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process from \"node:process\";\nimport { OpenAI } from \"npm:openai\";\n\nconst IMAGE_COST = 400;\nconst TEXT_COST = 400;\nconst FILE_ANALYSIS_COST = 400;\nconst SEARCH_COST = 400;\n\nconst BOT_ACCESS_KEY = process.env.BOT_ACCESS_KEY;\n\ninterface ModelConfig {\n provider: \"google\" | \"poe\";\n model: string;\n endpoint: string;\n headers: Record;\n}\n\nconst GEMINI_PRO_CONFIG: ModelConfig = {\n provider: \"google\",\n model: \"gemini-2.5-pro\",\n endpoint:\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst GEMINI_FLASH_CONFIG: ModelConfig = {\n provider: \"google\",\n model: \"gemini-1.5-flash\",\n endpoint:\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst GEMINI_2_5_FLASH_CONFIG: ModelConfig = {\n provider: \"google\",\n model: \"gemini-2.5-flash\",\n endpoint:\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst GEMINI_VISION_CONFIG: ModelConfig = {\n provider: \"google\",\n model: \"models/gemini-2.5-flash\",\n endpoint:\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst POE_MODELS = {\n imageGeneration: [\n \"Flux-schnell\",\n \"Flux-schnell-DI\",\n \"ImageCreatorSD\",\n \"Free-GPT-Image-1\",\n ],\n textGeneration: [\n \"Qwen3-235B-A22B\",\n \"Gemini-1.5-Flash\",\n \"Llama-3.1-8b-DI\",\n \"Mistral-7b-v0.3-DI\",\n ],\n claudeSpecific: [\n \"Free-claude-sonnet-4\",\n \"Free-Claude-Opus-4.1\",\n ],\n fileAnalysis: [\n \"Qwen3-235B-A22B\",\n \"Gemini-1.5-Flash\",\n \"Llama-3.1-8b-DI\",\n ],\n imageAnalysis: [\n \"Llama-3.1-8b-DI\",\n \"Qwen3-235B-A22B\",\n \"Gemini-1.5-Flash\",\n ],\n search: [\n \"Gemini-1.5-Flash-Search\",\n \"Multi-Web-Search\",\n \"Compound-Mini\",\n ],\n};\n\nconst POE_API_KEYS = [\n process.env.POE_API_KEY,\n process.env.POE_API_KEY2,\n process.env.POE_API_KEY3,\n].filter(Boolean);\n\nfunction authenticateRequest(\n req: Request,\n): { isValid: boolean; error?: string } {\n if (!BOT_ACCESS_KEY) {\n console.warn(\"Warning: BOT_ACCESS_KEY is not set in environment variables\");\n return { isValid: true };\n }\n\n const providedKey =\n req.headers.get(\"Authorization\")?.replace(\"Bearer \", \"\") ||\n req.headers.get(\"X-API-Key\") ||\n req.headers.get(\"X-Bot-Access-Key\");\n\n if (!providedKey) {\n return {\n isValid: false,\n error:\n \"Missing authentication. Please provide BOT_ACCESS_KEY in Authorization header, X-API-Key header, or X-Bot-Access-Key header.\",\n };\n }\n\n if (providedKey !== BOT_ACCESS_KEY) {\n return {\n isValid: false,\n error: \"Invalid BOT_ACCESS_KEY. Access denied.\",\n };\n }\n\n return { isValid: true };\n}\n\nfunction createPoeClient(apiKey: string): OpenAI {\n return new OpenAI({\n apiKey: apiKey || \"YOUR_POE_API_KEY\",\n baseURL: \"https://api.poe.com/v1\",\n });\n}\n\nfunction selectOptimalTokens(contentLength: number, taskType: string): number {\n if (taskType === \"Image Generation\") return 1024;\n if (contentLength < 100) return 1024;\n if (contentLength < 500) return 1500;\n return 2048;\n}\n\nasync function callSinglePoeModel(\n model: string,\n prompt: string,\n apiKeyIndex: number,\n taskType: string,\n timeout: number = 15000,\n isImageGeneration: boolean = false,\n): Promise {\n try {\n const apiKey = POE_API_KEYS[apiKeyIndex];\n const poeClient = createPoeClient(apiKey);\n const maxTokens = selectOptimalTokens(prompt.length, taskType);\n\n const chatPromise = poeClient.chat.completions.create({\n model: model,\n messages: [{ role: \"user\", content: prompt }],\n max_tokens: maxTokens,\n temperature: 0.8,\n });\n\n const timeoutPromise = new Promise((_, reject) => {\n setTimeout(() => reject(new Error(\"Request timeout\")), timeout);\n });\n\n const chat = await Promise.race([chatPromise, timeoutPromise]);\n const message = chat.choices[0]?.message;\n const content = message?.content;\n\n if (\n isImageGeneration && message?.attachments &&\n message.attachments.length > 0\n ) {\n const imageAttachment = message.attachments.find((att) =>\n att.content_type && att.content_type.startsWith(\"image/\")\n );\n if (imageAttachment && imageAttachment.url) {\n return imageAttachment.url;\n }\n }\n\n return content && content.trim().length > 0 ? content : null;\n } catch (error) {\n console.error(\n `Model ${model} failed with API key ${apiKeyIndex + 1}:`,\n error.message,\n );\n return null;\n }\n}\n\nasync function callPoeModelParallel(\n models: string[],\n prompt: string,\n send: SendEventFn,\n taskType: string,\n timeout: number = 15000,\n isImageGeneration: boolean = false,\n): Promise {\n const parallelModels = models.slice(0, 2);\n const remainingModels = models.slice(2);\n\n const promises: Promise<\n { result: string | null; model: string; keyIndex: number }\n >[] = [];\n\n for (\n let keyIndex = 0;\n keyIndex < Math.min(POE_API_KEYS.length, 2);\n keyIndex++\n ) {\n for (const model of parallelModels) {\n promises.push(\n callSinglePoeModel(\n model,\n prompt,\n keyIndex,\n taskType,\n timeout,\n isImageGeneration,\n )\n .then((result) => ({ result, model, keyIndex })),\n );\n }\n }\n\n try {\n const results = await Promise.allSettled(promises);\n\n const successful = results.find((r) =>\n r.status === \"fulfilled\" && r.value.result !== null\n ) as PromiseFulfilledResult<\n { result: string; model: string; keyIndex: number }\n >;\n\n if (successful) {\n console.log(\n `Success with ${successful.value.model} (API key ${\n successful.value.keyIndex + 1\n })`,\n );\n\n if (isImageGeneration) {\n const urlMatch = successful.value.result.match(\n /https?:\\/\\/[^\\s]+\\.(jpg|jpeg|png|gif|webp)/i,\n );\n if (urlMatch) {\n send(\"replace_response\", {\n text:\n `🎨 **${taskType} by Gemini-2.5-Pro-Omni completed**\\n\\n![Generated Image](${\n urlMatch[0]\n })`,\n });\n return urlMatch[0];\n } else {\n send(\"replace_response\", {\n text:\n `🎨 **${taskType} by Gemini-2.5-Pro-Omni completed**\\n\\n${successful.value.result}`,\n });\n }\n } else {\n send(\"replace_response\", {\n text:\n `✅ **${taskType} by Gemini-2.5-Pro-Omni completed**\\n\\n${successful.value.result}`,\n });\n }\n return successful.value.result;\n }\n } catch (error) {\n console.error(\"Parallel model execution failed:\", error);\n }\n\n if (remainingModels.length > 0) {\n console.log(\"Trying remaining models sequentially...\");\n for (const model of remainingModels) {\n for (let keyIndex = 0; keyIndex < POE_API_KEYS.length; keyIndex++) {\n const result = await callSinglePoeModel(\n model,\n prompt,\n keyIndex,\n taskType,\n timeout,\n isImageGeneration,\n );\n if (result) {\n if (isImageGeneration) {\n const urlMatch = result.match(\n /https?:\\/\\/[^\\s]+\\.(jpg|jpeg|png|gif|webp)/i,\n );\n if (urlMatch) {\n send(\"replace_response\", {\n text:\n `🎨 **${taskType} by ${model} completed**\\n\\n![Generated Image](${\n urlMatch[0]\n })`,\n });\n return urlMatch[0];\n } else {\n send(\"replace_response\", {\n text: `🎨 **${taskType} by ${model} completed**\\n\\n${result}`,\n });\n }\n } else {\n send(\"replace_response\", {\n text: `✅ **${taskType} by ${model} completed**\\n\\n${result}`,\n });\n }\n return result;\n }\n }\n }\n }\n\n return null;\n}\n\nasync function imageToBase64(\n imageUrl: string,\n): Promise<{ data: string; mimeType: string }> {\n try {\n const response = await fetch(imageUrl, { timeout: 8000 });\n if (!response.ok) {\n throw new Error(`Failed to fetch image: ${response.status}`);\n }\n\n const arrayBuffer = await response.arrayBuffer();\n const buffer = Buffer.from(arrayBuffer);\n const mimeType = response.headers.get(\"content-type\") || \"image/jpeg\";\n\n return { data: buffer.toString(\"base64\"), mimeType: mimeType };\n } catch (error) {\n console.error(\"Error converting image to base64:\", error);\n throw error;\n }\n}\n\nasync function callGeminiApi(\n config: ModelConfig,\n prompt: string,\n send: SendEventFn,\n taskType: string,\n imageUrl?: string,\n): Promise {\n try {\n console.log(`Trying ${taskType} with ${config.model}`);\n\n let parts: any[] = [{ text: prompt }];\n\n if (imageUrl) {\n const { data: imageBase64, mimeType } = await imageToBase64(imageUrl);\n parts.push({\n inline_data: {\n mime_type: mimeType,\n data: imageBase64,\n },\n });\n }\n\n const maxTokens = selectOptimalTokens(prompt.length, taskType);\n\n const payload = {\n contents: [{\n role: \"user\",\n parts: parts,\n }],\n generationConfig: {\n temperature: 0.4,\n maxOutputTokens: maxTokens,\n candidateCount: 1,\n },\n };\n\n const url = `${config.endpoint}?key=${process.env.GEMINI_API_KEY}`;\n\n const response = await fetch(url, {\n method: \"POST\",\n headers: config.headers,\n body: JSON.stringify(payload),\n timeout: 15000,\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(\n `${config.model} API error: ${response.status} - ${errorText}`,\n );\n }\n\n const result = await response.json();\n const content = result.candidates?.[0]?.content?.parts?.[0]?.text;\n\n if (content && content.trim().length > 0) {\n send(\"replace_response\", {\n text: `🚀 **${taskType} completed by Gemini-2.5-Pro-Omni`,\n });\n return content;\n } else {\n throw new Error(`No content from ${config.model}`);\n }\n } catch (error) {\n console.error(`${config.model} attempt failed:`, error);\n return null;\n }\n}\n\nasync function generateImage(prompt: string, send: SendEventFn): Promise {\n let cleanPrompt = prompt;\n if (cleanPrompt.endsWith(\"--image\")) {\n cleanPrompt = cleanPrompt.substring(0, cleanPrompt.length - 7).trim();\n }\n\n const imagePrompt = `Detailed ${cleanPrompt}`;\n\n send(\"text\", { text: `🎨 Generating image...` });\n\n const result = await callPoeModelParallel(\n POE_MODELS.imageGeneration,\n imagePrompt,\n send,\n \"Image Generation\",\n 15000,\n true,\n );\n\n if (!result) {\n send(\"error\", {\n text: `❌ Image generation failed: All models exhausted.`,\n allow_retry: true,\n });\n }\n}\n\nasync function generateText(\n prompt: string,\n send: SendEventFn,\n useClaudeModels: boolean = false,\n): Promise {\n send(\"text\", { text: `💬 Processing...` });\n\n const shouldUseFast = prompt.length < 200 && !useClaudeModels;\n\n if (shouldUseFast) {\n const flashResult = await callGeminiApi(\n GEMINI_FLASH_CONFIG,\n prompt,\n send,\n \"Text Generation\",\n );\n if (flashResult) return;\n } else {\n const proResult = await callGeminiApi(\n GEMINI_PRO_CONFIG,\n prompt,\n send,\n \"Text Generation\",\n );\n if (proResult) return;\n }\n\n const models = useClaudeModels\n ? POE_MODELS.claudeSpecific\n : POE_MODELS.textGeneration;\n\n const poeResult = await callPoeModelParallel(\n models,\n prompt,\n send,\n \"Text Generation\",\n 15000,\n );\n\n if (!poeResult) {\n send(\"error\", {\n text: `❌ Text generation failed: All models exhausted.`,\n allow_retry: true,\n });\n }\n}\n\nasync function analyzeFile(\n fileContent: string,\n fileName: string,\n userPrompt: string,\n send: SendEventFn,\n): Promise {\n if (!fileContent || fileContent.trim().length === 0) {\n send(\"error\", {\n text:\n `❌ File analysis failed: No content found in ${fileName}. Please ensure the file is not empty and is in a supported format.`,\n allow_retry: true,\n });\n return;\n }\n\n const maxContentLength = 8000;\n let processedContent = fileContent;\n\n if (fileContent.length > maxContentLength) {\n processedContent = fileContent.substring(0, maxContentLength) +\n \"\\n\\n[Content truncated for analysis...]\";\n }\n\n let analysisPrompt: string;\n if (!userPrompt || userPrompt.trim().length === 0) {\n analysisPrompt =\n `Analyze file (${fileName}). Provide key insights and summary:\\n\\n${processedContent}`;\n } else {\n analysisPrompt =\n `${userPrompt}\\n\\nFile (${fileName}):\\n${processedContent}`;\n }\n\n send(\"text\", { text: `📄 Processing file: ${fileName}...` });\n\n const flashResult = await callGeminiApi(\n GEMINI_FLASH_CONFIG,\n analysisPrompt,\n send,\n \"File Analysis\",\n );\n if (flashResult) return;\n\n const poeResult = await callPoeModelParallel(\n POE_MODELS.fileAnalysis,\n analysisPrompt,\n send,\n \"File Analysis\",\n 20000,\n );\n\n if (!poeResult) {\n send(\"error\", {\n text: `❌ File analysis failed: All models exhausted.`,\n allow_retry: true,\n });\n }\n}\n\nasync function analyzeImage(\n imageUrl: string,\n userPrompt: string,\n send: SendEventFn,\n): Promise {\n let analysisPrompt: string;\n if (!userPrompt || userPrompt.trim().length === 0) {\n analysisPrompt =\n `Analyze this image in detail. Provide a comprehensive analysis including what you see, colors, objects, people, text, context, and any other relevant details.`;\n } else {\n analysisPrompt = `${userPrompt}`;\n }\n\n send(\"text\", { text: `🔍 Analyzing image...` });\n\n // Try Gemini-2.5-Flash first as the default\n const gemini25FlashResult = await callGeminiApi(\n GEMINI_2_5_FLASH_CONFIG,\n analysisPrompt,\n send,\n \"Image Analysis\",\n imageUrl,\n );\n\n if (gemini25FlashResult) return;\n\n // Fallback to Gemini Pro\n const geminiProResult = await callGeminiApi(\n GEMINI_PRO_CONFIG,\n analysisPrompt,\n send,\n \"Image Analysis\",\n imageUrl,\n );\n\n if (geminiProResult) return;\n\n // Final fallback to POE models\n const llamaPrompt = `Analyze image: ${imageUrl}\\n\\nUser's request: ${\n userPrompt || \"Provide detailed analysis\"\n }`;\n\n const poeResult = await callPoeModelParallel(\n POE_MODELS.imageAnalysis,\n llamaPrompt,\n send,\n \"Image Analysis\",\n 15000,\n );\n\n if (!poeResult) {\n send(\"error\", {\n text: `❌ Image analysis failed: All models exhausted.`,\n allow_retry: true,\n });\n }\n}\n\nasync function performSearch(query: string, send: SendEventFn): Promise {\n const searchPrompt =\n `Search for comprehensive, current information about: ${query}\\n\\nProvide detailed, accurate information with sources and references where available. Focus on the most recent and relevant information.`;\n\n send(\"text\", { text: `🔍 Searching: ${query}...` });\n\n const poeResult = await callPoeModelParallel(\n POE_MODELS.search,\n searchPrompt,\n send,\n \"Web Search\",\n 15000,\n );\n\n if (!poeResult) {\n const geminiResult = await callGeminiApi(\n GEMINI_FLASH_CONFIG,\n searchPrompt,\n send,\n \"Web Search\",\n );\n\n if (!geminiResult) {\n send(\"error\", {\n text: `❌ Search failed: All models exhausted.`,\n allow_retry: true,\n });\n }\n }\n}\n\nfunction detectIntent(\n content: string,\n hasAttachments: boolean,\n): { intent: string; useClaudeModels: boolean } {\n const lowerContent = content.toLowerCase();\n let useClaudeModels = false;\n\n if (content.endsWith(\"--claude\") || content.endsWith(\"--opus\")) {\n useClaudeModels = true;\n }\n\n let cleanContent = content;\n if (cleanContent.endsWith(\"--claude\")) {\n cleanContent = cleanContent.substring(0, cleanContent.length - 8).trim();\n } else if (cleanContent.endsWith(\"--opus\")) {\n cleanContent = cleanContent.substring(0, cleanContent.length - 6).trim();\n }\n\n if (cleanContent.endsWith(\"--image\")) {\n return { intent: \"image_generation\", useClaudeModels };\n }\n\n if (\n cleanContent.startsWith(\"search for \") ||\n cleanContent.endsWith(\"--search\")\n ) {\n return { intent: \"search\", useClaudeModels };\n }\n\n if (hasAttachments) {\n if (\n lowerContent.includes(\"image\") ||\n lowerContent.includes(\"picture\") ||\n lowerContent.includes(\"photo\") ||\n lowerContent.includes(\"analyze\") ||\n lowerContent.includes(\"what\") ||\n lowerContent.includes(\"describe\")\n ) {\n return { intent: \"image_analysis\", useClaudeModels };\n }\n return { intent: \"file_analysis\", useClaudeModels };\n }\n\n if (\n lowerContent.includes(\"generate image\") ||\n lowerContent.includes(\"create image\") ||\n lowerContent.includes(\"draw\") ||\n lowerContent.includes(\"picture of\") ||\n lowerContent.includes(\"image of\")\n ) {\n return { intent: \"image_generation\", useClaudeModels };\n }\n\n if (\n lowerContent.includes(\"search\") ||\n lowerContent.includes(\"find information\") ||\n lowerContent.includes(\"look up\") ||\n lowerContent.includes(\"what is\") ||\n lowerContent.includes(\"do a search\")\n ) {\n return { intent: \"search\", useClaudeModels };\n }\n\n return { intent: \"text_generation\", useClaudeModels };\n}\n\nasync function getResponse(req: Query, send: SendEventFn) {\n send(\"meta\", { content_type: \"text/markdown\" });\n\n const messages = req.query.map((msg) => ({\n role: msg.role === \"bot\"\n ? \"assistant\"\n : msg.role as \"user\" | \"assistant\" | \"system\",\n content: msg.content,\n attachments: msg.attachments || [],\n }));\n\n const lastUserMessage = messages.filter((m) => m.role === \"user\").pop();\n\n if (!lastUserMessage) {\n send(\"text\", { text: \"👋 What would you like to do today?\" });\n send(\"done\", {});\n return;\n }\n\n const userContent = lastUserMessage.content;\n const attachments = lastUserMessage.attachments || [];\n\n const { intent, useClaudeModels } = detectIntent(\n userContent,\n attachments.length > 0,\n );\n\n try {\n switch (intent) {\n case \"image_generation\":\n await generateImage(userContent, send);\n break;\n\n case \"file_analysis\":\n if (attachments.length > 0) {\n const file = attachments[0];\n let fileContent = file.parsed_content;\n\n if (!fileContent && file.name) {\n fileContent =\n `File: ${file.name} (${file.content_type})\\nFile URL: ${file.url}\\n\\nNote: Content could not be extracted. Please describe what you'd like to know about this file.`;\n }\n\n if (!fileContent || fileContent.trim().length === 0) {\n send(\"error\", {\n text:\n `❌ Could not extract content from ${file.name}. Please ensure the file is in a supported format and not corrupted.`,\n allow_retry: true,\n });\n break;\n }\n\n await analyzeFile(fileContent, file.name, userContent, send);\n } else {\n send(\"error\", {\n text: \"Please attach a file for analysis.\",\n allow_retry: false,\n });\n }\n break;\n\n case \"image_analysis\":\n if (attachments.length > 0) {\n const imageAttachment = attachments.find((att) =>\n att.content_type && att.content_type.startsWith(\"image/\")\n );\n\n if (imageAttachment && imageAttachment.url) {\n await analyzeImage(imageAttachment.url, userContent, send);\n } else {\n send(\"error\", {\n text: \"This bot doesn't support this file type.\",\n allow_retry: false,\n });\n }\n } else {\n send(\"error\", {\n text: \"Please attach an image for analysis.\",\n allow_retry: false,\n });\n }\n break;\n\n case \"search\":\n let cleanQuery = userContent;\n if (cleanQuery.startsWith(\"search the web \")) {\n cleanQuery = cleanQuery.substring(15).trim();\n }\n if (cleanQuery.endsWith(\"--search\")) {\n cleanQuery = cleanQuery.substring(0, cleanQuery.length - 8).trim();\n }\n if (cleanQuery.endsWith(\"--claude\")) {\n cleanQuery = cleanQuery.substring(0, cleanQuery.length - 8).trim();\n } else if (cleanQuery.endsWith(\"--opus\")) {\n cleanQuery = cleanQuery.substring(0, cleanQuery.length - 6).trim();\n }\n\n await performSearch(cleanQuery || userContent, send);\n break;\n\n case \"text_generation\":\n default:\n let cleanPrompt = userContent;\n if (cleanPrompt.endsWith(\"--claude\")) {\n cleanPrompt = cleanPrompt.substring(0, cleanPrompt.length - 8).trim();\n } else if (cleanPrompt.endsWith(\"--opus\")) {\n cleanPrompt = cleanPrompt.substring(0, cleanPrompt.length - 6).trim();\n }\n\n await generateText(cleanPrompt, send, useClaudeModels);\n break;\n }\n } catch (error) {\n console.error(\"Error in getResponse:\", error);\n send(\"error\", {\n text: `An unexpected error occurred: ${error.message}. Please try again.`,\n allow_retry: true,\n });\n }\n\n send(\"done\", {});\n}\n\nasync function getBotSettings(): Promise {\n const rateCard = \"| Type | Price |\\n\" +\n \"|-----------|-------|\\n\" +\n `| Input (image) | ${IMAGE_COST} points/message |\\n` +\n `| Input (file) | ${FILE_ANALYSIS_COST} points/message |\\n` +\n `| Input (text) | ${TEXT_COST} points/message |\\n` +\n `| Output (search) | ${SEARCH_COST} points/message |\\n` +\n `| Output (image) | ${IMAGE_COST} points/message |\\n`;\n\n return {\n allow_attachments: true,\n expand_text_attachments: true,\n enable_multi_bot_chat_prompting: true,\n rate_card: rateCard,\n cost_label: \"400 points\",\n };\n}\n\ntype BotSettings = {\n allow_attachments?: boolean;\n expand_text_attachments?: boolean;\n enable_multi_bot_chat_prompting?: boolean;\n rate_card?: string;\n cost_label?: string;\n};\n\ntype Query = {\n version: string;\n type: \"query\";\n query: {\n role: \"system\" | \"user\" | \"bot\";\n content: string;\n content_type: string;\n timestamp: number;\n message_id: string;\n feedback: {\n type: \"like\" | \"dislike\";\n reason?: string;\n }[];\n attachments: {\n url: string;\n content_type: string;\n name: string;\n parsed_content?: string;\n }[];\n }[];\n message_id: string;\n user_id: string;\n conversation_id: string;\n metadata: string;\n language_code: string;\n};\n\ntype Events = {\n meta: { content_type: string };\n text: { text: string };\n replace_response: { text: string };\n suggested_reply: { text: string };\n error: { allow_retry?: boolean; text: string };\n done: {};\n};\n\ntype SendEventFn = (\n eventName: EventName,\n data: Events[EventName],\n) => void;\n\nfunction encodeEvent(event: string, data: any = {}) {\n return new TextEncoder().encode(\n `event: ${event}\\ndata: ${JSON.stringify(data)}\\n\\n`,\n );\n}\n\nexport default async function (req: Request): Promise {\n if (req.method !== \"OPTIONS\") {\n const authResult = authenticateRequest(req);\n if (!authResult.isValid) {\n return new Response(\n JSON.stringify({\n error: authResult.error,\n status: \"authentication_failed\",\n }),\n {\n status: 401,\n headers: {\n \"Content-Type\": \"application/json\",\n \"WWW-Authenticate\": 'Bearer realm=\"Bot API\"',\n },\n },\n );\n }\n }\n\n const reqBody = await req.json().catch((e) => {\n console.error(\"body parse error\", e);\n return {};\n });\n\n console.log(\"body\", reqBody);\n\n if (reqBody.type === \"settings\") {\n return new Response(JSON.stringify(await getBotSettings()), {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n },\n });\n }\n\n const body = new ReadableStream({\n start(controller) {\n const send = (event, data) => {\n controller.enqueue(encodeEvent(event, data));\n };\n\n return getResponse(reqBody, send)\n .catch((e) => {\n console.error(\"get response error\", e);\n send(\"error\", {\n text: `Fatal error: ${e.message}`,\n allow_retry: true,\n });\n })\n .finally(() => controller.close());\n },\n });\n\n return new Response(body, {\n status: 200,\n headers: {\n \"Content-Type\": \"text/event-stream\",\n },\n });\n}","deleted","label",[98],"permalink","https://www.val.town/x/RobooHood/Gemini-Nano-Banana-05/code/main.tsx","extension","actionData","errors"]