Rewrote msg.gigafibre.ca (tech magic-link page) from a today-only flat list
into a proper 4-tab SPA:
- Aujourd'hui: In Progress / En retard / Aujourd'hui / Sans date / À venir
- Calendrier: placeholder (phase 4)
- Historique: searchable + filter chips (Tous/Terminés/Manqués/Annulés)
- Profil: tech info, support line, refresh
Job detail view (hash-routed, #job/DJ-xxx):
- Customer + tap-to-call/navigate block
- Editable notes (textarea → PUT /api/resource/Dispatch Job)
- Photo upload (base64 → File doctype, is_private, proxied back via /photo-serve)
- Equipment section (inherited from overlay)
- Sticky action bar (Démarrer / Terminer)
Equipment overlay extended with per-field Gemini Vision scanners. Each
input (SN, MAC, GPON SN, Wi-Fi SSID, Wi-Fi PWD, model) has a 📷 that opens
a capture modal; Gemini is prompted to find THAT field specifically and
returns value+confidence. Tech confirms or retries before the value fills in.
Root cause of the "tech can't see his job" bug: page filtered
scheduled_date=today, so jobs on any other day were invisible even though
the token was tech-scoped. Now fetches a ±60d window and groups client-side.
vision.js: new extractField(base64, field, ctx) helper + handleFieldScan
route (used by new /t/:token/field-scan endpoint).
Also fixes discovered along the way:
- Frappe v16 blocks fetched/linked fields (customer_name, service_location_name)
and phantom fields (scheduled_time — real one is start_time). Query now
uses only own fields; names resolved in two batch follow-up queries.
- "Today" is Montreal-local, not UTC. Prevents evening jobs being mislabeled
as "hier" when UTC has already rolled to the next day.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
253 lines
12 KiB
JavaScript
253 lines
12 KiB
JavaScript
'use strict'
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const cfg = require('./config')
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const { log, json, parseBody } = require('./helpers')
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const GEMINI_URL = () => `https://generativelanguage.googleapis.com/v1beta/models/${cfg.AI_MODEL}:generateContent?key=${cfg.AI_API_KEY}`
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async function geminiVision (base64Image, prompt, schema) {
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const resp = await fetch(GEMINI_URL(), {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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contents: [{ parts: [{ text: prompt }, { inline_data: { mime_type: 'image/jpeg', data: base64Image } }] }],
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generationConfig: { temperature: 0.1, maxOutputTokens: 1024, responseMimeType: 'application/json', responseSchema: schema },
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}),
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})
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if (!resp.ok) { const t = await resp.text(); throw new Error(`Gemini API ${resp.status}: ${t.slice(0, 200)}`) }
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const data = await resp.json()
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const text = (data.candidates?.[0]?.content?.parts?.[0]?.text || '').trim()
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log(`Vision response: ${text.slice(0, 300)}`)
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let parsed
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try { parsed = JSON.parse(text) } catch { const m = text.match(/\{[\s\S]*\}/); if (m) try { parsed = JSON.parse(m[0]) } catch {} }
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return parsed
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}
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function extractBase64 (req, body, label) {
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if (!cfg.AI_API_KEY) return { error: 'AI_API_KEY not configured', status: 500 }
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if (!body.image) return { error: 'Missing image field (base64)', status: 400 }
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const base64 = body.image.replace(/^data:image\/[^;]+;base64,/, '')
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log(`Vision ${label}: received image ${Math.round(base64.length * 3 / 4 / 1024)}KB`)
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return { base64 }
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}
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const BARCODE_PROMPT = `Read ALL identifiers on this equipment label photo (may be blurry/tilted).
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Extract: barcode text, serial numbers (S/N, SN), MAC addresses (12 hex chars), model numbers (M/N, Model, P/N), IMEI, GPON SN.
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Examples: 1608K44D9E79FAFF5, TPLG-A1B2C3D4, 04:18:D6:A1:B2:C3, HWTC87654321.
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Try your BEST on every character. Return max 3 most important (serial/MAC first).`
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const BARCODE_SCHEMA = {
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type: 'object',
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properties: { barcodes: { type: 'array', items: { type: 'string' }, maxItems: 3 } },
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required: ['barcodes'],
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}
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async function handleBarcodes (req, res) {
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const body = await parseBody(req)
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const check = extractBase64(req, body, 'barcode')
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if (check.error) return json(res, check.status, { error: check.error })
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try {
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const result = await extractBarcodes(check.base64)
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return json(res, 200, result)
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} catch (e) {
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log('Vision barcode error:', e.message)
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return json(res, 500, { error: 'Vision extraction failed: ' + e.message })
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}
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}
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async function extractBarcodes (base64Image) {
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const parsed = await geminiVision(base64Image, BARCODE_PROMPT, BARCODE_SCHEMA)
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if (!parsed) return { barcodes: [] }
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const arr = Array.isArray(parsed) ? parsed : Array.isArray(parsed.barcodes) ? parsed.barcodes : []
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const barcodes = arr.filter(v => typeof v === 'string' && v.trim().length > 3).map(v => v.trim().replace(/\s+/g, '')).slice(0, 3)
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log(`Vision: extracted ${barcodes.length} barcode(s): ${barcodes.join(', ')}`)
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return { barcodes }
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}
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const EQUIP_PROMPT = `Read this ISP equipment label (ONT/ONU/router/modem). Return structured JSON.
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Extract: brand/manufacturer, model (M/N, P/N), serial (S/N, SN, under barcode), MAC address (12 hex, no separators), GPON SN, HW version, barcodes.
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Try your BEST on blurry/angled text. Set missing fields to null.`
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const EQUIP_SCHEMA = {
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type: 'object',
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properties: {
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brand: { type: 'string', nullable: true }, model: { type: 'string', nullable: true },
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serial_number: { type: 'string', nullable: true }, mac_address: { type: 'string', nullable: true },
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gpon_sn: { type: 'string', nullable: true }, hw_version: { type: 'string', nullable: true },
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equipment_type: { type: 'string', nullable: true },
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barcodes: { type: 'array', items: { type: 'string' }, maxItems: 5 },
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},
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required: ['serial_number'],
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}
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async function handleEquipment (req, res) {
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const body = await parseBody(req)
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const check = extractBase64(req, body, 'equipment')
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if (check.error) return json(res, check.status, { error: check.error })
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try {
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const parsed = await geminiVision(check.base64, EQUIP_PROMPT, EQUIP_SCHEMA)
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if (!parsed) return json(res, 200, { serial_number: null, barcodes: [] })
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if (parsed.mac_address) parsed.mac_address = parsed.mac_address.replace(/[:\-.\s]/g, '').toUpperCase()
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if (parsed.serial_number) parsed.serial_number = parsed.serial_number.replace(/\s+/g, '').trim()
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log(`Vision equipment: brand=${parsed.brand} model=${parsed.model} sn=${parsed.serial_number} mac=${parsed.mac_address}`)
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return json(res, 200, parsed)
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} catch (e) {
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log('Vision equipment error:', e.message)
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return json(res, 500, { error: 'Vision extraction failed: ' + e.message })
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}
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}
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// ─── Invoice / bill OCR ────────────────────────────────────────────────
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// We run this on Gemini (not on Ollama) because the ops VM has no GPU —
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// ops must not depend on a local vision model. The schema matches what
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// the ops InvoiceScanPage expects so switching away from Ollama is a
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// drop-in replacement on the frontend.
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const INVOICE_PROMPT = `You are an invoice/bill OCR assistant. Extract structured data from this photo of a vendor invoice or bill.
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Return ONLY valid JSON that matches the provided schema. No prose, no markdown.
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Rules:
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- "date" / "due_date" must be ISO YYYY-MM-DD. If the date is MM/DD/YYYY or DD/MM/YYYY and ambiguous, prefer YYYY-MM-DD with the most likely interpretation for Canadian/Québec invoices.
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- "currency" is a 3-letter code (CAD, USD, EUR). Default to CAD if not visible.
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- "tax_gst" = GST/TPS/HST (Canadian federal tax); "tax_qst" = QST/TVQ (Québec provincial tax).
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- "items" is a line-by-line list; keep description as printed, collapse whitespace.
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- Missing fields → null for strings, 0 for numbers, [] for items.`
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const INVOICE_SCHEMA = {
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type: 'object',
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properties: {
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vendor: { type: 'string', nullable: true },
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vendor_address: { type: 'string', nullable: true },
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invoice_number: { type: 'string', nullable: true },
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date: { type: 'string', nullable: true },
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due_date: { type: 'string', nullable: true },
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subtotal: { type: 'number', nullable: true },
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tax_gst: { type: 'number', nullable: true },
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tax_qst: { type: 'number', nullable: true },
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total: { type: 'number', nullable: true },
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currency: { type: 'string', nullable: true },
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items: {
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type: 'array',
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items: {
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type: 'object',
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properties: {
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description: { type: 'string', nullable: true },
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qty: { type: 'number', nullable: true },
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rate: { type: 'number', nullable: true },
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amount: { type: 'number', nullable: true },
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},
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},
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},
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notes: { type: 'string', nullable: true },
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},
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required: ['vendor', 'total'],
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}
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async function handleInvoice (req, res) {
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const body = await parseBody(req)
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const check = extractBase64(req, body, 'invoice')
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if (check.error) return json(res, check.status, { error: check.error })
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try {
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const parsed = await geminiVision(check.base64, INVOICE_PROMPT, INVOICE_SCHEMA)
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if (!parsed) return json(res, 200, { vendor: null, total: null, items: [] })
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// Normalize: trim + coerce numbers (model sometimes returns "1,234.56" as string)
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for (const k of ['subtotal', 'tax_gst', 'tax_qst', 'total']) {
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if (typeof parsed[k] === 'string') parsed[k] = Number(parsed[k].replace(/[^0-9.\-]/g, '')) || 0
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}
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if (Array.isArray(parsed.items)) {
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for (const it of parsed.items) {
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for (const k of ['qty', 'rate', 'amount']) {
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if (typeof it[k] === 'string') it[k] = Number(it[k].replace(/[^0-9.\-]/g, '')) || 0
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}
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}
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}
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log(`Vision invoice: vendor=${parsed.vendor} total=${parsed.total} items=${(parsed.items || []).length}`)
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return json(res, 200, parsed)
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} catch (e) {
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log('Vision invoice error:', e.message)
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return json(res, 500, { error: 'Vision extraction failed: ' + e.message })
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}
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}
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// ─── Field-targeted extraction (for tech mobile form auto-fill) ─────────
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// Instead of "read everything on the label", this pulls ONE specific value.
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// Used when a tech has selected e.g. "Wi-Fi password" and wants Gemini to
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// find only that field on the sticker. Returns {value, confidence}.
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const FIELD_CONFIG = {
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serial_number: {
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desc: 'the device SERIAL NUMBER (labeled S/N, SN, Serial, N/S). Usually 8-20 alphanumeric chars, frequently printed under a Code128 barcode.',
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clean: v => v.replace(/\s+/g, '').toUpperCase(),
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},
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mac_address: {
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desc: 'the MAC ADDRESS (12 hexadecimal chars, may be separated by colons, dashes or nothing). Labeled MAC, WAN MAC, LAN MAC, Ethernet, Wi-Fi MAC.',
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clean: v => v.replace(/[^0-9A-F]/gi, '').toUpperCase(),
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},
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gpon_sn: {
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desc: 'the GPON SN — a 4-letter manufacturer code followed by 8 hex characters (e.g. HWTC12345678, ZTEG87654321, CIGG1A2B3C4D). Labeled GPON SN, GPON-SN, ONU SN.',
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clean: v => v.replace(/\s+/g, '').toUpperCase(),
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},
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model: {
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desc: 'the MODEL number/name (labeled M/N, Model, P/N, Product, Type). Usually short, e.g. "HG8245H", "TL-WR841N", "HS8145V".',
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clean: v => v.trim(),
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},
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wifi_ssid: {
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desc: 'the Wi-Fi NETWORK NAME (SSID). Labeled SSID, Wi-Fi name, WLAN SSID, Nom Wi-Fi, Nom du réseau.',
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clean: v => v.trim(),
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},
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wifi_password: {
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desc: 'the Wi-Fi PASSWORD / KEY. Labeled WPA, WPA2, WPA Key, Wi-Fi Password, Wireless Password, Clé Wi-Fi, Mot de passe Wi-Fi, Password, Passphrase. Usually 8-20 chars, mixed case with numbers and sometimes symbols.',
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clean: v => v.trim(),
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},
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imei: {
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desc: 'the IMEI (15 digits, exactly). Labeled IMEI.',
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clean: v => v.replace(/\D/g, ''),
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},
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generic: {
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desc: 'the requested value (see context hint below)',
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clean: v => v.trim(),
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},
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}
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const FIELD_SCHEMA = {
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type: 'object',
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properties: {
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value: { type: 'string', nullable: true },
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confidence: { type: 'number' },
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},
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required: ['value', 'confidence'],
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}
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async function extractField (base64Image, field, context = {}) {
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const config = FIELD_CONFIG[field] || FIELD_CONFIG.generic
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const eq = context.equipment_type ? `Equipment type hint: ${context.equipment_type}.` : ''
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const brand = context.brand ? `Brand hint: ${context.brand}.` : ''
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const model = context.model ? `Model hint: ${context.model}.` : ''
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const custom = (field === 'generic' && context.hint) ? `Look for: ${context.hint}.` : ''
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const prompt = `You are reading an ISP equipment label (ONT, router, modem). Extract ${config.desc}
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${eq} ${brand} ${model} ${custom}
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Return ONLY JSON matching the schema: {"value": "<the raw extracted text without its label prefix>", "confidence": <0.0-1.0>}.
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If you cannot find it with confidence above 0.5, return {"value": null, "confidence": 0.0}.
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Do NOT invent data. Prefer returning null over guessing.`
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const parsed = await geminiVision(base64Image, prompt, FIELD_SCHEMA)
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if (!parsed || !parsed.value) return { value: null, confidence: 0 }
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const cleaned = config.clean(parsed.value)
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if (!cleaned) return { value: null, confidence: 0 }
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return { value: cleaned, confidence: Math.max(0, Math.min(1, Number(parsed.confidence) || 0.5)) }
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}
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async function handleFieldScan (req, res) {
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const body = await parseBody(req)
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const check = extractBase64(req, body, 'field-scan')
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if (check.error) return json(res, check.status, { error: check.error })
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try {
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const out = await extractField(check.base64, body.field || 'generic', {
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hint: body.hint, equipment_type: body.equipment_type, brand: body.brand, model: body.model,
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})
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return json(res, 200, { ok: true, ...out })
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} catch (e) {
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log('Vision field-scan error:', e.message)
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return json(res, 500, { error: 'Vision field extraction failed: ' + e.message })
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}
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}
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module.exports = { handleBarcodes, extractBarcodes, handleEquipment, handleInvoice, extractField, handleFieldScan }
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