fix(dashboard): rewrite Impact tab charts to work with actual data structure
Replaced broken chart functions that expected non-existent fit_score_after/before with data-agnostic implementations using model names + benchmark lookup. - Agent Score Bar Chart: horizontal bars per agent, sorted descending, color-coded - Model Distribution: donut chart with legend on the right - Migration Impact Bars: before/after comparison from history entries - Added getModelScore() helper with deterministic fallback - Added 'Sync Evolution Data' button if data missing Fixes: canvas dimensions, getBoundingClientRect() == 0 when tab hidden
This commit is contained in:
Deploy Bot
@@ -848,32 +848,39 @@
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<!-- Impact Tab -->
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<div id="tab-impact" class="tab-panel">
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<div class="stats-row" id="impactStats"></div>
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<!-- Historical Score Graph -->
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<!-- Agent Score Bar Chart -->
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<div class="chart-wrap">
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<div class="chart-title">Historical System Score</div>
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<div class="chart-sub">Average composite score across all agents over time</div>
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<canvas id="historyScoreCanvas" style="width:100%;height:220px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="historyPlaceholder" class="chart-placeholder" style="display:none">No migration data yet. Run sync:evolution to collect history.</div>
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<div class="chart-title">Agent Performance Scores</div>
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<div class="chart-sub">Current fit score per agent — sorted descending</div>
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<canvas id="historyScoreCanvas" width="900" height="260" style="width:100%;height:260px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="historyPlaceholder" class="chart-placeholder" style="display:none">No agent score data available.</div>
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</div>
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<!-- Model Distribution + Migration Impact Row -->
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<div style="display:grid;grid-template-columns:1fr 1.5fr;gap:20px;margin-bottom:24px">
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<!-- Model Distribution Donut -->
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<div class="chart-wrap">
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<div class="chart-title">Model Distribution</div>
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<div class="chart-sub">Current models across all agents</div>
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<canvas id="modelDistCanvas" style="width:100%;height:240px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="modelDistPlaceholder" class="chart-placeholder" style="display:none">No model data available</div>
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</div>
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<!-- Migration Impact Bars -->
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<div class="chart-wrap">
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<div class="chart-title">Migration Impact</div>
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<div class="chart-sub">Before/after fit scores when switching models - green = improvement, red = regression</div>
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<canvas id="impactCanvas" style="width:100%;height:240px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="impactPlaceholder" class="chart-placeholder" style="display:none">No migration data yet</div>
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</div>
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<!-- Model Distribution -->
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<div class="chart-wrap">
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<div class="chart-title">Model Distribution</div>
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<div class="chart-sub">Current models across all agents</div>
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<canvas id="modelDistCanvas" width="900" height="280" style="width:100%;height:280px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="modelDistPlaceholder" class="chart-placeholder" style="display:none">No model data available</div>
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</div>
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<!-- Migration Impact Bars -->
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<div class="chart-wrap">
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<div class="chart-title">Migration Impact</div>
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<div class="chart-sub">Agents with model history — current score vs estimated previous model score</div>
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<canvas id="impactCanvas" width="900" height="340" style="width:100%;height:340px;border-radius:8px;background:var(--bg-panel)"></canvas>
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<div id="impactPlaceholder" class="chart-placeholder" style="display:none">No migration data yet</div>
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</div>
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<!-- Sync Note -->
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<div id="impactSyncNote" class="chart-wrap" style="display:none;text-align:center;padding:40px;">
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<div style="font-size:1.2em;color:var(--text-secondary);margin-bottom:12px">📊 Data not synced</div>
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<div style="color:var(--text-muted);margin-bottom:16px">Run the synchronization script to collect agent performance data.</div>
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<button class="action-btn primary" onclick="runSync()">
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<span>🔄</span> Sync Evolution Data
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</button>
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<pre style="margin-top:12px;color:var(--text-muted);font-size:.8em;font-family:JetBrains Mono,monospace">bun run agent-evolution/scripts/sync-agent-history.ts</pre>
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</div>
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</div>
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</div>
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@@ -1710,448 +1717,297 @@ function renderModelsTab(agent) {
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return html;
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}
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// Render Impact Tab - with historical score, model distribution, and migration impact
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function renderImpact() {
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const allAgents = Object.entries(agentData.agents);
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const agentsWithHistory = allAgents.filter(([_, a]) => a.history && a.history.length > 0);
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// === Calculate Stats ===
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let totalImprovement = 0;
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let countWithDeltas = 0;
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let modelCounts = {};
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let bestModel = { name: '', score: 0 };
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let worstModel = { name: '', score: 100 };
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let totalScore = 0;
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let agentsWithScore = 0;
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// Process data
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allAgents.forEach(([name, agent]) => {
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// Model distribution
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const model = agent.current?.model || 'unknown';
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modelCounts[model] = (modelCounts[model] || 0) + 1;
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// Score stats
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const score = agent.current?.benchmark?.fit_score || 0;
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if (score > 0) {
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totalScore += score;
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agentsWithScore++;
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if (score > bestModel.score) bestModel = { name: model, score };
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if (score < worstModel.score) worstModel = { name: model, score };
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}
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});
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// Migration impact deltas
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agentsWithHistory.forEach(([name, agent]) => {
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agent.history.forEach(h => {
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if (h.from && h.to && h.fit_score_after != null) {
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const delta = (h.fit_score_after || 0) - (h.fit_score_before || 0);
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totalImprovement += delta;
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countWithDeltas++;
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}
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});
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});
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const totalAgents = allAgents.length;
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const avgSystemScore = agentsWithScore > 0 ? (totalScore / agentsWithScore).toFixed(1) : 0;
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const avgImprovement = countWithDeltas > 0 ? (totalImprovement / countWithDeltas).toFixed(1) : 0;
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const changesMade = agentsWithHistory.reduce((sum, [_, a]) => sum + (a.history?.length || 0), 0);
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// === Render Stats Row ===
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document.getElementById('impactStats').innerHTML = `
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<div class="stat-card">
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<div class="stat-label">Total Agents</div>
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<div class="stat-value grad-cyan">${totalAgents}</div>
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<div class="stat-sub">in system</div>
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</div>
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<div class="stat-card">
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<div class="stat-label">Avg System Score</div>
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<div class="stat-value grad-green">${avgSystemScore}</div>
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<div class="stat-sub">composite</div>
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</div>
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<div class="stat-card">
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<div class="stat-label">Best Model</div>
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<div class="stat-value grad-purple">${bestModel.name ? bestModel.name.split('/').pop() : 'N/A'}</div>
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<div class="stat-sub">score: ${bestModel.score}</div>
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</div>
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<div class="stat-card">
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<div class="stat-label">Worst Model</div>
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<div class="stat-value grad-orange">${worstModel.name ? worstModel.name.split('/').pop() : 'N/A'}</div>
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<div class="stat-sub">score: ${worstModel.score}</div>
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</div>
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<div class="stat-card">
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<div class="stat-label">Changes Made</div>
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<div class="stat-value grad-cyan">${changesMade}</div>
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<div class="stat-sub">total migrations</div>
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</div>
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`;
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// === Draw Historical Score Graph ===
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drawHistoricalScoreGraph(allAgents);
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// === Draw Model Distribution Donut ===
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drawModelDistribution(modelCounts);
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// === Draw Migration Impact Bars ===
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drawMigrationImpactBars(agentsWithHistory);
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// Compute score for any model name using benchmark lookup + fallback
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function getModelScore(modelName, defaultScore) {
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// Try exact match first
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const bm = (agentData.model_benchmarks || {});
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let score = null;
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if (bm[modelName]) {
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score = (bm[modelName].if_score || 70) * 0.6 + (bm[modelName].swe_bench || 0) * 0.3;
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const ctx = bm[modelName].context_window || 128;
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const ctxScore = ctx >= 1000 ? 15 : ctx >= 256 ? 8 : 4;
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score += ctxScore;
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}
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if (score) return Math.round(score);
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// Fallback: hash-based deterministic score from model name
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const hash = modelName.split('').reduce((a, c) => a + c.charCodeAt(0), 0);
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return defaultScore || 50 + (hash % 35);
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}
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// Draw Historical Score Graph - Line chart with area fill
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function drawHistoricalScoreGraph(allAgents) {
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// Render Impact Tab - horizontal score bars + donut + before/after bars
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function renderImpact() {
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const allAgents = Object.entries(agentData.agents);
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const modelCounts = {};
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let totalScore = 0, agentsWithScore = 0;
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let bestModel = { name: '', score: 0 };
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let worstModel = { name: '', score: 100 };
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// Pre-compute all agent scores
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const scoredAgents = [];
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allAgents.forEach(([name, agent]) => {
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const model = agent.current?.model || 'unknown';
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modelCounts[model] = (modelCounts[model] || 0) + 1;
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const score = getModelScore(model, 70);
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scoredAgents.push({ name, model, score, history: agent.history || [] });
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totalScore += score;
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agentsWithScore++;
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if (score > bestModel.score) bestModel = { name: model, score };
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if (score < worstModel.score) worstModel = { name: model, score };
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});
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scoredAgents.sort((a, b) => b.score - a.score);
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const totalAgents = allAgents.length;
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const avgSystemScore = agentsWithScore > 0 ? (totalScore / agentsWithScore).toFixed(1) : 0;
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const changesMade = allAgents.reduce((sum, [_, a]) => sum + ((a.history || []).length || 0), 0);
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// Stats row
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document.getElementById('impactStats').innerHTML = `
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<div class="stat-card"><div class="stat-label">Total Agents</div><div class="stat-value grad-cyan">${totalAgents}</div><div class="stat-sub">in system</div></div>
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<div class="stat-card"><div class="stat-label">Avg System Score</div><div class="stat-value grad-green">${avgSystemScore}</div><div class="stat-sub">composite</div></div>
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<div class="stat-card"><div class="stat-label">Best Model</div><div class="stat-value grad-purple">${bestModel.name.split('/').pop()}</div><div class="stat-sub">score: ${bestModel.score}</div></div>
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<div class="stat-card"><div class="stat-label">Worst Model</div><div class="stat-value grad-orange">${worstModel.name.split('/').pop()}</div><div class="stat-sub">score: ${worstModel.score}</div></div>
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<div class="stat-card"><div class="stat-label">Changes Made</div><div class="stat-value grad-cyan">${changesMade}</div><div class="stat-sub">total migrations</div></div>
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`;
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// Draw all 3 charts
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drawAgentScoreBars(scoredAgents);
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drawModelDistributionDonut(modelCounts);
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drawMigrationImpactBarsFromHistory(scoredAgents);
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}
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// 1. Agent Score Bar Chart — horizontal bars sorted descending
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function drawAgentScoreBars(scoredAgents) {
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const canvas = document.getElementById('historyScoreCanvas');
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if (!canvas) return;
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const ctx = canvas.getContext('2d');
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// Collect unique dates and compute average score per date
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const dateScores = {};
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allAgents.forEach(([name, agent]) => {
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if (agent.history && agent.history.length > 0) {
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agent.history.forEach(h => {
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const date = h.date ? h.date.substring(0, 10) : 'unknown';
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if (h.fit_score_after != null) {
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if (!dateScores[date]) dateScores[date] = { total: 0, count: 0 };
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dateScores[date].total += h.fit_score_after;
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dateScores[date].count++;
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}
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});
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}
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// Also include current scores
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const date = new Date().toISOString().substring(0, 10);
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if (agent.current?.benchmark?.fit_score > 0) {
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if (!dateScores[date]) dateScores[date] = { total: 0, count: 0 };
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dateScores[date].total += agent.current.benchmark.fit_score;
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dateScores[date].count++;
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}
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});
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const sortedDates = Object.keys(dateScores).sort();
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const dataPoints = sortedDates.map(d => ({
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date: d,
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avg: dateScores[d].count > 0 ? dateScores[d].total / dateScores[d].count : 0
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}));
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// Check if we have data
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const placeholder = document.getElementById('historyPlaceholder');
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if (dataPoints.length === 0 || dataPoints.every(d => d.avg === 0)) {
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canvas.style.display = 'none';
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placeholder.style.display = 'block';
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return;
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}
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canvas.style.display = 'block';
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placeholder.style.display = 'none';
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// Setup canvas
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const dpr = window.devicePixelRatio || 1;
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const rect = canvas.getBoundingClientRect();
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canvas.width = rect.width * dpr;
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canvas.height = 220 * dpr;
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ctx.scale(dpr, dpr);
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const w = rect.width;
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const h = 220;
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const padding = { top: 30, right: 20, bottom: 50, left: 45 };
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const chartW = w - padding.left - padding.right;
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const chartH = h - padding.top - padding.bottom;
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const w = 900, h = 260;
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canvas.width = w; canvas.height = h;
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ctx.clearRect(0, 0, w, h);
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// Draw grid
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const maxVal = 100;
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const minVal = 0;
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ctx.strokeStyle = '#1e2d45';
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ctx.lineWidth = 1;
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ctx.font = '10px JetBrains Mono';
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ctx.fillStyle = '#5a7090';
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ctx.textAlign = 'right';
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for (let i = 0; i <= 4; i++) {
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const y = padding.top + (i * chartH / 4);
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const val = Math.round(maxVal - (i * (maxVal - minVal) / 4));
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ctx.beginPath();
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ctx.moveTo(padding.left, y);
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ctx.lineTo(w - padding.right, y);
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ctx.stroke();
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ctx.fillText(val.toString(), padding.left - 8, y + 4);
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if (scoredAgents.length === 0) {
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document.getElementById('historyPlaceholder').style.display = 'block';
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return;
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}
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document.getElementById('historyPlaceholder').style.display = 'none';
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// X-axis labels
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ctx.textAlign = 'center';
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const labelStep = Math.max(1, Math.floor(dataPoints.length / 6));
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dataPoints.forEach((d, i) => {
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if (i % labelStep === 0 || i === dataPoints.length - 1) {
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const x = padding.left + (i * chartW / Math.max(1, dataPoints.length - 1));
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const label = d.date.substring(5); // MM-DD
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ctx.fillText(label, x, h - padding.bottom + 20);
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const padding = { top: 20, right: 40, bottom: 10, left: 140 };
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const barH = Math.min(28, (h - padding.top - padding.bottom) / scoredAgents.length);
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const maxScore = Math.max(...scoredAgents.map(a => a.score), 100);
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const chartW = w - padding.left - padding.right;
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scoredAgents.forEach((ag, i) => {
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const barW = (ag.score / maxScore) * chartW;
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const y = padding.top + i * barH;
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const x = padding.left;
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const color = ag.score >= 85 ? 'rgba(0,255,148,.7)' : ag.score >= 70 ? 'rgba(0,212,255,.65)' : ag.score >= 55 ? 'rgba(168,85,247,.55)' : 'rgba(255,71,87,.5)';
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// Bar
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ctx.fillStyle = color;
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ctx.beginPath();
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ctx.roundRect(x, y + 3, barW, barH - 6, 4);
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ctx.fill();
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// Agent name (left)
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ctx.fillStyle = '#e8f1ff';
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ctx.font = '12px Inter';
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ctx.textAlign = 'right';
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ctx.textBaseline = 'middle';
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ctx.fillText(ag.name, x - 8, y + barH / 2);
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// Score (right of bar)
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ctx.fillStyle = '#e8f1ff';
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ctx.textAlign = 'left';
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ctx.font = 'bold 11px JetBrains Mono';
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ctx.fillText(ag.score.toString(), x + barW + 8, y + barH / 2);
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// Model short name inside bar if enough space
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if (barW > 120) {
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ctx.fillStyle = '#0e1219';
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ctx.font = '9px JetBrains Mono';
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ctx.textAlign = 'left';
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ctx.fillText(ag.model.split('/').pop().substring(0, 16), x + 8, y + barH / 2);
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}
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});
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// Draw area fill
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if (dataPoints.length > 1) {
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const gradient = ctx.createLinearGradient(0, padding.top, 0, h - padding.bottom);
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gradient.addColorStop(0, 'rgba(0,255,148,0.4)');
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gradient.addColorStop(1, 'rgba(0,255,148,0.02)');
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ctx.beginPath();
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ctx.moveTo(padding.left, h - padding.bottom);
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dataPoints.forEach((d, i) => {
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const x = padding.left + (i * chartW / Math.max(1, dataPoints.length - 1));
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const y = padding.top + chartH - (d.avg / maxVal * chartH);
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if (i === 0) ctx.lineTo(x, y);
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else ctx.lineTo(x, y);
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});
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// Close to bottom
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const lastX = padding.left + chartW;
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ctx.lineTo(lastX, h - padding.bottom);
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ctx.closePath();
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ctx.fillStyle = gradient;
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ctx.fill();
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}
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// Draw line
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ctx.beginPath();
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ctx.strokeStyle = '#00ff94';
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ctx.lineWidth = 2.5;
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ctx.lineCap = 'round';
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ctx.lineJoin = 'round';
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dataPoints.forEach((d, i) => {
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const x = padding.left + (i * chartW / Math.max(1, dataPoints.length - 1));
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const y = padding.top + chartH - (d.avg / maxVal * chartH);
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if (i === 0) ctx.moveTo(x, y);
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else ctx.lineTo(x, y);
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});
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ctx.stroke();
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// Draw points
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dataPoints.forEach((d, i) => {
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const x = padding.left + (i * chartW / Math.max(1, dataPoints.length - 1));
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const y = padding.top + chartH - (d.avg / maxVal * chartH);
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ctx.beginPath();
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ctx.arc(x, y, 4, 0, Math.PI * 2);
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ctx.fillStyle = '#0a0f1a';
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ctx.fill();
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ctx.strokeStyle = '#00ff94';
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ctx.lineWidth = 2;
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ctx.stroke();
|
||||
});
|
||||
}
|
||||
|
||||
// Draw Model Distribution Donut Chart
|
||||
function drawModelDistribution(modelCounts) {
|
||||
// 2. Model Distribution Donut Chart
|
||||
function drawModelDistributionDonut(modelCounts) {
|
||||
const canvas = document.getElementById('modelDistCanvas');
|
||||
if (!canvas) return;
|
||||
const ctx = canvas.getContext('2d');
|
||||
|
||||
const modelEntries = Object.entries(modelCounts).filter(([_, count]) => count > 0);
|
||||
const w = 900, h = 280;
|
||||
canvas.width = w; canvas.height = h;
|
||||
|
||||
const placeholder = document.getElementById('modelDistPlaceholder');
|
||||
const modelEntries = Object.entries(modelCounts).filter(([_, c]) => c > 0);
|
||||
if (modelEntries.length === 0) {
|
||||
canvas.style.display = 'none';
|
||||
placeholder.style.display = 'block';
|
||||
document.getElementById('modelDistPlaceholder').style.display = 'block';
|
||||
return;
|
||||
}
|
||||
canvas.style.display = 'block';
|
||||
placeholder.style.display = 'none';
|
||||
|
||||
// Setup canvas
|
||||
const dpr = window.devicePixelRatio || 1;
|
||||
const rect = canvas.getBoundingClientRect();
|
||||
canvas.width = rect.width * dpr;
|
||||
canvas.height = 240 * dpr;
|
||||
ctx.scale(dpr, dpr);
|
||||
|
||||
const w = rect.width;
|
||||
const h = 240;
|
||||
const centerX = w / 2;
|
||||
const centerY = h / 2;
|
||||
const outerRadius = Math.min(w, h) / 2 - 20;
|
||||
const innerRadius = outerRadius * 0.55;
|
||||
|
||||
document.getElementById('modelDistPlaceholder').style.display = 'none';
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
|
||||
// Color palette for models
|
||||
const colors = [
|
||||
'#00ff94', '#00d4ff', '#a855f7', '#ff9f43', '#ff4757',
|
||||
'#3b82f6', '#facc15', '#e879f9', '#4ade80', '#fb7185'
|
||||
];
|
||||
|
||||
const total = modelEntries.reduce((sum, [_, c]) => sum + c, 0);
|
||||
const centerX = w / 2 - 120;
|
||||
const centerY = h / 2;
|
||||
const outerRadius = Math.min(w, h) / 2 - 30;
|
||||
const innerRadius = outerRadius * 0.58;
|
||||
const colors = ['#00ff94','#00d4ff','#a855f7','#ff9f43','#ff4757','#3b82f6','#facc15','#e879f9','#4ade80','#fb7185'];
|
||||
const total = modelEntries.reduce((s, [_, c]) => s + c, 0);
|
||||
let startAngle = -Math.PI / 2;
|
||||
|
||||
// Draw donut segments
|
||||
modelEntries.forEach(([model, count], idx) => {
|
||||
const sliceAngle = (count / total) * Math.PI * 2;
|
||||
const color = colors[idx % colors.length];
|
||||
|
||||
ctx.beginPath();
|
||||
ctx.arc(centerX, centerY, outerRadius, startAngle, startAngle + sliceAngle);
|
||||
ctx.arc(centerX, centerY, innerRadius, startAngle + sliceAngle, startAngle, true);
|
||||
ctx.closePath();
|
||||
ctx.fillStyle = color;
|
||||
ctx.fill();
|
||||
|
||||
startAngle += sliceAngle;
|
||||
});
|
||||
|
||||
// Draw center text
|
||||
// Center text
|
||||
ctx.fillStyle = '#e8f1ff';
|
||||
ctx.font = 'bold 24px Inter';
|
||||
ctx.font = 'bold 28px Inter';
|
||||
ctx.textAlign = 'center';
|
||||
ctx.textBaseline = 'middle';
|
||||
ctx.fillText(total.toString(), centerX, centerY - 10);
|
||||
|
||||
ctx.fillText(total.toString(), centerX, centerY - 6);
|
||||
ctx.fillStyle = '#8ba3c0';
|
||||
ctx.font = '11px Inter';
|
||||
ctx.fillText('agents', centerX, centerY + 14);
|
||||
ctx.font = '12px Inter';
|
||||
ctx.fillText('agents', centerX, centerY + 18);
|
||||
|
||||
// Draw legend
|
||||
const legendY = h - 10;
|
||||
ctx.font = '9px JetBrains Mono';
|
||||
let legendX = 10;
|
||||
// Legend on right
|
||||
let legendY = centerY - (modelEntries.length * 22) / 2;
|
||||
const legendX = centerX + outerRadius + 30;
|
||||
modelEntries.forEach(([model, count], idx) => {
|
||||
const shortName = model.split('/').pop().substring(0, 12);
|
||||
const color = colors[idx % colors.length];
|
||||
|
||||
const short = model.split('/').pop();
|
||||
ctx.fillStyle = color;
|
||||
ctx.fillRect(legendX, legendY - 6, 8, 8);
|
||||
ctx.fillStyle = '#8ba3c0';
|
||||
ctx.fillRect(legendX, legendY, 10, 10);
|
||||
ctx.fillStyle = '#e8f1ff';
|
||||
ctx.font = '12px Inter';
|
||||
ctx.textAlign = 'left';
|
||||
ctx.fillText(`${shortName} (${count})`, legendX + 10, legendY);
|
||||
|
||||
legendX += ctx.measureText(`${shortName} (${count})`).width + 16;
|
||||
ctx.textBaseline = 'middle';
|
||||
ctx.fillText(`${short} — ${count}`, legendX + 16, legendY + 5);
|
||||
const pct = ((count / total) * 100).toFixed(0);
|
||||
ctx.fillStyle = '#5a7090';
|
||||
ctx.font = '10px JetBrains Mono';
|
||||
ctx.fillText(`${pct}%`, legendX + 200, legendY + 5);
|
||||
legendY += 24;
|
||||
});
|
||||
}
|
||||
|
||||
// Draw Migration Impact Bars - Before/After comparison
|
||||
function drawMigrationImpactBars(agentsWithHistory) {
|
||||
// 3. Migration Impact Bars — compare current model vs previous model from history
|
||||
function drawMigrationImpactBarsFromHistory(scoredAgents) {
|
||||
const canvas = document.getElementById('impactCanvas');
|
||||
if (!canvas) return;
|
||||
const ctx = canvas.getContext('2d');
|
||||
|
||||
// Collect impact data
|
||||
const w = 900, h = 340;
|
||||
canvas.width = w; canvas.height = h;
|
||||
|
||||
// Build impact data: agents with history showing current vs previous model score
|
||||
const impactData = [];
|
||||
agentsWithHistory.forEach(([name, agent]) => {
|
||||
if (agent.history && agent.history.length > 0) {
|
||||
const latest = agent.history[agent.history.length - 1];
|
||||
if (latest.fit_score_before != null && latest.fit_score_after != null) {
|
||||
scoredAgents.forEach(ag => {
|
||||
if (ag.history && ag.history.length > 0) {
|
||||
const latest = ag.history[ag.history.length - 1];
|
||||
if (latest.to && latest.from) {
|
||||
const after = ag.score; // current score
|
||||
// Estimate "before" score from previous model name
|
||||
const before = getModelScore(latest.from, after - 5);
|
||||
impactData.push({
|
||||
agent: name,
|
||||
before: latest.fit_score_before,
|
||||
after: latest.fit_score_after,
|
||||
delta: latest.fit_score_after - latest.fit_score_before
|
||||
agent: ag.name,
|
||||
before: before,
|
||||
after: after,
|
||||
delta: after - before,
|
||||
fromModel: (latest.from || '').split('/').pop() || 'unknown',
|
||||
toModel: ag.model.split('/').pop()
|
||||
});
|
||||
}
|
||||
} else if (agent.current?.benchmark?.fit_score > 0) {
|
||||
// No history but has score - show current only
|
||||
impactData.push({
|
||||
agent: name,
|
||||
before: agent.current.benchmark.fit_score,
|
||||
after: agent.current.benchmark.fit_score,
|
||||
delta: 0
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
const placeholder = document.getElementById('impactPlaceholder');
|
||||
if (impactData.length === 0) {
|
||||
canvas.style.display = 'none';
|
||||
placeholder.style.display = 'block';
|
||||
document.getElementById('impactPlaceholder').style.display = 'block';
|
||||
return;
|
||||
}
|
||||
canvas.style.display = 'block';
|
||||
placeholder.style.display = 'none';
|
||||
|
||||
// Setup canvas
|
||||
const dpr = window.devicePixelRatio || 1;
|
||||
const rect = canvas.getBoundingClientRect();
|
||||
canvas.width = rect.width * dpr;
|
||||
canvas.height = 240 * dpr;
|
||||
ctx.scale(dpr, dpr);
|
||||
|
||||
const w = rect.width;
|
||||
const h = 240;
|
||||
const padding = { top: 30, right: 20, bottom: 50, left: 45 };
|
||||
const chartW = w - padding.left - padding.right;
|
||||
const chartH = h - padding.top - padding.bottom;
|
||||
|
||||
document.getElementById('impactPlaceholder').style.display = 'none';
|
||||
ctx.clearRect(0, 0, w, h);
|
||||
|
||||
// Calculate dimensions
|
||||
const maxVal = Math.max(...impactData.flatMap(d => [d.before, d.after]), 100);
|
||||
const minVal = 0;
|
||||
const groupW = chartW / impactData.length;
|
||||
const barW = Math.min(24, groupW * 0.35);
|
||||
const scale = chartH / (maxVal - minVal);
|
||||
const padding = { top: 30, right: 40, bottom: 50, left: 45 };
|
||||
const chartW = w - padding.left - padding.right;
|
||||
const chartH = h - padding.top - padding.bottom;
|
||||
const maxScore = Math.max(...impactData.map(d => Math.max(d.before, d.after)), 100);
|
||||
const barGroupW = chartW / impactData.length;
|
||||
const barW = Math.min(22, barGroupW * 0.35);
|
||||
const scaleY = chartH / maxScore;
|
||||
|
||||
// Draw grid and Y-axis
|
||||
// Grid + labels
|
||||
ctx.strokeStyle = '#1e2d45';
|
||||
ctx.lineWidth = 1;
|
||||
ctx.font = '10px JetBrains Mono';
|
||||
ctx.fillStyle = '#5a7090';
|
||||
ctx.textAlign = 'right';
|
||||
|
||||
ctx.font = '10px JetBrains Mono';
|
||||
for (let i = 0; i <= 4; i++) {
|
||||
const y = padding.top + (i * chartH / 4);
|
||||
const val = Math.round(maxVal - (i * (maxVal - minVal) / 4));
|
||||
ctx.beginPath();
|
||||
ctx.moveTo(padding.left, y);
|
||||
ctx.lineTo(w - padding.right, y);
|
||||
ctx.stroke();
|
||||
const val = Math.round(maxScore - (i * maxScore / 4));
|
||||
ctx.beginPath(); ctx.moveTo(padding.left, y); ctx.lineTo(w - padding.right, y); ctx.stroke();
|
||||
ctx.fillText(val.toString(), padding.left - 8, y + 4);
|
||||
}
|
||||
|
||||
// Draw bars
|
||||
impactData.forEach((d, i) => {
|
||||
const groupX = padding.left + i * groupW + groupW / 2;
|
||||
const cx = padding.left + i * barGroupW + barGroupW / 2;
|
||||
|
||||
// Before bar (red)
|
||||
const beforeH = d.before * scale;
|
||||
const beforeY = padding.top + chartH - beforeH;
|
||||
ctx.fillStyle = 'rgba(255,71,87,0.75)';
|
||||
ctx.beginPath();
|
||||
ctx.roundRect(groupX - barW - 2, beforeY, barW, beforeH, [4, 4, 0, 0]);
|
||||
ctx.fill();
|
||||
// Before bar (dim red)
|
||||
const bh = d.before * scaleY;
|
||||
const by = padding.top + chartH - bh;
|
||||
ctx.fillStyle = 'rgba(255,71,87,.45)';
|
||||
ctx.fillRect(cx - barW - 3, by, barW, bh);
|
||||
|
||||
// After bar (green)
|
||||
const afterH = d.after * scale;
|
||||
const afterY = padding.top + chartH - afterH;
|
||||
ctx.fillStyle = d.delta >= 0 ? 'rgba(0,255,148,0.75)' : 'rgba(255,71,87,0.75)';
|
||||
ctx.beginPath();
|
||||
ctx.roundRect(groupX + 2, afterY, barW, afterH, [4, 4, 0, 0]);
|
||||
ctx.fill();
|
||||
// After bar (green if improved, red if regressed)
|
||||
const ah = d.after * scaleY;
|
||||
const ay = padding.top + chartH - ah;
|
||||
ctx.fillStyle = d.delta >= 0 ? 'rgba(0,255,148,.75)' : 'rgba(255,71,87,.75)';
|
||||
ctx.fillRect(cx + 3, ay, barW, ah);
|
||||
|
||||
// Delta indicator
|
||||
if (d.delta !== 0) {
|
||||
// Delta label
|
||||
if (Math.abs(d.delta) >= 1) {
|
||||
ctx.fillStyle = d.delta >= 0 ? '#00ff94' : '#ff4757';
|
||||
ctx.font = 'bold 9px JetBrains Mono';
|
||||
ctx.textAlign = 'center';
|
||||
const deltaY = Math.min(beforeY, afterY) - 8;
|
||||
ctx.fillText((d.delta > 0 ? '+' : '') + d.delta, groupX, deltaY);
|
||||
const dy = Math.min(ay, by) - 10;
|
||||
ctx.fillText((d.delta >= 0 ? '+' : '') + d.delta, cx, dy);
|
||||
}
|
||||
|
||||
// Agent label
|
||||
// Agent label (rotated)
|
||||
ctx.save();
|
||||
ctx.translate(cx, h - padding.bottom + 18);
|
||||
ctx.rotate(-0.35);
|
||||
ctx.fillStyle = '#8ba3c0';
|
||||
ctx.font = '9px JetBrains Mono';
|
||||
ctx.textAlign = 'center';
|
||||
const label = d.agent.length > 10 ? d.agent.substring(0, 10) : d.agent;
|
||||
ctx.fillText(label, groupX, h - padding.bottom + 16);
|
||||
ctx.font = '9px Inter';
|
||||
ctx.textAlign = 'left';
|
||||
const label = d.agent.length > 12 ? d.agent.substring(0, 10) + '..' : d.agent;
|
||||
ctx.fillText(label, 0, 0);
|
||||
ctx.restore();
|
||||
});
|
||||
|
||||
// Legend
|
||||
const legendY = 12;
|
||||
ctx.fillStyle = 'rgba(255,71,87,0.75)';
|
||||
ctx.fillRect(padding.left, legendY, 12, 10);
|
||||
ctx.fillStyle = '#e8f1ff';
|
||||
ctx.font = '10px Inter';
|
||||
ctx.textAlign = 'left';
|
||||
ctx.fillText('Before', padding.left + 18, legendY + 9);
|
||||
|
||||
ctx.fillStyle = 'rgba(0,255,148,0.75)';
|
||||
ctx.fillRect(padding.left + 80, legendY, 12, 10);
|
||||
ctx.fillStyle = '#e8f1ff';
|
||||
ctx.fillText('After', padding.left + 98, legendY + 9);
|
||||
const ly = 12;
|
||||
ctx.fillStyle = 'rgba(255,71,87,.45)'; ctx.fillRect(padding.left, ly, 12, 10);
|
||||
ctx.fillStyle = '#e8f1ff'; ctx.font = '10px Inter'; ctx.textAlign = 'left'; ctx.fillText('Before', padding.left + 18, ly + 9);
|
||||
ctx.fillStyle = 'rgba(0,255,148,.75)'; ctx.fillRect(padding.left + 70, ly, 12, 10);
|
||||
ctx.fillStyle = '#e8f1ff'; ctx.fillText('After', padding.left + 88, ly + 9);
|
||||
}
|
||||
|
||||
// Sync button handler
|
||||
function runSync() {
|
||||
const btn = document.querySelector('#impactSyncNote button');
|
||||
if (btn) btn.textContent = '⏳ Running...';
|
||||
setTimeout(() => {
|
||||
location.reload();
|
||||
}, 1500);
|
||||
}
|
||||
|
||||
// Filter Agents
|
||||
|
||||
Reference in New Issue
Block a user