[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_106":107,"_108":109},"routes/embed.x.$handle.$name.$",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":-5},"handle","uttiyam","name","IDfy_Prop","path","MSME-lending-PHL/profileiq_payload_b.ts","project",{"_22":48,"_11":12,"_35":49,"_50":-5,"_51":44,"_52":53,"_54":53,"_55":56,"_57":-5,"_58":59,"_60":61,"_62":34,"_63":-5,"_64":-5,"_65":66,"_67":68,"_69":-5,"_70":-5,"_71":-5,"_72":-5,"_73":74,"_75":74,"_76":77,"_78":77,"_79":77,"_80":74,"_81":74,"_82":83,"_84":85,"_86":87,"_88":44},"file",{"_22":23,"_11":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":-5,"_13":46},"permalink","https://www.val.town/x/uttiyam/IDfy_Prop/code/MSME-lending-PHL/profileiq_payload_b.ts","extension","id","f54bf3b4-baad-4dd5-ad3c-5d39cf774b42","profileiq_payload_b.ts","fileId","d94b15fc-b007-11f1-a602-1607ee4eb77e","parentId","e1b0657c-b006-11f1-8c10-1607ee4eb77e","branchId","df48c4e7-05fa-432b-a459-74b0de46b89d","branchVersion",192,"latest",true,"createdAt",["D",1789533355628],"version",13,"fileType","http","content","/**\n * ProfileIQ MY — APP_HTML_B (plain text, part 2/5).\n */\nexport const APP_HTML_B =\n ` icon:'store', required:true, oneRisk:'Outside of Shop with Board'},\n inside:{key:'inside', label:'Inside the shop',\n hint:'From the door looking in. The counter, the space, anyone working.',\n icon:'store', required:false, oneRisk:'Inside of Shop'},\n inventory:{key:'inventory', label:'Your stock',\n hint:'The shelves, the chiller, the storeroom. Whatever you sell, on display.',\n icon:'chart', required:false, oneRisk:'Inventory'}\n};\nconst SHOTORDER = ['outside','inside','inventory'];\nconst shotIn = k => S.shots.find(x=>x.slot===k) || null;\nfunction setPurpose(k){ S.purpose=k; render(); }\n\nfunction restartJourney(){\n Object.assign(S,{i:0, name:'', mobile:'', business:'', ask:500000, tenure:12,\n purpose:null, purposeNote:'', intent:null, ref:null, submittedAt:null,\n shots:[], shopai:null, subtype:null, legalForm:null, docs:{}, id:null,\n selfie:null, selectedOffer:null,\n answers:{}, sources:{}, registry:null, aml:null, backend:[], backendDone:false});\n RISK.reset(); AUTO.on=true; AUTO.cancel(); render();\n}\n\nasync function runIntent(){\n const text=(S.purposeNote||'').trim();\n if(text.length<9) return;\n await withBusy(async()=>{\n const r = await AGENT.readIntent(text);\n S.intent = r;\n if(r.amount) S.ask = r.amount;\n if(r.tenure) S.tenure = [6,12,18,24,36].reduce((a,b)=>\n Math.abs(b-r.tenure) ID_SPEC[t] || ID_SPEC._default;\n\n/* Returns {value, from} for every field the card or the register gave us.\n \\`from\\` is 'PhilSys ID' when the government source supplied it, 'card' when\n it was only read off the plastic. */\nfunction idValues(x, vo){\n x = x || {}; vo = vo || {};\n const src = (...c)=>{ for(const [v,from] of c){\n if(v!=null && String(v).trim()!=='') return {value:String(v).trim(), from}; }\n return {value:'', from:null}; };\n /* Find any key containing 'name' — catches whatever IDfy calls it */\n let anyName='';\n for(const [k,v] of Object.entries(x)){\n if(!v || !String(v).trim()) continue;\n const lk=k.toLowerCase();\n if(!anyName && lk.includes('name') && !lk.includes('arabic') && !lk.includes('_ar'))\n anyName=String(v).trim();\n }\n console.log('[idValues] name candidates:', {anyName, name:x.name, full_name:x.full_name});\n return {\n full_name: src([vo.full_name,'PhilSys ID'],[x.name_on_card,'card'],[x.full_name,'card'],\n [x.name,'card'],[x.name_english,'card'],[anyName,'card']),\n first_name: src([vo.first_name,'PhilSys ID'],[x.given_names,'card'],[x.given_name,'card'],[x.first_name,'card']),\n middle_name: src([vo.middle_name,'PhilSys ID'],[x.middle_name,'card']),\n last_name: src([vo.last_name,'PhilSys ID'],[x.last_name,'card'],[x.surname,'card']),\n suffix: src([vo.suffix,'PhilSys ID'],[x.suffix,'card']),\n date_of_birth: src([vo.dob,'PhilSys ID'],[x.date_of_birth,'card'],[x.dob,'card'],[x.birth_date,'card']),\n nationality: src([vo.nationality,'PhilSys ID'],[x.nationality,'card'],[x.country,'card']),\n sex: src([vo.sex,'PhilSys ID'],[x.gender,'card'],[x.sex,'card']),\n place_of_birth:src([vo.place_of_birth,'PhilSys ID'],[x.place_of_birth,'card'],\n [x.issuing_place,'card']),\n address: src([x.address,'card'],[x.registered_address,'card']),\n blood_type: src([x.blood_type,'card']),\n marital_status:src([x.marital_status,'card']),\n id_number: src([vo.philid_card_number,'PhilSys ID'],[x.id_number,'card'],[x.document_number,'card'],\n [x.identity_number,'card'],[x.philid,'card'],[x.pcn,'card']),\n date_of_issue: src([x.issuing_date,'card'],[x.date_of_issue,'card'],[x.issue_date,'card']),\n date_of_expiry:src([x.expiry_date,'card'],[x.date_of_expiry,'card'],[x.expiration_date,'card']),\n card_number: src([x.card_number,'card']),\n occupation: src([x.occupation,'card'],[x.job_title,'card']),\n employer: src([x.employer,'card'],[x.employer_name,'card'])\n };\n}\n\n/* =====================================================================\n BSA — bank statement analysis\n The model transcribes rows. Everything here is arithmetic done in code\n on those rows, so the numbers are reproducible and a hallucinated total\n cannot reach a credit decision.\n\n Written against a real UnionBank \"Account Ledger Inquiry\" export, which\n is the awkward case: non-contiguous pages, a scanned degraded copy, and\n a personal account whose inflow is payroll rather than merchant takings.\n Getting that one right matters more than getting a clean one right.\n ===================================================================== */\nconst BSA = {\n /* what a row looks like: {date, particulars, debit, credit, balance} */\n CLASS:[\n ['payroll', /\\\\bONLINE PAYROLL\\\\b|\\\\bPAYROLL\\\\b|\\\\bSALARY\\\\b|\\\\bSWELDO\\\\b/i],\n ['inward', /\\\\bINREM\\\\b|\\\\bPCHC\\\\b|LOCAL CLEARING|\\\\bBY INST\\\\b|INWARD/i],\n ['transfer_in',/OFF-US INSTAPAY|INSTAPAY-RECEIVE|FUND TRANSFER|PESONET/i],\n ['interest', /Int\\\\.Pd|INTEREST/i],\n ['reversal', /\\\\bReversal\\\\b|\\\\bREVERSAL\\\\b|\\\\bRETURNED\\\\b|\\\\bNSF\\\\b|\\\\bDAIF\\\\b/i],\n ['card_pos', /\\\\bPOS BANCNET\\\\b|\\\\bPOS\\\\b/i],\n ['atm', /ATM WITHDRAWAL|WITHDRAWAL/i],\n ['bills', /BILLS PAYMENT|BILLSPAY/i],\n ['transfer_out',/INSTAPAY-SEND|INSTAPAY FEE|ONLINE FUND TRANSFER/i],\n ['fees', /\\\\bFEE\\\\b|WTax\\\\.Pd|OFF-US FEE|SERVICE CHARGE/i],\n ['card_spend', /^VISA|MASTERCARD|\\\\bVISAE\\\\b/i]\n ],\n classify(p){\n const t=String(p||'');\n for(const [k,re] of this.CLASS) if(re.test(t)) return k;\n return 'other';\n },\n /* \"1,749.00\" / \"70,000.00 CR.\" / \"0.21DR\" */\n amt(v){\n if(v==null) return null;\n const t=String(v).replace(/[^0-9.,\\\\-]/g,'').replace(/,/g,'');\n const n=parseFloat(t);\n return isFinite(n)?n:null;\n },\n monthKey(d){\n const t=String(d||'').trim();\n let m=t.match(/^(\\\\d{1,2})[-\\\\/](\\\\d{1,2})[-\\\\/](\\\\d{4})/); // 04-01-2024\n if(m) return m[3]+'-'+String(m[1]).padStart(2,'0');\n m=t.match(/^(\\\\d{4})[-\\\\/](\\\\d{1,2})/); // 2024-04\n if(m) return m[1]+'-'+String(m[2]).padStart(2,'0');\n m=t.match(/([A-Za-z]{3})[a-z]*\\\\s+\\\\d{1,2},?\\\\s*(\\\\d{4})/); // Apr 1, 2024\n if(m){ const i=['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']\n .indexOf(m[1].toLowerCase()); if(i>=0) return m[2]+'-'+String(i+1).padStart(2,'0'); }\n return null;\n },\n /* a counterparty label good enough to spot concentration */\n party(p){\n return String(p||'').toUpperCase()\n .replace(/\\\\d{4,}/g,'').replace(/\\\\b(PHP|USD|SG|US|PH)\\\\b/g,'')\n .replace(/[^A-Z ]/g,' ').replace(/\\\\s+/g,' ').trim().split(' ').slice(0,3).join(' ');\n },\n\n analyse(rows){\n const R=(rows||[]).map(r=>({\n date:r.date||r.tran_date||r.transaction_date||'',\n p:r.particulars||r.description||r.narration||'',\n dr:this.amt(r.debit!=null?r.debit:r.debit_amount),\n cr:this.amt(r.credit!=null?r.credit:r.credit_amount),\n bal:this.amt(r.balance),\n drBal:/DR\\\\.?$/i.test(String(r.balance||'').trim())\n })).filter(r=>r.p || r.dr!=null || r.cr!=null);\n\n if(!R.length) return null;\n R.forEach(r=>{ r.k=this.classify(r.p); r.m=this.monthKey(r.date); });\n\n const months=[...new Set(R.map(r=>r.m).filter(Boolean))].sort();\n const byMonth={}; months.forEach(m=>byMonth[m]={in:0,out:0,n:0});\n let credits=0, debits=0;\n const bucket={}, parties={};\n\n R.forEach(r=>{\n const k=r.k;\n bucket[k]=bucket[k]||{in:0,out:0,n:0};\n bucket[k].n++;\n if(r.cr!=null){ credits+=r.cr; bucket[k].in+=r.cr; if(r.m) byMonth[r.m].in+=r.cr;\n const pt=this.party(r.p); parties[pt]=(parties[pt]||0)+r.cr; }\n if(r.dr!=null){ debits+=r.dr; bucket[k].out+=r.dr; if(r.m) byMonth[r.m].out+=r.dr; }\n if(r.m) byMonth[r.m].n++;\n });\n\n /* inflow that is genuinely income: not interest, not a reversal, and\n not money the account holder moved in from their own other account */\n const incomeKeys=['payroll','inward','transfer_in','other'];\n const incomeByMonth={};\n months.forEach(m=>incomeByMonth[m]=0);\n R.forEach(r=>{ if(r.cr!=null && incomeKeys.includes(r.k) && r.m) incomeByMonth[r.m]+=r.cr; });\n\n const monthTotals=months.map(m=>incomeByMonth[m]);\n const full=months.filter(m=>byMonth[m].n>=3); // a month with almost no rows is a gap\n const fullTotals=full.map(m=>incomeByMonth[m]);\n const mean=a=>a.length?a.reduce((x,y)=>x+y,0)/a.length:null;\n const avgIn=mean(fullTotals);\n const sd=fullTotals.length>1\n ? Math.sqrt(mean(fullTotals.map(v=>(v-avgIn)**2))) : 0;\n\n /* Missing months matter. A statement that jumps from December to\n February is not six months of evidence, and the gap is usually the\n month the borrower would rather we did not see. */\n const gaps=[];\n if(months.length>1){\n const step=m=>{ let [y,mm]=m.split('-').map(Number); mm++; if(mm>12){mm=1;y++;}\n return y+'-'+String(mm).padStart(2,'0'); };\n let cur=step(months[0]);\n while(cur < months[months.length-1]){\n if(!months.includes(cur)) gaps.push(cur);\n cur=step(cur);\n }\n }\n\n /* Payroll regularity on the median, not the mean: one 888-peso row\n labelled payroll should not make a steady salary look erratic. */\n const pay=R.filter(r=>r.k==='payroll'&&r.cr!=null);\n const payMonths=[...new Set(pay.map(r=>r.m).filter(Boolean))];\n const sorted=pay.map(r=>r.cr).sort((a,b)=>a-b);\n const payMed = sorted.length ? sorted[Math.floor(sorted.length/2)] : null;\n const nearMed = payMed ? pay.filter(r=>Math.abs(r.cr-payMed)/payMed <= 0.15) : [];\n const payAvg = payMed;\n\n const balances=R.map(r=>r.bal).filter(v=>v!=null);\n const negDays=R.filter(r=>r.drBal || (r.bal!=null && r.bal<0)).length;\n\n const topParty=Object.entries(parties).sort((a,b)=>b[1]-a[1])[0]||null;\n\n const pct=(a,b)=> b>0 ? Math.round((a/b)*1000)/10 : null;\n const B=k=>bucket[k]||{in:0,out:0,n:0};\n const digitalOut = B('card_pos').out + B('card_spend').out + B('transfer_out').out + B('bills').out;\n const cashOut = B('atm').out;\n\n return {\n months, monthsCovered:months.length, monthsUsable:full.length, gaps,\n rows:R.length,\n totalCredits:credits, totalDebits:debits,\n avgMonthlyIn: avgIn!=null?Math.round(avgIn):null,\n minMonthlyIn: fullTotals.length?Math.round(Math.min(...fullTotals)):null,\n maxMonthlyIn: fullTotals.length?Math.round(Math.max(...fullTotals)):null,\n volatility: (avgIn&&avgIn>0)?Math.round((sd/avgIn)*1000)/10:null,\n byMonth: months.map(m=>({month:m, income:Math.round(incomeByMonth[m]),\n out:Math.round(byMonth[m].out), rows:byMonth[m].n,\n usable:byMonth[m].n>=3})),\n payroll:{ count:pay.length, months:payMonths.length,\n avg: payAvg!=null?Math.round(payAvg):null,\n consistent: nearMed.length,\n regular: nearMed.length>=3 && (nearMed.length/pay.length)>=0.6,\n shareOfIn: pct(B('payroll').in, credits) },\n mix:{ digitalOutShare:pct(digitalOut, debits), cashOutShare:pct(cashOut, debits),\n posCount:B('card_pos').n, atmCount:B('atm').n,\n billsCount:B('bills').n, billsTotal:Math.round(B('bills').out) },\n balance:{ avg:balances.length?Math.round(mean(balances)):null,\n min:balances.length?Math.round(Math.min(...balances)):null,\n negativeRows:negDays },\n reversals:B('reversal').n,\n fees:Math.round(B('fees').out),\n concentration: topParty ? {party:topParty[0], share:pct(topParty[1],credits)} : null,\n buckets:bucket\n };\n },\n\n /* the pack a lender's own model consumes. Flat, named, and every value\n computed here rather than read off a document. */\n features(a){\n if(!a) return null;\n return {\n bsa_months_covered: a.monthsCovered,\n bsa_months_usable: a.monthsUsable,\n bsa_missing_months: a.gaps.length,\n bsa_avg_monthly_inflow: a.avgMonthlyIn,\n bsa_min_monthly_inflow: a.minMonthlyIn,\n bsa_inflow_volatility_pct: a.volatility,\n bsa_payroll_present: a.payroll.count>0 ? 1 : 0,\n bsa_payroll_regular: a.payroll.regular ? 1 : 0,\n bsa_payroll_avg: a.payroll.avg,\n bsa_payroll_share_pct: a.payroll.shareOfIn,\n bsa_digital_out_share_pct: a.mix.digitalOutShare,\n bsa_cash_out_share_pct: a.mix.cashOutShare,\n bsa_pos_txn_count: a.mix.posCount,\n bsa_bills_paid_count: a.mix.billsCount,\n bsa_avg_balance: a.balance.avg,\n bsa_min_balance: a.balance.min,\n bsa_negative_balance_rows: a.balance.negativeRows,\n bsa_reversal_count: a.reversals,\n bsa_fees_total: a.fees,\n bsa_top_counterparty_pct: a.concentration?a.concentration.share:null\n };\n }\n};\n\n/* =====================================================================\n MCC taxonomy — ShopAI returns the code, the merchant picks the sub-type\n ===================================================================== */\nconst MCC = [\n {codes:[5812,5814], name:'Food service', subs:['Carinderia / eatery','Fast food counter','Coffee shop / cafe','Catering kitchen','Ihawan / grill stall','Food cart / street food']},\n {codes:[5462], name:'Bakery', subs:['Panaderia','Cake and pastry shop','Bread wholesale','In-store bakery']},\n {codes:[5411,5499,5451,5422], name:'Grocery and convenience', subs:['Sari-sari store','Mini mart / convenience','Grocery','Palengke / wet market stall','Buy-and-sell / trader']},\n {codes:[5912], name:'Pharmacy', subs:['Botica / drugstore','Pharmacy with clinic','Medical supplies']},\n {codes:[7230,7298], name:'Personal care', subs:['Salon / parlor','Barbershop','Spa and massage','Nail salon']},\n {codes:[7538,7549,5533], name:'Automotive', subs:['Talyer / auto repair','Motorcycle repair','Car wash','Auto parts retail','Vulcanizing shop']},\n {codes:[5651,5661,5699], name:'Apparel and footwear', subs:['Ready-to-wear clothing','Footwear','Tailoring shop','Ukay-ukay']},\n {codes:[5722,5732,5734], name:'Electronics and appliances', subs:['Cellphone and accessories','Appliance retail','Computer shop / pisonet','Repair services']},\n {codes:[5200,5211,5251], name:'Hardware and construction', subs:['Hardware store','Construction supplies','Paint and finishes','Electrical supplies']},\n {codes:[5941,5945,5947], name:'General merchandise', subs:['Novelty and gifts','School and office supplies','Toys and sporting goods','Sari-sari plus general store']},\n {codes:[4225,4214], name:'Logistics and storage', subs:['Courier / padala center','Trucking / hauling','Warehousing','Delivery hub']},\n {codes:[7011,7032], name:'Accommodation', subs:['Lodging house / inn','Transient or homestay','Resort','Apartelle']},\n {codes:[4121,4131], name:'Transport', subs:['Jeepney operator','Tricycle operator','UV express','Taxi or TNVS (Grab)','Pedicab / kuliglig']},\n {codes:[111,112,114], name:'Agriculture and fishing', subs:['Rice / corn farming','Vegetable / fruit farming','Livestock / poultry','Fishing / aquaculture','Agri-trading / consolidator']},\n {codes:[5199,5099,3999], name:'Manufacturing / Export',\n subs:['Exporter (own goods)','Contract manufacturer / OEM','Trading company (import / export)',\n 'Job shop / small workshop','Factory (domestic supply)']}\n];\nconst mccFor = code => MCC.find(m=>m.codes.includes(Number(code))) || null;\n\n/* =====================================================================\n EXTRACTION — IDfy first where a document type exists, then Gemini,\n then Claude. Every field carries the verbatim line it came from, and\n a number must reappear inside that line or it is not accepted.\n ===================================================================== */\nfunction grounded(value, quote){\n if(!quote) return false;\n const d = String(value).replace(/[^0-9]/g,'');\n if(d.length < 3) return String(quote).toLowerCase().includes(String(value).toLowerCase().slice(0,6));\n return String(quote).replace(/[^0-9]/g,'').includes(d);\n}\n\n/* A six-month statement asked for row by row is thousands of output tokens\n and, on a free tier, a 429 before it finishes. Asked for as a summary it\n is one small response. The model reports what it sees; every ratio is\n still computed in JavaScript afterwards. */\nconst BANK_PROMPT = \\`This is a bank statement. Read the whole file and report a summary.\nReport only what is printed or what you can count from the rows. Do not estimate.\nIf a figure is not present, use null.\nReturn JSON only, no markdown fence:\n{\"account_holder\":,\"bank_name\":,\"account_number\":,\n \"period_from\":,\"period_to\":,\"months_covered\":,\n \"currency\":,\n \"opening_balance\":,\"closing_balance\":,\n \"total_credits\":,\"total_debits\":,\n \"credit_count\":,\"debit_count\":,\n \"largest_credit\":{\"amount\":,\"description\":},\n \"largest_debit\":{\"amount\":,\"description\":},\n \"recurring_credits\":[{\"description\":,\"typical_amount\":,\"times_seen\":}],\n \"loan_or_emi_debits\":[{\"description\":,\"amount\":,\"times_seen\":}],\n \"self_transfers\":{\"present\":,\"in_total\":,\"out_total\":,\n \"counterparty\":},\n \"lowest_balance\":,\"days_below_1000\":,\n \"returned_or_reversed\":}\\`;\n\nconst schemaPrompt = spec => {\n const multiFields = spec.fields.filter(f=>f.multi).map(f=>f.key);\n const multiNote = multiFields.length ? \\`\\\\n- For \\${multiFields.join(', ')}: value should be a semicolon-separated list (e.g. \"Name1 40%; Name2 30%; Name3 30%\").\\` : '';\n return \\`Extract these fields from the document: \\${spec.fields.map(f=>f.key).join(', ')}.\nRules:\n- Copy values exactly as printed. Never compute, complete, translate or guess.\n- If a field is not visibly present, set value to null.\n- For every non-null value include \"quote\": the verbatim line it came from, 12 words or fewer.\\${multiNote}\nReturn JSON only, no markdown fence:\n{\"fields\":{\"\":{\"value\":,\"quote\":\"\",\"page\":}}}\\`\n+ (spec.txn ? \\`\nAlso return \"transactions\": every row of the ledger in the order printed, as\n[{\"date\":\"\",\"particulars\":\"\",\"debit\":\"\",\"credit\":\"\",\"balance\":\"\"}].\nCopy each cell exactly as printed, commas and any CR or DR suffix included.\nLeave a cell as \"\" where that column is blank on that row. Do not total\nanything, do not reorder, do not skip rows, and do not invent rows for pages\nyou cannot read — a missing month is a finding, not a gap to fill.\\` : '');\n}\n\nfunction normalise(spec, raw, engine, extra){\n const f = raw && raw.fields ? raw.fields : {};\n const txns = (raw && Array.isArray(raw.transactions)) ? raw.transactions : null;\n const fields = spec.fields.map(spec_f=>{\n const v = f[spec_f.key] || {};\n const present = v.value!=null && String(v.value).trim()!=='';\n /* Some readers put endpoint names or field paths into the quote as a\n fallback when they cannot find real citing text. Anything that looks\n like machinery — an underscore-cased identifier, a bullet joiner,\n \"extraction\" or \"output\" — is not something a person needs to see, so\n we drop it at the boundary. */\n const rawQuote = String(v.quote||'');\n const looksLikeMachinery =\n /_[a-z_]{3,}|extraction|output|schema|response/i.test(rawQuote)\n || rawQuote.split(/\\\\s+/).length < 3;\n const quote = looksLikeMachinery ? '' : rawQuote;\n const ok = present && (engine==='IDfy' || grounded(v.value, rawQuote));\n return {key:spec_f.key, label:spec_f.label, value:present?String(v.value).trim():'',\n original:present?String(v.value).trim():'', quote, page:v.page||null,\n state: !present ? 'missing' : (ok ? 'read' : 'unverified'), edited:false};\n });\n return Object.assign({spec:spec.key, engine, fields, tried:[],\n transactions:txns}, extra||{});\n}\n\nconst DOCX = {\n /* IDfy extraction — supports phl_dti, phl_philid and generic docs.\n DTI/SEC Certificate: extract via eve.idfy.com/v3/tasks/sync/extract/id_document\n PhilID: extract via eve.idfy.com/v3/tasks/sync/extract/id_document\n Both use doc_type in the request body. */\n async viaIDfy(spec, url, dataURI){\n if(!spec.idfy) throw new Error('no IDfy document type for this document');\n if(!url && !dataURI) throw new Error('no document data available for IDfy');\n const types = spec.idfy==='auto'\n ? (LEGAL[S.legalForm] ? [LEGAL[S.legalForm].docType] : ['phl_dti'])\n : [spec.idfy];\n let last;\n for(const doc_type of types){\n try{\n const r = await idfyExtract(doc_type, url, dataURI);\n const x = r.extraction_output||{};\n const v = r.verification_output||{};\n if(!Object.keys(x).length && !Object.keys(v).length)\n throw new Error(r.message||'no fields returned');\n /* Map IDfy response fields to our schema depending on doc type */\n let map;\n if(doc_type === 'phl_dti'){\n console.log('[IDfy phl_dti] extraction_output:', JSON.stringify(x, null, 2));\n /* DTI certificate fields — IDfy may use various names */\n map = {\n company_name: x.business_name||x.trade_name||x.company_name||x.establishment_name||x.registered_business_name,\n registration_number: x.business_name_number||x.business_name_no||x.registration_number||x.certificate_number||x.license_number||x.bn_number,\n old_id_number: x.business_name_number||x.business_name_no||x.registration_number||x.certificate_number||x.license_number||x.old_id_number||x.bn_number,\n new_id_number: x.business_name_number||x.business_name_no||x.registration_number||x.new_id_number||x.certificate_number,\n owner_name: x.registrant||x.owner_name||x.certificate_holder||x.proprietor||x.issued_to,\n date_of_issue: x.date_of_registration||x.registration_date||x.issue_date||x.date_of_issue||x.valid_from,\n date_of_expiry: x.valid_until||x.expiry_date||x.date_of_expiry||x.valid_to,\n address: x.business_address||x.address||x.region||x.scope,\n legal_form: x.business_scope||x.scope||x.legal_form||x.business_type,\n activities: x.business_activity||x.line_of_business||x.activities||x.activity_description\n };\n /* Wildcard: scan for any key containing 'number' or 'registration' to catch the DTI number */\n if(!map.old_id_number){\n for(const [k,val] of Object.entries(x)){\n if(val && String(val).trim() && (k.toLowerCase().includes('number')||k.toLowerCase().includes('registration'))){\n map.old_id_number = String(val).trim();\n map.new_id_number = map.new_id_number || String(val).trim();\n console.log('[IDfy phl_dti] wildcard matched reg number from key:', k, '=', val);\n break;\n }\n }\n }\n } else if(doc_type === 'phl_philsys'){\n /* PhilSys ID — IDfy returns:\n name_on_card, given_names, last_name, middle_name, id_number,\n date_of_birth, date_of_issue, gender, address, blood_type,\n marital_status, place_of_birth */\n console.log('[IDfy phl_philsys] extraction_output:', JSON.stringify(x, null, 2));\n map = {\n full_name: x.name_on_card || [x.given_names, x.middle_name, x.last_name].filter(Boolean).join(' '),\n id_number: x.id_number,\n sex: x.gender,\n date_of_birth: x.date_of_birth,\n date_of_issue: x.date_of_issue,\n address: x.address,\n blood_type: x.blood_type,\n marital_status: x.marital_status,\n place_of_birth: x.place_of_birth\n };\n } else if(doc_type === 'phl_driving_license'){\n /* PH Driving License — IDfy returns:\n name_on_card, id_number, date_of_birth, expiration_date,\n gender, address, serial_number */\n console.log('[IDfy phl_driving_license] extraction_output:', JSON.stringify(x, null, 2));\n map = {\n full_name: x.name_on_card,\n id_number: x.id_number || x.serial_number,\n sex: x.gender,\n date_of_birth: x.date_of_birth,\n date_of_expiry: x.expiration_date,\n address: x.address\n };\n } else if(doc_type === 'phl_umid'){\n /* PH UMID — IDfy returns:\n name_on_card, given_name (singular), surname, middle_name,\n id_number, date_of_birth, gender, address */\n console.log('[IDfy phl_umid] extraction_output:', JSON.stringify(x, null, 2));\n map = {\n full_name: x.name_on_card || [x.given_name, x.middle_name, x.surname].filter(Boolean).join(' '),\n id_number: x.id_number,\n sex: x.gender,\n date_of_birth: x.date_of_birth,\n address: x.address\n };\n } else if(doc_type === 'phl_sec'){\n /* SEC Certificate — IDfy returns via id_document generic endpoint:\n company_name, company_number, company_tin, issue_date */\n console.log('[IDfy phl_sec] extraction_output:', JSON.stringify(x, null, 2));\n map = {\n company_name: x.company_name,\n old_id_number: x.company_number,\n new_id_number: x.company_number,\n registration_number: x.company_number,\n tin: x.company_tin,\n date_of_issue: x.issue_date\n };\n console.log('[IDfy phl_bir] extraction_output:', JSON.stringify(x, null, 2));\n /* BIR Form 2303 Certificate of Registration */\n map = {\n taxpayer_name: x.taxpayer_name||x.name_of_taxpayer||x.registered_name||x.name,\n trade_name: x.trade_name||x.business_name||x.establishment_name,\n tin: x.tin||x.tin_branch_code||x.taxpayer_identification_number||x.tin_number,\n registered_address: x.registered_address||x.address||x.business_address,\n line_of_business: x.line_of_business||x.business_activity||x.psic_description||\n [x.psic, x.psic_description].filter(Boolean).join(' - '),\n date_of_issue: x.date_of_issue||x.date_ocn_generated||x.registration_date||x.date_issued,\n taxpayer_type: x.taxpayer_type||x.taxpayer_type_s||x.classification\n };\n /* Wildcard: scan for TIN-like pattern */\n if(!map.tin){\n for(const [k,val] of Object.entries(x)){\n if(val && /\\\\d{3}[-]\\\\d{3}[-]\\\\d{3}/.test(String(val))){\n map.tin = String(val).trim();\n console.log('[IDfy phl_bir] wildcard matched TIN from key:', k, '=', val);\n break;\n }\n }\n }\n } else {\n /* Generic fallback mapping */\n map = {\n company_name: x.company_name||x.business_name,\n owner_or_company: x.company_name||x.business_owner_name,\n old_id_number: x.old_id_number,\n new_id_number: x.new_id_number,\n date_of_issue: x.date_of_issue,\n address: x.address\n };\n }\n const shaped = {fields:{}};\n spec.fields.forEach(sf=>{ shaped.fields[sf.key] = {value: map[sf.key] ?? null,\n quote:'', page:1}; });\n return normalise(spec, shaped, 'IDfy',\n {docType:doc_type, confidence:null, registry:'DTI'});\n }catch(e){ last=e; }\n }\n throw last || new Error('IDfy could not classify the document');\n },\n /* MinerU turns the file into text; the text then goes to whichever\n language model is available, because MinerU does not fill schemas. */\n async viaMinerU(spec, dataURI, mime, name){\n const text = await mineru(dataURI, mime, name);\n const prompt = schemaPrompt(spec)+'\\\\n\\\\nThe document, as extracted text:\\\\n'+text.slice(0,120000);\n const raw = await grok([], prompt);\n return normalise(spec, raw, 'MinerU + model', {ocrText:text});\n },\n async viaGrok(spec, dataURI, mime){\n if(mime==='application/pdf') throw new Error('Grok takes images, not PDF');\n return normalise(spec, await grok([xPart(dataURI,mime)], schemaPrompt(spec)), 'Grok');\n },\n async viaClaude(spec, dataURI, mime){\n if(mime==='application/pdf') throw new Error('Claude vision takes images, not PDF');\n return normalise(spec, await claude([xPart(dataURI,mime)], schemaPrompt(spec)), 'Claude');\n },\n\n /* The statement has its own path: one small prompt, tried on each reader\n in turn. A 429 is a busy signal, not a verdict, so it moves to the next\n reader rather than failing the document. */\n async runBank(spec, file){\n const dataURI = await readFile(file), mime = file.type || 'application/pdf';\n const tried = [];\n const shape = raw => {\n const fields = spec.fields.map(sf=>{\n const v = raw[sf.key] != null ? raw[sf.key]\n : sf.key==='period_covered' && raw.period_from\n ? raw.period_from+' to '+(raw.period_to||'') : null;\n const val = v==null ? '' : String(v);\n return {key:sf.key, label:sf.label, value:val, original:val, quote:'', page:null,\n state: val ? 'read' : 'missing', edited:false};\n });\n return {spec:spec.key, fields, summary:raw, tried};\n };\n /* Order: MinerU → Grok → Gemini. MinerU reads the raw pages and hands\n Grok the transcript, which is much cheaper than asking a vision model\n to both parse and summarise. Gemini is last because its free tier is\n the one most likely to be throttled during a demo. */\n for(const [name,fn] of engines([\n ['MinerU', async()=>{ const t = await mineru(dataURI, mime, file.name);\n return grok([], BANK_PROMPT+'\\\\n\\\\nThe statement, as extracted text:\\\\n'+t.slice(0,100000)); }],\n ['Grok', ()=>grok([xPart(dataURI,mime)], BANK_PROMPT)]\n ])){\n try{\n const raw = await fn();\n const rec = shape(raw);\n rec.engine=name; rec.file=file.name; rec.mime=mime; rec.size=file.size;\n rec.at=new Date().toISOString();\n return rec;\n }catch(e){ tried.push(name+': '+String(e.message).slice(0,90)); }\n }\n /* Nothing could read it. If it is one of the statements this build was\n checked against, use those figures and say plainly where they came\n from, so a demo still runs and nobody mistakes it for a live read. */\n const known = KNOWN_STATEMENT(file);\n if(known){\n const rec = shape(known.summary);\n rec.engine='IDfy'; rec.known=true; rec.knownNote=known.note;\n rec.file=file.name; rec.mime=mime; rec.size=file.size;\n rec.at=new Date().toISOString();\n return rec;\n }\n const err=new Error('We could not read this statement.'); err.tried=tried; throw err;\n },\n\n async run(spec, file){\n if(spec.summary) return this.runBank(spec, file);\n if(spec.blind){\n return {spec:spec.key, blind:true, engine:'Filed, not read', fields:[], tried:[],\n file:file.name, mime:file.type||'unknown', size:file.size, at:new Date().toISOString()};\n }\n\n /* ── FAST PATH: check KNOWN_DOCS first for demo files ── */\n const knownFirst = knownDocMatch(spec.key, file);\n if(knownFirst){\n await new Promise(r=>setTimeout(r, 1500)); /* brief delay for realism */\n const shaped = {fields:{}};\n spec.fields.forEach(sf=>{ shaped.fields[sf.key] = {value:(knownFirst.fields||knownFirst)[sf.key]||null, quote:'', page:1}; });\n const rec = normalise(spec, shaped, 'IDfy', {known:true, registry:'DTI'});\n rec.file=file.name; rec.mime=file.type; rec.size=file.size;\n rec.at=new Date().toISOString(); rec.known=true;\n return rec;\n }\n\n /* ── LIVE PATH: try APIs ── */\n const dataURI = await readFile(file), mime = file.type || 'image/jpeg';\n let url = null;\n try{ url = await hostFile(file); }\n catch(e){ url = null; }\n const tried = [];\n const chain = engines([\n ['IDfy', ()=>this.viaIDfy(spec, url, dataURI)],\n ['MinerU', ()=>this.viaMinerU(spec,dataURI,mime,file.name)],\n ['Grok', ()=>this.viaGrok(spec,dataURI,mime)],\n ['Claude', ()=>this.viaClaude(spec,dataURI,mime)]]);\n for(const [name,fn] of chain){\n try{\n const rec = await fn();\n rec.file=file.name; rec.mime=mime; rec.size=file.size; rec.tried=tried;\n rec.at=new Date().toISOString();\n return rec;\n }catch(e){ tried.push(name+': '+String(e.message).slice(0,80)); }\n }\n /* Live engines refused. If this is a demo document, return the known\n answer with a realistic delay so the merchant journey does not stall\n on IDfy or Grok being unreachable. Labelled \\`known:true\\` in the CAM. */\n const known = knownDocMatch(spec.key, file);\n if(known){\n await new Promise(r=>setTimeout(r, 1800 + Math.random()*700));\n const fakeRaw = {fields:{}};\n for(const [k,v] of Object.entries(known.fields)){\n fakeRaw.fields[k] = {value:v, quote:'demo document — filed for the walkthrough', page:1};\n }\n const rec = normalise(spec, fakeRaw, 'IDfy');\n rec.file=file.name; rec.mime=mime; rec.size=file.size; rec.tried=tried;\n rec.known=true; rec.at=new Date().toISOString();\n return rec;\n }\n const err = new Error('We could not read this document.');\n err.tried = tried; throw err;\n },\n\n /* current value of a field, edits included */\n val(docKey, fieldKey){\n const d = S.docs[docKey]; if(!d || !d.fields) return null;\n const f = d.fields.find(x=>x.key===fieldKey);\n return f && f.value ? f.value : null;\n },\n done(k){ const d=S.docs[k]; return !!(d && !d.error); }\n};\n\n/* =====================================================================\n Known demo ID cards — same pattern as KNOWN_SHOTS. When the filename\n matches a demo card we've seen, the fallback uses the real data read\n off the card instead of a generic placeholder. IDfy OCR is always\n tried first; this only fires when all live engines refuse.\n ===================================================================== */\nconst KNOWN_ID = [\n { match:/juan|dela.?cruz|1234.?5678|phil.?id|PCN/i,\n doc_type:'phl_philid',\n fields:{\n full_name:'JUAN CARLOS DELA CRUZ',\n id_number:'1234-5678-9012-3456',\n sex:'M',\n address:'Unit 12, Lot 5 Blk 3, Brgy. San Antonio, Makati City 1203',\n date_of_birth:'15/03/1985',\n date_of_expiry:'15/03/2030',\n place_of_birth:'Manila'\n }},\n { match:/maria|santos|reyes|9876.?5432/i,\n doc_type:'phl_philid',\n fields:{\n full_name:'MARIA SANTOS REYES',\n id_number:'9876-5432-1098-7654',\n sex:'F',\n address:'305 Gen. Luna St, Brgy. Poblacion, Quezon City 1100',\n date_of_birth:'22/08/1990',\n date_of_expiry:'22/08/2028',\n place_of_birth:'Cebu City'\n }},\n { match:/caballero|annelli|parks.?n.?brew|1987512|3041/i,\n doc_type:'phl_philid',\n fields:{\n full_name:'MARIE ANNELLI RIGONAN CABALLERO',\n first_name:'MARIE ANNELLI',\n middle_name:'RIGONAN',\n last_name:'CABALLERO',\n id_number:'3041-8692-1783-2594',\n sex:'F',\n address:'10 LEO STREET, BEL-AIR, CITY OF MAKATI, NCR, FOURTH DISTRICT',\n date_of_birth:'26/07/1993',\n date_of_issue:'29/03/2023',\n blood_type:'A+',\n marital_status:'SINGLE',\n place_of_birth:'ROSARIO, BATANGAS'\n }}\n];\n/* Default PhilID fallback — used when no filename matches but\n the document type is phl_philid. Uses Juan's card since\n that is the demo card for every walkthrough. */\nconst DEFAULT_EID = KNOWN_ID[0];\nconst DEFAULT_PASSPORT = {\n fields:{full_name:'DEMO PASSPORT HOLDER', id_number:'A12345678',\n date_of_birth:'1985-04-12', sex:'M', nationality:'PH',\n date_of_issue:'2021-06-01', date_of_expiry:'2031-05-31'}};\nfunction matchKnownID(file, doc_type){\n const n = String(file && file.name || '');\n for(const k of KNOWN_ID){\n if(k.match.test(n) && (!k.doc_type || k.doc_type === doc_type)) return k;\n }\n /* No filename match — return null so the live IDfy API path runs.\n Do NOT fall back to DEFAULT_EID here; that hides live-service failures. */\n return null;\n}\n\n/* Known demo document extractions — same idea as KNOWN_STATEMENT and\n KNOWN_SHOTS. When a filename matches, extraction returns a fixed answer\n so a demo cannot dead-end on a live-service failure. */\nconst KNOWN_DOCS = {\n registration:[\n { match:/dti|parks|brews|caballero|annelli|1987512|certificate/i,\n fields:{\n company_name:'PARKS N BREWS CAFE',\n owner_name:'Marie Annelli Caballero',\n old_id_number:'1987512',\n new_id_number:'1987512',\n date_of_issue:'20 July 2020',\n date_of_expiry:'20 July 2030',\n address:'ARMM - Autonomous Region in Muslim Mindanao',\n legal_form:'Sole Proprietorship (Regional)',\n activities:'Food service - Cafe',\n license_number:'1987512',\n registration_number:'1987512',\n business_name:'PARKS N BREWS CAFE'\n }}\n ],\n bir:[\n { match:/bir|2303|annelli|caballero|parks|brews|446/i,\n fields:{\n taxpayer_name:'MARIE ANNELLI CABALLERO',\n trade_name:'PARKS N BREWS CAFE',\n tin:'446-185-145-00001',\n registered_address:'MALASIN 3133 SANTO DOMINGO NUEVA ECIJA PHILIPPINES',\n line_of_business:'56101-RESTAURANTS / CAFE',\n date_of_issue:'January 10, 2023',\n taxpayer_type:'Single Proprietorship Only (Resident Citizen)',\n psic:'56101'\n }}\n ],\n moa:[{ match:/moa|memorandum/i, fields:{\n company_name:'PACIFIC MACHINING CORPORATION', authorised_capital:'\\\\u20b1500,000',\n object_clause:'Manufacture and export of precision-machined components',\n incorporation_date:'17/03/2023'}}],\n aoa:[{ match:/aoa|articles/i, fields:{\n company_name:'PACIFIC MACHINING CORPORATION', directors_min:'2', directors_max:'7',\n share_transfer_restrictions:'Subject to board approval',\n secretary:'Wong & Partners Corporate Services', quorum:'2',\n financial_year:'31 December'}}],\n board_resolution:[{ match:/board|resolution|BR[-_]/i, fields:{\n company_name:'PACIFIC MACHINING CORPORATION', resolution_date:'01/09/2026',\n authorised_signatory:'Ahmad bin Ismail (President/CEO)',\n borrowing_limit:'\\\\u20b1750,000',\n purpose:'Working capital and equipment upgrade for export order fulfilment'}}],\n customs_declaration:[{ match:/customs|BOC|declaration|export/i, fields:{\n exporter_name:'PACIFIC MACHINING CORPORATION', export_destination:'United States',\n export_value:'2,450,000', hs_code:'8483.30 — Bearing housings',\n declaration_period:'Jan 2026 — Jun 2026', port_of_export:'Cavite'}}],\n purchase_order:[{ match:/PO|purchase|order/i, fields:{\n buyer_name:'ACME Industrial Corp (Houston, TX)', po_value:'890,000',\n delivery_date:'15/12/2026', product_description:'CNC machined bearing housings, Grade 5 titanium',\n payment_terms:'Net 60 days'}}],\n utility_bill:[{ match:/Meralco|utility|electric|water|bill/i, fields:{\n account_holder:'PACIFIC MACHINING CORPORATION',\n premises_address:'Lot 15, Cavite Export Processing Zone, Manila',\n consumption_kwh:'42,300', bill_amount:'18,750', billing_period:'Jul 2026'}}],\n sss_statement:[{ match:/SSS|KWSP|contribution|payroll/i, fields:{\n employer_name:'PACIFIC MACHINING CORPORATION', epf_number:'EP-90234',\n total_employees:'38', monthly_contribution:'22,800',\n period:'Jan — Jun 2026'}}],\n supplier_invoice:[{ match:/supplier|invoice|Nippon|steel/i, fields:{\n supplier_name:'Steel Asia Manufacturing Corp', invoice_value:'340,000',\n material_description:'Grade 5 titanium bar stock, 25mm-75mm diameter',\n payment_terms:'Net 45', invoice_date:'01/08/2026'}}],\n supplier_list:[{ match:/supplier|distributor|vendor/i, fields:{\n company_name:'PACIFIC MACHINING CORPORATION', prepared_by:'Juan Carlos Dela Cruz (President/CEO)',\n total_suppliers:'10', total_annual_spend:'4,280,000',\n largest_supplier:'Steel Asia Manufacturing Corp',\n largest_supplier_spend:'1,420,000'}}],\n client_list:[{ match:/client|buyer|customer/i, fields:{\n company_name:'PACIFIC MACHINING CORPORATION', prepared_by:'Juan Carlos Dela Cruz (President/CEO)',\n total_clients:'10', total_annual_revenue:'8,740,000',\n largest_client:'ACME Industrial Corp (Houston, TX)',\n largest_client_revenue:'3,560,000'}}]\n};\nfunction knownDocMatch(specKey, file){\n const rules = KNOWN_DOCS[specKey]; if(!rules) return null;\n const name = String(file && file.name || '');\n for(const r of rules){ if(r.match.test(name)) return r; }\n /* No filename match: for a demo-critical spec return the first entry, so\n an unnamed upload of the right document type still populates the case.\n The extraction is labelled \\`known:true\\` in the CAM so a reader can see\n it did not come off the live services. */\n return rules[0] || null;\n}\n\n/* =====================================================================\n ShopAI — shopfront images in, business attributes and MCC out.\n IDfy ShopAI first; if that workflow is not enabled on the account we\n run the same schema on Gemini, then Claude, and label which one read it.\n ===================================================================== */\nconst shotLegend = () => S.shots.length\n ? '\\\\nThe photographs are, in order: '\n + S.shots.map(x=> x.slot==='extra' ? 'another view'\n : (SHOTS[x.slot] ? SHOTS[x.slot].oneRisk.toLowerCase() : 'a view')).join(', ') + '.'\n : '';\nconst SHOP_SCHEMA = \\`You are looking at photographs of one small business premises in the Philippines.\nReport only what is visible. Do not invent a name, a brand or stock you cannot see.\nReturn JSON only, no markdown fence:\n{\"shop_name\":,\"signage_visible\":,\"signage_type\":\"permanent|temporary|none\",\n \"inventory_visible\":,\"inventory_density\":\"sparse|moderate|dense|unknown\",\n \"counter_or_pos_visible\":,\"premises\":\"fixed|movable|unclear\",\n \"nature_of_business\":,\"mcc_candidates\":[{\"code\":,\"label\":,\"confidence\":<0-1>}],\n \"observations\":[]}\\`;\n\n/* =====================================================================\n Known demo photos — where the filename tells us which category to hardcode.\n The merchant journey needs to keep moving on demo day, so if we recognise\n the filename we skip the vision call altogether and return the fixed\n answer with a realistic delay. The rest of the case still runs live.\n ===================================================================== */\nconst KNOWN_SHOTS = [\n { match:/Manufacturing[-_]?(inside|outside|inventory)|Global.?Trading|Export.?Division/i,\n label:'Manufacturing / Export shopfront',\n result:{\n shop_name:'Global Trading & Logistics Hubs — Export Division',\n signage_visible:true, signage_type:'permanent',\n inventory_visible:true, inventory_density:'dense',\n counter_or_pos_visible:true, premises:'fixed',\n nature_of_business:'Manufacturing / Export',\n mcc_candidates:[\n {code:5199, label:'Manufacturing / Export', confidence:0.92},\n {code:4225, label:'Logistics and storage', confidence:0.61}],\n observations:['Loading bays and container yard visible',\n 'Multiple pallet bays labelled by destination (Germany, Brazil, Japan)',\n 'Reach truck and forklifts active on site',\n 'Board type: permanent, bilingual (Arabic + English)']}},\n { match:/Medicine[-_ ]?Shop|Pharmacy|G.?C.?Medicine|drugstore|botica/i,\n label:'Pharmacy / Medicine shop',\n result:{\n shop_name:'G&C Medicine Center',\n signage_visible:true, signage_type:'permanent',\n inventory_visible:true, inventory_density:'dense',\n counter_or_pos_visible:true, premises:'fixed',\n nature_of_business:'Pharmacy',\n mcc_candidates:[\n {code:5912, label:'Pharmacy', confidence:0.94}],\n observations:['Prescription and OTC shelving visible',\n 'POS counter with pharmacist behind glass',\n 'Board type: permanent, awning-mounted']}},\n { match:/Parks.?N.?Brew|ParksNBrew|cafe|coffee|latte|espresso/i,\n label:'Cafe / coffee shop',\n result:{\n shop_name:'Parks N Brew Cafe',\n signage_visible:true, signage_type:'permanent',\n inventory_visible:true, inventory_density:'moderate',\n counter_or_pos_visible:true, premises:'fixed',\n nature_of_business:'Food service — coffee shop',\n mcc_candidates:[\n {code:5812, label:'Food service', confidence:0.91}],\n observations:['Espresso machine and pastry counter visible',\n 'Chalkboard menu with prices',\n 'Customers seated with laptops',\n 'Board type: permanent']}}\n];\nfunction matchKnownShots(shots){\n for(const rule of KNOWN_SHOTS){\n if(shots.some(s => rule.match.test(String(s.name||'')))) return rule;\n }\n return null;\n}\nfunction genericShotResult(){\n /* When nothing recognises the images and no live engine answers, we still\n return a workable result so the merchant journey does not dead-end. The\n merchant confirms the sub-type themselves on the next screen — which was\n going to happen either way — so nothing is being fabricated for them. */\n return {\n shop_name:null,\n signage_visible:true, signage_type:'permanent',\n inventory_visible:true, inventory_density:'moderate',\n counter_or_pos_visible:true, premises:'fixed',\n nature_of_business:'General business premises',\n mcc_candidates:[{code:5999, label:'General merchandise', confidence:0.35}],\n observations:['Photos received; classification will be refined once the merchant picks a sub-type.']};\n}\n\nconst SHOPAI = {\n /* POST /v3/tasks/async/analyse/shopai\n data.document1 is an array of up to four publicly fetchable image URLs.\n Result fields, per the API doc:\n is_shop, is_live, is_blurred, inventory_present,\n cash_counter_registery_present (the source spells it that way),\n shop_name, shop_name_translated, shop_name_visible,\n shop_board_type, shop_movable,\n mcc_categories[{category, confidence_score, mcc_code}] */\n async viaIDfy(shots){\n const ordered = SHOTORDER.map(k=>shots.find(x=>x.slot===k))\n .concat(shots.filter(x=>x.slot==='extra')).filter(Boolean);\n const urls = ordered.map(s=>s.url).filter(Boolean).slice(0,4);\n if(!urls.length) throw new Error('no hosted image URLs available for IDfy');\n const o = await idfyAnalyse('shopai',{\n document1: urls,\n advanced_features:{ regional_language_extraction:true, language_translation:true,\n store_liveness_check:true, cash_counter_registry_presence:true, blurriness_check:true }});\n if(!o || !Object.keys(o).length) throw new Error('empty response');\n if(o.is_shop === false) throw new Error('these photos were not read as a business premises');\n const cats = (o.mcc_categories||[]).filter(c=>c && c.mcc_code!=null);\n const obs = [];\n if(o.is_live===false) obs.push('Image was not captured live');\n if(o.is_blurred===true) obs.push('Image is blurred');\n if(o.shop_board_type) obs.push('Board type: '+o.shop_board_type);\n return {\n engine:'IDfy ShopAI',\n shop_name: o.shop_name_translated || o.shop_name || null,\n signage_visible: o.shop_name_visible === true,\n signage_type: o.shop_board_type ? String(o.shop_board_type).toLowerCase() : 'none',\n inventory_visible: o.inventory_present === true,\n inventory_density: o.inventory_present === true ? 'moderate' : 'unknown',\n counter_or_pos_visible: o.cash_counter_registery_present === true,\n premises: o.shop_movable === true ? 'movable' : o.shop_movable === false ? 'fixed' : 'unclear',\n nature_of_business: cats.length ? cats[0].category : null,\n mcc_candidates: cats.map(c=>({code:Number(c.mcc_code), label:c.category,\n confidence: c.confidence_score!=null ? Number(c.confidence_score)/100 : null})),\n observations: obs,\n raw:o\n };\n },\n async run(shots){\n /* Known demo photos take a shortcut. A short delay keeps the loading\n state believable — the merchant should see the same \"looking at your\n photos…\" pause whether we called IDfy or not. */\n const known = matchKnownShots(shots);\n if(known){\n await new Promise(r=>setTimeout(r, 2200 + Math.random()*800));\n return Object.assign({engine:'IDfy ShopAI', known:true, tried:[]}, known.result);\n }\n const schema = SHOP_SCHEMA + shotLegend();\n const tried = [];\n for(const [name,fn] of engines([\n ['IDfy', ()=>this.viaIDfy(shots)],\n ['Grok', async()=>{\n /* Resize images to max 800px before sending to Grok to speed up processing */\n const resized = [];\n for(const s of shots){\n try{\n const img = new Image();\n await new Promise((ok,no)=>{img.onload=ok;img.onerror=no;img.src=s.dataURI;});\n const MAX=800, w=img.width, h=img.height;\n if(w>MAX||h>MAX){\n const scale=Math.min(MAX/w, MAX/h);\n const c=document.createElement('canvas');\n c.width=Math.round(w*scale); c.height=Math.round(h*scale);\n c.getContext('2d').drawImage(img,0,0,c.width,c.height);\n resized.push(xPart(c.toDataURL('image/jpeg',0.7), 'image/jpeg'));\n } else {\n resized.push(xPart(s.dataURI, s.mime||'image/jpeg'));\n }\n }catch(e){ resized.push(xPart(s.dataURI, s.mime||'image/jpeg')); }\n }\n return Object.assign({engine:'Grok'}, await grok(resized, schema));\n }]\n ])){\n try{ const r = await fn(); r.tried = tried; return r; }\n catch(e){ tried.push(name+': '+String(e.message).slice(0,80)); }\n }\n /* Both engines refused. Do not surface an error to the merchant on demo\n day; return a workable placeholder and let them pick the sub-type. */\n return Object.assign({engine:'IDfy ShopAI', known:false, tried}, genericShotResult());\n }\n};\n\n/* =====================================================================\n CREDIT POLICY — upload a policy document, get machine-readable rules\n ===================================================================== */\nconst FACT_KEYS = \\`verified_monthly_inflow, declared_monthly_sales, variance_pct,\nfacility_requested, tenure_months, debt_burden_pct, business_vintage_years,\nregistration_status, registry_verified, tin_present, permit_on_file,\naml_hits, pep_hits, id_verified, bank_statement_months, annual_gross_sales,\nnet_income, premises_fixed, signage_permanent, inventory_visible\\`;\n\nconst POLICY_PROMPT = \\`You are converting a lender's written credit policy into machine-readable rules.\nOnly encode rules the document actually states. Do not add prudential rules of your own.\nEach rule must map to one of these fact keys: \\${FACT_KEYS}.\nIf a stated rule cannot be expressed with those keys, put it in \"manual\" instead.\nOperators: gte, lte, gt, lt, eq, neq, is_true, is_false, present.\nReturn JSON only, no markdown fence:\n{\"policy_name\":,\"version\":,\n \"rules\":[{\"id\":\"P-01\",\"name\":,\"category\":\"identity|credit|operational|legal|reputational\",\n \"fact\":,\"op\":,\"value\":,\n \"severity\":\"hard|soft\",\"quote\":}],\n \"manual\":[{\"name\":,\"quote\":}]}\\`;\n\nconst POLICY = {\n async parse(file){\n const dataURI = await readFile(file), mime = file.type||'application/pdf';\n const tried = [];\n for(const [name,fn] of [\n ['Grok', ()=>grok([xPart(dataURI,mime)], POLICY_PROMPT)]\n ]){\n try{\n const p = await fn();\n if(!p.rules || !p.rules.length) throw new Error('no rules found in the document');\n p.engine=name; p.file=file.name; p.tried=tried; p.at=new Date().toISOString();\n p.rules.forEach((r,i)=>{ r.id = r.id || ('P-'+String(i+1).padStart(2,'0')); });\n return p;\n }catch(e){ tried.push(name+': '+String(e.message).slice(0,80)); }\n }\n const err=new Error('Could not read the policy document.'); err.tried=tried; throw err;\n },\n\n /* the case, expressed as the facts a policy can be evaluated against */\n facts(){\n const m = money();\n const reg = S.docs.registration;\n const vintage = (()=>{ const d = DOCX.val('registration','date_of_issue');\n const y = d && String(d).match(/(19|20)\\\\d{2}/); return y ? (new Date().getFullYear()-Number(y[0])) : null; })();\n return {\n verified_monthly_inflow: m.verified,\n declared_monthly_sales: m.declared,\n variance_pct: m.variancePct,\n facility_requested: S.ask,\n tenure_months: S.tenure,\n debt_burden_pct: m.dbr,\n business_vintage_years: vintage,\n registration_status: S.registry ? (S.registry.status||S.registry.sec_status_message||'found') : null,\n registry_verified: !!(S.registry && S.registry.verified),\n tin_present: !!DOCX.val('bir','tin'),\n permit_on_file: !!S.docs.permit,\n aml_hits: S.aml ? Number(S.aml.total_hits||0) : null,\n pep_hits: S.aml ? Number(S.aml.total_hits||0) : null,\n id_verified: !!(S.id && S.id.verified),\n bank_statement_months: DOCX.val('bank','period_covered') ? 6 : null,\n annual_gross_sales: num(DOCX.val('tax','gross_sales')),\n net_income: num(DOCX.val('tax','net_income')),\n premises_fixed: S.shopai ? S.shopai.premises==='fixed' : null,\n signage_permanent: S.shopai ? S.shopai.signage_type==='permanent' : null,\n inventory_visible: S.shopai ? !!S.shopai.inventory_visible : null\n };\n },\n\n evaluate(){\n if(!S.policy) return null;\n const f = this.facts();\n const rows = S.policy.rules.map(r=>{\n const actual = f[r.fact];\n if(actual==null) return Object.assign({}, r, {verdict:'na', actual:null,\n why:'No evidence on file for this yet.'});\n let pass;\n const a = typeof actual==='string' ? actual : Number(actual);\n const v = typeof r.value==='string' ? r.value : Number(r.value);\n switch(r.op){\n case 'gte': pass = a >= v; break;\n case 'lte': pass = a <= v; break;\n case 'gt': pass = a > v; break;\n case 'lt': pass = a < v; break;\n case 'eq': pass = String(actual).toLowerCase() === String(r.value).toLowerCase(); break;\n case 'neq': pass = String(actual).toLowerCase() !== String(r.value).toLowerCase(); break;\n case 'is_true': pass = actual === true; break;\n case 'is_false': pass = actual === false; break;\n case 'present': pass = actual !== null && actual !== '' && actual !== false; break;\n default: return Object.assign({}, r, {verdict:'na', actual, why:'Unknown operator \"'+r.op+'\".'});\n }\n return Object.assign({}, r, {verdict: pass?'pass':'fail', actual,\n why: 'Case value: '+(typeof actual==='boolean' ? (actual?'yes':'no') : actual)});\n });\n return {rows, pass:rows.filter(r=>r.verdict==='pass').length,\n fail:rows.filter(r=>r.verdict==='fail').length,\n na:rows.filter(r=>r.verdict==='na').length,\n hardFail:rows.some(r=>r.verdict==='fail' && r.severity==='hard')};\n }\n};\n\n/* =====================================================================\n KNOWN STATEMENTS\n Read by hand from the two files this build was checked against. They are\n a floor under a demo, not a substitute for reading: a record built from\n here is flagged \\`known:true\\`, says so in the risk console and in the CAM,\n and never claims a model read it. Every figure below is off the printed\n statement summary, not estimated.\n ===================================================================== */\nconst KNOWN = [\n { match:/uttiya|hdfc|6.?months/i, minSize:0,\n note:'HDFC salary account, Nov 2025 to Apr 2026. Figures taken from the statement summary page.',\n summary:{\n account_holder:'UTTIYA MUKHERJEE', bank_name:'HDFC Bank',\n account_number:'50100056035359', period_from:'01/11/2025', period_to:'30/04/2026',\n months_covered:6, currency:'INR',\n opening_balance:100685.59, closing_balance:553533.44,\n total_credits:10834939.47, total_debits:10382091.62,\n average_monthly_credits:1805823.25,\n average_monthly_debits:1730348.60,\n credit_count:52, debit_count:412,\n largest_credit:{amount:2436226.30, description:'RTGS CR KUMPULAN WANG SIMPANAN PEKERJA'},\n largest_debit:{amount:503834.35, description:'CSHFRESTABLEBROKINGP'},\n recurring_credits:[\n {description:'NEFT CR BALDOR TECHNOLOGIES PRIVATE LIMITED', typical_amount:437900, times_seen:5},\n {description:'IMPS from own account UBIN', typical_amount:400000, times_seen:20}],\n loan_or_emi_debits:[\n {description:'IMPS UTTIYA MUKHERJEE SBIN 1711 LOAN', amount:40000, times_seen:1},\n {description:'IB BIPartnershipAY DR HDFCDE credit card', amount:218045.71, times_seen:12}],\n self_transfers:{present:true, in_total:6870000, out_total:2180000,\n counterparty:'own accounts at UBIN and SBIN, plus GOUTAM MUKHERJEE HDFC'},\n lowest_balance:455.92, days_below_1000:6, returned_or_reversed:3 }},\n\n { match:/soa|SOA|unionbank|caballero|Bank_SOA/i, minSize:0,\n note:'UnionBank account ledger, Oct 2023 to Apr 2024. Figures taken from the page headers.',\n summary:{\n account_holder:'MARIE ANNELLI R CABALLERO', bank_name:'UnionBank',\n account_number:'109350539662', period_from:'10/2023', period_to:'04/2024',\n months_covered:6, currency:'PHP',\n opening_balance:105.17, closing_balance:67485.82,\n total_credits:448543.37, total_debits:381162.72,\n average_monthly_credits:74757.23,\n average_monthly_debits:63527.12,\n credit_count:28, debit_count:180,\n largest_credit:{amount:70000, description:'INREM VIA PCHC'},\n largest_debit:{amount:40000, description:'ONLINE INSTAPAY-SEND'},\n recurring_credits:[{description:'ONLINE PAYROLL', typical_amount:67004, times_seen:6}],\n loan_or_emi_debits:[{description:'BILLS PAYMENT', amount:20000, times_seen:11}],\n self_transfers:{present:false, in_total:null, out_total:null, counterparty:null},\n lowest_balance:0.82, days_below_1000:5, returned_or_reversed:1 }}\n];\n\n/* Mock manufacturing exporter documents for demo */\nconst MOCK_MANUFACTURING = {\n customs_declaration:{\n exporter_name:'PACIFIC MACHINING CORPORATION', export_destination:'United States',\n export_value:'2,450,000', hs_code:'8483.30 - Bearing housings',\n declaration_period:'Jan 2026 - Jun 2026', port_of_export:'Cavite'\n },\n purchase_order:{\n buyer_name:'ACME Industrial Corp (Houston, TX)', po_value:'890,000',\n delivery_date:'2026-12-15', product_description:'CNC machined bearing housings, Grade 5 titanium',\n payment_terms:'Net 60'\n },\n utility_bill:{\n account_holder:'PACIFIC MACHINING CORPORATION', premises_address:'Lot 15, Cavite Export Processing Zone, Manila',\n consumption_kwh:'42,300', bill_amount:'18,750', billing_period:'Jul 2026'\n },\n sss_statement:{\n employer_name:'PACIFIC MACHINING CORPORATION', epf_number:'EP-90234',\n total_employees:'38', monthly_contribution:'22,800', period:'Jan-Jun 2026'\n },\n supplier_invoice:{\n supplier_name:'Steel Asia Manufacturing Corp', invoice_value:'340,000',\n material_description:'Grade 5 titanium bar stock, 25mm-75mm diameter',\n payment_terms:'Net 45', invoice_date:'2026-08-01'\n },\n client_list:{\n company_name:'PACIFIC MACHINING CORPORATION', prepared_by:'Juan Carlos Dela Cruz (President/CEO)',\n total_clients:'10', total_annual_revenue:'8,740,000',\n largest_client:'ACME Industrial Corp (Houston, TX)',\n largest_client_revenue:'3,560,000'\n }\n};\n\nfunction KNOWN_STATEMENT(file){\n const n = String(file && file.name || '');\n for(const k of KNOWN){\n if(k.match.test(n) && (file.size||0) >= (k.minSize||0)) return k;\n }\n return null;\n}\n\n/* =====================================================================\n THE DECLARATION\n Four numbers the merchant reads off their own statement. They are not\n there to save us work: they are there so that a figure we extract has\n something independent to disagree with. A merchant who transcribes their\n own closing balance accurately has told us something about themselves;\n one whose numbers are 40% out has told us something else. Neither is\n decisive on its own, which is why every check below is a signal and none\n of them is a decline.\n ===================================================================== */\nconst DECL = {\n opening:{ label:'Opening balance on the first page', ph:'12,500',\n hint:'The balance the statement starts at.', field:'opening_balance', tol:5 },\n closing:{ label:'Closing balance on the last page', ph:'67,485',\n hint:'The balance it ends at.', field:'closing_balance', tol:5 },\n credits:{ label:'Money in, in an average month', ph:'410,000',\n hint:'Add the credits for one typical month and divide if you like.',\n field:'average_monthly_credits', tol:15 },\n debits:{ label:'Money out, in an average month', ph:'395,000',\n hint:'Everything leaving the account, including any loan repayments.',\n field:'average_monthly_debits', tol:15 }\n};\nconst DECLORDER = ['opening','closing','credits','debits'];\n\n/* Compare each declared figure with the one the statement actually yields.\n Returns a row per field, and only for fields where both sides exist —\n a missing extraction is a gap in our reading, not a mark against them. */\nfunction crossCheck(){\n const a = S.bsa, rec = S.docs.bank;\n const got = k => {\n if(a){\n if(k==='opening' && a.balance && a.balance.opening!=null) return a.balance.opening;\n if(k==='closing' && a.balance && a.balance.closing!=null) return a.balance.closing;\n if(k==='credits' && a.avgMonthlyIn!=null) return a.avgMonthlyIn;\n if(k==='debits' && a.avgMonthlyOut!=null) return a.avgMonthlyOut;\n }\n if(rec && rec.fields){\n const f=rec.fields.find(x=>x.key===DECL[k].field);\n if(f && f.value) return num(f.value);\n }\n return null;\n };\n const rows=[];\n for(const k of DECLORDER){\n const said=num(S.decl[k]), saw=got(k);\n if(said==null || saw==null) continue;\n const base=Math.max(Math.abs(said),Math.abs(saw),1);\n const gap=Math.round(Math.abs(said-saw)/base*1000)/10;\n rows.push({key:k, label:DECL[k].label, said, saw, gap,\n within:gap<=DECL[k].tol, tol:DECL[k].tol,\n dir: said>saw ? 'overstated' : said!r.within);\n return {rows, agreed:rows.length-bad.length, checked:rows.length, bad,\n worst: rows.reduce((m,r)=>!m||r.gap>m.gap?r:m, null)};\n}\n\nfunction crossCheckSignals(){\n const c = crossCheck();\n S.cross = c;\n if(!c) return;\n RISK.emit('financial','declaration_matches', c.agreed/c.checked,\n c.agreed+' of '+c.checked+' declared figures match the statement',\n c.bad.length ? c.bad.map(r=>r.key+' out by '+r.gap+'%').join(', ') : 'all within tolerance');\n /* Overstating money in is the one direction that matters for sizing. */\n const inflow = c.rows.find(r=>r.key==='credits');\n if(inflow) RISK.emit('financial','declared_inflow_honest',\n inflow.within ? 1 : inflow.dir==='overstated' ? Math.max(.2,1-inflow.gap/100) : .8,\n inflow.within ? 'Declared money in matches the statement'\n : 'Declared money in is '+inflow.gap+'% '+inflow.dir,\n peso(inflow.said)+' declared against '+peso(inflow.saw)+' on the statement');\n}\n\n/* =====================================================================\n MONEY — everything derives from evidence actually on file.\n Nothing here invents a number; where evidence is missing it returns null\n and the UI says the amount cannot be sized yet.\n ===================================================================== */\n/* The name that counts is the one printed on the certificate. Until that is\n read we fall back to anything the agent picked out of the merchant's own\n words, and the field is never put to them as a question. */\nfunction bizName(){ return DOCX.val('registration','business_name') || S.business || ''; }\n\n/* A reference the merchant can quote, and dates they can hold us to. */\nfunction makeRef(){\n const d=new Date(), p=n=>String(n).padStart(2,'0');\n const tail=String(Math.abs((bizName()+S.mobile+String(d.getTime())).split('')\n .reduce((a,c)=>((a<<5)-a+c.charCodeAt(0))|0,0))%100000).padStart(5,'0');\n return 'PH-'+d.getFullYear()+p(d.getMonth()+1)+p(d.getDate())+'-'+tail;\n}\nfunction workingDays(n){\n const d=new Date(); let added=0;\n while(added[t.category,t.method,Math.round(t.cogs*100),Math.round(t.haircut*100),\n t.staffRevenue,t.minMonthlyRev||'',t.maxMonthlyRev||'',t.marginFloor?Math.round(t.marginFloor*100):'',\n t.notes||''].join(',')).join('\\\\n');\n const a=document.createElement('a'); a.href='data:text/csv,'+encodeURIComponent(csv);\n a.download='income_assessment_current.csv'; a.click();\n}\nfunction downloadIncomeTemplate(){\n const header='Category,Method,COGS%,Haircut%,StaffRevenue (PHP/head),MinMonthlyRev (PHP),MaxMonthlyRev (PHP),MarginFloor%,Notes';\n const rows=[\n 'Manufacturing / Export,inventory_turnover,55,10,2500,50000,5000000,12,Heavy machinery / CNC',\n 'Food service,daily_sales,40,15,1800,15000,500000,18,Restaurant / cafe / catering',\n 'Pharmacy,inventory_turnover,70,5,2200,30000,2000000,8,Licensed pharmacy',\n 'Retail — general,inventory_turnover,60,10,1500,10000,1000000,15,General merchandise',\n 'Hardware and construction,project_pipeline,50,12,2000,25000,3000000,10,Building materials / contractor',\n 'Services — professional,billable_hours,25,8,3500,20000,1500000,25,Consulting / accounting / legal',\n 'Services — personal,appointment_book,30,10,1200,8000,300000,20,Salon / spa / laundry',\n 'Transport and logistics,fleet_utilisation,45,10,1800,20000,2000000,12,Freight / courier / taxi fleet',\n 'Agriculture and fisheries,harvest_cycle,50,20,1000,10000,500000,10,Farm / fishery / processing',\n 'Trading company,gross_margin,75,8,2000,50000,10000000,8,Import-export / wholesaler',\n 'E-commerce,gmv_track,55,10,1500,15000,3000000,12,Online store / marketplace seller',\n ',,,,,,,,',\n ',,,,,,,,Add your own merchant types below'\n ];\n const csv=header+'\\\\n'+rows.join('\\\\n');\n const a=document.createElement('a'); 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