[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_158":159,"_160":161},"routes/_app.x.$handle.$name",{"_97":96,"_32":45,"_105":87,"_27":106,"_13":14,"_107":-5,"_11":108,"_109":110,"_111":112,"_113":114,"_115":116,"_20":-5,"_117":-5,"_118":-5,"_119":25,"_120":121,"_122":123,"_124":-5,"_125":-5,"_126":127},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":-5,"_53":-5,"_18":54,"_55":-5,"_56":25,"_57":-5,"_58":-5,"_59":60,"_61":62,"_63":64,"_65":66,"_67":68,"_69":-5,"_70":71,"_72":71,"_73":74,"_75":74,"_76":74,"_77":71,"_78":71,"_79":80,"_81":82,"_83":84,"_85":25},"version",39,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_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 =\n (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 / task you see clearly. Then solve the task to the best of your ability, in a way that would get full points from the grader. When writing the code, formulas, write the solution in fully english senteces, telling me what to type in, No Markdown, no LaTeX, no symbols. Say math in words: for example, 'd underscore k', 'n squared', 'big O of n log n', 'gamma', 'sigma'. If there is code, mathematical calculations, drawing or anything still have them. if anything needs to be drawn explain in detail.\";\n\n/** Fast + capable vision IDs (keep in sync with MODELS.txt / provider docs). */\nconst MODEL_CLAUDE = \"claude-sonnet-4-6\";\nconst MODEL_OPENAI = \"gpt-5.4-mini\";\nconst MODEL_GEMINI = \"gemini-2.5-pro\";\nconst MODEL_GEMINI_FALLBACKS = [\"gemini-2.5-flash\"] as const;\nconst CLAUDE_IMAGE_MAX_BYTES = 5 * 1024 * 1024;\nconst CLAUDE_TOO_LARGE_PREFIX = \"__CLAUDE_IMAGE_TOO_LARGE__:\";\n\n/** Gemini `maxOutputTokens` cap (below model limits). */\nconst GEMINI_MAX_OUTPUT_TOKENS = 65_536;\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\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/** `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/** `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 const anthropicMsgs: Array<{ role: \"user\" | \"assistant\"; content: unknown }> =\n [];\n for (const m of messages) {\n if (m.role === \"user\") {\n const { b64, mime, bytes } = await blobToBase64(\n m.image_blob_key,\n m.image_mime,\n );\n if (bytes > CLAUDE_IMAGE_MAX_BYTES) {\n return `${CLAUDE_TOO_LARGE_PREFIX}${bytes}`;\n }\n const media = mime === \"image/jpeg\" || mime === \"image/jpg\"\n ? \"image/jpeg\"\n : mime === \"image/webp\"\n ? \"image/webp\"\n : \"image/png\";\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: claudeMaxOutputTokensForModel(MODEL_CLAUDE),\n messages: anthropicMsgs,\n };\n if (claudeSupportsAgenticMessagesParams(MODEL_CLAUDE)) {\n body.thinking = { type: \"adaptive\" };\n body.output_config = { effort: \"max\" };\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 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: GEMINI_MAX_OUTPUT_TOKENS },\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 return `Session ${sessionOrdinal}. ${modelLabel} speaking. ${text}`;\n}\n\nasync function synthesizeElevenLabs(\n text: string,\n apiKey: string,\n voiceId: string,\n): Promise {\n const normalized = normalizeForTts(text);\n if (!normalized) throw new Error(\"empty tts text\");\n const voiceSettings = getElevenLabsVoiceSettings();\n const speed = getElevenLabsSpeed();\n const r = await fetch(\n `https://api.elevenlabs.io/v1/text-to-speech/${\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 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 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 // 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 const 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 imageMime = mime === \"image/jpg\" ? \"image/jpeg\" : 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 image limit. Using alternate model output. ${fallback}`;\n }\n\n hc.push({ role: \"assistant\", text: tc });\n ho.push({ role: \"assistant\", text: to });\n hg.push({ role: \"assistant\", text: tg });\n\n const sessionOrdinal = await getSessionOrdinal(sessionId, created_at);\n const spokenClaude = withSpeakerPrefix(sessionOrdinal, \"claude\", tc);\n const spokenOpenAI = withSpeakerPrefix(sessionOrdinal, \"openai\", to);\n const spokenGemini = withSpeakerPrefix(sessionOrdinal, \"gemini\", tg);\n\n let audioClaude: string | null = null;\n let audioOpenAI: string | null = null;\n let audioGemini: string | null = null;\n const [ac, ao, ag] = await Promise.allSettled([\n synthesizeElevenLabs(spokenClaude, eleven, elevenVoiceId),\n synthesizeElevenLabs(spokenOpenAI, eleven, elevenVoiceId),\n synthesizeElevenLabs(spokenGemini, eleven, elevenVoiceId),\n ]);\n if (ac.status === \"fulfilled\") {\n const k = `belt/${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. POST /capture with image; see README.\",\n {\n headers: { \"Content-Type\": \"text/plain; charset=utf-8\" },\n },\n );\n }\n\n return bad(\"Not found\", 404);\n}","/**\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 =\n (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 / task you see clearly. Then solve the task to the best of your ability, in a way that would get full points from the grader, but tell me what to write exactly (Specifically tell me / dictate what to write in the answer sheet). When writing the code, formulas, write the solution in fully english senteces, telling me what to type in, No Markdown, no LaTeX, no symbols. Say math in words: for example, 'd underscore k', 'n squared', 'big O of n log n', 'gamma', 'sigma'. Do this for code, mathematical calculations, drawing or anything else. if anything needs to be drawn explain in detail in words.\";\n\n/** Fast + capable vision IDs (keep in sync with MODELS.txt / provider docs). */\nconst MODEL_CLAUDE = \"claude-sonnet-4-6\";\nconst MODEL_OPENAI = \"gpt-5.4-mini\";\nconst MODEL_GEMINI = \"gemini-2.5-pro\";\nconst MODEL_GEMINI_FALLBACKS = [\"gemini-2.5-flash\"] 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 `maxOutputTokens` cap (below model limits). */\nconst GEMINI_MAX_OUTPUT_TOKENS = 65_536;\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, mime: outMime } = await normalizeImageForClaude(\n new Uint8Array(ab),\n mime,\n );\n const b64 = bytesToBase64(buf);\n return {\n b64,\n media: anthropicImageMediaType(outMime),\n bytes: buf.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/** `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: claudeMaxOutputTokensForModel(MODEL_CLAUDE),\n messages: anthropicMsgs,\n };\n if (claudeSupportsAgenticMessagesParams(MODEL_CLAUDE)) {\n body.thinking = { type: \"adaptive\" };\n body.output_config = { effort: \"max\" };\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 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: GEMINI_MAX_OUTPUT_TOKENS },\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 return `Session ${sessionOrdinal}. ${modelLabel} speaking. ${text}`;\n}\n\nasync function synthesizeElevenLabs(\n text: string,\n apiKey: string,\n voiceId: string,\n): Promise {\n const normalized = normalizeForTts(text);\n if (!normalized) throw new Error(\"empty tts text\");\n const voiceSettings = getElevenLabsVoiceSettings();\n const speed = getElevenLabsSpeed();\n const r = await fetch(\n `https://api.elevenlabs.io/v1/text-to-speech/${\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 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 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 // 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. ${fallback}`;\n }\n\n hc.push({ role: \"assistant\", text: tc });\n ho.push({ role: \"assistant\", text: to });\n hg.push({ role: \"assistant\", text: tg });\n\n const sessionOrdinal = await getSessionOrdinal(sessionId, created_at);\n const spokenClaude = withSpeakerPrefix(sessionOrdinal, \"claude\", tc);\n const spokenOpenAI = withSpeakerPrefix(sessionOrdinal, \"openai\", to);\n const spokenGemini = withSpeakerPrefix(sessionOrdinal, \"gemini\", tg);\n\n let audioClaude: string | null = null;\n let audioOpenAI: string | null = null;\n let audioGemini: string | null = null;\n const [ac, ao, ag] = await Promise.allSettled([\n synthesizeElevenLabs(spokenClaude, eleven, elevenVoiceId),\n synthesizeElevenLabs(spokenOpenAI, eleven, elevenVoiceId),\n synthesizeElevenLabs(spokenGemini, eleven, elevenVoiceId),\n ]);\n if (ac.status === \"fulfilled\") {\n const k = `belt/${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 · v39