[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_157":158,"_159":160},"routes/_app.x.$handle.$name",{"_96":95,"_32":44,"_104":86,"_27":105,"_13":14,"_106":-5,"_11":107,"_108":109,"_110":111,"_112":113,"_114":115,"_20":-5,"_116":-5,"_117":-5,"_118":25,"_119":120,"_121":122,"_123":-5,"_124":-5,"_125":126},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_42":43,"_32":44,"_37":45,"_46":-5,"_47":25,"_48":49,"_50":49,"_51":-5,"_52":-5,"_18":53,"_54":-5,"_55":25,"_56":-5,"_57":-5,"_58":59,"_60":61,"_62":63,"_64":65,"_66":67,"_68":-5,"_69":70,"_71":70,"_72":73,"_74":73,"_75":73,"_76":70,"_77":70,"_78":79,"_80":81,"_82":83,"_84":25},"version",49,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":36,"_37":38,"_39":14,"_40":41,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","b50311de-33a4-11f1-8e9d-42b51c65c3df","name","fileType","http","content","/**\n * Belt backend — paste into a Val Town HTTP val.\n * Secrets: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY,\n * ELEVENLABS_API_KEY, SHARED_SECRET\n *\n * export default matches Val Town’s HTTP trigger shape.\n */\nimport sqlite from \"https://esm.town/v/std/sqlite/main.ts\";\nimport { blob } from \"https://esm.town/v/std/blob\";\n\n/** Val Town / Deno — env secrets */\ndeclare const Deno: { env: { get(key: string): string | undefined } };\n\n/**\n * Val Town exposes secrets via the val’s Environment Variables sidebar.\n * Docs list both `Deno.env.get` and `process.env`; some runtimes only populate one.\n */\nfunction getEnv(name: string): string | undefined {\n const fromDeno = Deno.env.get(name);\n if (fromDeno != null && String(fromDeno).length > 0) return String(fromDeno);\n\n const proc = (globalThis as unknown as {\n process?: { env?: Record };\n })\n .process;\n const fromProcess = proc?.env?.[name];\n if (typeof fromProcess === \"string\" && fromProcess.length > 0) {\n return fromProcess;\n }\n\n return undefined;\n}\n\n// --- Tunables (user-facing text to all models on every image) ---\n\nexport const USER_PROMPT =\n `Describe the question, problem, or task you see clearly. Then solve it fully, step by step, in a way that would get full marks from the grader. Dictate exactly what I should write on the answer sheet.\n\nRules for how you dictate:\n\nMath and formulas: Write everything in fully spoken English sentences. No Markdown, no LaTeX, no raw symbols.\n\nGreek letters and special symbols: I do not know Greek letters. Whenever one appears, first tell me its name, then describe exactly what to draw on paper. For example:\n- \"epsilon naught — epsilon looks like a small backwards 3, then write a small zero at the bottom right as a subscript\"\n- \"mu naught — mu looks like a lowercase u with a tail going down on the left side, then write a small zero at the bottom right\"\n- \"delta — delta looks like a small triangle, like a lowercase d but with a curvy top that loops around\"\n- \"theta — theta looks like an O with a horizontal line through the middle\"\n- \"phi — phi looks like an O with a vertical line going through it from top to bottom\"\n- \"omega — omega looks like a lowercase w but rounder, like a small horseshoe shape\"\n- \"sigma — capital sigma looks like a sideways Z\" or \"lowercase sigma looks like a small o with a tail going to the right\"\n- \"the integral sign — draw a tall stretched-out S shape\"\n- \"partial derivative sign — it looks like a backwards 6\"\nDo this for every unfamiliar symbol, every single time it appears.\n\nVectors: Tell me explicitly when to draw an arrow on top of a letter. For example: \"write a capital E with a small right-pointing arrow sitting on top of the letter.\" For a unit vector, say: \"write r with a small pointy hat shape sitting on top, like a tiny upside-down V on top of the letter.\"\n\nUnits: Always tell me the units and how to write them. For example: \"write m slash s squared, meaning a lowercase m, then a forward slash, then a lowercase s, then a small 2 up in the top right corner of the s.\"\n\nSubscripts and superscripts: Describe the physical placement. For example: \"write a capital V, then down at the bottom right write a small letter A\" for V subscript A. Or \"write x, then up at the top right write a small 2\" for x squared.\n\nDiagrams and drawings: If anything needs to be drawn — a free body diagram, a circuit, a field line sketch, a graph, a Gaussian surface, a ray diagram — describe it in full detail. Tell me what shapes to draw, where to place arrows, how to label axes, what directions things point, where to put plus and minus signs.\n\nSign conventions and reasoning: State which direction is positive, explain sign choices, and include the physical reasoning the grader expects to see.\n\nFinal answer: Clearly state the final answer with its value, direction if applicable, and correct units. Tell me to box it or underline it.`;\n\n/** EASY TEST — keep in sync with MODELS.txt / provider docs. */\nconst MODEL_CLAUDE = \"claude-haiku-4-5-20251001\";\nconst MODEL_OPENAI = \"gpt-5.4-nano\";\nconst MODEL_GEMINI = \"gemini-2.5-flash\";\nconst MODEL_GEMINI_FALLBACKS = [\n \"gemini-2.5-flash-lite\",\n \"gemini-1.5-flash\",\n] as const;\n/**\n * Anthropic enforces ~5MB on the base64 `data` string (not decoded bytes).\n * 3.75MB raw ≈ 5MB base64; stay well under that.\n */\nconst CLAUDE_IMAGE_MAX_BASE64_CHARS = 5 * 1024 * 1024;\nconst CLAUDE_IMAGE_MAX_DECODED_BYTES =\n Math.floor((CLAUDE_IMAGE_MAX_BASE64_CHARS * 3) / 4) - 64 * 1024;\nconst CLAUDE_IMAGE_TARGET_BYTES = Math.floor(\n CLAUDE_IMAGE_MAX_DECODED_BYTES * 0.82,\n);\nconst CLAUDE_TOO_LARGE_PREFIX = \"__CLAUDE_IMAGE_TOO_LARGE__:\";\nconst CLAUDE_PRIOR_IMAGE_OMITTED =\n \"\\n\\n[Earlier screenshot in this session — image omitted to stay within Claude API limits.]\";\n\n/** Gemini output cap on /capture. Override: GEMINI_MAX_OUTPUT_TOKENS. */\nconst GEMINI_MAX_OUTPUT_TOKENS_DEFAULT = 16_384;\n\nfunction geminiCaptureMaxOutputTokens(): number {\n return parseNumberEnv(\n \"GEMINI_MAX_OUTPUT_TOKENS\",\n GEMINI_MAX_OUTPUT_TOKENS_DEFAULT,\n 256,\n 65_536,\n );\n}\n\n/** OpenAI Responses API output cap on /capture. Override: OPENAI_MAX_OUTPUT_TOKENS. */\nfunction openaiCaptureMaxOutputTokens(): number {\n return parseNumberEnv(\"OPENAI_MAX_OUTPUT_TOKENS\", 8_192, 256, 32_768);\n}\n\n/** ElevenLabs request size (after normalize). Override: ELEVENLABS_MAX_CHARACTERS. */\nfunction getElevenLabsMaxCharacters(): number {\n return parseNumberEnv(\"ELEVENLABS_MAX_CHARACTERS\", 9_500, 500, 10_000);\n}\n\n/**\n * Raw assistant text cap before TTS. Chunked synthesis allows longer Gemini answers.\n * Override: GEMINI_TTS_MAX_CHARS / OPENAI_TTS_MAX_CHARS.\n */\nfunction assistantMaxCharsForTts(model: LLMKey): number {\n if (model === \"gemini\") {\n return parseNumberEnv(\"GEMINI_TTS_MAX_CHARS\", 24_000, 500, 80_000);\n }\n if (model === \"openai\") {\n return parseNumberEnv(\"OPENAI_TTS_MAX_CHARS\", 8_000, 500, 50_000);\n }\n return 50_000;\n}\n\ntype LLMKey = \"claude\" | \"openai\" | \"gemini\";\n\ntype UserMsg = {\n role: \"user\";\n text: string;\n image_blob_key: string;\n image_mime: string;\n};\ntype AsstMsg = { role: \"assistant\"; text: string };\ntype Msg = UserMsg | AsstMsg;\n\ntype SessionRow = {\n id: string;\n created_at: number;\n updated_at: number;\n active_llm: LLMKey;\n history_claude: string;\n history_openai: string;\n history_gemini: string;\n audio_claude: string | null;\n audio_openai: string | null;\n audio_gemini: string | null;\n /** JSON string[] of blob keys in capture order (per model). */\n audio_claude_keys: string;\n audio_openai_keys: string;\n audio_gemini_keys: string;\n};\n\nfunction jsonResponse(\n data: unknown,\n status = 200,\n extraHeaders: HeadersInit = {},\n): Response {\n return new Response(JSON.stringify(data), {\n status,\n headers: {\n \"Content-Type\": \"application/json; charset=utf-8\",\n ...extraHeaders,\n },\n });\n}\n\nfunction bad(msg: string, status = 400): Response {\n return jsonResponse({ error: msg }, status);\n}\n\nfunction unauthorized(): Response {\n return new Response(\"Unauthorized\", { status: 401 });\n}\n\nfunction isUuid(id: string): boolean {\n return /^[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}$/i\n .test(\n id,\n );\n}\n\nasync function ensureSchema(): Promise {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS sessions (\n id TEXT PRIMARY KEY,\n created_at INTEGER NOT NULL,\n updated_at INTEGER NOT NULL,\n active_llm TEXT NOT NULL DEFAULT 'claude',\n history_claude TEXT NOT NULL DEFAULT '[]',\n history_openai TEXT NOT NULL DEFAULT '[]',\n history_gemini TEXT NOT NULL DEFAULT '[]',\n audio_claude TEXT,\n audio_openai TEXT,\n audio_gemini TEXT\n )\n `);\n await migrateSessionsAudioLists();\n}\n\nfunction parseStringArrayJson(s: string | null | undefined): string[] {\n try {\n const j = JSON.parse(s ?? \"[]\") as unknown;\n if (!Array.isArray(j)) return [];\n return j.filter((x): x is string => typeof x === \"string\" && x.length > 0);\n } catch {\n return [];\n }\n}\n\n/** Append a new blob key; seed from legacy single-pointer column when the list is still empty. */\nfunction appendAudioKeysJson(\n prevKeysJson: string | null | undefined,\n prevLatestPointer: string | null,\n newKey: string | null,\n): string {\n const arr = parseStringArrayJson(prevKeysJson);\n if (arr.length === 0 && prevLatestPointer) arr.push(prevLatestPointer);\n if (newKey) arr.push(newKey);\n return JSON.stringify(arr);\n}\n\nfunction isBeltSessionAudioBlobKey(key: string): boolean {\n return /^belt\\/[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}\\//i\n .test(\n key,\n );\n}\n\nasync function migrateSessionsAudioLists(): Promise {\n for (\n const col of [\n \"audio_claude_keys\",\n \"audio_openai_keys\",\n \"audio_gemini_keys\",\n ] as const\n ) {\n try {\n await sqlite.execute({\n sql:\n `ALTER TABLE sessions ADD COLUMN ${col} TEXT NOT NULL DEFAULT '[]'`,\n });\n } catch {\n /* column already exists */\n }\n }\n const res = await sqlite.execute({\n sql: `SELECT id, audio_claude, audio_openai, audio_gemini,\n audio_claude_keys, audio_openai_keys, audio_gemini_keys\n FROM sessions`,\n });\n for (const raw of res.rows ?? []) {\n const r = raw as {\n id: string;\n audio_claude: string | null;\n audio_openai: string | null;\n audio_gemini: string | null;\n audio_claude_keys: string;\n audio_openai_keys: string;\n audio_gemini_keys: string;\n };\n const kc = parseStringArrayJson(r.audio_claude_keys);\n const ko = parseStringArrayJson(r.audio_openai_keys);\n const kg = parseStringArrayJson(r.audio_gemini_keys);\n let nkc = r.audio_claude_keys;\n let nko = r.audio_openai_keys;\n let nkg = r.audio_gemini_keys;\n if (kc.length === 0 && r.audio_claude) {\n nkc = JSON.stringify([r.audio_claude]);\n }\n if (ko.length === 0 && r.audio_openai) {\n nko = JSON.stringify([r.audio_openai]);\n }\n if (kg.length === 0 && r.audio_gemini) {\n nkg = JSON.stringify([r.audio_gemini]);\n }\n if (\n nkc !== r.audio_claude_keys || nko !== r.audio_openai_keys ||\n nkg !== r.audio_gemini_keys\n ) {\n await sqlite.execute({\n sql:\n `UPDATE sessions SET audio_claude_keys = ?, audio_openai_keys = ?, audio_gemini_keys = ?\n WHERE id = ?`,\n args: [nkc, nko, nkg, r.id],\n });\n }\n }\n}\n\nfunction parseHist(s: string): Msg[] {\n try {\n const j = JSON.parse(s) as unknown;\n if (!Array.isArray(j)) return [];\n return j as Msg[];\n } catch {\n return [];\n }\n}\n\nfunction getAnthropicKey(): string | undefined {\n return getEnv(\"ANTHROPIC_API_KEY\");\n}\nfunction getOpenAIKey(): string | undefined {\n return getEnv(\"OPENAI_API_KEY\");\n}\nfunction getGoogleKey(): string | undefined {\n return getEnv(\"GOOGLE_API_KEY\");\n}\nfunction getElevenLabsKey(): string | undefined {\n return getEnv(\"ELEVENLABS_API_KEY\");\n}\nfunction getElevenLabsVoiceId(): string {\n // Default is a clear, neutral voice; override via ELEVENLABS_VOICE_ID.\n return getEnv(\"ELEVENLABS_VOICE_ID\") ?? \"EXAVITQu4vr4xnSDxMaL\";\n}\nfunction parseBoolEnv(name: string, fallback: boolean): boolean {\n const raw = getEnv(name);\n if (!raw) return fallback;\n const v = raw.trim().toLowerCase();\n if (v === \"1\" || v === \"true\" || v === \"yes\" || v === \"on\") return true;\n if (v === \"0\" || v === \"false\" || v === \"no\" || v === \"off\") return false;\n return fallback;\n}\nfunction parseNumberEnv(\n name: string,\n fallback: number,\n min: number,\n max: number,\n): number {\n const raw = getEnv(name);\n if (!raw) return fallback;\n const n = Number(raw);\n if (!Number.isFinite(n)) return fallback;\n return Math.max(min, Math.min(max, n));\n}\nfunction getElevenLabsVoiceSettings(): {\n stability: number;\n similarity_boost: number;\n style: number;\n use_speaker_boost: boolean;\n} {\n return {\n // More stable, less jittery default delivery.\n stability: parseNumberEnv(\"ELEVENLABS_STABILITY\", 0.82, 0, 1),\n similarity_boost: parseNumberEnv(\"ELEVENLABS_SIMILARITY_BOOST\", 0.75, 0, 1),\n style: parseNumberEnv(\"ELEVENLABS_STYLE\", 0.1, 0, 1),\n use_speaker_boost: parseBoolEnv(\"ELEVENLABS_USE_SPEAKER_BOOST\", true),\n };\n}\nfunction getElevenLabsSpeed(): number {\n // Slightly slower than real-time for clearer listening.\n return parseNumberEnv(\"ELEVENLABS_SPEED\", 0.86, 0.7, 1.2);\n}\nfunction getSharedSecret(): string | undefined {\n return getEnv(\"SHARED_SECRET\");\n}\n\n/** Returns a Response if the request is not allowed; `null` if OK. */\nfunction requireAuth(req: Request): Response | null {\n const want = getSharedSecret();\n if (!want) return secretNotConfigured();\n if (req.headers.get(\"X-Key\") !== want) return unauthorized();\n return null;\n}\n\n/** Use when auth is required but SHARED_SECRET was never set in the val’s env. */\nfunction secretNotConfigured(): Response {\n return jsonResponse(\n {\n error:\n \"SHARED_SECRET is not set. Open this val → Environment Variables (sidebar) and add SHARED_SECRET, then redeploy or save.\",\n },\n 503,\n );\n}\n\n/** Chunked base64 — avoids call stack limits on large screenshots. */\nfunction bytesToBase64(u8: Uint8Array): string {\n const chunk = 0x8000;\n const parts: string[] = [];\n for (let i = 0; i < u8.length; i += chunk) {\n parts.push(\n String.fromCharCode.apply(\n null,\n u8.subarray(i, i + chunk) as unknown as number[],\n ),\n );\n }\n return btoa(parts.join(\"\"));\n}\n\nfunction anthropicImageMediaType(\n mime: string,\n): \"image/jpeg\" | \"image/webp\" | \"image/png\" {\n const m = mime.toLowerCase();\n if (m === \"image/jpeg\" || m === \"image/jpg\") return \"image/jpeg\";\n if (m === \"image/webp\") return \"image/webp\";\n return \"image/png\";\n}\n\nfunction base64EncodedLength(decodedBytes: number): number {\n return Math.ceil(decodedBytes / 3) * 4;\n}\n\n/** True when both decoded size and base64 length fit Anthropic’s cap. */\nfunction claudeImageWithinLimits(u8: Uint8Array, b64?: string): boolean {\n if (u8.byteLength > CLAUDE_IMAGE_MAX_DECODED_BYTES) return false;\n const b64Len = b64 != null ? b64.length : base64EncodedLength(u8.byteLength);\n return b64Len <= CLAUDE_IMAGE_MAX_BASE64_CHARS;\n}\n\nfunction claudeBase64WithinLimits(b64: string): boolean {\n return b64.length <= CLAUDE_IMAGE_MAX_BASE64_CHARS;\n}\n\nfunction isJpegBytes(u8: Uint8Array): boolean {\n return u8.byteLength >= 3 && u8[0] === 0xff && u8[1] === 0xd8 &&\n u8[2] === 0xff;\n}\n\nfunction downscaleRgba(\n src: Uint8Array,\n srcW: number,\n srcH: number,\n dstW: number,\n dstH: number,\n): Uint8Array {\n const dst = new Uint8Array(dstW * dstH * 4);\n const xRatio = srcW / dstW;\n const yRatio = srcH / dstH;\n for (let y = 0; y < dstH; y++) {\n const sy = Math.min(srcH - 1, Math.floor(y * yRatio));\n for (let x = 0; x < dstW; x++) {\n const sx = Math.min(srcW - 1, Math.floor(x * xRatio));\n const si = (sy * srcW + sx) * 4;\n const di = (y * dstW + x) * 4;\n dst[di] = src[si]!;\n dst[di + 1] = src[si + 1]!;\n dst[di + 2] = src[si + 2]!;\n dst[di + 3] = src[si + 3]!;\n }\n }\n return dst;\n}\n\n/** Pure-JS JPEG path (Android /capture always sends JPEG). */\nasync function shrinkJpegWithJpegJs(\n input: Uint8Array,\n targetBytes: number,\n): Promise {\n const jpeg = await import(\"https://esm.sh/jpeg-js@0.4.4\");\n let decoded = jpeg.decode(input, { useTArray: true });\n let quality = 82;\n let scale = 1;\n let best = input;\n for (let attempt = 0; attempt < 28; attempt++) {\n const w = Math.max(240, Math.floor(decoded.width * scale));\n const h = Math.max(240, Math.floor(decoded.height * scale));\n const data = w < decoded.width || h < decoded.height\n ? downscaleRgba(decoded.data, decoded.width, decoded.height, w, h)\n : decoded.data;\n const encoded = jpeg.encode({ data, width: w, height: h }, quality);\n best = encoded.data;\n if (claudeImageWithinLimits(best)) return best;\n if (best.byteLength <= targetBytes) return best;\n if (quality > 48) {\n quality -= 8;\n continue;\n }\n quality = 82;\n scale *= 0.78;\n decoded = jpeg.decode(best, { useTArray: true });\n }\n return best;\n}\n\nasync function shrinkWithImageScript(\n input: Uint8Array,\n targetBytes: number,\n): Promise {\n const { Image } = await import(\n \"https://deno.land/x/imagescript@1.3.0/mod.ts\"\n );\n const decoded = await Image.decode(input);\n let quality = 85;\n let scale = 1;\n let best = input;\n for (let attempt = 0; attempt < 28; attempt++) {\n const w = Math.max(240, Math.floor(decoded.width * scale));\n const h = Math.max(240, Math.floor(decoded.height * scale));\n const frame = scale < 1 || w < decoded.width || h < decoded.height\n ? decoded.resize(w, h)\n : decoded;\n const jpeg = await frame.encodeJPEG(quality);\n best = jpeg;\n if (claudeImageWithinLimits(best)) return jpeg;\n if (jpeg.byteLength <= targetBytes) return jpeg;\n if (quality > 48) {\n quality -= 8;\n continue;\n }\n quality = 85;\n scale *= 0.78;\n }\n return best;\n}\n\n/** Downscale / re-encode until under decoded + base64 limits (best effort). */\nasync function shrinkImageUnderBytes(\n input: Uint8Array,\n targetBytes: number,\n): Promise {\n if (claudeImageWithinLimits(input)) return input;\n\n if (isJpegBytes(input)) {\n try {\n const jpegOut = await shrinkJpegWithJpegJs(input, targetBytes);\n if (claudeImageWithinLimits(jpegOut)) return jpegOut;\n if (jpegOut.byteLength < input.byteLength) return jpegOut;\n } catch {\n /* try ImageScript */\n }\n }\n\n try {\n const out = await shrinkWithImageScript(input, targetBytes);\n if (claudeImageWithinLimits(out)) return out;\n if (out.byteLength < input.byteLength) return out;\n } catch {\n /* fall through */\n }\n\n if (isJpegBytes(input)) {\n try {\n return await shrinkJpegWithJpegJs(input, Math.floor(targetBytes * 0.5));\n } catch {\n return input;\n }\n }\n return input;\n}\n\nasync function normalizeImageForClaude(\n input: Uint8Array,\n mime: string,\n): Promise<{ buf: Uint8Array; mime: string }> {\n let buf = input;\n let outMime = anthropicImageMediaType(mime);\n if (claudeImageWithinLimits(buf)) {\n return { buf, mime: outMime };\n }\n\n let target = CLAUDE_IMAGE_TARGET_BYTES;\n for (let round = 0; round < 10; round++) {\n const shrunk = await shrinkImageUnderBytes(buf, target);\n buf = shrunk;\n outMime = \"image/jpeg\";\n const b64 = bytesToBase64(buf);\n if (claudeImageWithinLimits(buf, b64)) {\n return { buf, mime: outMime };\n }\n target = Math.floor(target * 0.6);\n }\n\n return { buf, mime: outMime };\n}\n\nasync function prepareCaptureImage(\n input: Uint8Array,\n mime: string,\n): Promise<{ buf: Uint8Array; mime: string }> {\n return normalizeImageForClaude(input, mime);\n}\n\nasync function blobToBase64(\n key: string,\n mimeHint?: string,\n): Promise<{ b64: string; mime: string; bytes: number }> {\n const body = await blob.get(key);\n const ab = await body.arrayBuffer();\n const u8 = new Uint8Array(ab);\n const mime = mimeHint && mimeHint.startsWith(\"image/\")\n ? mimeHint\n : body.type && body.type !== \"application/octet-stream\"\n ? body.type\n : \"image/png\";\n return { b64: bytesToBase64(u8), mime, bytes: u8.byteLength };\n}\n\n/** Claude-only: shrink blobs before base64 (checks base64 length, not just raw bytes). */\nasync function blobToClaudeImageBase64(\n key: string,\n mimeHint?: string,\n): Promise<\n {\n b64: string;\n media: \"image/jpeg\" | \"image/webp\" | \"image/png\";\n bytes: number;\n }\n> {\n const body = await blob.get(key);\n const ab = await body.arrayBuffer();\n const mime = mimeHint && mimeHint.startsWith(\"image/\")\n ? mimeHint\n : body.type && body.type !== \"application/octet-stream\"\n ? body.type\n : \"image/png\";\n const buf = new Uint8Array(ab);\n if (claudeImageWithinLimits(buf)) {\n return {\n b64: bytesToBase64(buf),\n media: anthropicImageMediaType(mime),\n bytes: buf.byteLength,\n };\n }\n const { buf: norm, mime: outMime } = await normalizeImageForClaude(buf, mime);\n const b64 = bytesToBase64(norm);\n return {\n b64,\n media: anthropicImageMediaType(outMime),\n bytes: norm.byteLength,\n };\n}\n\n/** `thinking.type: \"adaptive\"` and `output_config` are rejected by Claude 3.x and Haiku-class endpoints. */\nfunction claudeSupportsAgenticMessagesParams(model: string): boolean {\n return (\n model.startsWith(\"claude-opus-4\") ||\n model.startsWith(\"claude-sonnet-4\") ||\n model.startsWith(\"claude-mythos\")\n );\n}\n\n/** Default `medium` — Anthropic’s speed/quality balance for Sonnet 4.6. Override: CLAUDE_EFFORT. */\nfunction claudeOutputEffort(): \"low\" | \"medium\" | \"high\" | \"max\" {\n const raw = getEnv(\"CLAUDE_EFFORT\")?.trim().toLowerCase();\n if (raw === \"low\" || raw === \"medium\" || raw === \"high\" || raw === \"max\") {\n return raw;\n }\n return \"medium\";\n}\n\n/** Adaptive thinking adds latency; off by default. Set CLAUDE_THINKING=adaptive to re-enable. */\nfunction claudeUsesAdaptiveThinking(): boolean {\n const raw = getEnv(\"CLAUDE_THINKING\")?.trim().toLowerCase();\n return raw === \"adaptive\" || raw === \"1\" || raw === \"true\" || raw === \"on\";\n}\n\n/** Caps generation time on /capture; enough for dictated answers. Override: CLAUDE_MAX_OUTPUT_TOKENS. */\nfunction claudeCaptureMaxOutputTokens(model: string): number {\n const cap = parseNumberEnv(\"CLAUDE_MAX_OUTPUT_TOKENS\", 16_384, 1024, 64_000);\n return Math.min(claudeMaxOutputTokensForModel(model), cap);\n}\n\n/** `max_tokens` must not exceed the model’s Messages API output cap (Anthropic returns 400 otherwise). */\nfunction claudeMaxOutputTokensForModel(model: string): number {\n if (model.startsWith(\"claude-haiku-4\")) return 64_000;\n if (model.startsWith(\"claude-sonnet-4\")) return 64_000;\n if (\n model.startsWith(\"claude-opus-4-7\") || model.startsWith(\"claude-opus-4-6\")\n ) return 128_000;\n if (model.startsWith(\"claude-opus-4-5\")) return 64_000;\n if (model.includes(\"opus-4-1\")) return 32_000;\n if (model.startsWith(\"claude-opus-4\")) return 32_000;\n if (model.startsWith(\"claude-mythos\")) return 64_000;\n return 64_000;\n}\n\nasync function callClaude(\n messages: Msg[],\n apiKey: string,\n): Promise {\n let lastUserIndex = -1;\n for (let i = 0; i < messages.length; i++) {\n if (messages[i].role === \"user\") lastUserIndex = i;\n }\n\n const anthropicMsgs: Array<{ role: \"user\" | \"assistant\"; content: unknown }> =\n [];\n for (let i = 0; i < messages.length; i++) {\n const m = messages[i];\n if (m.role === \"user\") {\n const includeImage = i === lastUserIndex;\n if (!includeImage) {\n anthropicMsgs.push({\n role: \"user\",\n content: [{\n type: \"text\",\n text: `${m.text}${CLAUDE_PRIOR_IMAGE_OMITTED}`,\n }],\n });\n continue;\n }\n const { b64, media, bytes } = await blobToClaudeImageBase64(\n m.image_blob_key,\n m.image_mime,\n );\n if (!claudeBase64WithinLimits(b64)) {\n return `${CLAUDE_TOO_LARGE_PREFIX}${bytes}:${b64.length}`;\n }\n anthropicMsgs.push({\n role: \"user\",\n content: [\n { type: \"text\", text: m.text },\n {\n type: \"image\",\n source: { type: \"base64\", media_type: media, data: b64 },\n },\n ],\n });\n } else {\n anthropicMsgs.push({\n role: \"assistant\",\n content: [{ type: \"text\", text: m.text }],\n });\n }\n }\n const body: Record = {\n model: MODEL_CLAUDE,\n max_tokens: claudeCaptureMaxOutputTokens(MODEL_CLAUDE),\n messages: anthropicMsgs,\n };\n if (\n claudeUsesAdaptiveThinking() &&\n claudeSupportsAgenticMessagesParams(MODEL_CLAUDE)\n ) {\n body.thinking = { type: \"adaptive\" };\n body.output_config = { effort: claudeOutputEffort() };\n }\n const r = await fetch(\"https://api.anthropic.com/v1/messages\", {\n method: \"POST\",\n headers: {\n \"x-api-key\": apiKey,\n \"anthropic-version\": \"2023-06-01\",\n \"content-type\": \"application/json\",\n },\n body: JSON.stringify(body),\n });\n const raw = await r.text();\n if (!r.ok) return `[claude error] ${r.status}: ${raw.slice(0, 400)}`;\n try {\n const data = JSON.parse(raw) as {\n content?: Array<{ type: string; text?: string }>;\n };\n const parts: string[] = [];\n for (const block of data.content ?? []) {\n if (block?.type === \"text\" && typeof block.text === \"string\") {\n parts.push(block.text);\n }\n }\n if (parts.length > 0) return parts.join(\"\");\n return `[claude unexpected] ${raw.slice(0, 400)}`;\n } catch {\n return `[claude parse] ${raw.slice(0, 400)}`;\n }\n}\n\nfunction extractOpenAIResponsesText(data: unknown): string | null {\n if (typeof data !== \"object\" || data === null) return null;\n const d = data as Record;\n if (typeof d.output_text === \"string\" && d.output_text.length > 0) {\n return d.output_text;\n }\n const out = d.output;\n if (!Array.isArray(out)) return null;\n const chunks: string[] = [];\n for (const item of out) {\n if (typeof item !== \"object\" || item === null) continue;\n const it = item as Record;\n if (it.type === \"message\" && Array.isArray(it.content)) {\n for (const part of it.content) {\n if (typeof part !== \"object\" || part === null) continue;\n const p = part as Record;\n if (p.type === \"output_text\" && typeof p.text === \"string\") {\n chunks.push(p.text);\n }\n }\n }\n }\n return chunks.length > 0 ? chunks.join(\"\") : null;\n}\n\n/** Full `gpt-5.4` can use `xhigh`; mini/nano are faster with `high` / `medium`. */\nfunction openaiReasoningEffort(model: string): \"medium\" | \"high\" | \"xhigh\" {\n if (model.includes(\"nano\")) return \"medium\";\n if (model.includes(\"mini\")) return \"high\";\n return \"xhigh\";\n}\n\nasync function callOpenAI(messages: Msg[], apiKey: string): Promise {\n type InputPart =\n | { type: \"input_text\"; text: string }\n | { type: \"input_image\"; image_url: string; detail?: string };\n const input: Array<{\n role: \"user\" | \"assistant\";\n content: string | InputPart[];\n }> = [];\n for (const m of messages) {\n if (m.role === \"user\") {\n const { b64, mime } = await blobToBase64(m.image_blob_key, m.image_mime);\n const mt = mime === \"image/jpeg\" || mime === \"image/jpg\"\n ? \"image/jpeg\"\n : mime === \"image/webp\"\n ? \"image/webp\"\n : \"image/png\";\n input.push({\n role: \"user\",\n content: [\n { type: \"input_text\", text: m.text },\n {\n type: \"input_image\",\n image_url: `data:${mt};base64,${b64}`,\n detail: \"high\",\n },\n ],\n });\n } else {\n input.push({ role: \"assistant\", content: m.text });\n }\n }\n const r = await fetch(\"https://api.openai.com/v1/responses\", {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${apiKey}`,\n \"content-type\": \"application/json\",\n },\n body: JSON.stringify({\n model: MODEL_OPENAI,\n reasoning: { effort: openaiReasoningEffort(MODEL_OPENAI) },\n max_output_tokens: openaiCaptureMaxOutputTokens(),\n input,\n }),\n });\n const raw = await r.text();\n if (!r.ok) return `[openai error] ${r.status}: ${raw.slice(0, 400)}`;\n try {\n const data = JSON.parse(raw) as unknown;\n const t = extractOpenAIResponsesText(data);\n if (typeof t === \"string\" && t.length > 0) return t;\n return `[openai unexpected] ${raw.slice(0, 400)}`;\n } catch {\n return `[openai parse] ${raw.slice(0, 400)}`;\n }\n}\n\nasync function callGemini(messages: Msg[], apiKey: string): Promise {\n type GPart = {\n text?: string;\n inline_data?: { mime_type: string; data: string };\n };\n type GContent = { role: \"user\" | \"model\"; parts: GPart[] };\n const contents: GContent[] = [];\n for (const m of messages) {\n if (m.role === \"user\") {\n const { b64, mime } = await blobToBase64(m.image_blob_key, m.image_mime);\n const mt = mime === \"image/jpeg\" || mime === \"image/jpg\"\n ? \"image/jpeg\"\n : mime === \"image/webp\"\n ? \"image/webp\"\n : \"image/png\";\n contents.push({\n role: \"user\",\n parts: [\n { text: m.text },\n { inline_data: { mime_type: mt, data: b64 } },\n ],\n });\n } else {\n contents.push({\n role: \"model\",\n parts: [{ text: m.text }],\n });\n }\n }\n const models = [MODEL_GEMINI, ...MODEL_GEMINI_FALLBACKS];\n let lastErr = \"[gemini error] unknown\";\n\n for (let i = 0; i < models.length; i++) {\n const model = models[i];\n const url =\n `https://generativelanguage.googleapis.com/v1beta/models/${model}:generateContent?key=${\n encodeURIComponent(apiKey)\n }`;\n const r = await fetch(url, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({\n contents,\n generationConfig: { maxOutputTokens: geminiCaptureMaxOutputTokens() },\n }),\n });\n const raw = await r.text();\n if (r.ok) {\n try {\n const data = JSON.parse(raw) as {\n candidates?: Array<\n { content?: { parts?: Array<{ text?: string }> } }\n >;\n };\n const parts = data.candidates?.[0]?.content?.parts;\n const text = parts?.map((p) => p.text ?? \"\").join(\"\") ?? \"\";\n if (text) return text;\n lastErr = `[gemini unexpected ${model}] ${raw.slice(0, 400)}`;\n } catch {\n lastErr = `[gemini parse ${model}] ${raw.slice(0, 400)}`;\n }\n continue;\n }\n\n lastErr = `[gemini error ${model}] ${r.status}: ${raw.slice(0, 400)}`;\n const shouldFallback = i < models.length - 1 &&\n isGeminiOverloadedOrUnavailable(r.status, raw);\n if (!shouldFallback) return lastErr;\n }\n\n return lastErr;\n}\n\nfunction isGeminiOverloadedOrUnavailable(status: number, raw: string): boolean {\n if (status === 429 || status === 503) return true;\n const text = raw.toLowerCase();\n return (\n text.includes(\"overloaded\") ||\n text.includes(\"overused\") ||\n text.includes(\"unavailable\") ||\n text.includes(\"resource exhausted\") ||\n text.includes(\"quota\") ||\n text.includes(\"temporarily\")\n );\n}\n\nfunction bytesToMbLabel(bytes: number): string {\n return `${(bytes / (1024 * 1024)).toFixed(2)}MB`;\n}\n\nasync function getSessionOrdinal(\n sessionId: string,\n createdAt: number,\n): Promise {\n const res = await sqlite.execute({\n sql: `SELECT COUNT(*) AS c\n FROM sessions\n WHERE created_at < ?\n OR (created_at = ? AND id < ?)`,\n args: [createdAt, createdAt, sessionId],\n });\n const row = res.rows?.[0] as { c?: number } | undefined;\n const before = Number(row?.c ?? 0);\n return before + 1;\n}\n\nfunction withSpeakerPrefix(\n sessionOrdinal: number,\n model: LLMKey,\n text: string,\n): string {\n const modelLabel = model === \"claude\"\n ? \"Claude\"\n : model === \"openai\"\n ? \"OpenAI\"\n : \"Gemini\";\n const body = model === \"claude\"\n ? text\n : limitAssistantTextForTts(model, text);\n return `Session ${sessionOrdinal}. ${modelLabel} speaking. ${body}`;\n}\n\nfunction limitAssistantTextForTts(model: LLMKey, text: string): string {\n const max = assistantMaxCharsForTts(model);\n if (text.length <= max) return text;\n return truncateAtSentenceBoundary(text, max);\n}\n\nfunction truncateAtSentenceBoundary(text: string, maxChars: number): string {\n if (text.length <= maxChars) return text;\n const slice = text.slice(0, maxChars);\n const punct = Math.max(\n slice.lastIndexOf(\". \"),\n slice.lastIndexOf(\"! \"),\n slice.lastIndexOf(\"? \"),\n slice.lastIndexOf(\".\\n\"),\n );\n if (punct >= Math.floor(maxChars * 0.4)) {\n return `${slice.slice(0, punct + 1).trim()} Answer shortened for audio.`;\n }\n const lastSpace = slice.lastIndexOf(\" \");\n const cut = lastSpace > maxChars * 0.5 ? slice.slice(0, lastSpace) : slice;\n return `${cut.trim()}… Answer shortened for audio.`;\n}\n\nfunction isTtsSkippableAssistantText(text: string): boolean {\n const t = text.trim();\n if (!t) return true;\n return /^\\[(claude|openai|gemini) (error|parse|unexpected|fallback)/i.test(t);\n}\n\nasync function synthesizeElevenLabs(\n text: string,\n apiKey: string,\n voiceId: string,\n): Promise {\n const normalized = normalizeForTts(text);\n if (!normalized) throw new Error(\"empty tts text\");\n const maxChars = getElevenLabsMaxCharacters();\n const chunks = splitTextForTts(normalized, maxChars);\n const parts: Uint8Array[] = [];\n for (const chunk of chunks) {\n parts.push(await synthesizeElevenLabsChunk(chunk, apiKey, voiceId));\n }\n return concatUint8Arrays(parts);\n}\n\nasync function synthesizeElevenLabsChunk(\n normalized: string,\n apiKey: string,\n voiceId: string,\n): Promise {\n const voiceSettings = getElevenLabsVoiceSettings();\n const speed = getElevenLabsSpeed();\n const r = await fetch(\n `https://api.elevenlabs.io/v1/text-to-speech/${\n encodeURIComponent(voiceId)\n }`,\n {\n method: \"POST\",\n headers: {\n \"xi-api-key\": apiKey,\n \"content-type\": \"application/json\",\n accept: \"audio/mpeg\",\n },\n body: JSON.stringify({\n text: normalized,\n model_id: \"eleven_multilingual_v2\",\n voice_settings: voiceSettings,\n speed,\n }),\n },\n );\n if (!r.ok) {\n const raw = await r.text();\n throw new Error(`elevenlabs ${r.status}: ${raw.slice(0, 300)}`);\n }\n return new Uint8Array(await r.arrayBuffer());\n}\n\nfunction splitTextForTts(text: string, maxChars: number): string[] {\n if (text.length <= maxChars) return [text];\n const chunks: string[] = [];\n let rest = text;\n while (rest.length > 0) {\n if (rest.length <= maxChars) {\n chunks.push(rest);\n break;\n }\n const slice = rest.slice(0, maxChars);\n const punct = Math.max(\n slice.lastIndexOf(\". \"),\n slice.lastIndexOf(\"! \"),\n slice.lastIndexOf(\"? \"),\n );\n let cut = punct >= Math.floor(maxChars * 0.25)\n ? punct + 1\n : slice.lastIndexOf(\" \");\n if (cut < Math.floor(maxChars * 0.15)) cut = maxChars;\n const piece = rest.slice(0, cut).trim();\n if (piece.length === 0) {\n chunks.push(rest.slice(0, maxChars));\n rest = rest.slice(maxChars).trim();\n } else {\n chunks.push(piece);\n rest = rest.slice(cut).trim();\n }\n }\n return chunks.filter((c) => c.length > 0);\n}\n\nfunction concatUint8Arrays(parts: Uint8Array[]): Uint8Array {\n if (parts.length === 0) return new Uint8Array(0);\n if (parts.length === 1) return parts[0]!;\n let total = 0;\n for (const p of parts) total += p.length;\n const out = new Uint8Array(total);\n let offset = 0;\n for (const p of parts) {\n out.set(p, offset);\n offset += p.length;\n }\n return out;\n}\n\nfunction maybeSynthesizeElevenLabs(\n assistantRaw: string,\n spoken: string,\n apiKey: string,\n voiceId: string,\n): Promise {\n if (isTtsSkippableAssistantText(assistantRaw)) {\n return Promise.reject(new Error(\"skip tts: no speakable assistant text\"));\n }\n return synthesizeElevenLabs(spoken, apiKey, voiceId);\n}\n\nfunction normalizeForTts(input: string): string {\n if (!input) return \"\";\n\n // Fix common UTF-8 mojibake sequences (often from smart punctuation).\n let s = input\n .replace(/ΓÇ£/g, '\"')\n .replace(/ΓÇ¥/g, '\"')\n .replace(/ΓÇÖ/g, \"'\")\n .replace(/ΓÇô/g, \"-\")\n .replace(/ΓÇö/g, \"-\")\n .replace(/ΓÇª/g, \"...\")\n .replace(/â€œ/g, '\"')\n .replace(/â€�/g, '\"')\n .replace(/â€˜/g, \"'\")\n .replace(/â€™/g, \"'\")\n .replace(/â€“/g, \"-\")\n .replace(/â€”/g, \"-\")\n .replace(/â€¦/g, \"...\");\n\n // Handle JSON-escaped newlines/tabs that sometimes leak into model output.\n s = s\n .replace(/\\\\r\\\\n/g, \". \")\n .replace(/\\\\n+/g, \". \")\n .replace(/\\\\t+/g, \" \");\n\n // Convert real line breaks and markdown separators into sentence pauses.\n s = s\n .replace(/\\r\\n/g, \"\\n\")\n .replace(/\\n{2,}/g, \". \")\n .replace(/\\n+/g, \". \")\n .replace(/^\\s*[-*]\\s+/gm, \"\")\n .replace(/^\\s*\\d+[.)]\\s+/gm, \"\");\n\n // Reduce common markdown noise that reads poorly in TTS.\n s = s\n .replace(/`{1,3}/g, \"\")\n .replace(/[*_#~]/g, \"\")\n .replace(/\\[(.*?)\\]\$(.*?)\$/g, \"$1\")\n .replace(/\$(https?:\\/\\/[^\\s)]+)\$/g, \"$1\");\n\n // Make grouping symbols explicit so TTS reads math/expressions correctly.\n // Skip heavy expansion on long answers (physics prompts blow past ElevenLabs limits).\n const compact = s.length > 3_500;\n if (compact) {\n s = s\n .replace(/\\s*\$\\s*/g, \" ( \")\n .replace(/\\s*\$\\s*/g, \") \")\n .replace(/\\s*\\[\\s*/g, \" [ \")\n .replace(/\\s*\\]\\s*/g, \"] \");\n } else {\n s = s\n .replace(/\$/g, \" open parenthesis \")\n .replace(/\$/g, \" close parenthesis \")\n .replace(/\\[/g, \" open bracket \")\n .replace(/\\]/g, \" close bracket \")\n .replace(/\\{/g, \" open brace \")\n .replace(/\\}/g, \" close brace \");\n }\n\n // Improve spoken pacing around punctuation and normalize whitespace.\n s = s\n .replace(/\\s*([,;:!?])\\s*/g, \"$1 \")\n .replace(/\\s*\\.\\s*/g, \". \")\n .replace(/\\s*-\\s*/g, \" - \")\n .replace(/\\s+/g, \" \")\n .trim();\n\n // Prevent awkward repeated pauses.\n s = s\n .replace(/([.!?]){2,}/g, \"$1\")\n .replace(/([,;:]){2,}/g, \"$1\")\n .replace(/(\\.\\s+){2,}/g, \". \");\n\n return s;\n}\n\nfunction latestAssistant(msgs: Msg[]): string {\n for (let i = msgs.length - 1; i >= 0; i--) {\n const m = msgs[i];\n if (m.role === \"assistant\") return m.text;\n }\n return \"\";\n}\n\nfunction previewFromSession(\n hc: string,\n ho: string,\n hg: string,\n): string {\n const a = latestAssistant(parseHist(hc));\n const b = latestAssistant(parseHist(ho));\n const c = latestAssistant(parseHist(hg));\n const pick = [a, b, c].find((s) => s.length > 0) ?? \"\";\n return pick.length > 160 ? pick.slice(0, 157) + \"…\" : pick;\n}\n\nfunction nextLlm(current: LLMKey): LLMKey {\n const order: LLMKey[] = [\"claude\", \"openai\", \"gemini\"];\n const i = order.indexOf(current);\n return order[(i + 1) % order.length];\n}\n\nfunction sessionPayload(\n row: SessionRow,\n origin: string,\n): Record {\n const hc = parseHist(row.history_claude);\n const ho = parseHist(row.history_openai);\n const hg = parseHist(row.history_gemini);\n const audioByLlm: Record = {\n claude: row.audio_claude,\n openai: row.audio_openai,\n gemini: row.audio_gemini,\n };\n const activeAudioBlob = audioByLlm[row.active_llm];\n const kc = (row as { audio_claude_keys?: string }).audio_claude_keys ?? \"[]\";\n const ko = (row as { audio_openai_keys?: string }).audio_openai_keys ?? \"[]\";\n const kg = (row as { audio_gemini_keys?: string }).audio_gemini_keys ?? \"[]\";\n return {\n id: row.id,\n created_at: row.created_at,\n updated_at: row.updated_at,\n active_llm: row.active_llm,\n latest: {\n claude: latestAssistant(hc),\n openai: latestAssistant(ho),\n gemini: latestAssistant(hg),\n },\n histories: {\n claude: hc,\n openai: ho,\n gemini: hg,\n },\n audio_history: {\n claude: parseStringArrayJson(kc),\n openai: parseStringArrayJson(ko),\n gemini: parseStringArrayJson(kg),\n },\n audio_url: activeAudioBlob ? `${origin}/audio/${row.id}` : null,\n };\n}\n\nexport default async function handler(req: Request): Promise {\n await ensureSchema();\n\n const url = new URL(req.url);\n const path = url.pathname.replace(/\\/$/, \"\") || \"/\";\n\n if (req.method === \"OPTIONS\") {\n return new Response(null, {\n status: 204,\n headers: {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, X-Key, X-Session-Id\",\n \"Access-Control-Max-Age\": \"86400\",\n },\n });\n }\n\n const isCapturePost = req.method === \"POST\" && path === \"/capture\";\n if (!isCapturePost) {\n const authErr = requireAuth(req);\n if (authErr) return authErr;\n }\n\n // --- POST /admin/clear-sessions ---\n if (req.method === \"POST\" && path === \"/admin/clear-sessions\") {\n await sqlite.execute({ sql: \"DELETE FROM sessions\" });\n return jsonResponse({ ok: true, cleared: true });\n }\n\n // --- GET /sessions ---\n if (req.method === \"GET\" && path === \"/sessions\") {\n const res = await sqlite.execute({\n sql:\n `SELECT id, created_at, updated_at, history_claude, history_openai, history_gemini\n FROM sessions ORDER BY updated_at DESC LIMIT 20`,\n });\n const rows = (res.rows ?? []) as Array<{\n id: string;\n created_at: number;\n updated_at: number;\n history_claude: string;\n history_openai: string;\n history_gemini: string;\n }>;\n const sessions = rows.map((r) => ({\n id: r.id,\n created_at: r.created_at,\n updated_at: r.updated_at,\n preview: previewFromSession(\n r.history_claude,\n r.history_openai,\n r.history_gemini,\n ),\n }));\n return jsonResponse({ sessions });\n }\n\n // --- GET /session/:id ---\n const sessionMatch = path.match(/^\\/session\\/([^/]+)$/);\n if (req.method === \"GET\" && sessionMatch) {\n const id = sessionMatch[1];\n if (!isUuid(id)) return bad(\"Invalid session id\");\n const res = await sqlite.execute({\n sql: \"SELECT * FROM sessions WHERE id = ?\",\n args: [id],\n });\n const row = res.rows?.[0] as SessionRow | undefined;\n if (!row) return bad(\"Not found\", 404);\n return jsonResponse(sessionPayload(row, url.origin));\n }\n\n // --- GET /audio-blob?key=belt/... (auth; for per-turn clips) ---\n if (req.method === \"GET\" && path === \"/audio-blob\") {\n const rawKey = url.searchParams.get(\"key\");\n if (!rawKey?.trim()) return bad(\"Missing key query parameter\");\n let blobKey: string;\n try {\n blobKey = decodeURIComponent(rawKey.trim());\n } catch {\n return bad(\"Invalid key encoding\");\n }\n if (!isBeltSessionAudioBlobKey(blobKey)) return bad(\"Invalid blob key\");\n try {\n const b = await blob.get(blobKey);\n const buf = await b.arrayBuffer();\n return new Response(buf, {\n headers: {\n \"Content-Type\": \"audio/mpeg\",\n \"Cache-Control\": \"private, max-age=3600\",\n },\n });\n } catch {\n return bad(\"Audio missing\", 404);\n }\n }\n\n // --- GET /audio/:id ---\n const audioMatch = path.match(/^\\/audio\\/([^/]+)$/);\n if (req.method === \"GET\" && audioMatch) {\n const id = audioMatch[1];\n if (!isUuid(id)) return bad(\"Invalid session id\");\n const res = await sqlite.execute({\n sql:\n \"SELECT active_llm, audio_claude, audio_openai, audio_gemini FROM sessions WHERE id = ?\",\n args: [id],\n });\n const row = res.rows?.[0] as {\n active_llm: LLMKey;\n audio_claude: string | null;\n audio_openai: string | null;\n audio_gemini: string | null;\n } | undefined;\n if (!row) return bad(\"Not found\", 404);\n const keyMap: Record = {\n claude: row.audio_claude,\n openai: row.audio_openai,\n gemini: row.audio_gemini,\n };\n const blobKey = keyMap[row.active_llm];\n if (!blobKey) return bad(\"No audio yet\", 404);\n try {\n const b = await blob.get(blobKey);\n const buf = await b.arrayBuffer();\n return new Response(buf, {\n headers: {\n \"Content-Type\": \"audio/mpeg\",\n \"Cache-Control\": \"private, max-age=60\",\n },\n });\n } catch {\n return bad(\"Audio missing\", 404);\n }\n }\n\n // --- POST /switch-llm/:id ---\n const switchMatch = path.match(/^\\/switch-llm\\/([^/]+)$/);\n if (req.method === \"POST\" && switchMatch) {\n const id = switchMatch[1];\n if (!isUuid(id)) return bad(\"Invalid session id\");\n const cur = await sqlite.execute({\n sql: \"SELECT active_llm FROM sessions WHERE id = ?\",\n args: [id],\n });\n const active = cur.rows?.[0] as { active_llm: LLMKey } | undefined;\n if (!active) return bad(\"Not found\", 404);\n const n = nextLlm(active.active_llm);\n const now = Date.now();\n await sqlite.execute({\n sql: \"UPDATE sessions SET active_llm = ?, updated_at = ? WHERE id = ?\",\n args: [n, now, id],\n });\n const full = await sqlite.execute({\n sql: \"SELECT * FROM sessions WHERE id = ?\",\n args: [id],\n });\n const row = full.rows?.[0] as SessionRow;\n const originBase = url.origin;\n return jsonResponse({\n ok: true,\n active_llm: n,\n ...sessionPayload(row, originBase),\n });\n }\n\n // --- POST /capture ---\n if (req.method === \"POST\" && path === \"/capture\") {\n const authErr = requireAuth(req);\n if (authErr) return authErr;\n\n const anth = getAnthropicKey();\n const oai = getOpenAIKey();\n const goog = getGoogleKey();\n const eleven = getElevenLabsKey();\n const elevenVoiceId = getElevenLabsVoiceId();\n if (!anth || !oai || !goog || !eleven) {\n return bad(\"Server missing API keys\", 500);\n }\n\n const ct = req.headers.get(\"Content-Type\") ?? \"image/png\";\n const mime = ct.split(\";\")[0].trim().toLowerCase();\n if (!mime.startsWith(\"image/\")) {\n return bad(\"Content-Type must be an image/*\");\n }\n\n let buf = new Uint8Array(await req.arrayBuffer());\n if (buf.length === 0) return bad(\"Empty body\");\n if (buf.length > 15 * 1024 * 1024) {\n return bad(\"Image too large (max ~15MB)\");\n }\n\n const headerSession = req.headers.get(\"X-Session-Id\")?.trim();\n let sessionId: string;\n let imageIndex: number;\n let hc: Msg[];\n let ho: Msg[];\n let hg: Msg[];\n let active_llm: LLMKey;\n let created_at: number;\n let previousRow: SessionRow | undefined;\n\n const now = Date.now();\n\n if (headerSession && isUuid(headerSession)) {\n const ex = await sqlite.execute({\n sql: \"SELECT * FROM sessions WHERE id = ?\",\n args: [headerSession],\n });\n const existing = ex.rows?.[0] as SessionRow | undefined;\n if (!existing) return bad(\"Unknown session id\", 404);\n previousRow = existing;\n sessionId = existing.id;\n created_at = existing.created_at;\n active_llm = existing.active_llm;\n hc = parseHist(existing.history_claude);\n ho = parseHist(existing.history_openai);\n hg = parseHist(existing.history_gemini);\n imageIndex = hc.filter((m) => m.role === \"user\").length;\n } else {\n sessionId = crypto.randomUUID();\n created_at = now;\n active_llm = \"claude\";\n hc = [];\n ho = [];\n hg = [];\n imageIndex = 0;\n }\n\n const imageMimeIn = mime === \"image/jpg\" ? \"image/jpeg\" : mime;\n const prepared = await prepareCaptureImage(buf, imageMimeIn);\n buf = prepared.buf;\n const imageMime = prepared.mime;\n const ext = imageMime === \"image/jpeg\"\n ? \"jpg\"\n : imageMime === \"image/webp\"\n ? \"webp\"\n : \"png\";\n const blobKey = `belt/${sessionId}/img/${imageIndex}.${ext}`;\n await blob.set(blobKey, buf);\n\n const userPart: UserMsg = {\n role: \"user\",\n text: USER_PROMPT,\n image_blob_key: blobKey,\n image_mime: imageMime,\n };\n hc.push(userPart);\n ho.push({ ...userPart });\n hg.push({ ...userPart });\n\n const [rc, ro, rg] = await Promise.allSettled([\n callClaude(hc, anth),\n callOpenAI(ho, oai),\n callGemini(hg, goog),\n ]);\n\n const to = ro.status === \"fulfilled\"\n ? ro.value\n : `[openai error] ${String(ro.reason).slice(0, 300)}`;\n const tg = rg.status === \"fulfilled\"\n ? rg.value\n : `[gemini error] ${String(rg.reason).slice(0, 300)}`;\n let tc = rc.status === \"fulfilled\"\n ? rc.value\n : `[claude error] ${String(rc.reason).slice(0, 300)}`;\n if (tc.startsWith(CLAUDE_TOO_LARGE_PREFIX)) {\n const rawBytes = Number(tc.slice(CLAUDE_TOO_LARGE_PREFIX.length));\n const sizeLabel = Number.isFinite(rawBytes)\n ? bytesToMbLabel(rawBytes)\n : \"unknown size\";\n const fallback = to.startsWith(\"[openai error]\") ? tg : to;\n tc =\n `[claude fallback] image ${sizeLabel} exceeds Claude 5MB base64 limit. Using alternate model output. ${fallback}`;\n }\n\n hc.push({ role: \"assistant\", text: tc });\n ho.push({ role: \"assistant\", text: to });\n hg.push({ role: \"assistant\", text: tg });\n\n const sessionOrdinal = await getSessionOrdinal(sessionId, created_at);\n const spokenClaude = withSpeakerPrefix(sessionOrdinal, \"claude\", tc);\n const spokenOpenAI = withSpeakerPrefix(sessionOrdinal, \"openai\", to);\n const spokenGemini = withSpeakerPrefix(sessionOrdinal, \"gemini\", tg);\n\n let audioClaude: string | null = null;\n let audioOpenAI: string | null = null;\n let audioGemini: string | null = null;\n const [ac, ao, ag] = await Promise.allSettled([\n maybeSynthesizeElevenLabs(tc, spokenClaude, eleven, elevenVoiceId),\n maybeSynthesizeElevenLabs(to, spokenOpenAI, eleven, elevenVoiceId),\n maybeSynthesizeElevenLabs(tg, spokenGemini, eleven, elevenVoiceId),\n ]);\n if (ac.status === \"fulfilled\") {\n const k = `belt/${sessionId}/audio/claude-${now}.mp3`;\n await blob.set(k, ac.value);\n audioClaude = k;\n }\n if (ao.status === \"fulfilled\") {\n const k = `belt/${sessionId}/audio/openai-${now}.mp3`;\n await blob.set(k, ao.value);\n audioOpenAI = k;\n }\n if (ag.status === \"fulfilled\") {\n const k = `belt/${sessionId}/audio/gemini-${now}.mp3`;\n await blob.set(k, ag.value);\n audioGemini = k;\n }\n\n const audio_claude_keys = appendAudioKeysJson(\n previousRow?.audio_claude_keys,\n previousRow?.audio_claude ?? null,\n audioClaude,\n );\n const audio_openai_keys = appendAudioKeysJson(\n previousRow?.audio_openai_keys,\n previousRow?.audio_openai ?? null,\n audioOpenAI,\n );\n const audio_gemini_keys = appendAudioKeysJson(\n previousRow?.audio_gemini_keys,\n previousRow?.audio_gemini ?? null,\n audioGemini,\n );\n\n const row: SessionRow = {\n id: sessionId,\n created_at,\n updated_at: now,\n active_llm,\n history_claude: JSON.stringify(hc),\n history_openai: JSON.stringify(ho),\n history_gemini: JSON.stringify(hg),\n audio_claude: audioClaude,\n audio_openai: audioOpenAI,\n audio_gemini: audioGemini,\n audio_claude_keys,\n audio_openai_keys,\n audio_gemini_keys,\n };\n\n if (headerSession && isUuid(headerSession)) {\n await sqlite.execute({\n sql: `UPDATE sessions SET\n updated_at = ?, history_claude = ?, history_openai = ?, history_gemini = ?,\n audio_claude = ?, audio_openai = ?, audio_gemini = ?,\n audio_claude_keys = ?, audio_openai_keys = ?, audio_gemini_keys = ?\n WHERE id = ?`,\n args: [\n now,\n row.history_claude,\n row.history_openai,\n row.history_gemini,\n row.audio_claude,\n row.audio_openai,\n row.audio_gemini,\n row.audio_claude_keys,\n row.audio_openai_keys,\n row.audio_gemini_keys,\n sessionId,\n ],\n });\n } else {\n await sqlite.execute({\n sql: `INSERT INTO sessions (\n id, created_at, updated_at, active_llm,\n history_claude, history_openai, history_gemini,\n audio_claude, audio_openai, audio_gemini,\n audio_claude_keys, audio_openai_keys, audio_gemini_keys\n ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n row.id,\n row.created_at,\n row.updated_at,\n row.active_llm,\n row.history_claude,\n row.history_openai,\n row.history_gemini,\n row.audio_claude,\n row.audio_openai,\n row.audio_gemini,\n row.audio_claude_keys,\n row.audio_openai_keys,\n row.audio_gemini_keys,\n ],\n });\n }\n\n const originBase = url.origin;\n return jsonResponse({\n ok: true,\n session_id: sessionId,\n ...sessionPayload(row, originBase),\n });\n }\n\n if (req.method === \"GET\" && path === \"/\") {\n return new Response(\n \"Belt backend OK. 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Diff · v49