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{ Annotation } from \"npm:@langchain/langgraph\";\nimport {\n BaseMessage,\n HumanMessage,\n AIMessage,\n SystemMessage,\n} from \"npm:@langchain/core/messages\";\n\nexport type ChatRole = \"user\" | \"assistant\";\n\nexport interface ChatMessage {\n role: ChatRole;\n content: string;\n}\n\nexport interface ImpactData {\n version?: string;\n inferenceTime?: { total: number; unit: string };\n energy?: { total: number; unit: string };\n emissions?: { total: number; unit: string };\n}\n\nexport interface ToolCallInfo {\n name: string;\n args: Record;\n}\n\nexport interface ApprovalPayload {\n toolCalls: ToolCallInfo[];\n threadId: string;\n}\n\nexport interface ChatResult {\n assistantMessage: string;\n messages: ChatMessage[];\n impact?: ImpactData;\n approvalRequest?: ApprovalPayload;\n}\n\n// LangGraph state schema\nexport const GraphState = Annotation.Root({\n messages: Annotation({\n reducer: (existing, update) => [...existing, ...update],\n default: () => [],\n }),\n assistantMessage: Annotation({\n reducer: (_prev, next) => next,\n default: () => \"\",\n }),\n impact: Annotation({\n reducer: (_prev, next) => next,\n default: () => undefined,\n }),\n sessionId: Annotation({\n reducer: (_prev, next) => next,\n default: () => \"\",\n }),\n});\n\nexport type GraphStateType = typeof GraphState.State;\n\n// Convert frontend ChatMessage[] to LangChain BaseMessage[]\nexport function toBaseMessages(messages: ChatMessage[]): BaseMessage[] {\n return messages.map((m) => {\n if (m.role === \"user\") return new HumanMessage(m.content);\n return new AIMessage(m.content);\n });\n}\n\n// Convert LangChain BaseMessage[] to frontend ChatMessage[]\nexport function fromBaseMessages(messages: BaseMessage[]): ChatMessage[] {\n const result: ChatMessage[] = [];\n for (const m of messages) {\n const type = m._getType();\n if (type === \"human\") {\n result.push({ role: \"user\", content: typeof m.content === \"string\" ? m.content : \"\" });\n } else if (type === \"ai\") {\n // Skip AI messages that are pure tool calls with no text content\n const content = typeof m.content === \"string\" ? m.content : \"\";\n if (content) {\n result.push({ role: \"assistant\", content });\n }\n }\n // Skip system, tool, and other message types\n }\n return result;\n}\n","import { Annotation, END } from \"npm:@langchain/langgraph\";\nimport {\n BaseMessage,\n HumanMessage,\n AIMessage,\n SystemMessage,\n ToolMessage,\n} from \"npm:@langchain/core/messages\";\nimport type OpenAI from \"npm:openai\";\n\n// ── Shared types ──────────────────────────────────────────────────────\n\nexport type ChatRole = \"user\" | \"assistant\";\n\nexport interface ChatMessage {\n role: ChatRole;\n content: string;\n}\n\nexport interface ImpactData {\n version?: string;\n inferenceTime?: { total: number; unit: string };\n energy?: { total: number; unit: string };\n emissions?: { total: number; unit: string };\n}\n\nexport interface ToolCallInfo {\n id: string;\n name: string;\n arguments: Record;\n}\n\nexport interface ApprovalRequest {\n toolCalls: ToolCallInfo[];\n}\n\n// ── Stream events emitted by the graph ────────────────────────────────\n\nexport type GraphStreamEvent =\n | { type: \"chunk\"; delta: string }\n | { type: \"thinking\" }\n | { type: \"tool_start\"; toolName: string; toolCallId: string }\n | { type: \"tool_result\"; toolName: string; toolCallId: string; result: string }\n | { type: \"approval_request\"; toolCalls: ToolCallInfo[] }\n | { type: \"post_process_start\" };\n\nexport type StreamEventHandler = (event: GraphStreamEvent) => void | Promise;\n\n// ── Graph result ──────────────────────────────────────────────────────\n\nexport interface ChatResult {\n assistantMessage: string;\n messages: ChatMessage[];\n impact?: ImpactData;\n approvalRequest?: ApprovalRequest;\n}\n\n// ── Graph state (Annotation.Root) ─────────────────────────────────────\n\nexport const GraphState = Annotation.Root({\n messages: Annotation({\n reducer: (curr: BaseMessage[], update: BaseMessage[]) => [...curr, ...update],\n default: () => [],\n }),\n assistantMessage: Annotation({\n reducer: (_: string, update: string) => update,\n default: () => \"\",\n }),\n impact: Annotation({\n reducer: (_: ImpactData | undefined, update: ImpactData | undefined) => update,\n default: () => undefined,\n }),\n sessionId: Annotation({\n reducer: (_: string, update: string) => update,\n default: () => \"\",\n }),\n approvalRequest: Annotation({\n reducer: (_: ApprovalRequest | undefined, update: ApprovalRequest | undefined) => update,\n default: () => undefined,\n }),\n});\n\nexport type GraphStateType = typeof GraphState.State;\n\n// ── Message conversion: ChatMessage[] → BaseMessage[] ─────────────────\n\nexport function chatMessagesToBaseMessages(\n messages: ChatMessage[],\n systemPrompt: string,\n): BaseMessage[] {\n const result: BaseMessage[] = [new SystemMessage(systemPrompt)];\n for (const msg of messages) {\n if (msg.role === \"user\") {\n result.push(new HumanMessage(msg.content));\n } else {\n result.push(new AIMessage(msg.content));\n }\n }\n return result;\n}\n\n// ── Message conversion: BaseMessage[] → OpenAI format ─────────────────\n\nexport function baseMessagesToOpenAI(\n messages: BaseMessage[],\n): OpenAI.ChatCompletionMessageParam[] {\n return messages.map((msg) => {\n if (msg._getType() === \"system\") {\n return { role: \"system\" as const, content: msg.content as string };\n }\n if (msg._getType() === \"human\") {\n return { role: \"user\" as const, content: msg.content as string };\n }\n if (msg._getType() === \"ai\") {\n const aiMsg = msg as AIMessage;\n const param: OpenAI.ChatCompletionAssistantMessageParam = {\n role: \"assistant\" as const,\n content: (aiMsg.content as string) || null,\n };\n if (aiMsg.tool_calls && aiMsg.tool_calls.length > 0) {\n param.tool_calls = aiMsg.tool_calls.map((tc) => ({\n id: tc.id ?? \"\",\n type: \"function\" as const,\n function: {\n name: tc.name,\n arguments: 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\"name\": \"office-window\",\n \"version\": \"1.0.0\",\n \"description\": \"This app is a starter template for client-side React and server-side Hono.\",\n \"main\": \"index.js\",\n \"scripts\": {\n \"test\": \"echo \\\"Error: no test specified\\\" && exit 1\"\n },\n \"keywords\": [],\n \"author\": \"\",\n \"license\": \"ISC\",\n \"type\": \"commonjs\"\n}\n","label",[143],"f21b43b4-1254-11f1-9d96-42dde27851f2","index.ts",109,["D",1772029147351],9,"http","import { serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { Hono } from \"npm:hono\";\nimport {\n runChatGraphStream,\n resumeAfterApproval,\n type ChatMessage,\n type ToolCallInfo,\n} from \"./backend/langgraph.ts\";\nimport { runMigrations } from \"./backend/database/migrations.ts\";\nimport { insertImpact, getGlobalStats } from \"./backend/database/queries.ts\";\n\nconst migrationDone = runMigrations().catch((err) => console.error(\"Migration failed:\", err));\n\nconst app = new Hono();\n\n// Serve index.html at the root /\napp.get(\"/\", () => serveFile(\"/frontend/index.html\"));\n\n// Serve all /frontend files\napp.get(\"/frontend/**/*\", (c) => serveFile(c.req.path));\n// Serve prompt/data config modules used by frontend imports\napp.get(\"/data/**/*\", (c) => serveFile(c.req.path));\n// Serve static assets (logos, icons, images)\napp.get(\"/assets/**/*\", (c) => serveFile(c.req.path));\n\nfunction isValidRole(role: unknown): role is ChatMessage[\"role\"] {\n return role === \"user\" || role === \"assistant\";\n}\n\nfunction isValidMessage(value: unknown): value is ChatMessage {\n if (!value || typeof value !== \"object\") {\n return false;\n }\n\n const candidate = value as Record;\n return (\n isValidRole(candidate.role) &&\n typeof candidate.content === \"string\" &&\n candidate.content.trim().length > 0\n );\n}\n\napp.get(\"/api/stats\", async (c) => {\n await migrationDone;\n try {\n const stats = await getGlobalStats();\n return c.json(stats);\n } catch (err) {\n console.error(\"Failed to fetch global stats:\", err);\n return c.json({ totalMessages: 0, totalEmissionsUg: 0, totalEnergyWms: 0, totalTimeMs: 0 });\n }\n});\n\nconst encoder = new TextEncoder();\nconst toSseEvent = (event: string, data: unknown): Uint8Array =>\n encoder.encode(`event: ${event}\\ndata: ${JSON.stringify(data)}\\n\\n`);\n\napp.post(\"/api/chat\", async (c) => {\n const payload = await c.req.json();\n const payloadRecord = payload as Record;\n const rawMessages = payloadRecord.messages;\n const sessionId =\n typeof payloadRecord.sessionId === \"string\" &&\n payloadRecord.sessionId.trim().length > 0\n ? payloadRecord.sessionId\n : crypto.randomUUID();\n\n if (!Array.isArray(rawMessages)) {\n return c.json({ error: \"`messages` must be an array\" }, 400);\n }\n\n if (!rawMessages.every(isValidMessage)) {\n return c.json(\n { error: \"Each message must include a valid role and non-empty content\" },\n 400,\n );\n }\n\n const messages = rawMessages as ChatMessage[];\n\n const stream = new ReadableStream({\n start(controller) {\n void (async () => {\n try {\n let streamedAssistantMessage = \"\";\n const graphResult = await runChatGraphStream(\n messages,\n sessionId,\n // onChunk\n (chunk) => {\n streamedAssistantMessage += chunk;\n controller.enqueue(\n toSseEvent(\"chunk\", {\n delta: chunk,\n assistantMessage: streamedAssistantMessage,\n }),\n );\n },\n // onToolStart\n (toolCall) => {\n controller.enqueue(\n toSseEvent(\"tool_start\", {\n name: toolCall.name,\n args: toolCall.args,\n }),\n );\n },\n // onToolResult\n (toolResult) => {\n controller.enqueue(\n toSseEvent(\"tool_result\", {\n name: toolResult.name,\n result: toolResult.args,\n }),\n );\n },\n // onApprovalNeeded\n (payload) => {\n controller.enqueue(\n toSseEvent(\"approval_request\", {\n toolCalls: payload.toolCalls,\n threadId: payload.threadId,\n }),\n );\n },\n );\n\n if (graphResult.approvalRequest) {\n // Graph ended at approval node — don't send \"done\", the approval_request\n // was already sent. Close the stream.\n controller.close();\n return;\n }\n\n controller.enqueue(\n toSseEvent(\"done\", {\n assistantMessage: graphResult.assistantMessage,\n messages: graphResult.messages,\n sessionId,\n impact: graphResult.impact,\n }),\n );\n\n if (graphResult.impact) {\n migrationDone\n .then(() =>\n insertImpact({\n sessionId,\n emissionsUg: graphResult.impact!.emissions?.total ?? 0,\n energyWms: graphResult.impact!.energy?.total ?? 0,\n inferenceTimeMs: graphResult.impact!.inferenceTime?.total ?? 0,\n }),\n )\n .catch((err) => console.error(\"Failed to record impact:\", err));\n }\n\n controller.close();\n } catch (error) {\n const message =\n error instanceof Error\n ? error.message\n : \"Failed to send chat message\";\n controller.enqueue(toSseEvent(\"error\", { error: message }));\n controller.close();\n }\n })();\n },\n });\n\n return new Response(stream, {\n headers: {\n \"content-type\": \"text/event-stream\",\n \"cache-control\": \"no-cache\",\n connection: \"keep-alive\",\n },\n });\n});\n\n// Human-in-the-loop: approval endpoint\napp.post(\"/api/chat/approve\", async (c) => {\n const payload = await c.req.json();\n const payloadRecord = payload as Record;\n const rawMessages = payloadRecord.messages;\n const sessionId =\n typeof payloadRecord.sessionId === \"string\"\n ? payloadRecord.sessionId\n : crypto.randomUUID();\n const approved = payloadRecord.approved === true;\n const pendingToolCalls = (payloadRecord.pendingToolCalls ?? []) as ToolCallInfo[];\n\n if (!Array.isArray(rawMessages) || !rawMessages.every(isValidMessage)) {\n return c.json({ error: \"Invalid messages\" }, 400);\n }\n\n const messages = rawMessages as ChatMessage[];\n\n const stream = new ReadableStream({\n start(controller) {\n void (async () => {\n try {\n let streamedAssistantMessage = \"\";\n const graphResult = await resumeAfterApproval(\n messages,\n sessionId,\n approved,\n pendingToolCalls,\n // onChunk\n (chunk) => {\n streamedAssistantMessage += chunk;\n controller.enqueue(\n toSseEvent(\"chunk\", {\n delta: chunk,\n assistantMessage: streamedAssistantMessage,\n }),\n );\n },\n // onToolStart\n (toolCall) => {\n controller.enqueue(\n toSseEvent(\"tool_start\", {\n name: toolCall.name,\n args: toolCall.args,\n }),\n );\n },\n // onToolResult\n (toolResult) => {\n controller.enqueue(\n toSseEvent(\"tool_result\", {\n name: toolResult.name,\n result: toolResult.args,\n }),\n );\n },\n );\n\n controller.enqueue(\n toSseEvent(\"done\", {\n assistantMessage: graphResult.assistantMessage,\n messages: graphResult.messages,\n sessionId,\n impact: graphResult.impact,\n }),\n );\n\n if (graphResult.impact) {\n migrationDone\n .then(() =>\n insertImpact({\n sessionId,\n emissionsUg: graphResult.impact!.emissions?.total ?? 0,\n energyWms: graphResult.impact!.energy?.total ?? 0,\n inferenceTimeMs: graphResult.impact!.inferenceTime?.total ?? 0,\n }),\n )\n .catch((err) => console.error(\"Failed to record impact:\", err));\n }\n\n controller.close();\n } catch (error) {\n const message =\n error instanceof Error\n ? error.message\n : \"Failed to process approval\";\n controller.enqueue(toSseEvent(\"error\", { error: message }));\n controller.close();\n }\n })();\n },\n });\n\n return new Response(stream, {\n headers: {\n \"content-type\": \"text/event-stream\",\n \"cache-control\": \"no-cache\",\n connection: \"keep-alive\",\n },\n });\n});\n\n// Unwrap and rethrow Hono errors as the original error\napp.onError((err) => Promise.reject(err));\n\n// HTTP vals expect an exported \"fetch handler\"\nexport default app.fetch;\n",[153],"7e29aa04-0da4-11f1-ab86-42dde27851f2","deno.json","906642c4-0cb7-11f1-ac61-42dde27851f2",64,["D",1771513556738],1,"{\n \"$schema\": \"https://raw.githubusercontent.com/denoland/deno/98f62cee78e85bfc47c62ed703777c6bc8794f1c/cli/schemas/config-file.v1.json\",\n \"lock\": false,\n \"compilerOptions\": {\n \"noImplicitAny\": false,\n \"strict\": true,\n \"types\": [\"https://www.val.town/types/valtown.d.ts\"],\n \"lib\": [\n \"dom\",\n \"dom.iterable\",\n \"dom.asynciterable\",\n \"deno.ns\",\n \"deno.unstable\"\n ]\n },\n \"lint\": {\n \"include\": [\"deno:/https/esm.town/**/*\"],\n \"rules\": {\n \"exclude\": [\"no-explicit-any\"]\n }\n },\n \"node_modules_dir\": false,\n \"experimental\": {\n \"unstable-node-globals\": true,\n \"unstable-temporal\": true,\n \"unstable-worker-options\": true,\n \"unstable-sloppy-imports\": true\n }\n}\n",[161],"aa56fcf6-0cb6-11f1-8b14-42dde27851f2","README.md","019c7059-0ed3-724f-869d-71f2afadb572",["D",1771411410564],"# React Hono Starter\n\nThis app is a starter template for client-side React and server-side Hono.\n\n## Getting started\n\nGet a copy of this starter template by clicking the **Remix** button in the\ntop-right.\n\n## How it works\n\n- The **entrypoint** is [`index.ts`](index.ts). That's the backend HTTP server,\n which also serves the all the frontend assets.\n\n- The **client-side entrypoint** is\n [`/frontend/index.html`](/frontend/index.html)\n - which in turn imports [`/frontend/index.tsx`](/frontend/index.tsx)\n - which in turn imports the React app from\n [`/frontend/components/App.tsx`](/frontend/components/App.tsx).\n\nSo if you wanted to get a sense of how this app comes together, we suggest\nreading the files in this order:\n\n[`index.ts`](index.ts) -> [`/frontend/index.html`](/frontend/index.html) ->\n[`/frontend/index.tsx`](/frontend/index.tsx) ->\n[`/frontend/components/App.tsx`](/frontend/components/App.tsx)\n\n## Further resources\n\n- [React Hono Example](https://www.val.town/x/stevekrouse/reactHonoExample) is a\n bigger example project, with a SQLite database table, queries, client-side\n CSS, a favicon, and shared code that runs on both client and server.",[169],"908d00bc-0cb7-11f1-8d87-42dde27851f2","AGENTS.md","908d018e-0cb7-11f1-8d87-42dde27851f2",7,["D",1771411796883],"You are an advanced assistant specialized in generating Val Town code.\n\n## Core Guidelines\n\n- Ask clarifying questions when requirements are ambiguous\n- Provide complete, functional solutions rather than skeleton implementations\n- Test your logic against edge cases before presenting the final solution\n- Ensure all code follows Val Town's specific platform requirements\n- If a section of code that you're working on is getting too complex, consider refactoring it into subcomponents\n\n## Code Standards\n\n- Generate code in TypeScript or TSX\n- Add appropriate TypeScript types and interfaces for all data structures\n- Prefer official SDKs or libraries than writing API calls directly\n- Ask the user to supply API or library documentation if you are at all unsure about it\n- **Never bake in secrets into the code** - always use environment variables\n- Include comments explaining complex logic (avoid commenting obvious operations)\n- Follow modern ES6+ conventions and functional programming practices if possible\n\n## Types of triggers\n\n### 1. HTTP Trigger\n\n- Create web APIs and endpoints\n- Handle HTTP requests and responses\n- Example structure:\n\n```ts\nexport default async function (req: Request) {\n return new Response(\"Hello World\");\n}\n```\n\nFiles that are HTTP triggers have http in their name like `foobar.http.tsx`\n\n### 2. Cron Triggers\n\n- Run on a schedule\n- Use cron expressions for timing\n- Example structure:\n\n```ts\nexport default async function () {\n // Scheduled task code\n}\n```\n\nFiles that are Cron triggers have cron in their name like `foobar.cron.tsx`\n\n### 3. Email Triggers\n\n- Process incoming emails\n- Handle email-based workflows\n- Example structure:\n\n```ts\nexport default async function (email: Email) {\n // Process email\n}\n```\n\nFiles that are Email triggers have email in their name like `foobar.email.tsx`\n\n\n## Val Town Standard Libraries\n\nVal Town provides several hosted services and utility functions.\n\n### Blob Storage\n\n```ts\nimport { blob } from \"https://esm.town/v/std/blob\";\nawait blob.setJSON(\"myKey\", { hello: \"world\" });\nlet blobDemo = await blob.getJSON(\"myKey\");\nlet appKeys = await blob.list(\"app_\");\nawait blob.delete(\"myKey\");\n```\n\n### SQLite\n\n```ts\nimport { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nconst TABLE_NAME = 'todo_app_users_2';\n// Create table - do this before usage and change table name when modifying schema\nawait sqlite.execute(`CREATE TABLE IF NOT EXISTS ${TABLE_NAME} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL\n)`);\n// Query data\nconst result = await sqlite.execute(`SELECT * FROM ${TABLE_NAME} WHERE id = ?`, [1]);\n```\n\nNote: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.\n\n### OpenAI\n\n```ts\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nconst openai = new OpenAI();\nconst completion = await openai.chat.completions.create({\n messages: [\n { role: \"user\", content: \"Say hello in a creative way\" },\n ],\n model: \"gpt-4o-mini\",\n max_tokens: 30,\n});\n```\n\n### Email\n\n```ts\nimport { email } from \"https://esm.town/v/std/email\";\n// By default emails the owner of the val\nawait email({ \n subject: \"Hi\", \n text: \"Hi\", \n html: \"

Hi

\"\n});\n```\n\n## Val Town Utility Functions\n\nVal Town provides several utility functions to help with common project tasks.\n\n### Importing Utilities\n\nAlways import utilities with version pins to avoid breaking changes:\n\n```ts\nimport { parseProject, readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n```\n\n### Available Utilities\n\n\n#### **serveFile** - Serve project files with proper content types\n\nFor example, in Hono:\n\n```ts\n// serve all files in frontend/ and shared/\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n```\n\n#### **readFile** - Read files from within the project:\n\n```ts\n// Read a file from the project\nconst fileContent = await readFile(\"/frontend/index.html\", import.meta.url);\n```\n\n#### **listFiles** - List all files in the project\n\n```ts\nconst files = await listFiles(import.meta.url);\n```\n\n#### **parseProject** - Extract information about the current project from import.meta.url\n\nThis is useful for including info for linking back to a val, ie in \"view source\" urls:\n\n```ts\nconst projectVal = parseProject(import.meta.url);\nconsole.log(projectVal.username); // Owner of the project\nconsole.log(projectVal.name); // Project name\nconsole.log(projectVal.version); // Version number\nconsole.log(projectVal.branch); // Branch name\nconsole.log(projectVal.links.self.project); // URL to the project page\n```\n\nHowever, it's *extremely importing* to note that `parseProject` and other Standard Library utilities ONLY RUN ON THE SERVER.\nIf you need access to this data on the client, run it in the server and pass it to the client by splicing it into the HTML page\nor by making an API request for it.\n\n## Val Town Platform Specifics\n\n- **Redirects:** Use `return new Response(null, { status: 302, headers: { Location: \"/place/to/redirect\" }})` instead of `Response.redirect` which is broken\n- **Images:** Avoid external images or base64 images. Use emojis, unicode symbols, or icon fonts/libraries instead\n- **AI Image:** To inline generate an AI image use: `

`\n- **Storage:** DO NOT use the Deno KV module for storage\n- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods\n- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified\n- **View Source:** Add a view source link by importing & using `import.meta.url.replace(\"ems.sh\", \"val.town)\"` (or passing this data to the client) and include `target=\"_top\"` attribute\n- **Error Debugging:** Add `` to HTML to capture client-side errors\n- **Error Handling:** Only use try...catch when there's a clear local resolution; Avoid catches that merely log or return 500s. Let errors bubble up with full context\n- **Environment Variables:** Use `Deno.env.get('keyname')` when you need to, but generally prefer APIs that don't require keys\n- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser\n- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites\n- **React Configuration:** When using React libraries, pin versions with `?deps=react@18.2.0,react-dom@18.2.0` and start the file with `/** @jsxImportSource https://esm.sh/react@18.2.0 */`\n- Ensure all React dependencies and sub-dependencies are pinned to the same version\n- **Styling:** Default to using TailwindCSS via `` unless otherwise specified\n\n## Project Structure and Design Patterns\n\n### Recommended Directory Structure\n```\n├── backend/\n│ ├── database/\n│ │ ├── migrations.ts # Schema definitions\n│ │ ├── queries.ts # DB query functions\n│ │ └── README.md\n│ └── routes/ # Route modules\n│ ├── [route].ts\n│ └── static.ts # Static file serving\n│ ├── index.ts # Main entry point\n│ └── README.md\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ └── [Component].tsx\n│ ├── favicon.svg\n│ ├── index.html # Main HTML template\n│ ├── index.tsx # Frontend JS entry point\n│ ├── README.md\n│ └── style.css\n├── README.md\n└── shared/\n ├── README.md\n └── utils.ts # Shared types and functions\n```\n\n### Backend (Hono) Best Practices\n\n- Hono is the recommended API framework\n- Main entry point should be `backend/index.ts`\n- **Static asset serving:** Use the utility functions to read and serve project files:\n ```ts\n import { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n \n // serve all files in frontend/ and shared/\n app.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\n app.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n \n // For index.html, often you'll want to bootstrap with initial data\n app.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject data to avoid extra round-trips\n const initialData = await fetchInitialData();\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n });\n ```\n- Create RESTful API routes for CRUD operations\n- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:\n ```ts\n // Unwrap Hono errors to see original error details\n app.onError((err, c) => {\n throw err;\n });\n ```\n\n### Database Patterns\n- Run migrations on startup or comment out for performance\n- Change table names when modifying schemas rather than altering\n- Export clear query functions with proper TypeScript typing\n\n## Common Gotchas and Solutions\n\n1. **Environment Limitations:** \n - Val Town runs on Deno in a serverless context, not Node.js\n - Code in `shared/` must work in both frontend and backend environments\n - Cannot use `Deno` keyword in shared code\n - Use `https://esm.sh` for imports that work in both environments\n\n2. **SQLite Peculiarities:**\n - Limited support for ALTER TABLE operations\n - Create new tables with updated schemas and copy data when needed\n - Always run table creation before querying\n\n3. **React Configuration:**\n - All React dependencies must be pinned to 18.2.0\n - Always include `@jsxImportSource https://esm.sh/react@18.2.0` at the top of React files\n - Rendering issues often come from mismatched React versions\n\n4. **File Handling:**\n - Val Town only supports text files, not binary\n - Use the provided utilities to read files across branches and forks\n - For files in the project, use `readFile` helpers\n\n5. **API Design:**\n - `fetch` handler is the entry point for HTTP vals\n - Run the Hono app with `export default app.fetch // This is the entry point for HTTP 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--destructive-foreground: oklch(0.985 0 0);\n \n --font-sans: \"Inter\", -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, sans-serif;\n --font-mono: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace;\n \n --radius-sm: 0.5rem;\n --radius-md: 0.75rem;\n --radius-lg: 1rem;\n --radius-xl: 1.5rem;\n --radius-full: 9999px;\n}\n\nbody {\n font-family: var(--font-sans);\n background-color: var(--background);\n color: var(--foreground);\n margin: 0;\n -webkit-font-smoothing: antialiased;\n}\n\n/* Layout */\n.chat-shell {\n display: flex;\n flex-direction: column;\n height: 100dvh;\n width: 100%;\n position: relative;\n background-color: var(--background);\n}\n\n/* Main Chat Area */\n.chat-messages {\n flex: 1;\n overflow-y: auto;\n padding: 2rem;\n display: flex;\n flex-direction: column;\n gap: 1.5rem;\n scroll-behavior: smooth;\n width: 100%;\n max-width: 48rem;\n margin: 0 auto;\n padding-bottom: 11rem; /* Space for floating input + session footer */\n}\n\n/* Empty State */\n.chat-empty {\n display: flex;\n flex-direction: column;\n align-items: center;\n justify-content: center;\n min-height: 80%;\n gap: 2rem;\n width: 100%;\n max-width: 56rem;\n margin: 0 auto;\n}\n\n.empty-header {\n text-align: center;\n margin-bottom: 2rem;\n}\n\n.empty-logo {\n width: 64px;\n height: 64px;\n background-color: var(--primary); /* Placeholder for logo */\n border-radius: 50%; /* Circle shape */\n margin: 0 auto 1.5rem;\n display: flex;\n align-items: center;\n justify-content: center;\n color: white;\n box-shadow: 0 10px 25px -5px oklch(var(--primary) / 0.3);\n}\n\n.empty-title {\n font-size: 2rem;\n font-weight: 700;\n margin: 0 0 0.5rem;\n color: var(--foreground);\n}\n\n.empty-subtitle {\n font-size: 1rem;\n color: var(--muted-foreground);\n margin: 0;\n max-width: 400px;\n line-height: 1.5;\n}\n\n.try-asking-label {\n font-size: 0.75rem;\n font-weight: 600;\n letter-spacing: 0.05em;\n text-transform: uppercase;\n color: var(--muted-foreground);\n display: flex;\n align-items: center;\n gap: 0.5rem;\n margin-bottom: 1rem;\n}\n\n.prompts-grid {\n display: grid;\n grid-template-columns: repeat(2, 1fr);\n gap: 1rem;\n width: 100%;\n max-width: 800px;\n}\n\n.prompt-card {\n background-color: var(--card);\n padding: 1.25rem;\n border-radius: var(--radius-lg);\n border: 1px solid transparent;\n text-align: left;\n cursor: pointer;\n transition: all 0.2s ease;\n box-shadow: 0 2px 8px oklch(0 0 0 / 0.04);\n font-size: 0.95rem;\n color: var(--foreground);\n font-weight: 500;\n display: flex;\n align-items: center;\n justify-content: space-between;\n}\n\n.prompt-card:hover {\n transform: translateY(-2px);\n box-shadow: 0 8px 16px oklch(0 0 0 / 0.08);\n border-color: oklch(0 0 0 / 0.05);\n}\n\n/* Chat Input */\n.chat-form {\n position: absolute;\n bottom: 2rem;\n left: 50%;\n transform: translateX(-50%);\n width: 100%;\n max-width: 48rem;\n padding: 0 1rem;\n z-index: 10;\n}\n\n.chat-input-container {\n background-color: var(--input-background);\n border-radius: var(--radius-full);\n padding: 0.5rem 0.75rem;\n display: flex;\n align-items: center;\n gap: 0.5rem;\n box-shadow: \n 0 4px 6px -1px oklch(0 0 0 / 0.05),\n 0 2px 4px -1px oklch(0 0 0 / 0.03),\n 0 0 0 1px oklch(0 0 0 / 0.05);\n transition: box-shadow 0.2s ease;\n}\n\n.chat-input-container:focus-within {\n box-shadow: \n 0 10px 15px -3px oklch(0 0 0 / 0.08),\n 0 4px 6px -2px oklch(0 0 0 / 0.04),\n 0 0 0 1px oklch(var(--primary) / 0.3);\n}\n\n.input-action-btn {\n width: 36px;\n height: 36px;\n border-radius: 50%;\n border: none;\n background: transparent;\n color: var(--muted-foreground);\n display: flex;\n align-items: center;\n justify-content: center;\n cursor: pointer;\n transition: all 0.2s;\n}\n\n.input-action-btn:hover {\n background-color: var(--muted);\n color: var(--foreground);\n}\n\n.chat-input {\n flex: 1;\n border: none;\n background: transparent;\n padding: 0.75rem;\n font-size: 1rem;\n font-family: var(--font-sans);\n color: var(--foreground);\n outline: none;\n resize: none;\n max-height: 120px;\n}\n\n.chat-input::placeholder {\n color: var(--muted-foreground);\n}\n\n.send-btn {\n width: 36px;\n height: 36px;\n border-radius: 50%;\n border: none;\n background-color: transparent;\n color: var(--muted-foreground);\n display: flex;\n align-items: center;\n justify-content: center;\n cursor: pointer;\n transition: all 0.2s;\n}\n\n.send-btn:not(:disabled) {\n background-color: var(--primary);\n color: white;\n}\n\n.send-btn:disabled {\n cursor: not-allowed;\n opacity: 0.5;\n}\n\n/* Chat Messages */\n.chat-row {\n display: flex;\n flex-direction: column;\n gap: 0.5rem;\n max-width: 100%;\n}\n\n.chat-row-user {\n align-items: flex-end;\n}\n\n.chat-role {\n font-size: 0.75rem;\n font-weight: 600;\n color: var(--muted-foreground);\n margin-left: 0.5rem;\n margin-bottom: 0.25rem;\n}\n\n.chat-bubble {\n padding: 1rem 1.5rem;\n border-radius: 1.5rem;\n background-color: white;\n color: var(--foreground);\n line-height: 1.6;\n box-shadow: 0 1px 2px oklch(0 0 0 / 0.05);\n max-width: 85%;\n}\n\n.chat-row-user .chat-bubble {\n background-color: var(--muted); /* Light grey for user */\n color: var(--foreground);\n border-bottom-right-radius: 0.25rem;\n}\n\n.chat-row:not(.chat-row-user) .chat-bubble {\n border-bottom-left-radius: 0.25rem;\n background-color: white;\n border: 1px solid var(--sidebar-border);\n box-shadow: 0 2px 4px oklch(0 0 0 / 0.02);\n}\n\n/* Loading & Error */\n.chat-loading {\n text-align: center;\n color: var(--muted-foreground);\n font-size: 0.875rem;\n padding: 1rem;\n}\n\n.chat-error {\n background-color: oklch(0.98 0.02 20);\n color: var(--destructive);\n padding: 1rem;\n border-radius: var(--radius-md);\n margin: 1rem 2rem;\n text-align: center;\n border: 1px solid oklch(0.9 0.05 20);\n}\n\n/* Prose */\n.prose {\n font-size: 1rem;\n}\n.prose p {\n margin-bottom: 0.75em;\n}\n.prose p:last-child {\n margin-bottom: 0;\n}\n.prose code {\n font-family: var(--font-mono);\n font-size: 0.9em;\n background: var(--muted);\n padding: 0.2em 0.4em;\n border-radius: 0.4em;\n}\n.prose pre {\n background: #1e1e1e;\n color: #d4d4d4;\n padding: 1rem;\n border-radius: 0.5rem;\n overflow-x: auto;\n margin: 0.5rem 0;\n}\n\n/* Infrastructure Badge */\n.infra-badge {\n display: flex;\n flex-direction: column;\n align-items: center;\n justify-content: center;\n gap: 0.15rem;\n padding: 0.4rem 1rem;\n background-color: oklch(0.97 0.03 145);\n color: oklch(0.35 0.12 145);\n font-size: 0.7rem;\n font-weight: 500;\n letter-spacing: 0.01em;\n border-bottom: 1px solid oklch(0.92 0.04 145);\n flex-shrink: 0;\n}\n\n.infra-badge-row {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 0.4rem;\n}\n\n.infra-badge-row svg {\n flex-shrink: 0;\n}\n\n.infra-stats {\n font-size: 0.65rem;\n color: oklch(0.45 0.1 145);\n}\n\n/* Per-message Impact Badge */\n.impact-badge {\n margin-left: 0.5rem;\n max-width: 85%;\n}\n\n.impact-badge-main {\n display: inline-flex;\n align-items: center;\n gap: 0.35rem;\n padding: 0.25rem 0.65rem;\n border: none;\n background-color: oklch(0.97 0.02 145);\n color: oklch(0.4 0.1 145);\n font-size: 0.7rem;\n font-weight: 500;\n border-radius: var(--radius-full);\n cursor: pointer;\n transition: background-color 0.15s ease;\n}\n\n.impact-badge-main:hover {\n background-color: oklch(0.94 0.04 145);\n}\n\n.impact-badge-main svg {\n flex-shrink: 0;\n}\n\n/* Comparison dropdown */\n.impact-comparison {\n margin-top: 0.4rem;\n padding: 0.6rem 0.8rem;\n background-color: var(--card);\n border: 1px solid var(--sidebar-border);\n border-radius: var(--radius-md);\n font-size: 0.7rem;\n}\n\n.comparison-row {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 0.2rem 0;\n}\n\n.comparison-label {\n color: var(--muted-foreground);\n}\n\n.comparison-value {\n font-weight: 600;\n}\n\n.comparison-green {\n color: oklch(0.5 0.18 145);\n}\n\n.comparison-grey {\n color: var(--muted-foreground);\n}\n\n.comparison-savings {\n margin-top: 0.3rem;\n padding-top: 0.3rem;\n border-top: 1px solid var(--sidebar-border);\n color: oklch(0.5 0.18 145);\n font-weight: 600;\n text-align: center;\n}\n\n/* Read more link */\n.impact-read-more {\n display: block;\n margin-top: 0.4rem;\n font-size: 0.65rem;\n color: oklch(0.5 0.15 145);\n text-align: center;\n text-decoration: none;\n}\n\n.impact-read-more:hover {\n text-decoration: underline;\n color: oklch(0.4 0.18 145);\n}\n\n/* Session Footer */\n.chat-bottom-bar {\n position: absolute;\n bottom: 5.5rem;\n left: 50%;\n transform: translateX(-50%);\n width: 100%;\n max-width: 48rem;\n padding: 0 1rem;\n z-index: 9;\n display: flex;\n flex-direction: column;\n align-items: center;\n gap: 0.3rem;\n}\n\n.session-footer {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 0.35rem;\n padding: 0.35rem 0.75rem;\n background-color: oklch(0.97 0.02 145 / 0.9);\n backdrop-filter: blur(6px);\n color: oklch(0.4 0.1 145);\n font-size: 0.7rem;\n font-weight: 500;\n border-radius: var(--radius-full);\n border: 1px solid oklch(0.92 0.04 145);\n}\n\n.session-footer svg {\n flex-shrink: 0;\n}\n\n.session-equivalent {\n color: oklch(0.5 0.12 145);\n font-style: italic;\n}\n\n/* Carbon Receipt */\n.carbon-receipt {\n display: flex;\n flex-direction: column;\n align-items: center;\n}\n\n.receipt-toggle {\n border: none;\n background: none;\n color: var(--muted-foreground);\n font-size: 0.65rem;\n cursor: pointer;\n text-decoration: underline;\n text-underline-offset: 2px;\n padding: 0.15rem 0.5rem;\n transition: color 0.15s ease;\n}\n\n.receipt-toggle:hover {\n color: var(--foreground);\n}\n\n.receipt-card {\n margin-bottom: 0.4rem;\n padding: 1rem 1.25rem;\n background-color: var(--card);\n border: 1px solid var(--sidebar-border);\n border-radius: var(--radius-lg);\n box-shadow: 0 4px 12px oklch(0 0 0 / 0.06);\n width: 20rem;\n max-width: 100%;\n}\n\n.receipt-title {\n font-size: 0.8rem;\n font-weight: 700;\n color: var(--foreground);\n margin-bottom: 0.6rem;\n text-align: center;\n}\n\n.receipt-rows {\n display: flex;\n flex-direction: column;\n gap: 0.3rem;\n}\n\n.receipt-row {\n display: flex;\n justify-content: space-between;\n font-size: 0.72rem;\n padding: 0.15rem 0;\n border-bottom: 1px solid oklch(0.95 0 0);\n}\n\n.receipt-row span:first-child {\n color: var(--muted-foreground);\n}\n\n.receipt-row span:last-child {\n font-weight: 600;\n color: var(--foreground);\n}\n\n.receipt-equivalent {\n margin-top: 0.6rem;\n padding: 0.4rem 0.6rem;\n background-color: oklch(0.97 0.02 145);\n color: oklch(0.4 0.12 145);\n font-size: 0.7rem;\n font-weight: 500;\n text-align: center;\n border-radius: var(--radius-sm);\n}\n\n.receipt-share {\n margin-top: 0.5rem;\n font-size: 0.65rem;\n color: var(--muted-foreground);\n text-align: center;\n font-style: italic;\n line-height: 1.4;\n}\n",[],[203,313],"aa56f030-0cb6-11f1-8b14-42dde27851f2","index.tsx","019c7059-0ed3-724f-869d-645b09fef5a9",["D",1771411410564],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { createRoot } from \"https://esm.sh/react-dom@18.2.0/client\";\nimport { App } from \"./components/App.tsx\";\n\nconst root = document.getElementById(\"root\");\nif (!root) {\n throw new Error(\"No root element found\");\n}\n\ncreateRoot(root).render();",[],[203,321],"0dcff062-0e5b-11f1-ad11-42dde27851f2","index.html","019c7059-0ed3-724f-869d-63b68cc06b7d",84,["D",1771591966166],5,"\n\n \n \n \n React Hono Val Town Starter\n \n \n \n \n \n \n \n

\n \n \n \n\n",[],[203,328],"aa56e6da-0cb6-11f1-8b14-42dde27851f2","favicon.svg","019c7059-0ed3-724f-869d-5ce97042364a",["D",1771411410564],"\n",[],[203,337],"007118d4-1247-11f1-b87e-42dde27851f2","App.tsx","019c7059-0ed3-724f-869d-558a3819401a","019c7059-0ed3-724f-869d-5b56e15573c6",104,["D",1772023158438],28,"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport {\n FormEvent,\n KeyboardEvent,\n useEffect,\n useMemo,\n useRef,\n useState,\n} from \"https://esm.sh/react@18.2.0\";\nimport { Streamdown } from \"https://esm.sh/streamdown?deps=react@18.2.0\";\nimport { STARTER_PROMPTS } from \"../../data/prompts.ts\";\n\n// Icons\nconst LogoIcon = () => (\n \n \n \n \n);\n\nconst EditIcon = () => (\n \n \n \n \n);\n\nconst SearchIcon = () => (\n \n \n \n \n);\n\nconst GridIcon = () => (\n \n \n \n \n \n \n);\n\nconst SunIcon = () => (\n \n \n \n \n \n \n \n \n \n \n \n);\n\nconst LogOutIcon = () => (\n \n \n \n \n \n);\n\nconst PlusIcon = () => (\n \n \n \n \n);\n\nconst MicIcon = () => (\n \n \n \n \n \n \n);\n\nconst SendIcon = () => (\n \n \n \n \n);\n\nconst SparklesIcon = () => (\n \n \n \n);\n\nconst LeafIcon = () => (\n \n \n \n \n);\n\n// Types\ntype ChatRole = \"user\" | \"assistant\";\n\ninterface ImpactData {\n version?: string;\n inferenceTime?: { total: number; unit: string };\n energy?: { total: number; unit: string };\n emissions?: { total: number; unit: string };\n}\n\ninterface ChatMessage {\n role: ChatRole;\n content: string;\n impact?: ImpactData;\n}\n\ninterface ChatDoneEvent {\n assistantMessage: string;\n messages: Array<{ role: ChatRole; content: string }>;\n sessionId: string;\n impact?: ImpactData;\n}\n\ninterface ChatChunkEvent {\n delta: string;\n assistantMessage: string;\n}\n\ninterface ChatErrorEvent {\n error: string;\n}\n\ninterface SessionImpact {\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n messageCount: number;\n}\n\ninterface GlobalStats {\n totalMessages: number;\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n}\n\n// Helpers\nfunction formatEmissions(ug: number): string {\n if (ug < 1000) return `${Math.round(ug)} \\u00b5g CO\\u2082e`;\n const mg = ug / 1000;\n if (mg < 1000) return `${mg.toFixed(1)} mg CO\\u2082e`;\n return `${(mg / 1000).toFixed(2)} g CO\\u2082e`;\n}\n\nfunction formatEnergy(wms: number): string {\n const wh = wms / 3_600_000;\n if (wh < 0.01) return `${(wh * 1000).toFixed(1)} mWh`;\n return `${wh.toFixed(2)} Wh`;\n}\n\nfunction formatTime(ms: number): string {\n if (ms < 1000) return `${Math.round(ms)} ms`;\n return `${(ms / 1000).toFixed(1)}s`;\n}\n\nfunction getRelatableEquivalent(totalUg: number): string {\n if (totalUg < 100) return \"Less CO\\u2082 than a single breath\";\n if (totalUg < 1000) return \"Less than 1 second of a light bulb\";\n if (totalUg < 10_000) return \"Equivalent to walking 1 cm\";\n if (totalUg < 100_000) return \"Less than 1 second of watching TV\";\n return \"Less than a minute of a light bulb\";\n}\n\nfunction getConventionalEstimate(ug: number): number {\n // GreenPT claims 40% lower CO2, so conventional = greenpt / 0.6\n return Math.round(ug / 0.6);\n}\n\nfunction parseSSEBuffer(buffer: string): {\n parsed: Array<{ type: string; data: string }>;\n remaining: string;\n} {\n const events: Array<{ type: string; data: string }> = [];\n const parts = buffer.split(\"\\n\\n\");\n\n for (let i = 0; i < parts.length - 1; i++) {\n const lines = parts[i].split(\"\\n\");\n let eventType = \"message\";\n let data = \"\";\n for (const line of lines) {\n if (line.startsWith(\"event: \")) {\n eventType = line.slice(7).trim();\n } else if (line.startsWith(\"data: \")) {\n data = line.slice(6);\n }\n }\n events.push({ type: eventType, data });\n }\n\n return { parsed: events, remaining: parts[parts.length - 1] };\n}\n\n// Components\nfunction ImpactBadge({ impact }: { impact: ImpactData }) {\n const [showComparison, setShowComparison] = useState(false);\n const emissionsUg = impact.emissions?.total ?? 0;\n const energyWms = impact.energy?.total ?? 0;\n const timeMs = impact.inferenceTime?.total ?? 0;\n const conventional = getConventionalEstimate(emissionsUg);\n\n return (\n

\n setShowComparison(!showComparison)}\n title=\"Powered by 100% renewable energy. Click for comparison.\"\n >\n \n \n {formatEmissions(emissionsUg)} · {formatEnergy(energyWms)} ·{\" \"}\n {formatTime(timeMs)}\n \n \n {showComparison && (\n

\n GreenPT (renewable)\n \n {formatEmissions(emissionsUg)}\n \n

\n Conventional estimate\n \n ~{formatEmissions(conventional)}\n \n

\n {Math.round(((conventional - emissionsUg) / conventional) * 100)}%\n less CO{\"\\u2082\"} with GreenPT\n

\n \n Read more about our calculations\n \n

\n )}\n

\n );\n}\n\nfunction SessionFooter({ session }: { session: SessionImpact }) {\n if (session.messageCount === 0) return null;\n\n return (\n

\n \n \n Session: {session.messageCount}{\" \"}\n {session.messageCount === 1 ? \"message\" : \"messages\"} ·{\" \"}\n {formatEmissions(session.totalEmissionsUg)} ·{\" \"}\n {formatEnergy(session.totalEnergyWms)} total\n \n \n — {getRelatableEquivalent(session.totalEmissionsUg)}\n \n

\n );\n}\n\nfunction CarbonReceipt({ session }: { session: SessionImpact }) {\n const [expanded, setExpanded] = useState(false);\n\n if (session.messageCount === 0) return null;\n\n return (\n

\n {expanded && (\n

Carbon Receipt

\n Messages\n {session.messageCount}\n

\n Total CO{\"\\u2082\"}\n {formatEmissions(session.totalEmissionsUg)}\n

\n Total energy\n {formatEnergy(session.totalEnergyWms)}\n

\n Total inference time\n {formatTime(session.totalTimeMs)}\n

\n Conventional estimate\n \n ~\n {formatEmissions(\n getConventionalEstimate(session.totalEmissionsUg),\n )}\n \n

\n {getRelatableEquivalent(session.totalEmissionsUg)}\n

\n My AI conversation used only{\" \"}\n {formatEmissions(session.totalEmissionsUg)} — powered by GreenPT\n

\n \n Read more about our calculations\n \n

\n )}\n setExpanded(!expanded)}\n >\n {expanded ? \"Hide\" : \"View\"} impact summary\n \n

\n );\n}\n\nfunction InfrastructureBadge({\n globalStats,\n}: {\n globalStats: GlobalStats | null;\n}) {\n return (\n

\n \n \n Hosted on 100% renewable energy · Scaleway, Paris · EU data residency\n \n

\n {globalStats && globalStats.totalMessages > 0 && (\n

\n \n {globalStats.totalMessages}{\" \"}\n {globalStats.totalMessages === 1 ? \"message\" : \"messages\"} served ·{\" \"}\n {formatEmissions(globalStats.totalEmissionsUg)} CO{\"\\u2082\"} total\n \n

\n )}\n

\n );\n}\n\nexport function App() {\n const [messages, setMessages] = useState([]);\n const [input, setInput] = useState(\"\");\n const [isLoading, setIsLoading] = useState(false);\n const [sessionId, setSessionId] = useState(null);\n const [errorMessage, setErrorMessage] = useState(null);\n const [sessionImpact, setSessionImpact] = useState({\n totalEmissionsUg: 0,\n totalEnergyWms: 0,\n totalTimeMs: 0,\n messageCount: 0,\n });\n const [globalStats, setGlobalStats] = useState(null);\n const messagesContainerRef = useRef(null);\n const hasStartedChat = messages.length > 0;\n\n const canSend = useMemo(\n () => input.trim().length > 0 && !isLoading,\n [input, isLoading],\n );\n\n useEffect(() => {\n if (messagesContainerRef.current) {\n messagesContainerRef.current.scrollTop =\n messagesContainerRef.current.scrollHeight;\n }\n }, [messages, isLoading]);\n\n useEffect(() => {\n fetch(\"/api/stats\")\n .then((res) => res.json())\n .then((data: GlobalStats) => setGlobalStats(data))\n .catch(() => {});\n }, []);\n\n async function sendMessage(nextMessage: string) {\n const trimmedInput = nextMessage.trim();\n if (!trimmedInput || isLoading) {\n return;\n }\n\n const nextMessages: ChatMessage[] = [\n ...messages,\n { role: \"user\", content: trimmedInput },\n ];\n setMessages(nextMessages);\n setInput(\"\");\n setIsLoading(true);\n setErrorMessage(null);\n\n try {\n const response = await fetch(\"/api/chat\", {\n method: \"POST\",\n headers: {\n \"content-type\": \"application/json\",\n },\n body: JSON.stringify({\n sessionId,\n messages: nextMessages.map(({ role, content }) => ({\n role,\n content,\n })),\n }),\n });\n\n if (!response.ok) {\n const errorBody = (await response.json()) as { error?: string };\n throw new Error(errorBody.error ?? \"Failed to send chat message\");\n }\n\n setMessages([...nextMessages, { role: \"assistant\", content: \"\" }]);\n\n const reader = response.body!.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n\n buffer += decoder.decode(value, { stream: true });\n const { parsed, remaining } = parseSSEBuffer(buffer);\n buffer = remaining;\n\n for (const event of parsed) {\n if (event.type === \"chunk\") {\n const data = JSON.parse(event.data) as ChatChunkEvent;\n setMessages([\n ...nextMessages,\n { role: \"assistant\", content: data.assistantMessage },\n ]);\n } else if (event.type === \"done\") {\n const data = JSON.parse(event.data) as ChatDoneEvent;\n setSessionId(data.sessionId);\n\n // Build messages with impact on the last assistant message\n const updatedMessages: ChatMessage[] = data.messages.map(\n (msg, i) => {\n if (\n data.impact &&\n msg.role === \"assistant\" &&\n i === data.messages.length - 1\n ) {\n return { ...msg, impact: data.impact };\n }\n return msg;\n },\n );\n\n // Preserve impact from prior messages\n const finalMessages = updatedMessages.map((msg, i) => {\n const prev = nextMessages[i];\n if (prev?.impact && !msg.impact) {\n return { ...msg, impact: prev.impact };\n }\n return msg;\n });\n\n setMessages(finalMessages);\n\n // Update session totals\n if (data.impact) {\n const emUg = data.impact!.emissions?.total ?? 0;\n const enWms = data.impact!.energy?.total ?? 0;\n const tMs = data.impact!.inferenceTime?.total ?? 0;\n\n setSessionImpact((prev) => ({\n totalEmissionsUg: prev.totalEmissionsUg + emUg,\n totalEnergyWms: prev.totalEnergyWms + enWms,\n totalTimeMs: prev.totalTimeMs + tMs,\n messageCount: prev.messageCount + 1,\n }));\n\n setGlobalStats((prev) =>\n prev\n ? {\n totalMessages: prev.totalMessages + 1,\n totalEmissionsUg: prev.totalEmissionsUg + emUg,\n totalEnergyWms: prev.totalEnergyWms + enWms,\n totalTimeMs: prev.totalTimeMs + tMs,\n }\n : {\n totalMessages: 1,\n totalEmissionsUg: emUg,\n totalEnergyWms: enWms,\n totalTimeMs: tMs,\n },\n );\n }\n } else if (event.type === \"error\") {\n const data = JSON.parse(event.data) as ChatErrorEvent;\n throw new Error(data.error);\n }\n }\n }\n } catch (error) {\n const message =\n error instanceof Error ? error.message : \"Failed to send chat message\";\n setErrorMessage(message);\n } finally {\n setIsLoading(false);\n }\n }\n\n function onSubmit(event: FormEvent) {\n event.preventDefault();\n sendMessage(input);\n }\n\n function onKeyDown(event: KeyboardEvent) {\n if (event.key === \"Enter\" && !event.shiftKey && canSend) {\n event.preventDefault();\n sendMessage(input);\n }\n }\n\n return (\n

\n \n \n {!hasStartedChat && (\n

\n \n

Ask your local AI

\n Start a conversation with our eco-friendly AI assistant.\n
\n Ask questions, get help, or just chat!\n

\n\n

\n \n Try Asking\n

\n\n

\n {STARTER_PROMPTS.map((prompt) => (\n sendMessage(prompt)}\n disabled={isLoading}\n >\n {prompt}\n \n ))}\n

\n )}\n\n {messages.map((message, index) => {\n const isStreamingMsg =\n isLoading &&\n message.role === \"assistant\" &&\n index === messages.length - 1;\n\n return (\n \n

\n {message.role === \"assistant\" ? \"AI Assistant\" : \"You\"}\n

\n \n \n {message.content}\n \n

\n {message.role === \"assistant\" && message.impact && (\n \n )}\n \n );\n })}\n\n {isLoading && (\n

Assistant is typing...

\n )}\n {errorMessage &&

{errorMessage}

}\n \n\n {hasStartedChat && (\n

\n \n \n

\n )}\n\n \n \n );\n}\n",[],[203,353,344],"components","aa56e478-0cb6-11f1-8b14-42dde27851f2",["D",1771411410564],[265],[203,353],["D",1771411410564],[264,266,267,268,269],[203],"c103f1c4-0e5b-11f1-aa50-42dde27851f2","prompts.ts","24a3046a-0d79-11f1-a265-42dde27851f2",87,["D",1771592266777],"export const STARTER_PROMPTS = [\n \"How does GreenPT reduce the environmental impact of AI?\",\n \"Are the chats I'm having with my private?\",\n \"What is the environment impact of AI?\",\n \"I want to use AI to have more time for my life. Is there an alternative?\",\n];\n",[],[208,362],"8ed00088-0d7c-11f1-9014-42dde27851f2","2472dac4-0d79-11f1-93af-42dde27851f2",33,["D",1771496404816],"# Data Configuration\n\n## Starter Prompts\n\nThe starter prompts displayed to the user before a chat begins are configured in `prompts.ts`.\n\nTo update the prompts:\n1. Open `data/prompts.ts`.\n2. Modify the `STARTER_PROMPTS` array.\n3. Add, remove, or edit the strings as needed.\n\nExample:\n```typescript\nexport const STARTER_PROMPTS = [\n \"New prompt 1\",\n \"New prompt 2\",\n];\n```\n\n## Assistant System Prompt\n\nThe backend assistant system prompt is managed in Langfuse Prompt Management.\n\n- Prompt name is configured with `LANGFUSE_SYSTEM_PROMPT_NAME`.\n- Optionally fetch by label with `LANGFUSE_SYSTEM_PROMPT_LABEL`.\n- Optionally pin a specific version via `LANGFUSE_SYSTEM_PROMPT_VERSION`.\n- If Langfuse prompt retrieval fails, the backend falls back to a local default system prompt so chat still works.\n",[],[208,169],["D",1771494937646],[261,262],[208],"b10348cc-1254-11f1-bc96-42dde27851f2","tools.ts","b10349b2-1254-11f1-bc96-42dde27851f2",107,["D",1772029038144],"import { tool } from \"npm:@langchain/core/tools\";\nimport { z } from \"npm:zod\";\n\nexport const TOOLS_REQUIRING_APPROVAL = new Set([\"web_search\"]);\n\nconst getCurrentTime = tool(\n async () => {\n return new Date().toISOString();\n },\n {\n name: \"get_current_time\",\n description: \"Get the current date and time in ISO format. Use this when the user asks about the current time or date.\",\n schema: z.object({}),\n },\n);\n\nconst calculateCarbonComparison = tool(\n async ({ activity, durationMinutes }) => {\n const emissionsPerMinute: Record = {\n watching_tv: 90_000,\n driving_car: 4_500_000,\n using_laptop: 30_000,\n led_lightbulb: 10_000,\n smartphone_charging: 5_000,\n boiling_kettle: 200_000,\n streaming_video: 60_000,\n };\n\n const rate = emissionsPerMinute[activity];\n if (!rate) {\n return JSON.stringify({\n error: `Unknown activity. Available: ${Object.keys(emissionsPerMinute).join(\", \")}`,\n });\n }\n\n const totalUg = rate * durationMinutes;\n return JSON.stringify({\n activity,\n durationMinutes,\n totalEmissionsUg: totalUg,\n formatted: `${(totalUg / 1_000_000).toFixed(4)} g CO2e`,\n });\n },\n {\n name: \"calculate_carbon_comparison\",\n description:\n \"Calculate approximate CO2 emissions for everyday activities to compare with AI usage. \" +\n \"Available activities: watching_tv, driving_car, using_laptop, led_lightbulb, smartphone_charging, boiling_kettle, streaming_video.\",\n schema: z.object({\n activity: z.string().describe(\"The activity to calculate emissions for\"),\n durationMinutes: z.number().describe(\"Duration of the activity in minutes\"),\n }),\n },\n);\n\nconst webSearch = tool(\n async ({ query }) => {\n return JSON.stringify({\n query,\n results: [\n {\n title: \"Search result placeholder\",\n snippet:\n \"Web search integration is pending. This is a placeholder result for the query: \" +\n query,\n },\n ],\n note: \"Web search integration pending. Using placeholder results.\",\n });\n },\n {\n name: \"web_search\",\n description:\n \"Search the web for current information. This tool requires user approval before execution.\",\n schema: z.object({\n query: z.string().describe(\"The search query\"),\n }),\n },\n);\n\nexport const allTools = [getCurrentTime, calculateCarbonComparison, webSearch];\n",[],[214,380],"dc796298-1254-11f1-894c-42dde27851f2","langgraph.ts","906ab7a0-0cb7-11f1-908d-42dde27851f2",108,["D",1772029111045],13,"import OpenAI from \"npm:openai\";\nimport { Langfuse } from \"npm:langfuse\";\nimport { ChatOpenAI } from \"npm:@langchain/openai\";\nimport { StateGraph, START, END, MemorySaver } from \"npm:@langchain/langgraph\";\nimport { ToolNode } from \"npm:@langchain/langgraph/prebuilt\";\nimport { SystemMessage, AIMessage, HumanMessage } from \"npm:@langchain/core/messages\";\n\nimport {\n GraphState,\n type GraphStateType,\n type ChatMessage,\n type ChatResult,\n type ImpactData,\n type ToolCallInfo,\n type ApprovalPayload,\n toBaseMessages,\n fromBaseMessages,\n} from \"./graph-state.ts\";\nimport { allTools, TOOLS_REQUIRING_APPROVAL } from \"./tools.ts\";\n\nexport type { ChatMessage, ChatResult, ImpactData, ToolCallInfo, ApprovalPayload };\n\ntype StreamChunkHandler = (chunk: string) => void | Promise;\ntype ToolEventHandler = (info: ToolCallInfo) => void | Promise;\ntype ApprovalHandler = (payload: ApprovalPayload) => void | Promise;\n\nconst OPENAI_MODEL = Deno.env.get(\"OPENAI_MODEL\") ?? \"gpt-4o-mini\";\nconst OPENAI_BASE_URL = Deno.env.get(\"OPENAI_BASE_URL\");\nconst POST_PROCESS_TOKEN_THRESHOLD = 80;\nconst FALLBACK_SYSTEM_PROMPT =\n \"You are a clear, practical assistant that helps users make informed choices about AI usage, privacy, and environmental impact. Be concise, factual, and transparent about uncertainty.\";\n\nfunction requireEnv(name: string): string {\n const value = Deno.env.get(name);\n if (!value) throw new Error(`Missing required environment variable: ${name}`);\n return value;\n}\n\nfunction getLangfuseCredentials() {\n return {\n publicKey: requireEnv(\"LANGFUSE_PUBLIC_KEY\"),\n secretKey: requireEnv(\"LANGFUSE_SECRET_KEY\"),\n baseUrl: Deno.env.get(\"LANGFUSE_BASE_URL\") ?? \"https://langfuse.greenpt.ai\",\n };\n}\n\nfunction parsePromptVersion(value: string | undefined): number | undefined {\n if (!value) return undefined;\n const parsed = Number.parseInt(value, 10);\n return Number.isFinite(parsed) ? parsed : undefined;\n}\n\nfunction extractPromptContent(prompt: unknown): string {\n if (typeof prompt === \"string\") return prompt;\n if (!prompt || typeof prompt !== \"object\") return \"\";\n const candidate = prompt as Record;\n if (typeof candidate.prompt === \"string\") return candidate.prompt;\n if (typeof candidate.content === \"string\") return candidate.content;\n if (typeof candidate.text === \"string\") return candidate.text;\n if (Array.isArray(candidate.prompt)) {\n return candidate.prompt\n .filter((item) => item && typeof item === \"object\")\n .map((item) => item as Record)\n .filter((item) => item.role === \"system\" && typeof item.content === \"string\")\n .map((item) => item.content as string)\n .join(\"\\n\")\n .trim();\n }\n return \"\";\n}\n\ninterface ResolvedSystemPrompt {\n content: string;\n source: \"langfuse\" | \"fallback\";\n}\n\nasync function resolveSystemPrompt(langfuse: Langfuse): Promise {\n const promptName = Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_NAME\");\n if (!promptName) {\n return { content: FALLBACK_SYSTEM_PROMPT, source: \"fallback\" };\n }\n const promptLabel = Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_LABEL\");\n const configuredVersion = parsePromptVersion(Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_VERSION\"));\n try {\n const prompt =\n configuredVersion !== undefined\n ? await langfuse.getPrompt(promptName, configuredVersion)\n : promptLabel\n ? await langfuse.getPrompt(promptName, undefined, { label: promptLabel })\n : await langfuse.getPrompt(promptName);\n const content = extractPromptContent(prompt).trim();\n if (content.length > 0) {\n return { content, source: \"langfuse\" };\n }\n } catch {\n // Fallback below\n }\n return { content: FALLBACK_SYSTEM_PROMPT, source: \"fallback\" };\n}\n\n// --- Impact extraction via direct OpenAI call (dual-call approach) ---\nasync function extractImpact(\n messages: OpenAI.ChatCompletionMessageParam[],\n): Promise {\n const client = new OpenAI({\n apiKey: requireEnv(\"OPENAI_API_KEY\"),\n baseURL: OPENAI_BASE_URL || undefined,\n });\n\n try {\n const stream = await client.chat.completions.create({\n model: OPENAI_MODEL,\n messages,\n max_tokens: 1,\n stream: true,\n });\n\n let impact: ImpactData | undefined;\n for await (const chunk of stream) {\n const rawImpact = (chunk as Record).impact;\n if (rawImpact && typeof rawImpact === \"object\") {\n impact = rawImpact as ImpactData;\n }\n }\n return impact;\n } catch (err) {\n console.error(\"[impact-extraction] Failed:\", err);\n return undefined;\n }\n}\n\n// --- Graph construction ---\nfunction buildGraph(systemPromptContent: string) {\n const llm = new ChatOpenAI({\n model: OPENAI_MODEL,\n configuration: {\n baseURL: OPENAI_BASE_URL || undefined,\n apiKey: requireEnv(\"OPENAI_API_KEY\"),\n },\n }).bindTools(allTools);\n\n const postProcessLlm = new ChatOpenAI({\n model: OPENAI_MODEL,\n configuration: {\n baseURL: OPENAI_BASE_URL || undefined,\n apiKey: requireEnv(\"OPENAI_API_KEY\"),\n },\n });\n\n const toolNode = new ToolNode(allTools);\n\n // --- Nodes ---\n async function chatNode(state: GraphStateType): Promise> {\n const messagesWithSystem = [\n new SystemMessage(systemPromptContent),\n ...state.messages,\n ];\n const response = await llm.invoke(messagesWithSystem);\n return { messages: [response] };\n }\n\n async function postProcessNode(state: GraphStateType): Promise> {\n const lastMessage = state.messages[state.messages.length - 1];\n const originalContent =\n lastMessage._getType() === \"ai\" && typeof lastMessage.content === \"string\"\n ? lastMessage.content\n : \"\";\n\n if (!originalContent) {\n return { assistantMessage: \"\" };\n }\n\n // Skip post-processing for short responses\n const wordCount = originalContent.split(/\\s+/).length;\n if (wordCount <= POST_PROCESS_TOKEN_THRESHOLD) {\n return { assistantMessage: originalContent };\n }\n\n const result = await postProcessLlm.invoke([\n new SystemMessage(\n \"You are an editor. Rewrite the following AI response to be more concise and direct. \" +\n \"Remove filler phrases, unnecessary qualifications, and wordiness. \" +\n \"Preserve all factual content, formatting, and code blocks. \" +\n \"Do not add new information. Output only the rewritten response.\",\n ),\n new HumanMessage(originalContent),\n ]);\n\n const conciseContent =\n typeof result.content === \"string\" ? result.content : originalContent;\n return { assistantMessage: conciseContent };\n }\n\n // Approval node returns early — the graph pauses and the API layer handles the rest\n async function approvalNode(state: GraphStateType): Promise> {\n // This node is a signal. When the graph reaches here, we stop streaming\n // and return an approval request to the client. The actual pause is handled\n // by checking for this node in the streaming loop.\n return {};\n }\n\n // --- Routing ---\n function routeAfterChat(state: GraphStateType): string {\n const lastMessage = state.messages[state.messages.length - 1];\n // deno-lint-ignore no-explicit-any\n const toolCalls = (lastMessage as any).tool_calls ?? [];\n\n if (toolCalls.length === 0) {\n return \"postProcess\";\n }\n\n // deno-lint-ignore no-explicit-any\n const needsApproval = toolCalls.some((tc: any) =>\n TOOLS_REQUIRING_APPROVAL.has(tc.name),\n );\n\n if (needsApproval) {\n return \"approval\";\n }\n\n return \"tools\";\n }\n\n // --- Build graph ---\n const workflow = new StateGraph(GraphState)\n .addNode(\"chat\", chatNode)\n .addNode(\"tools\", toolNode)\n .addNode(\"approval\", approvalNode)\n .addNode(\"postProcess\", postProcessNode)\n .addEdge(START, \"chat\")\n .addConditionalEdges(\"chat\", routeAfterChat, {\n tools: \"tools\",\n approval: \"approval\",\n postProcess: \"postProcess\",\n })\n .addEdge(\"tools\", \"chat\")\n .addEdge(\"approval\", END) // Approval node ends the graph; frontend handles resume\n .addEdge(\"postProcess\", END);\n\n return workflow.compile({ checkpointer: new MemorySaver() });\n}\n\n// --- Main entry point ---\nexport async function runChatGraphStream(\n messages: ChatMessage[],\n sessionId: string,\n onChunk?: StreamChunkHandler,\n onToolStart?: ToolEventHandler,\n onToolResult?: ToolEventHandler,\n onApprovalNeeded?: ApprovalHandler,\n): Promise {\n const langfuse = new Langfuse(getLangfuseCredentials());\n const resolvedSystemPrompt = await resolveSystemPrompt(langfuse);\n console.log(\n `[system-prompt] source=${resolvedSystemPrompt.source}, length=${resolvedSystemPrompt.content.length}`,\n );\n\n const trace = langfuse.trace({\n sessionId,\n metadata: {\n runtime: \"val-town\",\n promptSource: resolvedSystemPrompt.source,\n graphEnabled: true,\n },\n });\n\n const graph = buildGraph(resolvedSystemPrompt.content);\n const langchainMessages = toBaseMessages(messages);\n\n const config = {\n configurable: { thread_id: sessionId },\n callbacks: [],\n };\n\n let assistantMessage = \"\";\n let approvalRequest: ApprovalPayload | undefined;\n\n // Stream the graph execution\n const streamResult = graph.stream(\n {\n messages: langchainMessages,\n sessionId,\n assistantMessage: \"\",\n impact: undefined,\n },\n { ...config, streamMode: \"updates\" as const },\n );\n\n for await (const update of await streamResult) {\n for (const [nodeName, nodeOutput] of Object.entries(update)) {\n const output = nodeOutput as Partial;\n\n if (nodeName === \"tools\" && output.messages) {\n // Tool execution results — notify frontend\n for (const msg of output.messages) {\n if (msg._getType() === \"tool\") {\n const toolName = (msg as { name?: string }).name ?? \"unknown\";\n const content = typeof msg.content === \"string\" ? msg.content : \"\";\n if (onToolResult) {\n await onToolResult({ name: toolName, args: { result: content } });\n }\n }\n }\n }\n\n if (nodeName === \"chat\" && output.messages) {\n // Check for tool calls to notify frontend\n for (const msg of output.messages) {\n // deno-lint-ignore no-explicit-any\n const toolCalls = (msg as any).tool_calls ?? [];\n for (const tc of toolCalls) {\n if (onToolStart) {\n await onToolStart({ name: tc.name, args: tc.args });\n }\n }\n }\n }\n\n if (nodeName === \"approval\" && output.messages) {\n // The approval node was reached — extract tool call info from the\n // preceding AI message (last message before this node's output)\n // We need the AI message that triggered the approval\n // It's in the graph state's messages, right before the approval node output\n // Actually the approval node adds no messages — we look at the stream\n // state accumulated so far. We saved tool calls from the chat node above.\n }\n\n if (nodeName === \"postProcess\") {\n assistantMessage = output.assistantMessage ?? \"\";\n }\n }\n }\n\n // Check if we ended at the approval node (approval request scenario)\n const finalState = await graph.getState(config);\n const allMessages = finalState.values.messages ?? [];\n if (allMessages.length > 0) {\n const lastAiMessage = allMessages[allMessages.length - 1];\n // deno-lint-ignore no-explicit-any\n const toolCalls = (lastAiMessage as any).tool_calls ?? [];\n const needsApproval = toolCalls.some(\n // deno-lint-ignore no-explicit-any\n (tc: any) => TOOLS_REQUIRING_APPROVAL.has(tc.name),\n );\n\n if (needsApproval && toolCalls.length > 0) {\n approvalRequest = {\n threadId: sessionId,\n toolCalls: toolCalls.map(\n // deno-lint-ignore no-explicit-any\n (tc: any) => ({ name: tc.name, args: tc.args ?? {} }),\n ),\n };\n\n if (onApprovalNeeded) {\n await onApprovalNeeded(approvalRequest);\n }\n }\n }\n\n // If no post-processing happened (e.g., approval flow), extract text from the\n // last AI message directly\n if (!assistantMessage && !approvalRequest) {\n for (let i = allMessages.length - 1; i >= 0; i--) {\n if (allMessages[i]._getType() === \"ai\") {\n const content = allMessages[i].content;\n if (typeof content === \"string\" && content) {\n assistantMessage = content;\n break;\n }\n }\n }\n }\n\n // Stream the final message to the frontend in chunks if we have one and\n // the post-process node produced it (not already streamed)\n if (assistantMessage && onChunk) {\n // Send the full message as a single chunk since we're using \"updates\" mode\n // rather than token-level streaming. Token-level streaming can be added\n // by switching to streamMode: \"messages\" in a future iteration.\n await onChunk(assistantMessage);\n }\n\n // Impact extraction: parallel direct OpenAI call\n const openaiMessages: OpenAI.ChatCompletionMessageParam[] = [\n { role: \"system\", content: resolvedSystemPrompt.content },\n ...messages.map((m) => ({\n role: m.role as \"user\" | \"assistant\",\n content: m.content,\n })),\n ];\n\n let impact: ImpactData | undefined;\n if (!approvalRequest) {\n impact = await extractImpact(openaiMessages);\n }\n\n // Langfuse generation tracking\n const generation = trace.generation({\n name: \"chat-graph\",\n model: OPENAI_MODEL,\n input: openaiMessages,\n });\n generation.end({ output: assistantMessage });\n await langfuse.flushAsync();\n\n const resultMessages = fromBaseMessages(allMessages);\n // Ensure the final assistant message is in the result\n if (\n assistantMessage &&\n (resultMessages.length === 0 ||\n resultMessages[resultMessages.length - 1].content !== assistantMessage)\n ) {\n // Replace the last assistant message content with post-processed version\n const lastIdx = resultMessages.length - 1;\n if (lastIdx >= 0 && resultMessages[lastIdx].role === \"assistant\") {\n resultMessages[lastIdx] = { role: \"assistant\", content: assistantMessage };\n }\n }\n\n return {\n assistantMessage,\n messages: resultMessages,\n impact,\n approvalRequest,\n };\n}\n\n// Resume after approval decision\nexport async function resumeAfterApproval(\n messages: ChatMessage[],\n sessionId: string,\n approved: boolean,\n pendingToolCalls: ToolCallInfo[],\n onChunk?: StreamChunkHandler,\n onToolStart?: ToolEventHandler,\n onToolResult?: ToolEventHandler,\n): Promise {\n if (!approved) {\n // User rejected — re-run the graph with a message saying tools were declined\n const augmentedMessages: ChatMessage[] = [\n ...messages,\n {\n role: \"user\",\n content:\n \"I declined the tool usage for: \" +\n pendingToolCalls.map((tc) => tc.name).join(\", \") +\n \". Please answer without using those tools.\",\n },\n ];\n return runChatGraphStream(augmentedMessages, sessionId, onChunk, onToolStart, onToolResult);\n }\n\n // User approved — re-run but this time the tools won't need approval\n // since the user already approved. We modify the approval set temporarily.\n // For v1 we simply re-run the conversation which will re-trigger the tools.\n // The approval node won't be hit because we remove the tools from the approval set.\n const approvedToolNames = new Set(pendingToolCalls.map((tc) => tc.name));\n for (const name of approvedToolNames) {\n TOOLS_REQUIRING_APPROVAL.delete(name);\n }\n\n try {\n return await runChatGraphStream(\n messages,\n sessionId + \"-approved\",\n onChunk,\n onToolStart,\n onToolResult,\n );\n } finally {\n // Restore the approval set\n for (const name of approvedToolNames) {\n TOOLS_REQUIRING_APPROVAL.add(name);\n }\n }\n}\n",[],[214,388],"2a52c126-1255-11f1-8391-42dde27851f2",["D",1772029241640],[],[214,33],"2f1461d2-0e69-11f1-8f46-42dde27851f2","queries.ts","2f146290-0e69-11f1-8f46-42dde27851f2","2ef968b4-0e69-11f1-a786-42dde27851f2",97,["D",1771598034934],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\nconst TABLE = \"greenpt_impact_v1\";\n\nexport interface ImpactRecord {\n sessionId: string;\n emissionsUg: number;\n energyWms: number;\n inferenceTimeMs: number;\n}\n\nexport interface GlobalStats {\n totalMessages: number;\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n}\n\nexport async function insertImpact(record: ImpactRecord): Promise {\n await sqlite.execute({\n sql: `INSERT INTO ${TABLE} (session_id, emissions_ug, energy_wms, inference_time_ms) VALUES (?, ?, ?, ?)`,\n args: [record.sessionId, record.emissionsUg, record.energyWms, record.inferenceTimeMs],\n });\n}\n\nexport async function getGlobalStats(): Promise {\n const result = await sqlite.execute(\n `SELECT COUNT(*) as cnt, COALESCE(SUM(emissions_ug), 0) as emissions, COALESCE(SUM(energy_wms), 0) as energy, COALESCE(SUM(inference_time_ms), 0) as time_ms FROM ${TABLE}`,\n );\n const row = result.rows[0];\n return {\n totalMessages: Number(row.cnt),\n totalEmissionsUg: Number(row.emissions),\n totalEnergyWms: Number(row.energy),\n totalTimeMs: Number(row.time_ms),\n };\n}\n",[],[214,409,401],"database","2f1f5786-0e69-11f1-9845-42dde27851f2","migrations.ts","2f1f586c-0e69-11f1-9845-42dde27851f2",100,["D",1771598034934],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\nconst TABLE = \"greenpt_impact_v1\";\n\nexport async function runMigrations() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n session_id TEXT,\n emissions_ug REAL NOT NULL DEFAULT 0,\n energy_wms REAL NOT NULL DEFAULT 0,\n inference_time_ms REAL NOT NULL DEFAULT 0,\n created_at TEXT NOT NULL DEFAULT (datetime('now'))\n )`);\n}\n",[],[214,409,411],"2ef967e2-0e69-11f1-a786-42dde27851f2",96,["D",1771598034757],[255,256],[214,409],["D",1771411796427],[254,257,258,259],[214],"8ea42c6a-0d7c-11f1-85b2-42dde27851f2",".gitkeep","8ea42d3c-0d7c-11f1-85b2-42dde27851f2",["D",1771496404535],"\n",[],[220,427],["D",1771496404338],[252],[220],["D",1771597882207],[],[226],"33fd7ed2-1250-11f1-b679-42dde27851f2","settings.local.json","0e28cc12-0e53-11f1-8dba-42dde27851f2",105,["D",1772027110400],3,"{\n \"permissions\": {\n \"allow\": [\n \"WebSearch\",\n \"WebFetch(domain:greenpt.com)\",\n \"mcp__ide__getDiagnostics\",\n \"WebFetch(domain:docs.langchain.com)\"\n ]\n }\n}\n",[],[232,440],["D",1771588530588],[249],[232],["D",1771411410564],{"_97":96,"_105":101},{"_97":96,"_98":99,"_100":101,"_18":-5,"_102":103,"_104":25},{"_13":86,"_32":87,"_44":43,"_88":45,"_89":-5,"_90":-5,"_91":-5,"_92":-5,"_93":-5,"_94":46,"_95":96},"ctx",{"_79":-5,"_125":488,"_489":490,"_491":492,"_493":25,"_494":25,"_495":25,"_496":123,"_497":-5,"_498":-7,"_499":-7,"_500":63,"_501":25,"_502":25,"_503":-5,"_504":25,"_505":25,"_506":25,"_507":-5},"ENV",{"_459":460,"_461":462,"_463":87,"_464":465,"_466":467,"_468":469,"_470":471,"_472":473,"_474":475,"_476":477,"_478":479,"_480":481,"_482":483,"_484":485,"_486":487},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","5015b8df46402d8bba4f485ecc37aec202b0e06b","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v110