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cogniflow-admin/cogniflow-excel-addin

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cogniflow-excel-addin
name: Cogniflow-Excel-AddIn-v2
description: ''
host: EXCEL
api_set: {}
script:
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strokeWidth: \"14px\" }, points: \"75.47 79.2 47.03 119.07 75.47 164.07\" }), c(\"polyline\", { class: \"cls-3\", style: { fill: \"none\", stroke: \"#eb5e60\", strokeLinecap: \"round\", strokeMiterlimit: 10, strokeWidth: \"14px\" }, points: \"113.2 79.2 84.76 119.07 113.2 164.07\" }), c(\"line\", { class: \"cls-3\", x1: \"101.72\", y1: \"145.91\", x2: \"133.07\", y2: \"104.48\" })] }), gn = () => c(\"svg\", { xmlns: \"http://www.w3.org/2000/svg\", width: \"24\", height: \"24\", viewBox: \"0 0 24 24\", style: \"fill: rgba(0, 0, 0, 1);transform: ;msFilter:;\", children: [c(\"path\", { d: \"M16 13v-2H7V8l-5 4 5 4v-3z\" }), c(\"path\", { d: \"M20 3h-9c-1.103 0-2 .897-2 2v4h2V5h9v14h-9v-4H9v4c0 1.103.897 2 2 2h9c1.103 0 2-.897 2-2V5c0-1.103-.897-2-2-2z\" })] }), yn = () => c(\"svg\", { xmlns: \"http://www.w3.org/2000/svg\", width: \"24\", height: \"24\", viewBox: \"0 0 24 24\", style: \"fill: rgba(0, 0, 0, 1);transform: ;msFilter:;\", children: c(\"path\", { d: \"m6.293 13.293 1.414 1.414L12 10.414l4.293 4.293 1.414-1.414L12 7.586z\" }) }), Tn = () => c(\"svg\", { xmlns: \"http://www.w3.org/2000/svg\", width: \"24\", height: \"24\", viewBox: \"0 0 24 24\", style: \"fill: rgba(0, 0, 0, 1);transform: ;msFilter:;\", children: c(\"path\", { d: \"M16.293 9.293 12 13.586 7.707 9.293l-1.414 1.414L12 16.414l5.707-5.707z\" }) }), An = () => c(\"svg\", { xmlns: \"http://www.w3.org/2000/svg\", width: \"24\", height: \"24\", viewBox: \"0 0 24 24\", style: \"fill: rgba(0, 0, 0, 1);transform: ;msFilter:;\", children: c(\"path\", { d: \"M13.293 6.293 7.586 12l5.707 5.707 1.414-1.414L10.414 12l4.293-4.293z\" }) }), Cn = \"_logoWrapper_1phh2_1\", bn = \"_formWrapper_1phh2_12\", En = \"_subtitle_1phh2_17\", wn = \"_formField_1phh2_23\", In = \"_loginButton_1phh2_52\", kn = \"_info_1phh2_74\", Sn = \"_link_1phh2_81\", Nn = \"_errorWrapper_1phh2_88\", M = { logoWrapper: Cn, formWrapper: bn, subtitle: En, formField: wn, loginButton: In, info: kn, link: Sn, errorWrapper: Nn }, Rn = () => { const { login: e } = fe(), [t, n] = N({ email: \"\", password: \"\" }), [o, i] = N(!1), r = () => { if (!t.email || !t.password) { i(\"Please fill all fields\"); return } i(!1), e(t.email, t.password).then(a => { W(\"/dashboard\", !0) }).catch(a => { let _ = `Error: ${a.message}`; switch (a.status) { case 400: _ = \"Invalid email or password\" }i(_) }) }, l = a => { n({ ...t, [a.target.name]: a.target.value }) }; return c(\"div\", { className: M.loginPage, children: [c(\"div\", { className: M.logoWrapper, children: c(vn, {}) }), c(\"div\", { className: M.formWrapper, children: [c(\"h3\", { className: M.subtitle, children: \"Sign in to Cogniflow\" }), c(\"div\", { className: M.formField, children: [c(\"label\", { children: \"Email\" }), c(\"input\", { placeholder: \"youremail@gmail.com\", name: \"email\", onChange: l, type: \"email\" })] }), c(\"div\", { className: M.formField, children: [c(\"label\", { children: \"Password\" }), c(\"input\", { placeholder: \"******\", onChange: l, name: \"password\", type: \"password\" })] }), c(\"div\", { className: M.info, children: c(\"small\", { children: [\"You need to have a Cogniflow password to access our Excel Add-in, if you don't have one, create it\", \" \", c(\"a\", { className: M.link, href: \"https://app.cogniflow.ai/settings/profile\", target: \"_blank\", children: \"here\" })] }) }), c(\"button\", { className: M.loginButton, onClick: r, children: \"Login\" }), o && c(\"div\", { className: M.errorWrapper, children: c(\"div\", { className: M.error, children: c(\"small\", { children: o }) }) })] })] }) }, On = { \"lds-ring\": \"_lds-ring_1im9c_1\" }, Tt = () => c(\"div\", { className: On[\"lds-ring\"], children: [c(\"div\", {}), c(\"div\", {}), c(\"div\", {}), c(\"div\", {})] }), xn = (e, t) => { K(() => { function n(o) { e.current && !e.current.contains(o.target) && t() } return document.addEventListener(\"mousedown\", n), () => { document.removeEventListener(\"mousedown\", n) } }, [e]) }; function $n(e) { return e && e.__esModule && Object.prototype.hasOwnProperty.call(e, \"default\") ? e.default : e } var At = { exports: {} };/*!\n\tCopyright (c) 2018 Jed Watson.\n\tLicensed under the MIT License (MIT), see\n\thttp://jedwatson.github.io/classnames\n*/(function (e) { (function () { var t = {}.hasOwnProperty; function n() { for (var o = [], i = 0; i < arguments.length; i++) { var r = arguments[i]; if (r) { var l = typeof r; if (l === \"string\" || l === \"number\") o.push(r); else if (Array.isArray(r)) { if (r.length) { var a = n.apply(null, r); a && o.push(a) } } else if (l === \"object\") { if (r.toString !== Object.prototype.toString && !r.toString.toString().includes(\"[native code]\")) { o.push(r.toString()); continue } for (var _ in r) t.call(r, _) && r[_] && o.push(_) } } } return o.join(\" \") } e.exports ? (n.default = n, e.exports = n) : window.classNames = n })() })(At); var Mn = At.exports; const Ct = $n(Mn), Pn = \"_dropdown_7fpzl_19\", Ln = \"_active_7fpzl_51\", te = { \"trigger-button\": \"_trigger-button_7fpzl_1\", \"dropdown-wrapper\": \"_dropdown-wrapper_7fpzl_19\", dropdown: Pn, active: Ln }, ce = ({ items: e = [], initialValue: t, className: n, onChange: o }) => { const [i, r] = N(!1), [l, a] = N(t), _ = Pt(null); xn(_, () => { r(!1) }); const h = d => { a(d), r(!1), o(d) }; return c(\"div\", { class: Ct(te[\"dropdown-wrapper\"], n), ref: _, children: [c(\"div\", { onClick: () => { r(d => !d) }, className: te[\"trigger-button\"], children: [l.label, i ? c(yn, {}) : c(Tn, {})] }), c(\"ul\", { className: i ? te.active : \"\", children: e.map(({ label: d, value: s }) => c(\"li\", { onClick: () => h({ label: d, value: s }), className: te.dropdownItem, children: d })) })] }) }, v = { TEXT_CLASSIFICATION: 0, TEXT_TRANSLATION: 1, TEXT_NER_RESPONSE: 2, TEXT_QANDA: 3, SMART_EXTRACTOR: 4, IMAGE_CLASSIFICATION: 100, IMAGE_OBJECT_DETECTION: 101, IMAGE_OCR: 102, IMAGE_FACE_SIMILARITY: 103, IMAGE_CUSTOM_ENTITY_EXTRACTOR: 4, AUDIO_CLASSIFICATION: 200, AUDIO_SPEECH2TEXT: 201 }, Dn = [v.TEXT_CLASSIFICATION, v.TEXT_TRANSLATION, v.TEXT_NER_RESPONSE, v.TEXT_QANDA, v.SMART_EXTRACTOR, v.IMAGE_CLASSIFICATION, v.IMAGE_OBJECT_DETECTION, v.IMAGE_OCR, v.AUDIO_CLASSIFICATION, v.AUDIO_SPEECH2TEXT], re = { TEXT: 0, IMAGE: 1, AUDIO: 2 }, bt = { [re.TEXT]: \"Text\", [re.IMAGE]: \"Image\", [re.AUDIO]: \"Audio\" }, Hn = { [v.TEXT_CLASSIFICATION]: \"Classification\", [v.AUDIO_CLASSIFICATION]: \"Classification\", [v.AUDIO_SPEECH2TEXT]: \"Transcription\", [v.TEXT_TRANSLATION]: \"Translation\", [v.TEXT_NER_RESPONSE]: \"Named Entity Recognition (NER)\", [v.TEXT_QANDA]: \"Question-Answering\", [v.IMAGE_OCR]: \"OCR\", [v.SMART_EXTRACTOR]: \"Smart extractor\", [v.IMAGE_CLASSIFICATION]: \"Classification\", [v.IMAGE_OBJECT_DETECTION]: \"Object detection\", [v.IMAGE_FACE_SIMILARITY]: \"Image verification\" }, Un = { [v.TEXT_CLASSIFICATION]: \"#ffe5e3\", [v.AUDIO_CLASSIFICATION]: \"#ffede3\", [v.AUDIO_SPEECH2TEXT]: \"#fff5e3\", [v.TEXT_TRANSLATION]: \"#fffae3\", [v.TEXT_NER_RESPONSE]: \"#fffde3\", [v.TEXT_QANDA]: \"#ffffe3\", [v.IMAGE_OCR]: \"#f2ffe3\", [v.SMART_EXTRACTOR]: \"#e6ffe3\", [v.IMAGE_CLASSIFICATION]: \"#d9ffe3\", [v.IMAGE_OBJECT_DETECTION]: \"#e3ffec\", [v.IMAGE_FACE_SIMILARITY]: \"#e3fff4\" }, Fn = \"_nav_1arwg_1\", Xn = \"_logout_1arwg_19\", Wn = \"_logoutItems_1arwg_33\", Bn = \"_dropdowns_1arwg_49\", Vn = \"_dropdownLabel_1arwg_60\", Gn = \"_experimentItem_1arwg_67\", qn = \"_content_1arwg_71\", jn = \"_cardsWrapper_1arwg_75\", zn = \"_loader_1arwg_83\", P = { nav: Fn, logout: Xn, logoutItems: Wn, dropdowns: Bn, dropdownLabel: Vn, experimentItem: Gn, content: qn, cardsWrapper: jn, loader: zn }, Zn = \"_card_1pg1x_1\", Jn = \"_wave_1pg1x_16\", Kn = \"_imgBackground_1pg1x_25\", Qn = \"_title_1pg1x_35\", Yn = \"_experimentClassification_1pg1x_44\", eo = \"_image_1pg1x_53\", to = \"_dot_1pg1x_74\", no = \"_cardFooter_1pg1x_82\", H = { card: Zn, wave: Jn, imgBackground: Kn, title: Qn, experimentClassification: Yn, image: eo, dot: to, cardFooter: no }, oo = ({ title: e, type: t, task: n, logo: o, ...i }) => { const r = o ? \"https://s3.us-east-2.amazonaws.com/static-cogniflow-prod/\" + o : \"https://app.cogniflow.ai/static/media/img-placeholder.3c18e621361480e8272e676738157c04.svg\"; return c(\"div\", { className: H.card, children: [c(\"div\", { className: H.wave, children: c(\"div\", { className: H.imgBackground }) }), c(\"p\", { className: H.title, children: e }), c(\"div\", { className: H.image, children: c(\"img\", { src: r }) }), c(\"div\", { className: H.cardFooter, children: [c(\"div\", { className: H.dot, style: { backgroundColor: Un[n] } }), c(\"p\", { className: H.experimentClassification, children: [bt[t], \" | \", Hn[n]] })] })] }) }, Je = [{ label: \"Public\", value: 1 }, { label: \"Private\", value: 0 }, { label: \"Community\", value: 3 }, { label: \"Organization\", value: 2 }], Ke = [{ label: \"All\", value: \"\" }, { label: \"Text\", value: 0 }, { label: \"Image\", value: 1 }, { label: \"Audio\", value: 2 }], ro = () => { const [e, t] = N(!1), [n, o] = N([]), { logout: i } = fe(), [r, l] = N(0), [a, _] = N(\"\"); K(() => { t(!0), en({ visibility: r, type: a }).then(f => { const u = f.filter(m => Dn.includes(m.task)); o(u), t(!1) }).catch(f => { t(!1), console.log(\"failing\", f) }) }, [r, a]); const h = f => { W(`/test/${f}`) }, d = () => { i(), W(\"/login\") }, s = f => { l(f.value) }, g = f => { _(f.value) }; return c(\"div\", { children: [c(\"div\", { className: P.nav, children: [c(\"div\", { className: P.logout, children: c(\"div\", { className: P.logoutItems, onClick: d, children: c(gn, {}) }) }), c(\"h4\", { children: \"Experiments\" }), c(\"div\", {})] }), c(\"div\", { className: P.content, children: [c(\"div\", { className: P.dropdowns, children: [c(\"div\", { children: [c(\"small\", { className: P.dropdownLabel, children: \"Visibility:\" }), c(ce, { items: Je, initialValue: Je[1], onChange: s })] }), c(\"div\", { children: [c(\"small\", { className: P.dropdownLabel, children: \"Type:\" }), c(ce, { items: Ke, initialValue: Ke[0], onChange: g })] })] }), e && c(\"div\", { className: P.loader, children: c(Tt, {}) }), !e && n.length ? c(\"div\", { className: P.cardsWrapper, children: n.map(({ id: f, ...u }) => c(\"div\", { className: P.experimentItem, onClick: () => h(f), children: c(oo, { ...u }) })) }) : c(\"div\", { children: c(\"h3\", { style: { textAlign: \"center\", fontWeight: \"400\" }, children: \"No experiments found\" }) })] })] }) }, io = \"_nav_r1i7b_1\", so = \"_title_r1i7b_20\", lo = \"_icon_r1i7b_24\", ao = \"_content_r1i7b_32\", co = \"_dropdowns_r1i7b_36\", _o = \"_runModel_r1i7b_42\", uo = \"_subtitleBlock_r1i7b_68\", fo = \"_checkboxes_r1i7b_76\", po = \"_checkboxText_r1i7b_83\", ho = \"_loading_r1i7b_87\", mo = \"_runModelDisabled_r1i7b_93\", vo = \"_warning_r1i7b_108\", S = { nav: io, title: so, icon: lo, content: ao, dropdowns: co, runModel: _o, subtitleBlock: uo, checkboxes: fo, checkboxText: po, loading: ho, runModelDisabled: mo, warning: vo }, G = { TEXT: 0, IMAGE: 1, AUDIO: 2 }, x = { TEXT_CLASSIFICATION: 0, TEXT_TRANSLATION: 1, TEXT_QANDA: 3, IMAGE_CLASSIFICATION: 100, IMAGE_OBJECT_DETECTION: 101, IMAGE_OCR: 102, AUDIO_CLASSIFICATION: 200, SMART_EXTRACTOR: 4 }, ke = { [G.TEXT]: { time: 1e3, limit: 3 }, [G.IMAGE]: { time: 1e3, limit: 2 }, [G.AUDIO]: { time: 1e3, limit: 1 }, index: 0 }; async function go(e) { await Excel.run(async t => { const n = t.workbook.worksheets.getActiveWorksheet(), o = bo(t); let i = t.workbook.getSelectedRange(); if (!e.hasHeaders && x.SMART_EXTRACTOR === e.task && wo(t), await o(i, [\"isEntireColumn\"]), i.isEntireColumn) { const _ = i.getUsedRange(); await o(_, [\"values\", \"address\"]); const h = await Eo(_, t); i = n.getRange(h) } await o(i, [\"values\", \"address\"]), await Io({ range: i, task: e.task, config: e.experimentConfig, context: t, sheet: n, parameters: e }); const r = i.values.map(([_]) => _), l = yo({ parameters: e, cells: r }); e.task === x.SMART_EXTRACTOR && e.hasHeaders && l.shift(); const a = ko(l, ke[e.type].limit); switch (e.task) { case x.SMART_EXTRACTOR: await To({ requests: a, requestRange: i, config: e.experimentConfig, sheet: n, context: t, type: e.type, hasHeaders: e.hasHeaders, callback: e.predictionCallback }); return; default: Ao({ sheet: n, context: t, requestRange: i, chunks: a, includeConfidence: e.includeConfidence, type: e.type, task: e.task, callback: e.predictionCallback }) } }) } function yo({ parameters: e, cells: t }) { var l; const n = []; let o = 0; const r = { [x.TEXT_QANDA]: { result: [{ answer: \"\" }] }, [x.SMART_EXTRACTOR]: [(l = e.experimentConfig.entities) == null ? void 0 : l.reduce((a, _) => ({ ...a, [_.name]: \"\" }), {})] }[e.task] || { result: \"\" }; for (const a of t) { if (e.hasHeaders && o === 0) { n.push(Promise.resolve({ result: r })), o++; continue } n.push(fetch(`${e.url}/${e.modelId}`, { method: \"POST\", headers: { \"Content-Type\": \"application/json\", \"x-api-key\": e.apikey }, body: JSON.stringify({ [So(e.type, e.task)]: a, format: \"jpg\" }) }).then(_ => _.ok ? _.json() : new Promise((h, d) => { _.json().then(s => { h({ message: \"error\", status: _.status, result: s }) }) }))), o++ } return n } function Et(e, t) { switch (e) { case 400: return \"Bad Request: The request was unacceptable, often due to missing a required parameter.\"; case 401: return \"Unauthorized: Prediction quota exceede or no valid API key provided.\"; case 403: return \"Forbidden: The API key doesn’t have permissions to perform the request.\"; case 404: return \"Not Found: The requested resource doesn’t exist.\"; case 429: return \"Too Many Requests: Too many requests hit the API too quickly.\"; case 500: return \"Internal Server Error: We had a problem with our server. Try again later.\"; case 503: return (t.includes(\"OpenAI\") ? \"Please enter your OpenAI API key in https://app.cogniflow.ai/settings/apikeys to enable the model to work.\" : \"\") || \"Service Unavailable: We’re temporarily offline for maintenance. Please try again later.\"; default: return \"Unknown error\" } } async function To({ requests: e, requestRange: t, config: n, sheet: o, context: i, type: r, hasHeaders: l, callback: a }) { const _ = n.entities.map(({ name: d }) => d); let h = t.getOffsetRange(l ? 1 : 0, 1); for (const d of e) { const s = await Promise.allSettled(d), g = h.getCell(0, 0), f = h.getCell(s.length - 1, _.length - 1); for (const y of [g, f]) y.load(\"address\"); await i.sync(); const u = `${_e(g.address)}:${_e(f.address)}`, m = []; for (const { value: y } of s) { if (y.message === \"error\") { m.push(_.map((I, w) => { var C; return w === 0 ? Et(y.status, (C = y == null ? void 0 : y.result) == null ? void 0 : C.detail) : \"\" })); continue } const T = _.map(I => { const w = y.result[0][I] || \"\"; return Array.isArray(w) ? JSON.stringify(w) : w }); T.length || T.push(\"---\"), m.push(T) } const E = o.getRange(u); E.values = m, h = E.getLastRow().getRowsBelow(1), await Co(ke[r].time) } a && a() } async function Ao({ sheet: e, context: t, requestRange: n, chunks: o, includeConfidence: i, type: r, task: l, callback: a }) { let _ = Qe(n.address).startNumber; const { startLetter: h } = Qe(n.address); for (const d of o) { const s = await Promise.allSettled(d), [g, f] = [`${h}${_}`, `${h}${_ + d.length - 1}`], u = e.getRange(`${g}:${f}`), m = s.map(E => [No(E.value, l)]); u.values = m, i && (u.getColumnsAfter(1).values = s.map(E => [Ro(E.value, l) || -1])), await t.sync(), await new Promise(E => setTimeout(E, ke[r].time)), _ += d.length } a && a() } function Co(e) { return new Promise(t => setTimeout(t, e)) } function bo(e) { return async (t, n) => { t.load(n), await e.sync() } } async function Eo(e, t) { const n = e.address.split(\"!\")[1].split(\":\")[0]; let o = 0; for (const [l] of e.values) { if (!l) break; o++ } const i = e.getCell(o - 1, 0); i.load(\"address\"), await t.sync(); const r = i.address.split(\"!\")[1]; return `${n}:${r}` } function wo(e) { e.workbook.worksheets.getActiveWorksheet().getRange(\"A1:A1\").getCell(0, 0).getEntireRow().insert(Excel.InsertShiftDirection.down) } async function Io({ range: e, task: t, config: n, context: o, sheet: i, parameters: r }) { switch (t) { case x.SMART_EXTRACTOR: const l = n.entities.map(({ name: g }) => g); if (r.action == \"insert\" && l.forEach(() => { e.getEntireColumn().getColumnsAfter(1).insert(\"Right\") }), r.hasHeaders) return; const a = Oo(e.address), _ = i.getRange(`${a}2:${a}2`), h = _.getRowsAbove(1).getCell(0, 1), d = _.getRowsAbove(1).getCell(0, l.length); for (const g of [h, d]) g.load(\"address\"); await o.sync(); const s = `${_e(h.address)}:${_e(d.address)}`; i.getRange(s).values = [l]; return; default: r.action == \"insert\" && (e.getEntireColumn().getColumnsAfter(1).insert(\"Right\"), r.includeConfidence && e.getEntireColumn().getColumnsAfter(1).insert(\"Right\")), r.hasHeaders && r.task === x.SMART_EXTRACTOR && (e.getCell(-1, 1).values = [[r.experiment]], r.includeConfidence && (e.getCell(-1, 2).values = [[\"Confidence\"]])) } } function _e(e) { return e.split(\"!\")[1] } function Qe(e) { const t = e.split(\"!\")[1], [n, ...o] = t.split(\":\")[0].split(\"\"); return { startLetter: String.fromCharCode(n.charCodeAt(0) + 1), startNumber: parseInt(o.join(\"\")) } } function ko(e, t) { const n = []; for (let o = 0; o < e.length; o += t) { const i = e.slice(o, o + t); n.push(i) } return n } function So(e, t) { switch (e) { case G.TEXT: return t === x.TEXT_QANDA ? \"question\" : \"text\"; case G.IMAGE: return \"url\"; case G.AUDIO: return \"url\"; default: return \"url\" } } function No(e, t) { if (e.message === \"error\") return Et(e.status); switch (t) { case x.TEXT_QANDA: return e.result[0].answer; case x.SMART_EXTRACTOR: return JSON.stringify(e.result); case x.IMAGE_OBJECT_DETECTION: return JSON.stringify(e.result); case x.IMAGE_OCR: return JSON.stringify(e.result); default: return e.result } } function Ro(e, t) { switch (t) { case x.TEXT_QANDA: return -1; default: return e.confidence_score } } function Oo(e) { const t = e.split(\"!\")[1].match(/[A-Za-z]+/); return t ? t[0] : null } const xo = { [v.TEXT_CLASSIFICATION]: \"text/classification/predict\", [v.TEXT_NER_RESPONSE]: \"text/information-extraction/extract-entities\", [v.TEXT_TRANSLATION]: \"text/translation/translate\", [v.TEXT_QANDA]: \"text/question-answering/ask\", [v.SMART_EXTRACTOR]: \"smart-extraction/extract\", [v.IMAGE_CLASSIFICATION]: \"image/classification/predict\", [v.IMAGE_OBJECT_DETECTION]: \"image/object-detection/detect-from-web\", [v.IMAGE_OCR]: \"image/ocr/recognize-from-web\", [v.IMAGE_FACE_SIMILARITY]: \"face-similarity/predict-from-web\", [v.AUDIO_CLASSIFICATION]: \"audio/classification/predict-from-web\", [v.AUDIO_SPEECH2TEXT]: \"audio/speech2text/transcribe-from-web\" }, $o = \"https://predict.cogniflow.ai\", ge = [{ label: \"Insert results at the right\", value: \"insert\" }, { label: \"Replace results at the right\", value: \"replace\" }], Mo = 0, Po = ({ id: e }) => { const t = () => { W(\"/dashboard\") }, [n, o] = N(null), [i, r] = N(!1), [l, a] = N(ge[0]), [_, h] = N(\"\"), [d, s] = N(!1), { user: g } = fe(); K(() => { r(!0), tn(e).then(({ title: b, type: k, task: $, models: R, config: B, ...Do }) => { o({ title: b, type: k, task: $, models: R, config: B }), h(R[0].id), r(!1) }) }, []); const { api_keys: f } = g, u = n && !!n.models.length && n.models[0], [m, E] = N({ hasHeaders: !1, includeConfidence: !1 }), y = b => { E(k => ({ ...k, [b.target.name]: b.target.checked })) }, T = b => { a(b) }, I = b => { h(b.id) }, w = () => { s(!1) }, C = async () => { let b = xo[n.task]; const k = re.IMAGE === n.type ? \"-from-web\" : \"\"; n.task === v.SMART_EXTRACTOR && (b = `${bt[n.type].toLowerCase()}/${b}`), b = `${$o}/${b}${k}`, s(!0), await go({ ...m, modelId: _, action: l.value, experiment: n.title, experimentConfig: n.config, type: n.type, task: n.task, url: b, apikey: f.find($ => $.provider === Mo).key, predictionCallback: w }) }; return c(\"div\", { children: [c(\"div\", { className: S.nav, children: [c(\"div\", { children: c(\"div\", { className: S.icon, onClick: t, children: c(An, {}) }) }), c(\"h4\", { className: S.title, children: \"Run model\" }), c(\"div\", {})] }), i && c(\"div\", { className: S.loading, children: c(Tt, {}) }), n && c(\"div\", { className: S.content, children: [c(\"div\", { className: S.title, children: c(\"h3\", { children: n && n.title }) }), c(\"div\", { className: S.dropdowns, children: [c(\"div\", { className: S.subtitleBlock, children: [c(\"p\", { children: \"Model to use\" }), c(ce, { initialValue: { label: u.name, id: u.id }, items: n.models.map(({ name: b, id: k }) => ({ label: b, value: k })), onChange: I })] }), c(\"div\", { className: S.subtitleBlock, children: [c(\"p\", { children: \"Place result\" }), c(ce, { initialValue: ge[0], items: ge, onChange: T })] }), c(\"div\", { children: [v.SMART_EXTRACTOR !== n.task && c(\"div\", { className: S.checkboxes, children: [c(\"input\", { type: \"checkbox\", id: \"confidence\", name: \"includeConfidence\", value: m.includeConfidence, onChange: y }), \" \", c(\"label\", { className: S.checkboxText, htmlFor: \"confidence\", children: \"Show confidence\" })] }), c(\"div\", { className: S.checkboxes, children: [c(\"input\", { type: \"checkbox\", id: \"headers\", name: \"hasHeaders\", value: m.hasHeaders, onChange: y }), \" \", c(\"label\", { className: S.checkboxText, htmlFor: \"headers\", children: \"My sheet includes headers\" })] })] })] }), d && c(\"div\", { className: S.warning, children: \"Please do not edit the document while the model is running.\" }), c(\"button\", { className: Ct({ [S.runModel]: !0, [S.runModelDisabled]: d }), onClick: C, disabled: d, children: d ? \"Running model...\" : \"Run model\" })] })] }) }; function Lo() { const { isAuthenticated: e } = fe(); return c(ht, { onChange: async n => { switch (n.url) { case \"/\": e && W(\"/dashboard\", !0); default: e || W(\"/\", !0) } }, children: [c(Rn, { path: \"/\" }), c(ro, { path: \"/dashboard\" }), c(Po, { path: \"/test/:id\" })] }) } xt(c(on, { children: c(Lo, {}) }), document.getElementById(\"app\"));\n"
language: typescript
template:
content: <div id="app"></div>
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style:
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input:focus{outline:none;-webkit-box-shadow:0px 5px 10px -5px
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svg{width:18px;height:18px}._logoutItems_1arwg_33{display:flex;align-items:center;justify-content:center;width:24px;height:24px;border-radius:50%;background-color:var(--gray-200);padding:3px;cursor:pointer}._dropdowns_1arwg_49{display:flex;gap:12px;margin-bottom:14px}._dropdowns_1arwg_49>div{width:100%}._dropdownLabel_1arwg_60{font-weight:600;margin-bottom:8px;font-size:12px}._experimentItem_1arwg_67{cursor:pointer}._content_1arwg_71{margin:0
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(max-width:
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(max-width:
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language: css
libraries: |
https://appsforoffice.microsoft.com/lib/1/hosted/office.js
@types/office-js
office-ui-fabric-js@1.4.0/dist/css/fabric.min.css
office-ui-fabric-js@1.4.0/dist/css/fabric.components.min.css
core-js@2.4.1/client/core.min.js
@types/core-js
jquery@3.1.1
@types/jquery@3.3.1
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