Move ONNX inference to Web Worker for smooth animations

Run maia3 inference in a dedicated Web Worker so the main thread is
never blocked. Board animations, eval bar transitions, and UI
interactions stay fully responsive while the ~120ms batch inference
runs in the background. Restore all 21 rating levels (step 100).

- Add public/maia-worker.js with model loading, caching, and inference
- Add public/ort/ with onnxruntime-web WASM runtime files
- Refactor maia.ts to communicate via postMessage/onmessage
- Simplify useEngineAnalysis back to single batch (no chunking needed)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: BasedestBerry

public/maia-worker.js

@@ -0,0 +1,201 @@
+/**
+ * Maia3 Web Worker — runs ONNX inference off the main thread.
+ *
+ * Messages FROM main thread:
+ *   { type: 'init', modelUrl, modelVersion }
+ *   { type: 'download' }
+ *   { type: 'inference', id, tokens, eloSelfs, eloOppos, batchSize }
+ *
+ * Messages TO main thread:
+ *   { type: 'status', status }
+ *   { type: 'progress', progress }
+ *   { type: 'error', message, id? }
+ *   { type: 'inference-result', id, logitsMove, logitsValue }
+ */
+
+importScripts('/ort/ort.wasm.min.js')
+
+const ORT = ort
+ORT.env.wasm.wasmPaths = '/ort/'
+
+// ── IndexedDB storage (mirrors MaiaModelStorage) ─────────────────────────────
+
+const DB_NAME = 'MaiaModels'
+const STORE_NAME = 'models'
+const MODEL_KEY = 'maia-rapid-model'
+
+function openDB() {
+  return new Promise((resolve, reject) => {
+    const request = indexedDB.open(DB_NAME, 1)
+    request.onerror = () => reject(request.error)
+    request.onsuccess = () => resolve(request.result)
+    request.onupgradeneeded = (event) => {
+      const db = event.target.result
+      if (!db.objectStoreNames.contains(STORE_NAME)) {
+        db.createObjectStore(STORE_NAME, { keyPath: 'id' })
+      }
+    }
+  })
+}
+
+async function getCachedModel(modelUrl, modelVersion) {
+  const db = await openDB()
+  const tx = db.transaction([STORE_NAME], 'readonly')
+  const store = tx.objectStore(STORE_NAME)
+
+  const data = await new Promise((resolve, reject) => {
+    const req = store.get(MODEL_KEY)
+    req.onsuccess = () => resolve(req.result || null)
+    req.onerror = () => reject(req.error)
+  })
+
+  if (!data) return null
+
+  if (data.version && data.version !== modelVersion) {
+    const rwTx = db.transaction([STORE_NAME], 'readwrite')
+    rwTx.objectStore(STORE_NAME).delete(MODEL_KEY)
+    return null
+  }
+
+  return await data.data.arrayBuffer()
+}
+
+async function storeModel(modelUrl, modelVersion, buffer) {
+  const db = await openDB()
+  const tx = db.transaction([STORE_NAME], 'readwrite')
+  const store = tx.objectStore(STORE_NAME)
+
+  await new Promise((resolve, reject) => {
+    const req = store.put({
+      id: MODEL_KEY,
+      url: modelUrl,
+      version: modelVersion,
+      data: new Blob([buffer]),
+      timestamp: Date.now(),
+      size: buffer.byteLength,
+    })
+    req.onsuccess = () => resolve()
+    req.onerror = () => reject(req.error)
+  })
+}
+
+// ── Worker state ─────────────────────────────────────────────────────────────
+
+let session = null
+let modelUrl = null
+let modelVersion = null
+
+async function initSession(buffer) {
+  session = await ORT.InferenceSession.create(buffer)
+}
+
+// ── Message handler ──────────────────────────────────────────────────────────
+
+self.onmessage = async (e) => {
+  const msg = e.data
+
+  try {
+    switch (msg.type) {
+      case 'init': {
+        modelUrl = msg.modelUrl
+        modelVersion = msg.modelVersion
+        postMessage({ type: 'status', status: 'loading' })
+
+        const buffer = await getCachedModel(modelUrl, modelVersion)
+        if (buffer) {
+          await initSession(buffer)
+          postMessage({ type: 'status', status: 'ready' })
+        } else {
+          postMessage({ type: 'status', status: 'no-cache' })
+        }
+        break
+      }
+
+      case 'download': {
+        const response = await fetch(modelUrl)
+        if (!response.ok) throw new Error('Failed to fetch model')
+
+        const reader = response.body.getReader()
+        const contentLength = +(response.headers.get('Content-Length') || 0)
+        const chunks = []
+        let receivedLength = 0
+        let lastReportedProgress = 0
+
+        while (true) {
+          const { done, value } = await reader.read()
+          if (done) break
+          chunks.push(value)
+          receivedLength += value.length
+          const currentProgress = Math.floor(
+            (receivedLength / contentLength) * 100,
+          )
+          if (currentProgress >= lastReportedProgress + 10) {
+            postMessage({ type: 'progress', progress: currentProgress })
+            lastReportedProgress = currentProgress
+          }
+        }
+
+        const buffer = new Uint8Array(receivedLength)
+        let position = 0
+        for (const chunk of chunks) {
+          buffer.set(chunk, position)
+          position += chunk.length
+        }
+
+        await storeModel(modelUrl, modelVersion, buffer.buffer)
+        await initSession(buffer.buffer)
+        postMessage({ type: 'status', status: 'ready' })
+        break
+      }
+
+      case 'inference': {
+        if (!session) {
+          postMessage({
+            type: 'error',
+            message: 'Model not initialized',
+            id: msg.id,
+          })
+          return
+        }
+
+        const { id, tokens, eloSelfs, eloOppos, batchSize } = msg
+
+        const feeds = {
+          tokens: new ORT.Tensor('float32', new Float32Array(tokens), [
+            batchSize,
+            64,
+            12,
+          ]),
+          elo_self: new ORT.Tensor('float32', new Float32Array(eloSelfs), [
+            batchSize,
+          ]),
+          elo_oppo: new ORT.Tensor('float32', new Float32Array(eloOppos), [
+            batchSize,
+          ]),
+        }
+
+        const result = await session.run(feeds)
+
+        const logitsMove = new Float32Array(result.logits_move.data)
+        const logitsValue = new Float32Array(result.logits_value.data)
+
+        postMessage(
+          {
+            type: 'inference-result',
+            id,
+            logitsMove: logitsMove.buffer,
+            logitsValue: logitsValue.buffer,
+          },
+          [logitsMove.buffer, logitsValue.buffer],
+        )
+        break
+      }
+    }
+  } catch (err) {
+    postMessage({
+      type: 'error',
+      message: err.message || 'Unknown worker error',
+      id: msg.id,
+    })
+  }
+}