWW_BASE}orders/${orderId}/`, {\n method: \"PATCH\",\n headers: BREWW_HEADERS,\n body: JSON.stringify({ order_status: 3 }),\n });\n}\n\nasync function sendSummaryEmail(\n token: string,\n processed: ProcessedItem[],\n review: ReviewItem[],\n) {\n if (processed.length === 0 && review.length === 0) return; // nothing to report\n\n const rows = (arr: string[]) => arr.map((r) => `

${r}

`).join(\"\");\n const processedRows = rows(\n processed.map(\n (p) =>\n `${p.subject} — Order ${p.orderNumber}, £${\n p.total.toFixed(2)\n } — ` +\n `view in Breww`,\n ),\n );\n const reviewRows = rows(\n review.map((r) => `${r.subject} — needs manual review: ${r.reason}`),\n );\n\n const html = `\n

B2B order processing run — ${new Date().toISOString()}

\n ${\n processed.length\n ? `

Auto-accepted (${processed.length})

${processedRows}`\n : \"\"\n }\n ${\n review.length\n ? `

Needs manual review (${review.length})

${reviewRows}`\n : \"\"\n }\n `;\n\n await fetch(\n `https://graph.microsoft.com/v1.0/users/${NOTIFY_FROM}/sendMail`,\n {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${token}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n message: {\n subject:\n `B2B orders processed: ${processed.length} accepted, ${review.length} need review`,\n body: { contentType: \"HTML\", content: html },\n toRecipients: [{ emailAddress: { address: NOTIFY_TO } }],\n },\n saveToSentItems: true,\n }),\n },\n );\n}\n\nasync function processOrders() {\n const token = await getGraphToken();\n const processedIds: string[] =\n (await blob.getJSON(\"lifeblood_orders_processed\")) ?? [];\n // Learned map of sender email domain -> Breww customer ID, built up\n // automatically as orders are matched. Makes repeat senders instant and\n // resilient to their PO wording changing between orders.\n const domainCache: Record =\n (await blob.getJSON(\"lifeblood_customer_domain_cache\")) ?? {};\n const emails = await getUnreadOrderEmails(token);\n const allCustomers = await getAllBrewwCustomers();\n const customersById = new Map(allCustomers.map((c) => [c.id, c]));\n\n const processed: ProcessedItem[] = [];\n const review: ReviewItem[] = [];\n\n for (const email of emails) {\n if (processedIds.includes(email.id)) continue;\n\n const senderDomain =\n email.from?.emailAddress?.address?.split(\"@\")[1]?.toLowerCase() ?? \"\";\n if (IGNORE_SENDER_DOMAINS.includes(senderDomain)) continue;\n if (IGNORE_SUBJECT_PATTERNS.some((p) => p.test(email.subject))) continue;\n\n const poText = await getAttachmentText(token, email.id);\n if (!poText) {\n review.push({\n subject: email.subject,\n reason: \"No readable PDF attachment\",\n messageId: email.id,\n });\n continue;\n }\n\n // Fast path: we've matched this sender domain to a customer before.\n let account: BrewwCustomer | null = null;\n if (domainCache[senderDomain]) {\n account = customersById.get(domainCache[senderDomain]) ?? null;\n }\n // Otherwise, fuzzy-match against the live customer list. This is what\n // makes brand-new customers work with zero setup — no table to update.\n if (!account) {\n account = findCustomerInText(allCustomers, poText);\n if (account && senderDomain) {\n domainCache[senderDomain] = account.id; // learn it for next time\n }\n }\n\n if (!account) {\n review.push({\n subject: email.subject,\n reason:\n \"Could not confidently match a customer — either they're not in Breww yet, \" +\n \"or their name matched more than one record. Create/fix the customer record, \" +\n \"then re-forward or let the next run pick it up.\",\n messageId: email.id,\n });\n await markRead(token, email.id);\n processedIds.push(email.id);\n continue;\n }\n if (!account.price_book_in_use?.id) {\n review.push({\n subject: email.subject,\n reason:\n `Matched customer \"${account.name}\" but they have no price book set in Breww`,\n messageId: email.id,\n });\n await markRead(token, email.id);\n processedIds.push(email.id);\n continue;\n }\n\n const lines = extractLines(poText);\n if (lines.length === 0) {\n review.push({\n subject: email.subject,\n reason: \"Could not parse any order lines\",\n messageId: email.id,\n });\n await markRead(token, email.id);\n processedIds.push(email.id);\n continue;\n }\n\n const products = await getBrewwProducts();\n const orderLines: { product: number; quantity: number }[] = [];\n let allMatched = true;\n for (const line of lines) {\n const product = products.find((p: any) =>\n p.name?.toLowerCase().includes(line.flavour.toLowerCase()) &&\n p.name?.toLowerCase().includes(\"12 x 330ml\")\n );\n if (!product) {\n allMatched = false;\n break;\n }\n orderLines.push({ product: product.id, quantity: line.quantity });\n }\n\n if (!allMatched) {\n review.push({\n subject: email.subject,\n reason: \"One or more product lines could not be matched confidently\",\n messageId: email.id,\n });\n await markRead(token, email.id);\n processedIds.push(email.id);\n continue;\n }\n\n const poNumberMatch = poText.match(/Order number\\s*\\n?\\s*([\\w-]+)/i);\n const poNumber = poNumberMatch?.[1] ?? email.subject;\n\n const order = await createOrder(\n account.id,\n account.price_book_in_use!.id,\n poNumber,\n orderLines,\n );\n\n if (!order?.id) {\n review.push({\n subject: email.subject,\n reason: \"Breww order creation failed\",\n messageId: email.id,\n });\n await markRead(token, email.id);\n processedIds.push(email.id);\n continue;\n }\n\n // Sanity check: total should roughly match quantity * book price sum.\n // If Breww's returned value is 0 or wildly off, don't auto-accept.\n // Also never auto-accept anything with \"test\" in the subject — lets us\n // safely send dummy POs through the real pipeline without risking a\n // fake order landing as Accepted.\n const isTestOrder = /test/i.test(email.subject);\n if (!order.value || order.value <= 0) {\n review.push({\n subject: email.subject,\n reason:\n `Order ${order.number} created but value looks wrong (£${order.value}) — left as Draft`,\n messageId: email.id,\n });\n } else if (isTestOrder) {\n review.push({\n subject: email.subject,\n reason:\n `Order ${order.number} matched cleanly (£${order.value}) but subject contains \"test\" — left as Draft, not auto-accepted`,\n messageId: email.id,\n });\n } else {\n await acceptOrder(order.id);\n processed.push({\n subject: email.subject,\n orderNumber: order.number,\n orderId: order.id,\n total: order.value,\n breww_url: `https://app.breww.com/orders/${order.id}/`,\n });\n }\n\n await markRead(token, email.id);\n processedIds.push(email.id);\n }\n\n await blob.setJSON(\"lifeblood_orders_processed\", processedIds.slice(-500)); // keep log bounded\n await blob.setJSON(\"lifeblood_customer_domain_cache\", domainCache);\n await sendSummaryEmail(token, processed, review);\n\n return { processed: processed.length, review: review.length };\n}\n\n// Cron val entry point. Val Town cron triggers call this with an Interval\n// object ({ lastRunAt: Date | undefined }) on the schedule set in the val's\n// Cron trigger settings, and ignore whatever is returned.\nexport default async function (interval: { lastRunAt?: Date }) {\n return await processOrders();\n}","// process-b2b-orders.ts\n// Val Town interval val — polls orders@sodafountain.uk for genuine B2B/wholesale\n// POs, matches customer + products in Breww, creates the order at book price,\n// and accepts it automatically when confident. Falls back to a Draft + email\n// flag when the customer or product match is ambiguous. See SKILL.md\n// (lifeblood-order-processing) for the full business logic this implements.\n\nimport { blob } from \"https://esm.town/v/std/blob\";\n\n// ---------- Secrets (set these as Val Town secrets on this val) ----------\nconst TENANT_ID = \"2ff21b32-fd4b-4dc0-b43b-8d66bbe9d9fa\";\nconst CLIENT_ID = \"865646e4-96c0-4b95-aa0d-4520883e005f\";\nconst GRAPH_CLIENT_SECRET = Deno.env.get(\"GRAPH_CLIENT_SECRET\")!;\nconst BREWW_API_KEY = Deno.env.get(\"BREWW_API_KEY\")!;\nconst MAILBOX = \"orders@sodafountain.uk\";\nconst NOTIFY_TO = \"chris@sodafountain.uk\";\nconst NOTIFY_FROM = \"lucy@drinklifeblood.com\";\n\nconst BREWW_BASE = \"https://breww.com/api/\";\nconst BREWW_HEADERS = {\n Authorization: `Bearer ${BREWW_API_KEY}`,\n \"User-Agent\":\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 Chrome/124.0.0.0 Safari/537.36\",\n Accept: \"application/json\",\n \"Content-Type\": \"application/json\",\n};\n\n// ---------- Dynamic customer matching ----------\n// No hardcoded account table. Every run pulls the live Breww customer list\n// and matches purely on whether a customer's own name/address text appears\n// in the incoming PO — since a customer's PO will always contain their own\n// business name somewhere (letterhead, delivery address, stock location).\n// This means brand-new customers work automatically the moment they exist\n// as a Breww record; nothing needs to be added here by hand.\n//\n// A learned cache (sender email domain -> customer ID) is kept in a Val\n// Town blob so repeat orders from the same sender skip the fuzzy scan\n// entirely after the first successful match — faster, and immune to PO\n// wording changing between orders.\nconst MIN_MATCH_LENGTH = 6; // ignore customer names/fragments shorter than this to avoid false positives\n\ninterface BrewwCustomer {\n id: number;\n name: string;\n price_book_in_use?: { id: number; name: string } | null;\n billing_address?: { first_line?: string };\n delivery_address?: { first_line?: string };\n entity_type_display?: string;\n}\n\n// Price book names that mean \"this is a DTC/Shopify consumer, not a B2B\n// trade account\" — excluded from the matching pool entirely. This is what\n// cuts ~900 total customer records down to the ~100 that are actually\n// wholesale/trade accounts, both for speed and to stop a DTC customer's\n// personal name ever being mistaken for a trade account.\nconst NON_B2B_PRICE_BOOKS = [\"D2C\", \"tester\"];\n\nasync function getAllBrewwCustomers(): Promise {\n let url = BREWW_BASE + \"customers-suppliers/?page_size=200\";\n const all: BrewwCustomer[] = [];\n while (url) {\n const res = await fetch(url, { headers: BREWW_HEADERS });\n const data = await res.json();\n all.push(...(data.results ?? []));\n url = data.next ?? null;\n }\n // Only match against real, B2B trade customers — not leads, suppliers,\n // or DTC/Shopify consumers (identified by price_book_in_use.name).\n return all.filter(\n (c) =>\n c.entity_type_display === \"Customer\" &&\n c.price_book_in_use?.name &&\n !NON_B2B_PRICE_BOOKS.includes(c.price_book_in_use.name),\n );\n}\n\nfunction matchableStrings(c: BrewwCustomer): string[] {\n const strings = [\n c.name,\n c.billing_address?.first_line,\n c.delivery_address?.first_line,\n ]\n .filter((s): s is string => !!s && s.length >= MIN_MATCH_LENGTH);\n // Also match on just the first two words of the customer's name (e.g.\n // \"Forest Farm\" for \"Forest Farm The Organic Dairy\") — real customers\n // very often refer to themselves by a shortened version of their full\n // registered name. If this shorter fragment happens to collide with\n // another customer's, findCustomerInText's ambiguity check catches it\n // and falls back to manual review rather than picking one at random.\n if (c.name) {\n const firstTwoWords = c.name.split(/\\s+/).slice(0, 2).join(\" \");\n if (firstTwoWords.length >= MIN_MATCH_LENGTH && firstTwoWords !== c.name) {\n strings.push(firstTwoWords);\n }\n }\n return strings;\n}\n\n// Finds the customer whose name/address text appears in the PO, preferring\n// the longest (most specific) match. Returns null if zero or multiple\n// distinct customers match, so ambiguous cases fall to manual review\n// instead of guessing.\nfunction findCustomerInText(customers: BrewwCustomer[], text: string) {\n const lowerText = text.toLowerCase();\n let best: { customer: BrewwCustomer; matchLength: number } | null = null;\n const distinctMatches = new Set();\n\n for (const c of customers) {\n for (const s of matchableStrings(c)) {\n if (lowerText.includes(s.toLowerCase())) {\n distinctMatches.add(c.id);\n if (!best || s.length > best.matchLength) {\n best = { customer: c, matchLength: s.length };\n }\n }\n }\n }\n\n if (distinctMatches.size !== 1 || !best) return null; // none, or ambiguous\n return best.customer;\n}\n\n// Flavour keyword -> product name fragment (case-insensitive substring match\n// against Breww's live product list, so this survives product ID changes).\nconst FLAVOUR_KEYWORDS: { keyword: RegExp; nameContains: string }[] = [\n { keyword: /original/i, nameContains: \"The Original\" },\n { keyword: /hopical/i, nameContains: \"Hopical Punch\" },\n { keyword: /mango(?:saurus)?/i, nameContains: \"Mangosaurus\" },\n { keyword: /lemon(?:drop)?/i, nameContains: \"Lemondrop\" },\n { keyword: /ginger/i, nameContains: \"Ginger Beer\" },\n];\n\n// Ignore these — DTC Shopify order emails, never touch.\nconst IGNORE_SENDER_DOMAINS = [\"drinklifeblood.com\"];\nconst IGNORE_SUBJECT_PATTERNS = [/^\\[Raw Culture\\] Order/i];\n\ninterface ReviewItem {\n subject: string;\n reason: string;\n messageId: string;\n}\ninterface ProcessedItem {\n subject: string;\n orderNumber: string;\n orderId: number;\n total: number;\n breww_url: string;\n}\n\nasync function getGraphToken(): Promise {\n const res = await fetch(\n `https://login.microsoftonline.com/${TENANT_ID}/oauth2/v2.0/token`,\n {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/x-www-form-urlencoded\" },\n body: new URLSearchParams({\n grant_type: \"client_credentials\",\n client_id: CLIENT_ID,\n client_secret: GRAPH_CLIENT_SECRET,\n scope: \"https://graph.microsoft.com/.default\",\n }),\n },\n );\n const data = await res.json();\n return data.access_token;\n}\n\nasync function getUnreadOrderEmails(token: string) {\n // No longer filters on hasAttachments — plain-text email orders (no PDF)\n // are handled too now. Body is included so we don't need a second call\n // per message just to get the text when there's no attachment.\n const url =\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/mailFolders/inbox/messages` +\n `?$filter=isRead eq false` +\n `&$top=25&$orderby=receivedDateTime desc` +\n `&$select=id,subject,from,receivedDateTime,hasAttachments,body`;\n const res = await fetch(url, {\n headers: { Authorization: `Bearer ${token}` },\n });\n const data = await res.json();\n return data.value ?? [];\n}\n\nfunction stripHtml(html: string): string {\n return html\n .replace(//gi, \"\")\n .replace(/<[^>]+>/g, \" \")\n .replace(/ /gi, \" \")\n .replace(/\\s+/g, \" \")\n .trim();\n}\n\n// Returns the best available text to parse an order from: a PDF attachment\n// if there is one, otherwise the plain email body itself. Real B2B\n// customers sometimes just type an order straight into the email rather\n// than attaching a formal PO, so both are treated as valid order sources.\nasync function getOrderText(token: string, email: any): Promise {\n if (email.hasAttachments) {\n const url =\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/messages/${email.id}/attachments`;\n const res = await fetch(url, {\n headers: { Authorization: `Bearer ${token}` },\n });\n const data = await res.json();\n const pdfAttachment = (data.value ?? []).find((a: any) =>\n a.name?.toLowerCase().endsWith(\".pdf\")\n );\n if (pdfAttachment?.contentBytes) {\n const pdfParse = (await import(\"npm:pdf-parse\")).default;\n const buffer = Uint8Array.from(\n atob(pdfAttachment.contentBytes),\n (c) => c.charCodeAt(0),\n );\n const parsed = await pdfParse(buffer);\n if (parsed.text?.trim()) return parsed.text as string;\n // fall through to body text if the PDF had no extractable text\n }\n }\n const body = email.body?.content;\n if (!body) return null;\n return email.body.contentType === \"html\" ? stripHtml(body) : body;\n}\n\nasync function markRead(token: string, messageId: string) {\n await fetch(\n `https://graph.microsoft.com/v1.0/users/${MAILBOX}/messages/${messageId}`,\n {\n method: \"PATCH\",\n headers: {\n Authorization: `Bearer ${token}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ isRead: true }),\n },\n );\n}\n\nasync function getBrewwProducts() {\n const res = await fetch(BREWW_BASE + \"products/?page_size=200\", {\n headers: BREWW_HEADERS,\n });\n const data = await res.json();\n return data.results ?? data;\n}\n\n// Line-item extraction handles two styles of wording:\n// - Structured PO tables (e.g. \"2.00 Case 12 21.50 ... Original\")\n// - Plain English typed straight into an email (\"1 case of the original,\n// 34 cases of hopical, 2 cases of lemon\")\n// Deliberately conservative — if it can't parse a line confidently, it\n// should surface a review item rather than guess.\nfunction extractLines(text: string) {\n const lines: { flavour: string; quantity: number }[] = [];\n for (const { keyword, nameContains } of FLAVOUR_KEYWORDS) {\n const patterns = [\n // \"2.00 Case 12 ... Original\" (structured PO table)\n new RegExp(\n `(\\\\d+(?:\\\\.\\\\d+)?)\\\\s*Case[\\\\s\\\\S]{0,40}${keyword.source}`,\n \"i\",\n ),\n // \"1 case of the original\" / \"34 cases of hopical\" (plain English)\n new RegExp(\n `(\\\\d+(?:\\\\.\\\\d+)?)\\\\s*cases?\\\\s*(?:of\\\\s+)?(?:the\\\\s+)?[\\\\s]{0,15}${keyword.source}`,\n \"i\",\n ),\n ];\n for (const pattern of patterns) {\n const lineMatch = text.match(pattern);\n if (lineMatch) {\n lines.push({\n flavour: nameContains,\n quantity: Math.round(parseFloat(lineMatch[1])),\n });\n break; // don't double-count the same flavour against both patterns\n }\n }\n }\n return lines;\n}\n\nasync function createOrder(\n customerId: number,\n priceBook: number,\n poNumber: string,\n orderLines: { product: number; quantity: number }[],\n) {\n const payload = {\n customer: customerId,\n price_book: priceBook,\n issue_date: new Date().toISOString().slice(0, 10),\n po_number: poNumber,\n order_status: 1, // draft first, promote after sanity check\n fulfillment_required: true,\n order_lines: orderLines,\n };\n const res = await fetch(BREWW_BASE + \"orders/\", {\n method: \"POST\",\n headers: BREWW_HEADERS,\n body: JSON.stringify(payload),\n });\n return res.json();\n}\n\nasync function acceptOrder(orderId: number) {\n await fetch(`${BREWW_BASE}orders/${orderId}/`, {\n method: \"PATCH\",\n headers: BREWW_HEADERS,\n body: JSON.stringify({ order_status: 3 }),\n });\n}\n\nasync function sendSummaryEmail(\n token: string,\n processed: ProcessedItem[],\n review: ReviewItem[],\n) {\n if (processed.length === 0 && review.length === 0) return; // nothing to report\n\n const rows = (arr: string[]) => arr.map((r) => `

${r}

B2B order processing run — ${new Date().toISOString()}

\n ${\n processed.length\n ? `

Auto-accepted (${processed.length})

${processedRows}`\n : \"\"\n }\n ${\n review.length\n ? `

Diff · v23