[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_180":181,"_182":183},"routes/_app.x.$handle.$name",{"_106":105,"_38":51,"_115":96,"_33":116,"_13":14,"_117":-5,"_11":118,"_119":120,"_121":122,"_123":124,"_125":126,"_26":-5,"_127":-5,"_128":-5,"_129":31,"_130":131,"_132":65,"_133":-5,"_134":-5,"_135":136},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":-5},"project",{"_49":50,"_38":51,"_44":52,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":69,"_70":71,"_72":73,"_74":75,"_76":77,"_78":-5,"_79":80,"_81":80,"_82":83,"_84":83,"_85":83,"_86":80,"_87":80,"_88":89,"_90":91,"_92":93,"_94":31},"version",8,"changes",[25],"aiChatInfo",{"_20":21,"_22":-5,"_23":-5,"_24":-5},"userId","aiChatMessageId","946744a7-6965-45b6-a08e-f298a658db04","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle",{"_26":27,"_28":29,"_30":31,"_32":31,"_33":34,"_35":34},"file",{"_36":37,"_38":34,"_39":40,"_41":43,"_44":45,"_46":14,"_47":48,"_30":31},"previous",{"_36":37,"_38":34,"_39":40,"_41":42},"deleted",false,"deletedHere","path","lib.ts","latestPath","fileId","f9788d16-ae16-11f1-ad57-1607ee4eb77e","name","fileType","script","content","// Shared FAQ + personalities logic for the FAQ chatbot.\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport const SHEET_URL =\n \"https://docs.google.com/spreadsheets/d/e/2PACX-1vSerSBZETsDKevnovm-yTNi79xVoemUOdATL-rgxz99VoeNhG6q1L131fnjEsJr4gPMg-88jDSD7yM9/pub?output=csv\";\n\n// Cache the sheet so we don't refetch on every request.\nlet cached: { q: string; answers: string[] }[] | null = null;\nlet cachedAt = 0;\nconst CACHE_MS = 60_000;\n\nexport async function loadFaq(): Promise<{ q: string; answers: string[] }[]> {\n if (cached && Date.now() - cachedAt < CACHE_MS) return cached;\n const res = await fetch(SHEET_URL);\n const csv = await res.text();\n const rows = csv.trim().split(\"\\n\").map(splitCsv);\n\n const questions: { num: string; text: string }[] = [];\n const answersByQuestion: Record = {};\n let currentGroup: string | null = null;\n\n for (const [a, b] of rows) {\n const aTrim = (a ?? \"\").trim();\n const bTrim = (b ?? \"\").trim();\n\n const qMatch = aTrim.match(/^Pregunta\\s*(\\d+)/i);\n if (qMatch) {\n questions.push({ num: qMatch[1], text: bTrim });\n continue;\n }\n\n const rMatch = aTrim.match(/^Respuestas?\\s+a\\s+Pregunta\\s*(\\d+)/i);\n if (rMatch) {\n currentGroup = rMatch[1];\n answersByQuestion[currentGroup] = [bTrim];\n continue;\n }\n\n if (!aTrim && bTrim && currentGroup) {\n answersByQuestion[currentGroup].push(bTrim);\n }\n }\n\n cached = questions\n .map((q) => ({ q: q.text, answers: answersByQuestion[q.num] ?? [] }))\n .filter((f) => f.q && f.answers.length > 0);\n\n cachedAt = Date.now();\n return cached;\n}\n\nfunction splitCsv(line: string): string[] {\n const out: string[] = [];\n let cur = \"\";\n let inQuotes = false;\n for (const ch of line) {\n if (ch === '\"') inQuotes = !inQuotes;\n else if (ch === \",\" && !inQuotes) {\n out.push(cur);\n cur = \"\";\n } else cur += ch;\n }\n out.push(cur);\n return out;\n}\n\n// Fallback (no API key): pick the best matching question by keyword overlap,\n// return a random one of its answers.\nexport function keywordAnswer(\n question: string,\n faq: { q: string; answers: string[] }[],\n): string {\n const words = question.toLowerCase().split(/\\W+/).filter(Boolean);\n let best: { item: { q: string; answers: string[] }; score: number } | null = null;\n for (const item of faq) {\n const qWords = item.q.toLowerCase().split(/\\W+/).filter(Boolean);\n let score = 0;\n for (const w of words) if (qWords.includes(w)) score++;\n if (!best || score > best.score) best = { item, score };\n }\n if (!best || best.score === 0) {\n return \"Hmm, no tengo eso todavía. Intenta reformular, o pregúntame algo del FAQ.\";\n }\n const answers = best.item.answers;\n return answers[Math.floor(Math.random() * answers.length)];\n}\n\n// ------- Personalities -------\n// Each personality shapes how the bot talks while still grounded in the FAQ.\nexport const PERSONALITIES: Record = {\n amable: {\n name: \"Amable 🤗\",\n system:\n \"Sos un asistente amable, cálido y servicial que responde en español rioplatense (voseo). \" +\n \"Respuestas cortas y directas, con un toque de onda. Basate SIEMPRE en la info del FAQ que se te da. \" +\n \"Si no sabés, decilo con honestidad y buena onda.\",\n },\n docente: {\n name: \"Docente 📚\",\n system:\n \"Sos un profesor claro y didáctico que responde en español. Explicá lo que se pregunta de forma breve pero completa. \" +\n \"Basate SIEMPRE en la info del FAQ. Aportá 1 consejo corto para estudiar cuando aplique.\",\n },\n chistoso: {\n name: \"Chistoso 😜\",\n system:\n \"Sos un compa sarcástico pero piola que responde en español rioplatense (voseo). \" +\n \"Dá la respuesta corta del FAQ y sumale un toque de humor o chiste breve. No te pases de largo: la data primero.\",\n },\n breve: {\n name: \"Breve ⚡\",\n system:\n \"Sos ultralacónico. Respondé en español con UNA frase cortísima o incluso una palabra. \" +\n \"Basate SIEMPRE en la info del FAQ. Nada de rodeos.\",\n },\n};\n\nexport const DEFAULT_PERSONALITY = \"amable\";\n\n// ------- LLM layer -------\n// Uses OpenAI-compatible API. Works out of the box with OPENAI_API_KEY.\n// If it's an Anthropic key, set LLM_BASE_URL to https://api.anthropic.com/v1\n// and LLM_MODEL to e.g. claude-sonnet-4-20250514.\n// Without a key, falls back to keywordAnswer().\nconst LLM_BASE_URL = Deno.env.get(\"LLM_BASE_URL\") ?? \"https://api.openai.com/v1\";\nconst LLM_MODEL = Deno.env.get(\"LLM_MODEL\") ?? \"gpt-4o-mini\";\nconst LLM_API_KEY = Deno.env.get(\"LLM_API_KEY\") ?? Deno.env.get(\"OPENAI_API_KEY\");\n\n// A cheap fingerprint of the FAQ so the cache invalidates when the sheet changes.\nfunction faqSignature(faq: { q: string; answers: string[] }[]): string {\n let s = \"\";\n for (const f of faq) s += f.q + \"|\" + f.answers.join(\"^\") + \"\\n\";\n return s.slice(0, 2000);\n}\n\nasync function ensureCacheDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS answer_cache (\n key TEXT PRIMARY KEY, -- normalized question + personality\n faq_sig TEXT NOT NULL,\n answer TEXT NOT NULL,\n created_at INTEGER NOT NULL\n )`);\n}\n\n// Cost-saver: only hit the LLM the FIRST time a question is asked. Reuse the\n// stored answer for repeated questions until the FAQ changes.\nasync function cached(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n): Promise {\n await ensureCacheDb();\n const sig = faqSignature(faq);\n const key = personality + \"::\" + question.toLowerCase().trim();\n const r = await sqlite.execute({\n sql: \"SELECT answer, faq_sig FROM answer_cache WHERE key = ?\",\n args: [key],\n });\n const row = (r.rows ?? [])[0] as { answer: string; faq_sig: string } | undefined;\n if (!row) return null;\n if (row.faq_sig !== sig) return null; // sheet changed → re-ask\n return row.answer;\n}\n\nasync function storeCached(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n answer: string,\n) {\n await ensureCacheDb();\n await sqlite.execute({\n sql: \"INSERT OR REPLACE INTO answer_cache (key, faq_sig, answer, created_at) VALUES (?, ?, ?, ?)\",\n args: [personality + \"::\" + question.toLowerCase().trim(), faqSignature(faq), answer, Date.now()],\n });\n}\n\nexport async function llmAnswer(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n): Promise<{ text: string; usedAI: boolean }> {\n const persona = PERSONALITIES[personality] ?? PERSONALITIES[DEFAULT_PERSONALITY];\n\n // No API key configured → fall back to keyword matching (free).\n if (!LLM_API_KEY) {\n return { text: keywordAnswer(question, faq), usedAI: false };\n }\n\n // Reuse a prior answer for this question+personality when the FAQ unchanged.\n const hit = await cached(question, faq, personality);\n if (hit) {\n return { text: hit, usedAI: true };\n }\n\n const faqBlock = faq\n .map((f) => `P: ${f.q}\\nR: ${f.answers.join(\" | \")}`)\n .join(\"\\n\\n\");\n\n const res = await fetch(`${LLM_BASE_URL}/chat/completions`, {\n method: \"POST\",\n headers: {\n \"content-type\": \"application/json\",\n authorization: `Bearer ${LLM_API_KEY}`,\n },\n body: JSON.stringify({\n model: LLM_MODEL,\n temperature: 0.7,\n max_tokens: 150,\n messages: [\n { role: \"system\", content: persona.system },\n {\n role: \"system\",\n content:\n \"Este es el FAQ de referencia. Respondé basandote en el. \" +\n \"Si el usuario pregunta algo que NO esta en el FAQ, respondé en el mismo tono que no tenés esa info.\" +\n \"\\n\\n\" + faqBlock,\n },\n { role: \"user\", content: question },\n ],\n }),\n });\n\n if (!res.ok) {\n const err = await res.text();\n console.error(\"LLM error\", res.status, err);\n return { text: keywordAnswer(question, faq), usedAI: false };\n }\n\n const data = await res.json();\n const text = data?.choices?.[0]?.message?.content?.trim();\n if (!text) return { text: keywordAnswer(question, faq), usedAI: false };\n return { text, usedAI: true };\n}\n\n// ------- Conversation history (WhatsApp-style) -------\n// Persist messages per conversation id so a refresh keeps the thread.\nexport async function ensureDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS messages (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n convo TEXT NOT NULL,\n role TEXT NOT NULL, -- 'user' | 'bot'\n text TEXT NOT NULL,\n created_at INTEGER NOT NULL\n )`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_convo ON messages (convo, id)`);\n}\n\nexport async function addMessage(convo: string, role: string, text: string) {\n await ensureDb();\n await sqlite.execute({\n sql: \"INSERT INTO messages (convo, role, text, created_at) VALUES (?, ?, ?, ?)\",\n args: [convo, role, text, Date.now()],\n });\n}\n\nexport async function getMessages(convo: string) {\n await ensureDb();\n const r = await sqlite.execute({\n sql: \"SELECT role, text, created_at FROM messages WHERE convo = ? ORDER BY id ASC LIMIT 200\",\n args: [convo],\n });\n return (r.rows ?? []) as { role: string; text: string; created_at: number }[];\n}\n\nexport async function clearMessages(convo: string) {\n await ensureDb();\n await sqlite.execute({\n sql: \"DELETE FROM messages WHERE convo = ?\",\n args: [convo],\n });\n}\n","// Shared FAQ + personalities logic for the FAQ chatbot.\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport const SHEET_URL =\n \"https://docs.google.com/spreadsheets/d/e/2PACX-1vSerSBZETsDKevnovm-yTNi79xVoemUOdATL-rgxz99VoeNhG6q1L131fnjEsJr4gPMg-88jDSD7yM9/pub?output=csv\";\n\n// Cache the sheet so we don't refetch on every request.\nlet cached: { q: string; answers: string[] }[] | null = null;\nlet cachedAt = 0;\nconst CACHE_MS = 60_000;\n\nexport async function loadFaq(): Promise<{ q: string; answers: string[] }[]> {\n if (cached && Date.now() - cachedAt < CACHE_MS) return cached;\n const res = await fetch(SHEET_URL);\n const csv = await res.text();\n const rows = csv.trim().split(\"\\n\").map(splitCsv);\n\n const questions: { num: string; text: string }[] = [];\n const answersByQuestion: Record = {};\n let currentGroup: string | null = null;\n\n for (const [a, b] of rows) {\n const aTrim = (a ?? \"\").trim();\n const bTrim = (b ?? \"\").trim();\n\n const qMatch = aTrim.match(/^Pregunta\\s*(\\d+)/i);\n if (qMatch) {\n questions.push({ num: qMatch[1], text: bTrim });\n continue;\n }\n\n const rMatch = aTrim.match(/^Respuestas?\\s+a\\s+Pregunta\\s*(\\d+)/i);\n if (rMatch) {\n currentGroup = rMatch[1];\n answersByQuestion[currentGroup] = [bTrim];\n continue;\n }\n\n if (!aTrim && bTrim && currentGroup) {\n answersByQuestion[currentGroup].push(bTrim);\n }\n }\n\n cached = questions\n .map((q) => ({ q: q.text, answers: answersByQuestion[q.num] ?? [] }))\n .filter((f) => f.q && f.answers.length > 0);\n\n cachedAt = Date.now();\n return cached;\n}\n\nfunction splitCsv(line: string): string[] {\n const out: string[] = [];\n let cur = \"\";\n let inQuotes = false;\n for (const ch of line) {\n if (ch === '\"') inQuotes = !inQuotes;\n else if (ch === \",\" && !inQuotes) {\n out.push(cur);\n cur = \"\";\n } else cur += ch;\n }\n out.push(cur);\n return out;\n}\n\n// Fallback (no API key): pick the best matching question by keyword overlap,\n// return a random one of its answers.\nexport function keywordAnswer(\n question: string,\n faq: { q: string; answers: string[] }[],\n): string {\n const words = question.toLowerCase().split(/\\W+/).filter(Boolean);\n let best: { item: { q: string; answers: string[] }; score: number } | null = null;\n for (const item of faq) {\n const qWords = item.q.toLowerCase().split(/\\W+/).filter(Boolean);\n let score = 0;\n for (const w of words) if (qWords.includes(w)) score++;\n if (!best || score > best.score) best = { item, score };\n }\n if (!best || best.score === 0) {\n return \"Hmm, no tengo eso todavía. Intenta reformular, o pregúntame algo del FAQ.\";\n }\n const answers = best.item.answers;\n return answers[Math.floor(Math.random() * answers.length)];\n}\n\n// ------- Personalities -------\n// Each personality shapes how the bot talks while still grounded in the FAQ.\nexport const PERSONALITIES: Record = {\n amable: {\n name: \"Amable 🤗\",\n system:\n \"Sos un asistente amable, cálido y servicial que responde en español rioplatense (voseo). \" +\n \"Respuestas cortas y directas, con un toque de onda. Basate SIEMPRE en la info del FAQ que se te da. \" +\n \"Si no sabés, decilo con honestidad y buena onda.\",\n },\n docente: {\n name: \"Docente 📚\",\n system:\n \"Sos un profesor claro y didáctico que responde en español. Explicá lo que se pregunta de forma breve pero completa. \" +\n \"Basate SIEMPRE en la info del FAQ. Aportá 1 consejo corto para estudiar cuando aplique.\",\n },\n chistoso: {\n name: \"Chistoso 😜\",\n system:\n \"Sos un compa sarcástico pero piola que responde en español rioplatense (voseo). \" +\n \"Dá la respuesta corta del FAQ y sumale un toque de humor o chiste breve. No te pases de largo: la data primero.\",\n },\n breve: {\n name: \"Breve ⚡\",\n system:\n \"Sos ultralacónico. Respondé en español con UNA frase cortísima o incluso una palabra. \" +\n \"Basate SIEMPRE en la info del FAQ. Nada de rodeos.\",\n },\n};\n\nexport const DEFAULT_PERSONALITY = \"amable\";\n\n// ------- LLM layer -------\n// Uses OpenAI-compatible API. Works out of the box with OPENAI_API_KEY.\n// If it's an Anthropic key, set LLM_BASE_URL to https://api.anthropic.com/v1\n// and LLM_MODEL to e.g. claude-sonnet-4-20250514.\n// Without a key, falls back to keywordAnswer().\nconst LLM_BASE_URL = Deno.env.get(\"LLM_BASE_URL\") ?? \"https://api.openai.com/v1\";\nconst LLM_MODEL = Deno.env.get(\"LLM_MODEL\") ?? \"gpt-4o-mini\";\nconst LLM_API_KEY = Deno.env.get(\"LLM_API_KEY\") ?? Deno.env.get(\"OPENAI_API_KEY\");\n\n// A cheap fingerprint of the FAQ so the cache invalidates when the sheet changes.\nfunction faqSignature(faq: { q: string; answers: string[] }[]): string {\n let s = \"\";\n for (const f of faq) s += f.q + \"|\" + f.answers.join(\"^\") + \"\\n\";\n return s.slice(0, 2000);\n}\n\nasync function ensureCacheDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS answer_cache (\n key TEXT PRIMARY KEY, -- normalized question + personality\n faq_sig TEXT NOT NULL,\n answer TEXT NOT NULL,\n created_at INTEGER NOT NULL\n )`);\n}\n\n// Cost-saver: only hit the LLM the FIRST time a question is asked. Reuse the\n// stored answer for repeated questions until the FAQ changes.\nasync function cached(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n): Promise {\n await ensureCacheDb();\n const sig = faqSignature(faq);\n const key = personality + \"::\" + question.toLowerCase().trim();\n const r = await sqlite.execute({\n sql: \"SELECT answer, faq_sig FROM answer_cache WHERE key = ?\",\n args: [key],\n });\n const row = (r.rows ?? [])[0] as { answer: string; faq_sig: string } | undefined;\n if (!row) return null;\n if (row.faq_sig !== sig) return null; // sheet changed → re-ask\n return row.answer;\n}\n\nasync function storeCached(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n answer: string,\n) {\n await ensureCacheDb();\n await sqlite.execute({\n sql: \"INSERT OR REPLACE INTO answer_cache (key, faq_sig, answer, created_at) VALUES (?, ?, ?, ?)\",\n args: [personality + \"::\" + question.toLowerCase().trim(), faqSignature(faq), answer, Date.now()],\n });\n}\n\nexport async function llmAnswer(\n question: string,\n faq: { q: string; answers: string[] }[],\n personality: string,\n): Promise<{ text: string; usedAI: boolean }> {\n const persona = PERSONALITIES[personality] ?? PERSONALITIES[DEFAULT_PERSONALITY];\n\n // No API key configured → fall back to keyword matching (free).\n if (!LLM_API_KEY) {\n return { text: keywordAnswer(question, faq), usedAI: false };\n }\n\n // Reuse a prior answer for this question+personality when the FAQ unchanged.\n const hit = await cached(question, faq, personality);\n if (hit) {\n return { text: hit, usedAI: true };\n }\n\n const faqBlock = faq\n .map((f) => `P: ${f.q}\\nR: ${f.answers.join(\" | \")}`)\n .join(\"\\n\\n\");\n\n const res = await fetch(`${LLM_BASE_URL}/chat/completions`, {\n method: \"POST\",\n headers: {\n \"content-type\": \"application/json\",\n authorization: `Bearer ${LLM_API_KEY}`,\n },\n body: JSON.stringify({\n model: LLM_MODEL,\n temperature: 0.7,\n max_tokens: 150,\n messages: [\n { role: \"system\", content: persona.system },\n {\n role: \"system\",\n content:\n \"Este es el FAQ de referencia. Respondé basandote en el. \" +\n \"Si el usuario pregunta algo que NO esta en el FAQ, respondé en el mismo tono que no tenés esa info.\" +\n \"\\n\\n\" + faqBlock,\n },\n { role: \"user\", content: question },\n ],\n }),\n });\n\n if (!res.ok) {\n const err = await res.text();\n console.error(\"LLM error\", res.status, err);\n return { text: keywordAnswer(question, faq), usedAI: false };\n }\n\n const data = await res.json();\n const text = data?.choices?.[0]?.message?.content?.trim();\n if (!text) return { text: keywordAnswer(question, faq), usedAI: false };\n await storeCached(question, faq, personality, text); // save for next time\n return { text, usedAI: true };\n}\n\n// ------- Conversation history (WhatsApp-style) -------\n// Persist messages per conversation id so a refresh keeps the thread.\nexport async function ensureDb() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS messages (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n convo TEXT NOT NULL,\n role TEXT NOT NULL, -- 'user' | 'bot'\n text TEXT NOT NULL,\n created_at INTEGER NOT NULL\n )`);\n await sqlite.execute(`CREATE INDEX IF NOT EXISTS idx_convo ON messages (convo, id)`);\n}\n\nexport async function addMessage(convo: string, role: string, text: string) {\n await ensureDb();\n await sqlite.execute({\n sql: \"INSERT INTO messages (convo, role, text, created_at) VALUES (?, ?, ?, ?)\",\n args: [convo, role, text, Date.now()],\n });\n}\n\nexport async function getMessages(convo: string) {\n await ensureDb();\n const r = await sqlite.execute({\n sql: \"SELECT role, text, created_at FROM messages WHERE convo = ? ORDER BY id ASC LIMIT 200\",\n args: [convo],\n });\n return (r.rows ?? []) as { role: string; text: string; created_at: number }[];\n}\n\nexport async function clearMessages(convo: string) {\n await ensureDb();\n await sqlite.execute({\n sql: \"DELETE FROM messages WHERE convo = ?\",\n args: [convo],\n });\n}\n","createdAt",["D",1789155336688],"branchVersion","branchId","73555ffa-82f1-460d-966b-d6fe95aff5be","id","fa71ddb1-4d13-45e3-a4b2-9fb14ebe52e9","faq-chatbot",["D",1789154591216],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","FAQ chatbot reading from a Google Sheet CSV","imageUrl","19ccde2f-c4b0-46a6-82af-561e6df89055","pinnedFileId","3dc18bf4-ae16-11f1-b07b-1607ee4eb77e","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","01a091ea-ed57-7183-b991-f2c54fa7bd7e","tursoName","fa71ddb14d1345e3a4b29fb14ebe52e9","tursoHostname","fa71ddb14d1345e3a4b29fb14ebe52e9-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789503703835],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_106":105,"_114":110},"org",{"_106":105,"_107":108,"_109":110,"_19":61,"_111":112,"_113":31},"branch",{"_13":95,"_38":96,"_49":48,"_97":50,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":-5,"_103":51,"_104":105},"isOwned",49,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","diegoravecca","handle","displayName","Diego Ravecca","avatar","https://lh3.googleusercontent.com/a/ACg8ocJdEvC5DSZOpC-To6ZUw6Oc_ZfdazqGtQfI9BhzAz_txuHSM_Sw=s96-c","tier","pro","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_49":50,"_38":51,"_44":175,"_53":-5,"_54":31,"_55":56,"_57":56,"_58":59,"_60":-5,"_19":61,"_62":63,"_64":65,"_66":-5,"_67":-5,"_68":69,"_70":71,"_72":73,"_74":75,"_76":176,"_78":-5,"_79":80,"_81":80,"_82":83,"_84":83,"_85":83,"_86":80,"_87":80,"_88":177,"_90":178,"_92":179,"_94":31},"tree",[166,167],"files",[166,167],"counts",{"_155":69,"_156":157,"_158":69,"_159":160,"_161":162},"contents",[137,138],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_49":151,"_38":34,"_36":37,"_141":-5,"_47":48,"_46":14,"_143":31,"_44":152,"_13":153,"_39":40,"_41":43,"_30":31,"_148":-5,"_33":154},{"_49":139,"_38":140,"_36":63,"_141":-5,"_47":48,"_46":142,"_143":31,"_44":144,"_13":145,"_39":146,"_41":147,"_30":31,"_148":149,"_33":150},"92fd344b-e43f-473f-b332-c2c4d9d1e254","main.ts","parentId",6,"latest",["D",1789155034329],4,"http","// FAQ Chatbot — WhatsApp-style chat UI with personality + conversation history.\nimport {\n loadFaq,\n llmAnswer,\n addMessage,\n getMessages,\n clearMessages,\n PERSONALITIES,\n DEFAULT_PERSONALITY,\n} from \"./lib.ts\";\n\n/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from \"https://esm.sh/react@18.2.0\";\nimport { renderToStaticMarkup } from \"https://esm.sh/react-dom@18.2.0/server\";\n\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n const faq = await loadFaq();\n\n // A conversation id lets each \"thread\" keep its own history.\n const convo = url.searchParams.get(\"convo\") ?? \"default\";\n\n // POST /api/chat → receive { text }, answer, add messages, return new messages\n if (url.pathname === \"/api/chat\" && req.method === \"POST\") {\n const body = await req.json().catch(() => ({}));\n const text = (body.text ?? \"\").toString().trim();\n const personality: string = body.personality ?? DEFAULT_PERSONALITY;\n\n await addMessage(convo, \"user\", text);\n const { text: reply } = await llmAnswer(text, faq, personality);\n await addMessage(convo, \"bot\", reply);\n\n const history = await getMessages(convo);\n return new Response(JSON.stringify({ reply, history }), {\n headers: { \"content-type\": \"application/json\" },\n });\n }\n\n // POST /api/clear → wipe the conversation\n if (url.pathname === \"/api/clear\" && req.method === \"POST\") {\n await clearMessages(convo);\n return new Response(\"{}\", { headers: { \"content-type\": \"application/json\" } });\n }\n\n // GET \"/\" → render the chat UI (history + personality picker)\n const history = await getMessages(convo);\n\n const personas = Object.entries(PERSONALITIES).map(\n ([key, p]) => ``,\n ).join(\"\");\n\n const bubbles = history\n .map((m) => {\n const side = m.role === \"user\" ? \"user\" : \"bot\";\n const when = new Date(Number(m.created_at)).toLocaleTimeString(\"es-AR\", {\n hour: \"2-digit\",\n minute: \"2-digit\",\n });\n return `

${escapeHtml(m.text)}${when}

`;\n })\n .join(\"\");\n\n const page = renderToStaticMarkup(\n React.createElement(\n \"html\",\n null,\n React.createElement(\"head\", null,\n React.createElement(\"meta\", { charSet: \"utf-8\" }),\n React.createElement(\"title\", null, \"FAQ Chatbot\"),\n React.createElement(\"meta\", {\n name: \"viewport\",\n content: \"width=device-width, initial-scale=1\",\n }),\n React.createElement(\"style\", null, CSS),\n ),\n React.createElement(\"body\", null,\n React.createElement(\"div\", { id: \"app\" },\n React.createElement(\"header\", null,\n React.createElement(\"h1\", null, \"🤖 FAQ Chatbot\"),\n React.createElement(\"select\", { id: \"personality\", dangerouslySetInnerHTML: { __html: personas } }),\n React.createElement(\"button\", { id: \"clear\", className: \"clear\" }, \"🧹\"),\n ),\n React.createElement(\"div\", {\n id: \"messages\",\n dangerouslySetInnerHTML: { __html: bubbles },\n }),\n React.createElement(\"div\", { id: \"inputbar\" },\n React.createElement(\"input\", {\n id: \"input\",\n placeholder: \"Escribí tu pregunta…\",\n autocomplete: \"off\",\n }),\n React.createElement(\"button\", { id: \"send\" }, \"➤\"),\n ),\n ),\n React.createElement(\"script\", {\n dangerouslySetInnerHTML: {\n __html: \"window.__initialHistory = \" + JSON.stringify(history) + \";\",\n },\n }),\n React.createElement(\"script\", { dangerouslySetInnerHTML: { __html: CLIENT_JS } }),\n ),\n ),\n );\n\n return new Response(\"\\n\" + page, {\n headers: { \"content-type\": \"text/html; charset=utf-8\" },\n });\n}\n\nfunction escapeHtml(s: string): string {\n return s\n .replace(/&/g, \"&\")\n .replace(//g, \">\")\n .replace(/\"/g, \""\");\n}\n\nconst CSS = `\n * { box-sizing: border-box; }\n body { margin: 0; font-family: -apple-system, system-ui, sans-serif; background: #e5ddd5; }\n #app { display: flex; flex-direction: column; height: 100vh; max-width: 720px; margin: 0 auto; background: #efe7dd; border-left: 1px solid #d5cdc5; border-right: 1px solid #d5cdc5; }\n header { display: flex; align-items: center; gap: 10px; padding: 10px 14px; background: #075e54; color: #fff; position: sticky; top: 0; }\n header h1 { font-size: 17px; flex: 1; margin: 0; }\n header select { padding: 6px 8px; border-radius: 6px; border: none; font-size: 14px; }\n header .clear { background: rgba(255,255,255,.2); color: #fff; border: none; border-radius: 6px; padding: 6px 10px; cursor: pointer; font-size: 15px; }\n #messages { flex: 1; overflow-y: auto; padding: 14px; display: flex; flex-direction: column; gap: 4px; }\n .row { display: flex; width: 100%; }\n .row.user { justify-content: flex-end; }\n .row.bot { justify-content: flex-start; }\n .bubble { max-width: 78%; padding: 8px 12px; border-radius: 10px; font-size: 15px; line-height: 1.4; position: relative; box-shadow: 0 1px 1px rgba(0,0,0,.08); white-space: pre-wrap; }\n .bubble.user { background: #dcf8c6; border-bottom-right-radius: 2px; }\n .bubble.bot { background: #fff; border-bottom-left-radius: 2px; }\n .bubble .time { float: right; margin-left: 8px; margin-top: 6px; font-size: 11px; color: #999; }\n #inputbar { display: flex; gap: 8px; padding: 10px 14px; background: #f0f0f0; border-top: 1px solid #ddd; }\n #inputbar input { flex: 1; padding: 12px 14px; font-size: 16px; border: none; border-radius: 24px; outline: none; background: #fff; }\n #inputbar button { width: 46px; height: 46px; border-radius: 50%; border: none; background: #075e54; color: #fff; font-size: 18px; cursor: pointer; }\n`;\n\nconst CLIENT_JS = `\n const convo = new URLSearchParams(location.search).get(\"convo\") || \"default\";\n const messages = document.getElementById(\"messages\");\n const input = document.getElementById(\"input\");\n const send = document.getElementById(\"send\");\n const clearBtn = document.getElementById(\"clear\");\n const persona = document.getElementById(\"personality\");\n\n function scrollBottom() { messages.scrollTop = messages.scrollHeight; }\n\n // Re-render our bubbles from history sent by the server.\n function render(history) {\n messages.innerHTML = (history || []).map(m => {\n const side = m.role === \"user\" ? \"user\" : \"bot\";\n const when = new Date(Number(m.created_at)).toLocaleTimeString(\"es-AR\", { hour: \"2-digit\", minute: \"2-digit\" });\n return '

' + esc(m.text) + '' + when + '

';\n }).join(\"\");\n scrollBottom();\n }\n\n function esc(s) {\n return (s || \"\").replace(/&/g, \"&\").replace(//g, \">\");\n }\n\n async function sendMessage() {\n const text = input.value.trim();\n if (!text) return;\n input.value = \"\";\n // Optimistic bubble.\n render((window.__history || []).concat([\n { role: \"user\", text: text, created_at: Date.now() },\n { role: \"bot\", text: \"…\", created_at: Date.now() },\n ]));\n const res = await fetch(\"/api/chat?convo=\" + convo, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ text, personality: persona.value }),\n });\n const data = await res.json();\n window.__history = data.history;\n render(data.history);\n }\n\n send.addEventListener(\"click\", sendMessage);\n input.addEventListener(\"keydown\", (e) => { if (e.key === \"Enter\") sendMessage(); });\n clearBtn.addEventListener(\"click\", async () => {\n await fetch(\"/api/clear?convo=\" + convo, { method: \"POST\" });\n window.__history = [];\n render([]);\n });\n\n // Store the initial history handed to us in the HTML a moment ago. We read it\n // from the server-rendered DOM: there are no bubbles yet on first load if empty.\n window.__history = window.__initialHistory || [];\n scrollBottom();\n`;\n","label","primeroprimera",[140],"5cf25f47-cfd4-48f6-9c07-9de2163420f7",["D",1789155336688],2,[34],"pulls","branches",1,"forks","envVars",{"_163":157,"_165":69},"connections",{"_163":69,"_164":69},"count","unfilledCount","emptyCount",{"_49":151,"_38":34,"_36":37,"_141":-5,"_47":48,"_46":14,"_143":31,"_44":172,"_13":153,"_39":40,"_41":43,"_30":31,"_148":-5,"_169":173,"_33":174},{"_49":139,"_38":140,"_36":63,"_141":-5,"_47":48,"_46":142,"_143":31,"_44":168,"_13":145,"_39":146,"_41":147,"_30":31,"_148":149,"_169":170,"_33":171},["D",1789155034329],"children",[],[140],["D",1789155336688],[],[34],["D",1789154591216],["D",1789503703835],{"_106":105,"_114":110},{"_106":105,"_107":108,"_109":110,"_19":61,"_111":112,"_113":31},{"_13":95,"_38":96,"_49":48,"_97":50,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":-5,"_103":51,"_104":105},"ctx",{"_88":-5,"_134":213,"_214":215,"_216":217,"_218":31,"_219":31,"_220":31,"_221":65,"_222":-5,"_223":-7,"_224":-7,"_225":69,"_226":31,"_227":31,"_228":-5,"_229":31,"_230":31,"_231":31,"_232":-5},"ENV",{"_184":185,"_186":187,"_188":96,"_189":190,"_191":192,"_193":194,"_195":196,"_197":198,"_199":200,"_201":202,"_203":204,"_205":206,"_207":208,"_209":210,"_211":212},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","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 · v8