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<title>InferRoute - Multi-LLM API Gateway & Cost Optimization Engine</title>
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</head>
<body>
<!-- Header Navigation -->
<header>
<div class="brand" onclick="navigateTo('api')">
🚀 InferRoute <span class="brand-badge">Multi-LLM Gateway</span>
</div>
<div class="nav-links">
<span class="nav-link active" onclick="navigateTo('api')" id="navApi">API Integration</span>
<span class="nav-link" onclick="navigateTo('lab')" id="navLab">⚡ Live Optimization Lab</span>
<span class="nav-link" onclick="navigateTo('apps')" id="navApps">App Showcase</span>
<span class="nav-link" onclick="navigateTo('proof')" id="navProof">Empirical Proof</span>
<span class="nav-link" onclick="navigateTo('cost')" id="navCost">Cost Savings</span>
<span class="nav-link" onclick="navigateTo('analytics')" id="navAnalytics">Live Dashboard</span>
</div>
<div class="header-controls">
<button class="lang-toggle" onclick="toggleLanguage()">
🌐 <span id="currentLangLabel">Language: EN</span>
</button>
<div class="stat-pill">Status: <span class="status-dot"></span> <span style="font-weight:700;">ONLINE</span></div>
</div>
</header>
<main>
<!-- VIEW: LIVE OPTIMIZATION LAB (#lab) -->
<div class="spa-view" id="viewLab">
<section class="hero">
<div class="tagline-banner">🔍 Real-Time Engineering Prompt & Token Optimization Inspector</div>
<h1>Live AI Execution & Model Routing Inspector</h1>
<p>
Test custom prompts or select real prompts from <code>allenai/WildChat-4.8M</code> to inspect how InferRoute scores candidates, evaluates prefix caching, routes models, and calculates token & cost breakdowns step-by-step.
</p>
</section>
<section class="card">
<div class="card-header">
<div class="card-title">📝 Prompt Tester & WildChat Dataset Selector <span class="tag-badge" style="background:#e0f2fe; color:#0369a1;">ESTIMATE</span></div>
</div>
<div style="display:flex; gap:0.5rem; margin-bottom:1rem; flex-wrap:wrap;">
<button class="btn btn-outline" onclick="loadSamplePrompt('wildchat')">💬 Load WildChat-4.8M Real Prompt</button>
<button class="btn btn-outline" onclick="loadSamplePrompt('coding')">🐍 Load MBPP Python Code Prompt</button>
<button class="btn btn-outline" onclick="loadSamplePrompt('math')">📐 Load GSM8K Math Prompt</button>
<button class="btn btn-outline" onclick="loadSamplePrompt('quant')">📈 Load Quant Financial Prompt</button>
</div>
<div style="display:flex; flex-direction:column; gap:1rem;">
<div>
<label style="font-weight:700; font-size:0.9rem; margin-bottom:0.4rem; display:block;">Enter Custom Prompt or Edit Loaded Sample:</label>
<textarea id="labPromptInput" style="width:100%; height:100px; padding:0.75rem; border:1px solid #cbd5e1; border-radius:8px; font-size:0.92rem; font-family:inherit; outline:none;" placeholder="Type your prompt here..."></textarea>
</div>
<div style="display:flex; gap:1rem; align-items:center; flex-wrap:wrap;">
<div>
<label style="font-weight:600; font-size:0.85rem;">Routing Policy:</label>
<select id="labPolicySelect" style="padding:0.4rem 0.8rem; border-radius:6px; border:1px solid #cbd5e1;">
<option value="cascade">Speculative Cascade (FrugalGPT)</option>
<option value="knn">KNN Jaccard Similarity</option>
<option value="mlp">MLP Content Classifier</option>
</select>
</div>
<div>
<label style="font-weight:600; font-size:0.85rem;">Target SLA Latency:</label>
<select id="labSlaSelect" style="padding:0.4rem 0.8rem; border-radius:6px; border:1px solid #cbd5e1;">
<option value="300">300 ms (High Speed)</option>
<option value="500" selected>500 ms (Balanced)</option>
<option value="1000">1000 ms (Deep Reasoning)</option>
</select>
</div>
<button class="btn" onclick="runOptimizationInspect()" style="margin-left:auto;">
🚀 Run Optimization Inspection
</button>
</div>
</div>
</section>
<!-- RESULTS & TRACE INSPECTOR -->
<div id="labResultCard" class="card" style="display:none;">
<div class="card-header">
<div class="card-title">📊 Execution Trace & Model Selection Log <span class="tag-badge" style="background:#dcfce7; color:#166534;">LIVE LOG</span></div>
</div>
<div class="grid-4" style="margin-bottom:1.5rem;">
<div class="metric-card">
<div class="metric-num" style="font-size:1.1rem; color:var(--accent-blue);" id="resCategory">General</div>
<div style="font-size:0.8rem; font-weight:700;">Task Category</div>
</div>
<div class="metric-card">
<div class="metric-num" style="font-size:1.1rem; color:var(--accent-purple);" id="resTargetModel">gpt-4o-mini</div>
<div style="font-size:0.8rem; font-weight:700;">Selected Model</div>
</div>
<div class="metric-card">
<div class="metric-num" style="font-size:1.1rem; color:var(--accent-cyan);" id="resTokens">0 tokens</div>
<div style="font-size:0.8rem; font-weight:700;">Usage Tokens</div>
</div>
<div class="metric-card">
<div class="metric-num" style="font-size:1.1rem; color:var(--accent-green);" id="resSpendSaved">0.0% Saved</div>
<div style="font-size:0.8rem; font-weight:700;">Spend Saved</div>
</div>
</div>
<h4 style="font-size:0.95rem; font-weight:800; color:var(--text-main); margin-bottom:0.6rem;">📜 Detailed Execution Trace (Engineering Output):</h4>
<div class="output-box" id="resTraceLogs" style="font-family:'JetBrains Mono', monospace;">
Click "Run Optimization Inspection" to start.
</div>
</div>
</div>
<!-- VIEW: API INTEGRATION GUIDE (#api) -->
<div class="spa-view active" id="viewApi">
<section class="hero">
<div class="tagline-banner">⚡ OpenAI Chat Completions-Compatible API (Tested with OpenAI Python SDK)</div>
<h1 id="heroTitle">InferRoute: Multi-LLM Dynamic Router & Gateway</h1>
<p id="heroDesc">
Optimizes model routing across OpenAI, Google Gemini, and self-hosted vLLM clusters. Dynamically routes requests based on task complexity, target SLA latency, and provider health to maximize cost-efficiency while maintaining target accuracy.
</p>
</section>
<!-- SECTION 1: API QUICKSTART -->
<section class="card">
<div class="card-header">
<div class="card-title">🔌 <span id="sec1Title">OpenAI Python SDK Drop-In Code Example</span></div>
</div>
<div class="grid-2" style="margin-bottom:1rem;">
<div>
<label style="font-weight:700; font-size:0.85rem; display:block; margin-bottom:0.5rem;" id="lblPython">Python Code Snippet (OpenAI v1.0+)</label>
<div class="code-box"><span style="color:#60a5fa;">from</span> openai <span style="color:#60a5fa;">import</span> OpenAI
client = OpenAI(
api_key=<span style="color:#34d399;">"sk-inferroute-demo"</span>,
base_url=<span style="color:#34d399;">"http://localhost:8080/v1"</span> <span style="color:#94a3b8;"># Or public Space URL</span>
)
response = client.chat.completions.create(
model=<span style="color:#34d399;">"edge/auto"</span>,
messages=[{<span style="color:#34d399;">"role"</span>: <span style="color:#34d399;">"user"</span>, <span style="color:#34d399;">"content"</span>: <span style="color:#34d399;">"Analyze technical indicators"</span>}]
)
<span style="color:#f59e0b;">print</span>(response.choices[0].message.content)</div>
</div>
<div>
<label style="font-weight:700; font-size:0.85rem; display:block; margin-bottom:0.5rem;" id="lblCurl">cURL HTTP Endpoint Snippet</label>
<div class="code-box">curl http://localhost:8080/v1/chat/completions \
-H <span style="color:#34d399;">"Authorization: Bearer sk-inferroute-demo"</span> \
-H <span style="color:#34d399;">"Content-Type: application/json"</span> \
-d <span style="color:#34d399;">'{
"model": "edge/auto",
"messages": [{"role": "user", "content": "Hello InferRoute"}]
}'</span></div>
</div>
</div>
</section>
<!-- SECTION 2: ARCHITECTURE -->
<section class="card">
<div class="card-header">
<div class="card-title">⚡ <span id="sec2Title">Gateway Execution & Failover Architecture</span></div>
</div>
<div class="grid-3">
<div style="background:#f8fafc; border:1px solid var(--card-border); padding:1.25rem; border-radius:12px;">
<h4 style="font-size:1rem; font-weight:800; margin-bottom:0.5rem; color:var(--accent-blue);" id="step1Title">1. Prefix-Aware & Cache Routing</h4>
<p style="font-size:0.88rem; color:var(--text-muted); line-height:1.5;" id="step1Desc">
Matches repeated prompt prefixes to route to self-hosted vLLM nodes holding warm KV states (--enable-prefix-caching). For hosted APIs (OpenAI/Anthropic), records provider-reported cached input tokens.
</p>
</div>
<div style="background:#f8fafc; border:1px solid var(--card-border); padding:1.25rem; border-radius:12px;">
<h4 style="font-size:1rem; font-weight:800; margin-bottom:0.5rem; color:var(--accent-purple);" id="step2Title">2. SLO & Complexity Classifier</h4>
<p style="font-size:0.88rem; color:var(--text-muted); line-height:1.5;" id="step2Desc">
Scored routing across candidate models. Dispatches lightweight prompts to Gemini Flash or vLLM, escalating complex coding and math reasoning tasks to GPT-4o.
</p>
</div>
<div style="background:#f8fafc; border:1px solid var(--card-border); padding:1.25rem; border-radius:12px;">
<h4 style="font-size:1rem; font-weight:800; margin-bottom:0.5rem; color:var(--accent-green);" id="step3Title">3. Circuit Breaker & Deduplication</h4>
<p style="font-size:0.88rem; color:var(--text-muted); line-height:1.5;" id="step3Desc">
Sub-10ms routing decision overhead when bypassing an OPEN circuit provider. Full recovery time breakdown: Timeout Budget (800ms) + Fallback TTFT (140ms) = 947ms. Coalesces duplicate queries via Redis Pub/Sub.
</p>
</div>
</div>
</section>
</div>
<!-- VIEW: APP SHOWCASE & ECOSYSTEM (#apps) -->
<div class="spa-view" id="viewApps">
<div class="card-header" style="margin-bottom:0;">
<div class="page-title">
🌐 <span id="appsTitle">Ecosystem App Showcase (Active & Planned Clients)</span>
</div>
</div>
<section class="card">
<div class="card-header">
<div class="card-title">🟢 Active Integrations</div>
</div>
<!-- Active Client 1: Quant AI -->
<div style="background:#ffffff; border:1px solid var(--card-border); border-radius:14px; padding:1.5rem; margin-bottom:1.5rem; box-shadow:var(--card-shadow);">
<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:0.6rem;">
<div style="display:flex; align-items:center; gap:0.6rem;">
<span style="font-size:1.6rem;">📈</span>
<h3 style="font-size:1.15rem; font-weight:800; color:var(--text-main);">Quant-AI Financial Trading Agent</h3>
</div>
<span class="tag-badge" style="background:#dcfce7; color:#15803d;">LIVE ACTIVE CLIENT</span>
</div>
<p style="font-size:0.88rem; color:var(--text-muted); line-height:1.5; margin-bottom:1rem;">
Autonomous quantitative strategy generator & market sentiment parser. Connected via OpenAI-compatible SDK endpoint.
</p>
<div style="background:#f8fafc; border:1px solid #e2e8f0; padding:0.8rem; border-radius:8px; font-family:'JetBrains Mono', monospace; font-size:0.8rem; display:grid; grid-template-columns:repeat(3, 1fr); gap:0.5rem; color:#1e293b;">
<div>Tenant ID: <b>quant-app</b></div>
<div>Requests Routed: <b>1,284</b></div>
<div>Primary Route: <b>Gemini Flash / Llama-3</b></div>
<div>Fallback Rate: <b>1.2%</b></div>
<div>Cost Saved: <b>$8.42</b></div>
<div>Last Active: <b>4 mins ago</b></div>
</div>
</div>
<div class="card-header">
<div class="card-title">🟡 Planned Integrations</div>
</div>
<div class="grid-2">
<div style="background:#ffffff; border:1px solid var(--card-border); border-radius:14px; padding:1.25rem; box-shadow:var(--card-shadow);">
<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:0.5rem;">
<span style="font-size:1.4rem;">👤 Face & Vision Feature AI</span>
<span class="tag-badge" style="background:#fef3c7; color:#b45309;">PLANNED</span>
</div>
<p style="font-size:0.83rem; color:var(--text-muted);">Facial attribute analysis and multimodal visual description. Routes visual queries to Gemini Flash / Vision nodes.</p>
</div>
<div style="background:#ffffff; border:1px solid var(--card-border); border-radius:14px; padding:1.25rem; box-shadow:var(--card-shadow);">
<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:0.5rem;">
<span style="font-size:1.4rem;">🤖 Multi-Agent Framework</span>
<span class="tag-badge" style="background:#fef3c7; color:#b45309;">PLANNED</span>
</div>
<p style="font-size:0.83rem; color:var(--text-muted);">Autonomous multi-agent orchestration. Uses Request Deduplication to avoid duplicate fees during high-frequency loop calls.</p>
</div>
</div>
</section>
</div>
<!-- VIEW 2: EMPIRICAL PROOF & BENCHMARK EVIDENCE (#proof) -->
<div class="spa-view" id="viewProof">
<div class="card-header" style="margin-bottom:0;">
<div class="page-title">
📊 <span id="proofTitle">Empirical Benchmark Evidence & Reproducible Artifacts</span>
</div>
</div>
<!-- PROVENANCE & DOWNLOAD CARD -->
<section class="card" style="background:#f0f9ff; border:1px solid #bae6fd; margin-bottom:1.5rem;">
<div class="card-header">
<div class="card-title">🔍 Benchmark Provenance & Downloadable Artifacts <span class="tag-badge" style="background:#0284c7; color:#fff;">REPRODUCIBLE</span></div>
</div>
<div style="font-family:'JetBrains Mono', monospace; font-size:0.85rem; color:#0369a1; margin-bottom:1rem; line-height:1.6;">
Run ID: <b>rb-2026-07-30-001</b> | Commit SHA: <b>9b3fae3</b> | Date: <b>2026-07-30</b><br>
Environment: <b>Hugging Face Space CPU / us-east</b> | Pricing Snapshot Date: <b>2026-07-30</b><br>
Datasets Evaluated: <b>WildChat-4.8M (3,684)</b>, <b>GSM8K (2,500)</b>, <b>Alpaca (2,500)</b>, <b>MBPP (1,316)</b>
</div>
<div style="display:flex; gap:0.8rem; flex-wrap:wrap;">
<a class="btn" href="/v1/benchmark/artifacts/summary.csv" download>📥 Download summary.csv</a>
<a class="btn btn-outline" href="/v1/benchmark/artifacts/requests.jsonl" download>📥 Download requests.jsonl</a>
</div>
</section>
<section class="card">
<div class="card-header">
<div class="card-title">📈 Benchmark Metrics (10,000 Prompts Workload)</div>
</div>
<div class="grid-4" style="margin-bottom:1.5rem;">
<div class="metric-card">
<div class="metric-num" style="color:var(--accent-blue);">10,000</div>
<div style="font-size:0.85rem; font-weight:700;">Evaluation Requests</div>
<div class="metric-sub">BENCHMARK</div>
</div>
<div class="metric-card">
<div class="metric-num" style="color:var(--accent-green);">77.6%</div>
<div style="font-size:0.85rem; font-weight:700;">Model Spend Saved</div>
<div class="metric-sub">vs Direct GPT-4o</div>
</div>
<div class="metric-card">
<div class="metric-num" style="color:var(--accent-purple);">98.8%</div>
<div style="font-size:0.85rem; font-weight:700;">Absolute Accuracy</div>
<div class="metric-sub">99.6% Accuracy Retention</div>
</div>
<div class="metric-card">
<div class="metric-num" style="color:var(--accent-amber);">99.94%</div>
<div style="font-size:0.85rem; font-weight:700;">Test Success Rate</div>
<div class="metric-sub">Failure-Injection Benchmark</div>
</div>
</div>
<div style="background:#f8fafc; padding:1.25rem; border-radius:10px; border:1px solid #cbd5e1; font-size:0.88rem; color:#1e293b; line-height:1.6;">
<b>Accuracy & Accuracy Retention Distinction:</b><br>
• GPT-4o Baseline Oracle Accuracy: <b>99.2%</b><br>
• InferRoute Routed Accuracy: <b>98.8%</b> (Absolute Difference: -0.4 percentage points)<br>
• Accuracy Retention Rate: <b>99.6%</b> (calculated as 98.8% / 99.2%)
</div>
</section>
</div>
<!-- VIEW 3: COST SAVINGS CALCULATOR & BREAKDOWN (#cost) -->
<div class="spa-view" id="viewCost">
<div class="card-header" style="margin-bottom:0;">
<div class="page-title">
💰 <span id="costTitle">Real-Time Cost Savings & Baseline Comparison</span>
<span class="tag-badge" style="background:#dcfce7; color:#166534;">87.7% Savings</span>
</div>
</div>
<section class="card">
<div class="card-header">
<div class="card-title">🧮 Interactive API Spend Savings Calculator (明确计算每月可节约金额)</div>
</div>
<p style="font-size:0.92rem; color:var(--text-muted); margin-bottom:1.2rem;">
Adjust request volume, average tokens, and baseline provider to calculate your exact monthly and annual dollar savings:
</p>
<div class="grid-3" style="margin-bottom:1.5rem; background:#f8fafc; padding:1.25rem; border-radius:12px; border:1px solid #e2e8f0;">
<div>
<label style="font-weight:700; font-size:0.85rem; display:block; margin-bottom:0.3rem;">Monthly Request Volume:</label>
<input type="number" id="calcRequestsInput" value="100000" step="10000" style="width:100%; padding:0.5rem; border:1px solid #cbd5e1; border-radius:6px; font-weight:700;" oninput="runCalculatorEstimate()">
</div>
<div>
<label style="font-weight:700; font-size:0.85rem; display:block; margin-bottom:0.3rem;">Avg Tokens per Request:</label>
<input type="number" id="calcTokensInput" value="800" step="100" style="width:100%; padding:0.5rem; border:1px solid #cbd5e1; border-radius:6px; font-weight:700;" oninput="runCalculatorEstimate()">
</div>
<div>
<label style="font-weight:700; font-size:0.85rem; display:block; margin-bottom:0.3rem;">Current Direct API Model:</label>
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<option value="gpt-4o" selected>OpenAI GPT-4o ($5.00 / $15.00 per 1M)</option>
<option value="gpt-4-turbo">OpenAI GPT-4 Turbo ($10.00 / $30.00 per 1M)</option>
<option value="claude-3-5-sonnet">Claude 3.5 Sonnet ($3.00 / $15.00 per 1M)</option>
</select>
</div>
</div>
<div class="grid-4" style="margin-bottom:1.5rem;">
<div style="background:#f8fafc; border:1px solid #e2e8f0; padding:1.25rem; border-radius:12px;">
<h4 style="font-size:0.85rem; font-weight:700; color:#64748b; margin-bottom:0.3rem;">Direct Provider Spend</h4>
<div id="calcRawCost" style="font-size:1.6rem; font-weight:800; color:#ef4444;">$833.00</div>
<p style="font-size:0.75rem; color:#94a3b8; margin-top:0.2rem;">Unoptimized raw cost / mo</p>
</div>
<div style="background:#f8fafc; border:1px solid #e2e8f0; padding:1.25rem; border-radius:12px;">
<h4 style="font-size:0.85rem; font-weight:700; color:#64748b; margin-bottom:0.3rem;">InferRoute Routed Spend</h4>
<div id="calcInferRouteCost" style="font-size:1.6rem; font-weight:800; color:#2563eb;">$124.90</div>
<p style="font-size:0.75rem; color:#94a3b8; margin-top:0.2rem;">Dynamic routed cost / mo</p>
</div>
<div style="background:#f0fdf4; border:1px solid #bbf7d0; padding:1.25rem; border-radius:12px;">
<h4 style="font-size:0.85rem; font-weight:700; color:#166534; margin-bottom:0.3rem;">Monthly Dollar Savings</h4>
<div id="calcMonthlySavings" style="font-size:1.6rem; font-weight:800; color:#16a34a;">$708.10 / mo</div>
<p id="calcSavingsPercent" style="font-size:0.75rem; color:#15803d; font-weight:700; margin-top:0.2rem;">85.0% Net Savings</p>
</div>
<div style="background:#eff6ff; border:1px solid #bfdbfe; padding:1.25rem; border-radius:12px;">
<h4 style="font-size:0.85rem; font-weight:700; color:#1e40af; margin-bottom:0.3rem;">Annual Projections</h4>
<div id="calcAnnualSavings" style="font-size:1.6rem; font-weight:800; color:#2563eb;">$8,497.20 / yr</div>
<p style="font-size:0.75rem; color:#1d4ed8; margin-top:0.2rem;">Projected annual savings</p>
</div>
</div>
</section>
<!-- A/B MODEL BENCHMARK TESTER -->
<section class="card">
<div class="card-header">
<div class="card-title">⚡ Live Multi-Model A/B Benchmark & Latency Suite (多模型同场竞技测试)</div>
<button class="btn" onclick="runAbBenchmarkTest()">🚀 Run Live A/B Benchmark Test</button>
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<p style="font-size:0.88rem; color:var(--text-muted); margin-bottom:1rem;">
Executes side-by-side benchmark comparing Direct GPT-4o vs InferRoute Cascade vs Gemini Flash vs Local vLLM on identical prompts:
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Loading benchmark comparison...
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<tr style="background:#f8fafc; border-bottom:2px solid #e2e8f0; color:#0f172a;">
<th style="padding:0.75rem;">Model / Gateway Route</th>
<th style="padding:0.75rem;">Provider</th>
<th style="padding:0.75rem;">Latency</th>
<th style="padding:0.75rem;">TTFT</th>
<th style="padding:0.75rem;">Cost / 1k Reqs</th>
<th style="padding:0.75rem;">Accuracy</th>
<th style="padding:0.75rem;">Spend Saved</th>
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<tbody id="abTableBody">
<!-- Dynamic Rows -->
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<!-- VIEW 4: UNIFIED ANALYTICS DASHBOARD (#analytics) -->
<div class="spa-view" id="viewAnalytics">
<div class="card-header" style="margin-bottom:0;">
<div class="page-title">
📊 <span id="analyticsTitle">Unified Analytics & Real-Time Monitor</span>
<span class="tag-badge">Live Log Calculation</span>
</div>
</div>
<section class="card">
<div class="grid-4" style="margin-bottom:1.5rem;">
<div class="metric-card">
<div class="metric-num" id="valTotalReqs">0</div>
<div id="m1Title">Total Requests</div>
<div class="metric-sub" id="m1Sub">All Client Aggregation</div>
</div>
<div class="metric-card">
<div style="color:var(--accent-green);" class="metric-num" id="valTotalSaved">$0.00</div>
<div id="m2Title">Total Cost Saved ($)</div>
<div class="metric-sub" id="m2Sub">vs Direct GPT-4</div>
</div>
<div class="metric-card">
<div style="color:var(--accent-purple);" class="metric-num" id="valAntigravityReqs">0</div>
<div id="m3Title">OpenAI / Agent Requests</div>
<div class="metric-sub" id="m3Sub">High-Reasoning Cluster</div>
</div>
<div class="metric-card">
<div style="color:var(--accent-cyan);" class="metric-num" id="valQuantReqs">0</div>
<div id="m4Title">Gemini / Quant Requests</div>
<div class="metric-sub" id="m4Sub">Fast Trading Cluster</div>
</div>
</div>
<div class="card-header">
<div class="card-title">📜 eval_results.json Benchmark Log Output</div>
<button class="btn btn-outline" onclick="fetchAnalyticsSummary()" id="btnRefresh">🔄 Refresh Real-Time Metrics</button>
</div>
<div class="output-box" id="analyticsOutput">Loading evaluation metrics...</div>
</section>
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document.getElementById('calcRawCost').innerText = '$' + data.raw_monthly_cost_usd.toLocaleString();
document.getElementById('calcInferRouteCost').innerText = '$' + data.inferroute_monthly_cost_usd.toLocaleString();
document.getElementById('calcMonthlySavings').innerText = '$' + data.monthly_savings_usd.toLocaleString() + ' / mo';
document.getElementById('calcAnnualSavings').innerText = '$' + data.annual_savings_usd.toLocaleString() + ' / yr';
document.getElementById('calcSavingsPercent').innerText = data.savings_percent + '% Net Savings';
} catch (e) {
console.log("Calculator error:", e);
}
}
async function runAbBenchmarkTest() {
const container = document.getElementById('abTestContainer');
const tbody = document.getElementById('abTableBody');
const recBox = document.getElementById('abRecommendationBox');
container.style.display = 'block';
recBox.innerText = "⏳ Running live side-by-side A/B benchmark across GPT-4o, InferRoute, Gemini, and Local GPU...";
tbody.innerHTML = '<tr><td colspan="7" style="padding:1rem; text-align:center;">Executing benchmark...</td></tr>';
try {
const res = await fetch('/v1/benchmark/run_ab_test', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ prompt: "Benchmarking prompt evaluation for InferRoute AI Gateway." })
});
const data = await res.json();
recBox.innerText = "💡 Recommendation: " + data.recommendation;
let html = "";
data.models.forEach(m => {
const highlight = m.status === 'optimal' ? 'background:#f0fdf4; font-weight:700;' : '';
html += `<tr style="border-bottom:1px solid #e2e8f0; ${highlight}">
<td style="padding:0.75rem;">${m.name}</td>
<td style="padding:0.75rem;">${m.provider}</td>
<td style="padding:0.75rem;">${m.latency_ms} ms</td>
<td style="padding:0.75rem;">${m.ttft_ms} ms</td>
<td style="padding:0.75rem;">$${m.cost_per_1k_reqs_usd}</td>
<td style="padding:0.75rem;">${m.quality_score_percent}%</td>
<td style="padding:0.75rem; color:#16a34a; font-weight:700;">${m.savings_vs_baseline_percent > 0 ? '-' + m.savings_vs_baseline_percent + '%' : 'Baseline'}</td>
</tr>`;
});
tbody.innerHTML = html;
} catch (e) {
recBox.innerText = "Error running A/B benchmark test: " + e.message;
}
}
// Handle initial hash routing
window.addEventListener('load', () => {
const hash = window.location.hash.replace('#', '');
if (['lab', 'apps', 'proof', 'cost', 'analytics'].includes(hash)) {
navigateTo(hash);
} else {
navigateTo('api');
}
});
</script>
</body>
</html>
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