[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":57,"_121":-5,"_122":-5,"_123":124},"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":31,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",6,"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","9757dbc0-a546-11f0-8cdd-0224a6c84d84","name","fileType","http","content","// @ts-ignore\nimport { Hono } from \"npm:hono@4.4.12\";\n// @ts-ignore\nimport { cors } from \"npm:hono@4.4.12/cors\";\n// @ts-ignore\nimport { blob } from \"https://esm.town/v/std/blob?v=11\";\n// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n\n// --- TYPE DEFINITIONS ---\ninterface DataObject {\n content_hash: string;\n content: string;\n summary: string;\n created_at: string;\n metadata: { key: string; value: string }[];\n relationships: {\n target_hash: string;\n target_summary?: string; // Optional for rendering\n description: string;\n }[];\n}\n\n// --- CONSTANTS FOR BLOB STORAGE KEYS ---\nconst MASTER_INDEX_KEY = \"master_index\";\nconst objectKey = (hash: string) => `object:${hash}`;\n\n// --- AI BEHAVIORAL GUIDELINES ---\nconst GENERATION_STYLE_GUIDE =\n \"As a data analysis engine, your operation must be precise and systematic. Adhere strictly to the requested JSON schema. All textual output, such as summaries and descriptions, should be articulated with clarity and professional diction, avoiding colloquialisms and speculative language. The objective is to produce structured data that is immediately and reliably machine-parsable.\";\n\nconst METADATA_EXTRACTION_PROMPT = `\nYou are a data analysis engine. Your function is to process unstructured text and convert it into a structured, machine-readable format.\nGiven the user's input text and a JSON string of existing data objects (represented by their content hash and summary), perform a comprehensive analysis.\nYour output must be a single, minified JSON object and nothing else.\n\nThe analysis must include:\n1. A one-sentence, neutral summary of the input text.\n2. Up to 5 relevant keyword tags for indexing.\n3. Key named entities (people, places, organizations, technical concepts).\n4. A quantitative sentiment analysis of the text.\n5. Proposed relationships between this new text and any of the provided existing data objects. A relationship should only be proposed if a clear, logical connection (e.g., refutation, expansion, citation) can be established.\n\nRespond with a JSON object conforming to this exact structure:\n{\n \"summary\": \"string\",\n \"tags\": [\"string\"],\n \"entities\": [{ \"name\": \"string\", \"type\": \"Person | Place | Organization | Concept\" }],\n \"sentiment\": { \"label\": \"positive | negative | neutral\", \"score\": number },\n \"relationships\": [{ \"target_hash\": \"string\", \"description\": \"string\" }]\n}\n\n${GENERATION_STYLE_GUIDE}\n`;\n\n// --- UTILITY FUNCTIONS ---\n/**\n * A simple, reliable SHA-256 hashing function for Deno.\n * @param text The string to hash.\n * @returns A hex-encoded SHA-256 hash.\n */\nasync function sha256(text: string): Promise {\n const ancome = new TextEncoder().encode(text);\n const hashBuffer = await crypto.subtle.digest(\"SHA-256\", ancome);\n const hashArray = Array.from(new Uint8Array(hashBuffer));\n return hashArray.map((b) => b.toString(16).padStart(2, \"0\")).join(\"\");\n}\n\n/**\n * Extracts a JSON object from a string, which may contain conversational text.\n * It finds the first '{' and the last '}' to isolate the JSON.\n * @param text The string containing the JSON.\n * @returns The parsed JSON object or null if parsing fails.\n */\nfunction extractJson(text: string): any | null {\n try {\n const jsonStart = text.indexOf(\"{\");\n const jsonEnd = text.lastIndexOf(\"}\");\n if (jsonStart === -1 || jsonEnd === -1) {\n return null;\n }\n const jsonString = text.substring(jsonStart, jsonEnd + 1);\n return JSON.parse(jsonString);\n } catch (error) {\n console.error(\"JSON extraction failed:\", error);\n return null;\n }\n}\n\n// --- HONO WEB APPLICATION ---\nconst app = new Hono();\n\napp.use(\"/api/*\", cors());\n\n// API Endpoint: Ingest a new data object.\napp.post(\"/api/ingest\", async (c) => {\n const { content } = await c.req.json();\n if (!content || typeof content !== \"string\") {\n return c.json({ error: \"Invalid content provided.\" }, 400);\n }\n\n const content_hash = await sha256(content);\n const existing = await blob.getJSON(objectKey(content_hash));\n\n if (existing) {\n return c.json({ hash: content_hash, status: \"exists\" }, 200);\n }\n\n // For relationship context, fetch a sample of existing object summaries.\n const masterIndex: string[] = await blob.getJSON(MASTER_INDEX_KEY) || [];\n const contextObjects = await Promise.all(\n masterIndex.slice(0, 50).map(async (hash) => {\n const obj: DataObject = await blob.getJSON(objectKey(hash));\n return { content_hash: obj.content_hash, summary: obj.summary };\n }),\n );\n\n // AI-powered metadata extraction.\n const openai = new OpenAI({ apiKey: Deno.env.get(\"OPENAI_API_KEY\") });\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: METADATA_EXTRACTION_PROMPT },\n {\n role: \"user\",\n content: `Input Text: \"${content}\"\\n\\nExisting Objects: ${\n JSON.stringify(contextObjects)\n }`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n\n const aiResponse = completion.choices[0].message.content;\n const analysis = extractJson(aiResponse);\n\n if (!analysis || !analysis.summary) {\n return c.json(\n { error: \"Failed to receive a valid analysis from AI.\" },\n 500,\n );\n }\n\n // Construct the full data object.\n const metadata: { key: string; value: string }[] = [];\n analysis.tags?.forEach((tag) => metadata.push({ key: \"tag\", value: tag }));\n analysis.entities?.forEach((e) =>\n metadata.push({ key: `entity:${e.type}`, value: e.name })\n );\n if (analysis.sentiment) {\n metadata.push({ key: \"sentiment_label\", value: analysis.sentiment.label });\n metadata.push({\n key: \"sentiment_score\",\n value: analysis.sentiment.score.toString(),\n });\n }\n\n const newObject: DataObject = {\n content_hash,\n content,\n summary: analysis.summary,\n created_at: new Date().toISOString(),\n metadata,\n relationships: analysis.relationships || [],\n };\n\n // Atomically store the new object and update the master index.\n await blob.setJSON(objectKey(content_hash), newObject);\n const updatedIndex = [content_hash, ...masterIndex];\n await blob.setJSON(MASTER_INDEX_KEY, updatedIndex);\n\n return c.json({ hash: content_hash, status: \"created\", analysis }, 201);\n});\n\n// API Endpoint: Retrieve all data objects with their metadata.\napp.get(\"/api/objects\", async (c) => {\n const masterIndex: string[] = await blob.getJSON(MASTER_INDEX_KEY) || [];\n if (masterIndex.length === 0) {\n return c.json([]);\n }\n\n // Fetch all objects in parallel.\n const objects: DataObject[] = (await Promise.all(\n masterIndex.map((hash) => blob.getJSON(objectKey(hash))),\n )).filter(Boolean); // Filter out any nulls from potential race conditions.\n\n // Enrich relationships with target summaries for easier rendering.\n const summaryMap = new Map(objects.map((o) => [o.content_hash, o.summary]));\n for (const obj of objects) {\n for (const rel of obj.relationships) {\n rel.target_summary = summaryMap.get(rel.target_hash) || \"Unknown Object\";\n }\n }\n\n // Sort by date descending.\n objects.sort((a, b) =>\n new Date(b.created_at).getTime() - new Date(a.created_at).getTime()\n );\n\n return c.json(objects);\n});\n\n// Main Route: Serve the HTML user interface.\napp.get(\"*\", (c) => {\n const sourceUrl = new URL(c.req.url).origin + \"/\";\n return c.html(generateHtml(sourceUrl));\n});\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string) {\n return `\n\n\n \n \n Semantic Lake :: Abstracted Storage Paradigm\n \n \n \n\n\n\n

Semantic Lake

\n \n

Lake Contents

\n\n\n\n`;\n}\n\n// Export the Hono app fetch handler for the Val Town runtime.\nexport default app.fetch;","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { Walrus } from \"npm:@wal-app/sdk\";\n\n// --- AI BEHAVIORAL GUIDELINES ---\nconst GENERATION_STYLE_GUIDE =\n \"As the AI strategist, you must adopt the persona of a senior brand consultant from a top-tier firm. Your analysis and suggestions must be articulated using precise, intelligent language. Avoid generic marketing phrases and clichés. The objective is to provide recommendations that are perceived as insightful and authentically composed by a human expert, not artificially generated.\";\n\nconst AI_AUDITOR_PROMPT = `\nYou are a world-class social media strategist and brand consultant.\nYou will be provided with a JSON array of a user's social media profiles.\nFor each profile, you must conduct a professional audit based on the provided data.\nYour analysis should be insightful, actionable, and communicated with professional, intelligent language.\n\nYou MUST respond ONLY with a single, minified JSON object.\nThe root object must have one key: \"audits\".\nThe \"audits\" value must be an array of objects, one for each network provided in the input.\nEach audit object must have the following keys:\n- \"platform\": The name of the social media platform (string).\n- \"username\": The user's handle on that platform (string).\n- \"positive_feedback\": One concise sentence highlighting a strength inferred from the data (e.g., follower count suggests strong community engagement) (string).\n- \"improvement_suggestion\": One actionable recommendation for enhancing the profile's effectiveness (string).\n- \"suggested_bio\": A new, professionally crafted biography for that specific platform, not exceeding 150 characters (string).\n\n${GENERATION_STYLE_GUIDE}\n`;\n\n// --- HTML & FRONTEND ---\nfunction generateHtml(sourceUrl: string) {\n return `\n\n\n \n \n AI Social Auditor\n \n \n \n\n\n\n

\n \n\n

Initiate an audit of the social media profiles connected to your Walrus account.

\n \n

\n\n

\n AI strategist is analyzing your profiles...\n

\n\n

\n \n

\n\n\n\n`;\n}\n\n// --- VAL TOWN HTTP HANDLER ---\nexport default async function (req: Request) {\n const url = new URL(req.url);\n\n // Handle the POST request for the audit action\n if (req.method === \"POST\") {\n try {\n const walrusApiKey = Deno.env.get(\"WALRUS_API_KEY\");\n if (!walrusApiKey) {\n throw new Error(\n \"The WALRUS_API_KEY environment variable is not set. Please add it to your val's secrets.\",\n );\n }\n\n // 1. Initialize Walrus and fetch social networks\n const walrus = new Walrus({ apiKey: walrusApiKey });\n const networks = await walrus.networks.get();\n\n if (!networks || networks.length === 0) {\n return new Response(JSON.stringify({ audits: [] }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n // 2. 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Diff · v6