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{\n Bot,\n InputFile,\n webhookCallback,\n} from \"https://deno.land/x/grammy@v1.35.0/mod.ts\";\n\nif (!Deno.env.get(\"TELEGRAM_TOKEN\")) {\n throw new Error(\"TELEGRAM_TOKEN is not set\");\n}\nconst telegramToken = Deno.env.get(\"TELEGRAM_TOKEN\")!;\n\nif (!Deno.env.get(\"OPENROUTER_API_KEY\")) {\n throw new Error(\"OPENROUTER_API_KEY is not set\");\n}\nconst openrouterApiKey = Deno.env.get(\"OPENROUTER_API_KEY\")!;\nconst openrouterApiKey2 = Deno.env.get(\"OPENROUTER_API_KEY2\");\n\nconst poeApiKey = Deno.env.get(\"POE_API_KEY\");\n\nconst bot = new Bot(telegramToken);\n\nconst OPENROUTER_MODELS = [\n \"meta-llama/llama-3.2-3b-instruct:free\",\n \"nousresearch/hermes-3-llama-3.1-405b:free\",\n \"arcee-ai/trinity-mini:free\"\n];\n\nconst VISION_MODELS = [\n \"nvidia/nemotron-nano-9b-v2:free\",\n \"nvidia/nemotron-nano-12b-v2-vl:free\",\n \"google/gemini-2.0-flash-exp:free\",\n \"google/gemini-flash-1.5-8b:free\",\n \"meta-llama/llama-3.2-11b-vision-instruct:free\"\n];\n\nconst DOCUMENT_MODELS = [\n \"tngtech/deepseek-r1t2-chimera:free\",\n \"tngtech/deepseek-r1t-chimera:free\",\n \"nex-agi/deepseek-v3.1-nex-n1:free\"\n];\n\nconst POE_IMAGE_BOTS = [\n \"Flux-schnell-DI\",\n \"Flux-1-Schnell-FW\",\n \"Flux-1-Schnell\",\n \"ImageResponseBotDemo\"\n];\n\nasync function generateWithOpenRouter(\n message: string,\n systemPrompt: string = \"You are a chat user. Answer concise.\",\n isSearch: boolean = false\n): Promise {\n const apiKeys = [openrouterApiKey];\n if (openrouterApiKey2) {\n apiKeys.push(openrouterApiKey2);\n }\n\n for (const apiKey of apiKeys) {\n for (const model of OPENROUTER_MODELS) {\n try {\n const response = await fetch(\"https://openrouter.ai/api/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: model,\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: message }\n ],\n }),\n });\n\n if (!response.ok) {\n console.error(`Model ${model} failed: ${response.status}`);\n continue;\n }\n\n const data = await response.json();\n return data.choices[0].message.content;\n } catch (error) {\n console.error(`Error with ${model}:`, error);\n continue;\n }\n }\n }\n throw new Error(\"All models failed\");\n}\n\nasync function generateWithVision(\n imageBase64: string,\n prompt: string\n): Promise {\n const apiKeys = [openrouterApiKey];\n if (openrouterApiKey2) {\n apiKeys.push(openrouterApiKey2);\n }\n\n for (const apiKey of apiKeys) {\n for (const model of VISION_MODELS) {\n try {\n console.log(`Vision attempt: ${model}`);\n const response = await fetch(\"https://openrouter.ai/api/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: model,\n messages: [\n {\n role: \"user\",\n content: [\n { type: \"text\", text: prompt },\n {\n type: \"image_url\",\n image_url: { url: `data:image/jpeg;base64,${imageBase64}` }\n }\n ]\n }\n ],\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Vision ${model} failed: ${response.status} - ${errorText}`);\n continue;\n }\n\n const data = await response.json();\n return data.choices[0].message.content;\n } catch (error) {\n console.error(`Vision error ${model}:`, error);\n continue;\n }\n }\n }\n throw new Error(\"All vision models failed\");\n}\n\nasync function extractTextFromDocument(fileBase64: string, fileName: string): Promise {\n try {\n const decoder = new TextDecoder(\"utf-8\", { fatal: false, ignoreBOM: true });\n const bytes = Uint8Array.from(atob(fileBase64), c => c.charCodeAt(0));\n\n const isPDF = bytes[0] === 0x25 && bytes[1] === 0x50 && bytes[2] === 0x44 && bytes[3] === 0x46;\n\n if (isPDF) {\n console.log(\"Detected PDF file - extracting text\");\n const pdfText = decoder.decode(bytes);\n const textChunks: string[] = [];\n\n const btMatches = pdfText.matchAll(/BT\\s+(.*?)\\s+ET/gs);\n for (const match of btMatches) {\n const content = match[1];\n const tjMatches = content.matchAll(/\\((.*?)\\)\\s*Tj/g);\n for (const tjMatch of tjMatches) {\n textChunks.push(tjMatch[1]);\n }\n }\n\n if (textChunks.length > 0) {\n return textChunks.join(' ').trim();\n }\n\n return pdfText.replace(/[^\\x20-\\x7E\\n\\r]/g, ' ')\n .replace(/\\s+/g, ' ')\n .trim();\n }\n\n const textContent = decoder.decode(bytes);\n const cleanText = textContent\n .replace(/\\r\\n/g, '\\n')\n .replace(/[^\\x20-\\x7E\\n\\r\\t]/g, '')\n .trim();\n\n return cleanText;\n } catch (error) {\n console.error(\"Text extraction error:\", error);\n throw new Error(`Failed to extract text: ${error instanceof Error ? error.message : 'Unknown error'}`);\n }\n}\n\nasync function processDocument(fileBase64: string, fileName: string, prompt: string): Promise {\n const apiKeys = [openrouterApiKey];\n if (openrouterApiKey2) {\n apiKeys.push(openrouterApiKey2);\n }\n\n const textContent = await extractTextFromDocument(fileBase64, fileName);\n\n if (textContent.length < 10) {\n throw new Error(\"Insufficient text content extracted from document\");\n }\n\n console.log(`Extracted ${textContent.length} characters from ${fileName}`);\n\n const maxLength = 12000;\n const documentContent = textContent.length > maxLength\n ? textContent.substring(0, maxLength) + \"\\n\\n[Document truncated...]\"\n : textContent;\n\n const systemPrompt = `analyze attached document: \"${fileName}\"`;\n const userMessage = `Document content:\\n\\n${documentContent}\\n\\nUser request: ${prompt}`;\n\n for (const apiKey of apiKeys) {\n for (const model of DOCUMENT_MODELS) {\n try {\n console.log(`Document processing attempt: ${model}`);\n const response = await fetch(\"https://openrouter.ai/api/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: model,\n messages: [\n { role: \"system\", content: systemPrompt },\n { role: \"user\", content: userMessage }\n ],\n max_tokens: 2000,\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Document model ${model} failed: ${response.status} - ${errorText}`);\n continue;\n }\n\n const data = await response.json();\n const result = data.choices[0].message.content;\n console.log(`Document processing success with ${model}`);\n return result;\n } catch (error) {\n console.error(`Document error ${model}:`, error);\n continue;\n }\n }\n }\n\n // Fallback: return extracted text\n return `📄 Document: ${fileName}\\n\\nExtracted text (first 1000 chars):\\n\\n${textContent.substring(0, 1000)}${textContent.length > 1000 ? '...' : ''}`;\n}\n\n// IMAGE poe\n\ninterface PoeAttachment {\n url: string;\n content_type: string;\n name: string;\n}\n\n/**\n * Parse SSE (Server-Sent Events) stream from Poe API\n * Returns the final text and any attachments\n */\nasync function parsePoeSSEResponse(response: Response): Promise<{ text: string; attachments: PoeAttachment[] }> {\n const reader = response.body?.getReader();\n if (!reader) throw new Error(\"No response body\");\n\n const decoder = new TextDecoder();\n let buffer = \"\";\n let finalText = \"\";\n const attachments: PoeAttachment[] = [];\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 lines = buffer.split(\"\\n\");\n buffer = lines.pop() || \"\";\n\n for (const line of lines) {\n if (line.startsWith(\"data: \")) {\n const dataStr = line.slice(6).trim();\n if (dataStr === \"[DONE]\") continue;\n\n try {\n const data = JSON.parse(dataStr);\n\n // Handle text content\n if (data.text) {\n finalText = data.text;\n }\n\n // Handle attachments (images)\n if (data.attachments && Array.isArray(data.attachments)) {\n for (const att of data.attachments) {\n if (att.url && att.content_type?.startsWith(\"image/\")) {\n attachments.push({\n url: att.url,\n content_type: att.content_type,\n name: att.name || \"image\"\n });\n }\n }\n }\n } catch {\n // Skip malformed JSON\n }\n }\n }\n }\n\n return { text: finalText, attachments };\n}\n\n/**\n * Generate image using Poe API with specified bot\n */\nasync function generateImageWithPoeBot(prompt: string, botName: string): Promise {\n if (!poeApiKey) {\n console.log(\"POE_API_KEY not configured\");\n return null;\n }\n\n try {\n console.log(`Trying Poe bot: ${botName}`);\n\n const response = await fetch(`https://api.poe.com/bot/${botName}`, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${poeApiKey}`,\n \"Content-Type\": \"application/json\",\n \"Accept\": \"text/event-stream\",\n },\n body: JSON.stringify({\n version: \"1.0\",\n type: \"query\",\n query: [\n {\n role: \"user\",\n content: prompt,\n content_type: \"text/markdown\"\n }\n ],\n user_id: \"\",\n conversation_id: \"\",\n message_id: crypto.randomUUID(),\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Poe ${botName} failed: ${response.status} - ${errorText}`);\n return null;\n }\n\n const { attachments } = await parsePoeSSEResponse(response);\n\n if (attachments.length > 0) {\n console.log(`Poe ${botName} generated image successfully`);\n return attachments[0].url;\n }\n\n console.log(`Poe ${botName} returned no image attachments`);\n return null;\n } catch (error) {\n console.error(`Poe ${botName} error:`, error);\n return null;\n }\n}\n\nasync function generateImageWithPoe(prompt: string): Promise {\n if (!poeApiKey) {\n console.log(\"POE_API_KEY not configured, skipping Poe\");\n return null;\n }\n\n for (const botName of POE_IMAGE_BOTS) {\n const imageUrl = await generateImageWithPoeBot(prompt, botName);\n if (imageUrl) {\n return imageUrl;\n }\n }\n\n console.log(\"All Poe image bots failed\");\n return null;\n}\n\n/**\n * Method: Pollinations.ai - Completely FREE, no API key needed\n * Uses FLUX model, returns image URL directly\n */\nasync function generateImageWithPollinations(prompt: string): Promise {\n try {\n console.log(\"Trying Pollinations.ai (free, no API key)\");\n\n const encodedPrompt = encodeURIComponent(prompt);\n const imageUrl = `https://image.pollinations.ai/prompt/${encodedPrompt}?width=1024&height=1024&nologo=true`;\n\n const checkResponse = await fetch(imageUrl, { method: \"HEAD\" });\n\n if (checkResponse.ok) {\n console.log(\"Pollinations.ai image URL generated successfully\");\n return imageUrl;\n }\n\n console.error(`Pollinations check failed: ${checkResponse.status}`);\n return null;\n } catch (error) {\n console.error(\"Pollinations.ai error:\", error);\n return null;\n }\n}\n\nasync function generateImageWithHuggingFace(prompt: string): Promise {\n try {\n console.log(\"Trying HuggingFace Inference API\"); // remove hugging face API logic\n\n const response = await fetch(\n \"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0\",\n {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ inputs: prompt }),\n }\n );\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(`HuggingFace failed: ${response.status} - ${errorText}`);\n return null;\n }\n\n const imageBlob = await response.blob();\n const arrayBuffer = await imageBlob.arrayBuffer();\n const base64 = btoa(String.fromCharCode(...new Uint8Array(arrayBuffer)));\n\n console.log(\"HuggingFace image generated successfully\");\n return `data:image/png;base64,${base64}`;\n } catch (error) {\n console.error(\"HuggingFace error:\", error);\n return null;\n }\n}\n\nasync function generateImage(prompt: string): Promise<{ url: string; isBase64: boolean } | null> {\n // Try Poe API first (higher quality, if configured)\n const poeUrl = await generateImageWithPoe(prompt);\n if (poeUrl) {\n return { url: poeUrl, isBase64: false };\n }\n\n // Try Pollinations (always free, no API key)\n const pollinationsUrl = await generateImageWithPollinations(prompt);\n if (pollinationsUrl) {\n return { url: pollinationsUrl, isBase64: false };\n }\n\n // Try HuggingFace as last resort\n const hfResult = await generateImageWithHuggingFace(prompt);\n if (hfResult) {\n const isBase64 = hfResult.startsWith(\"data:\");\n return { url: hfResult, isBase64 };\n }\n\n console.error(\"All image generation methods failed\");\n return null;\n}\n\ninterface AutoSearch {\n chatId: number;\n query: string;\n lastRun: number;\n}\n\nconst autoSearches = new Map();\n\nasync function performWebSearch(query: string): Promise {\n try {\n return await generateWithOpenRouter(query, \"You are a chat user. Answer concise.\", true);\n } catch (error) {\n console.error(\"Search error:\", error);\n return \"Search unavailable.\";\n }\n}\n\nasync function runAutoSearches() {\n const now = Date.now();\n const eightHours = 8 * 60 * 60 * 1000;\n\n for (const [key, search] of autoSearches.entries()) {\n if (now - search.lastRun >= eightHours) {\n try {\n const result = await performWebSearch(search.query);\n await bot.api.sendMessage(\n search.chatId,\n `🔍 Auto Search: \"${search.query}\"\\n\\n${result}`\n );\n search.lastRun = now;\n autoSearches.set(key, search);\n } catch (error) {\n console.error(`Auto search failed for ${search.chatId}:`, error);\n }\n }\n }\n}\n\nsetInterval(runAutoSearches, 30 * 60 * 1000);\n\nfunction isImageGenerationRequest(text: string): boolean {\n if (!text) return false;\n const lower = text.toLowerCase().trim();\n const keywords = [\"generate image\",\"Generate image\",\"Generate Image\", \"gpt photo\", \"create image\",\"Create image\", \"image of\", \"photo of\", \"draw\", \"illustrate\", \"/image\", \"--image\"];\n return keywords.some(k => lower.startsWith(k));\n}\n\nasync function downloadFile(fileId: string): Promise {\n try {\n const file = await bot.api.getFile(fileId);\n const url = `https://api.telegram.org/file/bot${telegramToken}/${file.file_path}`;\n const response = await fetch(url);\n if (!response.ok) {\n throw new Error(`File download failed: ${response.status}`);\n }\n return new Uint8Array(await response.arrayBuffer());\n } catch (error) {\n console.error(\"Download error:\", error);\n throw error;\n }\n}\n\n// ============ BOT COMMANDS ============\n\nbot.command(\"start\", (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) {\n return ctx.reply(\"Join group: https://t.me/chatgpt_deutsch\");\n }\n return ctx.reply(\"Poe AI access: https://poe.com/Open-Hat-Lab\");\n});\n\nbot.command(\"search\", async (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) return ctx.reply(\"Group only: https://t.me/chatgpt_deutsch\");\n\n const query = ctx.message?.text?.replace(\"/search\", \"\").trim();\n if (!query) return ctx.reply(\"Usage: /search \");\n\n try {\n await ctx.replyWithChatAction(\"typing\");\n const response = await generateWithOpenRouter(query, \"You are a chat user. Answer concise.\", true);\n await ctx.reply(response);\n } catch (error) {\n console.error(\"Search error:\", error);\n await ctx.reply(\"Search failed. Try: https://poe.com/Open-Hat-Lab\");\n }\n});\n\nbot.command(\"auto_search\", async (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) return ctx.reply(\"Group only\");\n\n const query = ctx.message?.text?.replace(\"/auto_search\", \"\").trim();\n if (!query) return ctx.reply(\"Usage: /auto_search \");\n\n const chatId = ctx.chat.id;\n const key = `${chatId}:${query}`;\n\n autoSearches.set(key, { chatId, query, lastRun: Date.now() });\n\n await ctx.replyWithChatAction(\"typing\");\n const result = await performWebSearch(query);\n await ctx.reply(`✅ Auto search: \"${query}\"\\nUpdates every 8h.\\n\\n${result}`);\n});\n\nbot.command(\"list_searches\", (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) return ctx.reply(\"Group only\");\n\n const searches = Array.from(autoSearches.values()).filter(s => s.chatId === ctx.chat.id);\n if (searches.length === 0) return ctx.reply(\"No active searches\");\n\n const list = searches.map((s, i) => `${i + 1}. \"${s.query}\"`).join(\"\\n\");\n return ctx.reply(`Active searches:\\n\\n${list}`);\n});\n\nbot.command(\"stop_search\", (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) return ctx.reply(\"Group only\");\n\n const query = ctx.message?.text?.replace(\"/stop_search\", \"\").trim();\n if (!query) return ctx.reply(\"Usage: /stop_search \");\n\n const key = `${ctx.chat.id}:${query}`;\n if (autoSearches.delete(key)) {\n return ctx.reply(`✅ Stopped: \"${query}\"`);\n }\n return ctx.reply(`❌ Not found: \"${query}\"`);\n});\n\nbot.command(\"image\", async (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) return ctx.reply(\"Group only: https://t.me/chatgpt_deutsch\");\n\n const prompt = ctx.message?.text?.replace(\"/image\", \"\").trim();\n if (!prompt || prompt.length < 3) {\n return ctx.reply(\"Usage: /image \");\n }\n\n try {\n await ctx.replyWithChatAction(\"upload_photo\");\n const result = await generateImage(prompt);\n\n if (result) {\n if (result.isBase64) {\n const base64Data = result.url.split(\",\")[1];\n const imageBuffer = Uint8Array.from(atob(base64Data), c => c.charCodeAt(0));\n await ctx.replyWithPhoto(new InputFile(imageBuffer, \"image.png\"), { caption: `🎨 \"${prompt}\"` });\n } else {\n await ctx.replyWithPhoto(result.url, { caption: `🎨 \"${prompt}\"` });\n }\n } else {\n await ctx.reply(\"Image generation failed. All providers unavailable.\");\n }\n } catch (error) {\n console.error(\"Image error:\", error);\n await ctx.reply(`Generation error: ${error instanceof Error ? error.message : 'Unknown'}`);\n }\n});\n\nbot.on(\"message\", async (ctx) => {\n const isGroup = ctx.chat?.type === \"group\" || ctx.chat?.type === \"supergroup\";\n if (!isGroup) {\n return ctx.reply(\"Join: https://t.me/chatgpt_deutsch\");\n }\n\n const messageText = ctx.message?.text || ctx.message?.caption || \"\";\n const photo = ctx.message?.photo;\n const document = ctx.message?.document;\n\n if (isImageGenerationRequest(messageText)) {\n let prompt = messageText;\n for (const prefix of [\"generate image\", \"Generate image\",\"Generate Image\", \"gpt photo\", \"create image\", \"Create image\",\"Create Image\", \"image of\", \"/image\"]) {\n if (prompt.toLowerCase().startsWith(prefix)) {\n prompt = prompt.substring(prefix.length).trim();\n break;\n }\n }\n if (prompt.length < 3) return ctx.reply(\"Provide image description\");\n\n try {\n await ctx.replyWithChatAction(\"upload_photo\");\n const result = await generateImage(prompt);\n\n if (result) {\n if (result.isBase64) {\n const base64Data = result.url.split(\",\")[1];\n const imageBuffer = Uint8Array.from(atob(base64Data), c => c.charCodeAt(0));\n await ctx.replyWithPhoto(new InputFile(imageBuffer, \"image.png\"), { caption: `🎨 \"${prompt}\"` });\n } else {\n await ctx.replyWithPhoto(result.url, { caption: `🎨 \"${prompt}\"` });\n }\n } else {\n await ctx.reply(\"All image providers unavailable.\");\n }\n } catch (error) {\n console.error(\"Auto image error:\", error);\n await ctx.reply(\"Use /image \");\n }\n return;\n }\n\n if (photo) {\n try {\n await ctx.replyWithChatAction(\"typing\");\n const largestPhoto = photo[photo.length - 1];\n console.log(`Processing photo: ${largestPhoto.file_id}`);\n\n const imageData = await downloadFile(largestPhoto.file_id);\n const base64 = btoa(String.fromCharCode(...imageData));\n const prompt = messageText || \"Describe image concise\";\n\n console.log(`Image size: ${imageData.length} bytes, prompt: ${prompt}`);\n\n const response = await generateWithVision(base64, prompt);\n await ctx.reply(response);\n } catch (error) {\n console.error(\"Vision error:\", error);\n await ctx.reply(`Image analysis failed: ${error instanceof Error ? error.message : 'Unknown error'}`);\n }\n return;\n }\n\n if (document) {\n try {\n await ctx.replyWithChatAction(\"typing\");\n const fileName = document.file_name || \"document\";\n console.log(`Processing document: ${fileName}, size: ${document.file_size} bytes`);\n\n if (document.file_size && document.file_size > 20 * 1024 * 1024) {\n return ctx.reply(\"Maximum size: 20MB\");\n }\n\n const fileData = await downloadFile(document.file_id);\n const base64 = btoa(String.fromCharCode(...fileData));\n const prompt = messageText || \"Summarize this document.\";\n\n console.log(`Document downloaded: ${fileData.length} bytes, processing with prompt: \"${prompt}\"`);\n\n const response = await processDocument(base64, fileName, prompt);\n await ctx.reply(response);\n } catch (error) {\n console.error(\"Document error:\", error);\n await ctx.reply(`Document processing failed: ${error instanceof Error ? error.message : 'Unknown error'}`);\n }\n return;\n }\n});\n\nbot.catch((err) => {\n console.error(\"Bot error:\", err);\n});\n\nconst SECRET_TOKEN = Deno.env.get(\"OPENROUTER_API_KEY\") || crypto.randomUUID();\nconst handleUpdate = webhookCallback(bot, \"std/http\", undefined, undefined, SECRET_TOKEN);\n\nexport default async (req: Request): Promise => {\n try {\n const url = new URL(req.url);\n const webhookUrl = `${url.protocol}//${url.host}${url.pathname}`;\n await bot.api.setWebhook(webhookUrl, { secret_token: SECRET_TOKEN });\n } catch (error) {\n console.error(\"Webhook setup error:\", error);\n }\n\n if (req.method === \"POST\") {\n try {\n return await handleUpdate(req);\n } catch (error) {\n console.error(\"Update error:\", error);\n return new Response(\"OK\", { status: 200 });\n }\n }\n\n return new Response(\"Bot active\", { status: 200, headers: { \"content-type\": \"text/html\" } });\n};","deleted","label",[99],"permalink","https://www.val.town/x/RobooHood/Free-Omni-Telegram/code/main.tsx","extension","actionData","errors"]