The project shipped a black-and-white theme that was only half converted: the login page looked right, the message page was halfway, and the settings page had not been started. This finishes the move to the CYBER reference in prototype_reference/. Colors now come from a single token source (styles/variables.scss), where every semantic color is declared twice: once as OKLCH for the CSS, and once as an RGB triple so translucent variants can be built as rgba(var(--*-rgb), alpha). Cyan is the system itself, amber means the user has to intervene, green means the machine is working, and red only ever means something broke. The RGB triples are derived from the OKLCH values so the two declarations cannot drift apart. Pages adopt the .cy-* shell classes from the reference (view, panel, kpis, tabs, grid, table, field) instead of ad-hoc markup, which is what makes 19 of 26 views consistent. The remaining seven are deliberately bespoke: chat streams, the kanban board, the workflow canvas, the terminal, and the missing-agent screen. The chart palette and the kanban column colors move into single-source modules so they cannot drift either. A new guard test rejects raw hex colors, named colors, and literal RGB triples anywhere in the client source, with an explicit exemption list for the places that genuinely cannot read a CSS variable. The existing chat semantics spec keeps the chat chain honest. No behavior changes: no logic, no i18n keys, no data structures, and the /chat-run event contract is untouched.
208 lines
6.1 KiB
TypeScript
208 lines
6.1 KiB
TypeScript
import { fetchUsageStats, type UsageStatsResponse } from '@/api/hermes/sessions'
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import { CHART_CATEGORICAL } from '@/styles/chart-palette'
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import { defineStore } from 'pinia'
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import { computed, ref } from 'vue'
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interface DailyUsage {
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date: string
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input_tokens: number
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output_tokens: number
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cache_read_tokens: number
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cache_write_tokens: number
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sessions: number
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errors: number
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cost: number
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visualTokens: number
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inputPercent: number
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outputPercent: number
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cachePercent: number
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}
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interface ModelUsage {
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model: string
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inputTokens: number
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outputTokens: number
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cacheTokens: number
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cacheWriteTokens: number
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totalTokens: number
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visualTokens: number
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sessions: number
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color: string
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inputPercent: number
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outputPercent: number
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cachePercent: number
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}
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interface AgentUsage {
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agent: string
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inputTokens: number
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outputTokens: number
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cacheTokens: number
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cacheWriteTokens: number
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totalTokens: number
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visualTokens: number
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sessions: number
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color: string
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inputPercent: number
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outputPercent: number
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cachePercent: number
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}
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// 模型 / agent 配色取统一图表分类色板,见 styles/chart-palette.ts。
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const MODEL_COLORS = CHART_CATEGORICAL
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function normalizeModel(model: string | null | undefined): string {
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const trimmed = (model || '').trim()
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return trimmed || 'unknown'
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}
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function percent(part: number, total: number): number {
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if (total <= 0) return 0
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return part / total * 100
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}
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function getModelColor(model: string): string {
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const normalized = normalizeModel(model)
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let hash = 0
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for (let i = 0; i < normalized.length; i += 1) {
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hash = ((hash << 5) - hash) + normalized.charCodeAt(i)
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hash |= 0
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}
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return MODEL_COLORS[Math.abs(hash) % MODEL_COLORS.length]
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}
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export const useUsageStore = defineStore('usage', () => {
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const stats = ref<UsageStatsResponse | null>(null)
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const isLoading = ref(false)
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let latestRequestId = 0
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async function loadSessions(days = 30) {
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const requestId = ++latestRequestId
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isLoading.value = true
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try {
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const response = await fetchUsageStats(days)
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if (requestId === latestRequestId) {
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stats.value = response
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}
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} catch (err) {
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if (requestId === latestRequestId) {
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console.error('Failed to load usage stats:', err)
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}
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} finally {
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if (requestId === latestRequestId) {
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isLoading.value = false
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}
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}
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}
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const hasData = computed(() => !!stats.value && stats.value.total_sessions > 0)
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const totalInputTokens = computed(() => stats.value?.total_input_tokens ?? 0)
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const totalOutputTokens = computed(() => stats.value?.total_output_tokens ?? 0)
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const totalTokens = computed(() => totalInputTokens.value + totalOutputTokens.value)
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const totalSessions = computed(() => stats.value?.total_sessions ?? 0)
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const totalCacheTokens = computed(() => stats.value?.total_cache_read_tokens ?? 0)
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const cacheHitRate = computed(() => {
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const total = totalInputTokens.value + totalCacheTokens.value
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if (total === 0) return null
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return ((totalCacheTokens.value / total) * 100)
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})
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const estimatedCost = computed(() => stats.value?.total_cost ?? 0)
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const modelUsage = computed<ModelUsage[]>(() => {
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if (!stats.value) return []
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return stats.value.model_usage.map(m => {
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const model = normalizeModel(m.model)
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const totalTokens = m.input_tokens + m.output_tokens
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const visualTokens = totalTokens + m.cache_read_tokens
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return {
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model,
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inputTokens: m.input_tokens,
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outputTokens: m.output_tokens,
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cacheTokens: m.cache_read_tokens,
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cacheWriteTokens: m.cache_write_tokens,
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totalTokens,
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visualTokens,
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sessions: m.sessions,
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color: getModelColor(model),
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inputPercent: percent(m.input_tokens, visualTokens),
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outputPercent: percent(m.output_tokens, visualTokens),
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cachePercent: percent(m.cache_read_tokens, visualTokens),
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}
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}).sort((a, b) => b.visualTokens - a.visualTokens)
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})
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const modelLegend = computed(() => {
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const seen = new Set<string>()
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return modelUsage.value.filter(m => {
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if (seen.has(m.model)) return false
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seen.add(m.model)
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return true
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}).map(m => ({ model: m.model, color: m.color }))
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})
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const agentUsage = computed<AgentUsage[]>(() => {
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if (!stats.value) return []
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return (stats.value.agent_usage ?? []).map(a => {
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const agent = (a.agent || '').trim() || 'unknown'
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const totalTokens = a.input_tokens + a.output_tokens
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const visualTokens = totalTokens + a.cache_read_tokens
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return {
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agent,
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inputTokens: a.input_tokens,
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outputTokens: a.output_tokens,
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cacheTokens: a.cache_read_tokens,
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cacheWriteTokens: a.cache_write_tokens,
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totalTokens,
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visualTokens,
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sessions: a.sessions,
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color: getModelColor(`agent:${agent}`),
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inputPercent: percent(a.input_tokens, visualTokens),
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outputPercent: percent(a.output_tokens, visualTokens),
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cachePercent: percent(a.cache_read_tokens, visualTokens),
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}
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}).sort((a, b) => b.visualTokens - a.visualTokens)
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})
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const dailyUsage = computed<DailyUsage[]>(() => (stats.value?.daily_usage ?? []).map(d => {
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const visualTokens = d.input_tokens + d.output_tokens + d.cache_read_tokens
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return {
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...d,
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visualTokens,
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inputPercent: percent(d.input_tokens, visualTokens),
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outputPercent: percent(d.output_tokens, visualTokens),
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cachePercent: percent(d.cache_read_tokens, visualTokens),
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}
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}))
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const avgSessionsPerDay = computed(() => {
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if (!stats.value || stats.value.daily_usage.length === 0) return 0
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const daysWithActivity = stats.value.daily_usage.filter(d => d.sessions > 0).length
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const days = Math.max(1, daysWithActivity)
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return totalSessions.value / days
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})
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return {
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stats,
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isLoading,
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hasData,
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loadSessions,
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totalInputTokens,
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totalOutputTokens,
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totalTokens,
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totalSessions,
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totalCacheTokens,
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cacheHitRate,
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estimatedCost,
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modelUsage,
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modelLegend,
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agentUsage,
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dailyUsage,
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avgSessionsPerDay,
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getModelColor,
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}
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})
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