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Image Generator: 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simple poe bot\n\nthings to note:\n- don't put underscores (_) in the name, it stops working\n","deleted","label","children",[],[154],{"_104":174,"_95":99,"_155":175,"_157":-5,"_158":140,"_159":101,"_161":22,"_106":176,"_100":177,"_164":178,"_166":179,"_168":22,"_169":-5,"_170":180,"_98":181},"1a46f2ce-93a8-11f0-81ee-0224a6c84d84","019956ce-060b-771f-803f-06388b474b24",["D",1758101065299],4,"http","import process from \"node:process\";\nimport { OpenAI } from \"npm:openai\";\n\nconst IMAGE_COST = 200;\nconst TEXT_COST = 200;\nconst FILE_ANALYSIS_COST = 200;\nconst SEARCH_COST = 200;\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: \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst GEMINI_FALLBACK: ModelConfig = {\n provider: \"google\",\n model: \"gemini-2.0-flash-lite\",\n endpoint: \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-lite: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-pro\",\n endpoint: \"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n};\n\nconst POE_MODELS = {\n imageGeneration: [\n \"ImageCreatorSD\",\n \"Free-GPT-Image-1\",\n \"Flux-schnell-DI\",\n \"Flux-schnell\"\n ],\n textGeneration: [\n \"Llama-3.1-8b-DI\",\n \"Qwen3-235B-A22B\",\n \"Mistral-7b-v0.3-DI\",\n \"Gemini-1.5-Flash\"\n ],\n claudeSpecific: [\n \"Free-Claude-Opus-4.1\",\n \"Free-claude-sonnet-4\"\n ],\n fileAnalysis: [\n \"Llama-3.1-8b-DI\",\n \"Qwen3-235B-A22B\",\n \"Mistral-7b-v0.3-DI\",\n \"Gemini-1.5-Flash\"\n ],\n imageAnalysis: [\n \"Llama-3.1-8b-DI\",\n \"Qwen3-235B-A22B\",\n \"Mistral-7b-v0.3-DI\",\n \"Gemini-1.5-Flash\"\n ],\n search: [\n \"Multi-Web-Search\",\n \"Compound-Mini\",\n \"Compound-Online\",\n \"Gemini-1.5-Flash-Search\"\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 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\nasync function tryPoeModelsFirst(models: string[]): Promise {\n return models;\n}\n\nasync function callPoeModel(\n models: string[],\n prompt: string,\n send: SendEventFn,\n taskType: string,\n timeout: number = 30000,\n isImageGeneration: boolean = false\n): Promise {\n const orderedModels = await tryPoeModelsFirst(models);\n \n for (let keyIndex = 0; keyIndex < POE_API_KEYS.length; keyIndex++) {\n const apiKey = POE_API_KEYS[keyIndex];\n const poeClient = createPoeClient(apiKey);\n \n for (let modelIndex = 0; modelIndex < orderedModels.length; modelIndex++) {\n const model = orderedModels[modelIndex];\n \n try {\n console.log(`Trying ${taskType} with model: ${model} (API key ${keyIndex + 1})`);\n \n const chatPromise = poeClient.chat.completions.create({\n model: model,\n messages: [{ role: \"user\", content: prompt }],\n max_tokens: 2048,\n temperature: 0.7,\n });\n \n const timeoutPromise = new Promise((_, reject) => {\n setTimeout(() => reject(new Error('Request timeout')), timeout);\n });\n \n const chat = await Promise.race([chatPromise, timeoutPromise]);\n const message = chat.choices[0]?.message;\n const content = message?.content;\n \n // Handle image generation responses\n if (isImageGeneration && message?.attachments && message.attachments.length > 0) {\n const imageAttachment = message.attachments.find(att => \n att.content_type && att.content_type.startsWith('image/')\n );\n \n if (imageAttachment && imageAttachment.url) {\n send(\"replace_response\", { \n text: `🎨 **${taskType} completed**\\n\\n![Generated Image](${imageAttachment.url})` \n });\n return imageAttachment.url;\n }\n }\n \n // Handle regular text responses\n if (content && content.trim().length > 0) {\n if (isImageGeneration) {\n // For image generation, look for URLs in the content\n const urlMatch = content.match(/https?:\\/\\/[^\\s]+\\.(jpg|jpeg|png|gif|webp)/i);\n if (urlMatch) {\n send(\"replace_response\", { \n text: `🎨 **${taskType} completed**\\n\\n![Generated Image](${urlMatch[0]})` \n });\n return urlMatch[0];\n } else {\n send(\"replace_response\", { \n text: `🎨 **${taskType} completed**\\n\\n${content}` \n });\n }\n } else {\n send(\"text\", { text: `✅ **${taskType} completed.**` });\n }\n return content;\n }\n } catch (error) {\n console.error(`Model ${model} failed with API key ${keyIndex + 1}:`, error.message);\n send(\"text\", { text: `⏳ Main model failed, trying next model...` });\n continue;\n }\n }\n }\n \n return null;\n}\n\nasync function imageToBase64(imageUrl: string): Promise<{ data: string; mimeType: string }> {\n try {\n const response = await fetch(imageUrl, { timeout: 10000 });\n if (!response.ok) {\n throw new Error(`Failed to fetch image: ${response.status}`);\n }\n const arrayBuffer = await response.arrayBuffer();\n const buffer = Buffer.from(arrayBuffer);\n const mimeType = response.headers.get('content-type') || 'image/jpeg';\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 callGeminiPro(\n prompt: string,\n send: SendEventFn,\n taskType: string,\n imageUrl?: string\n): Promise {\n try {\n console.log(`Trying ${taskType} with Gemini 2.5 Pro`);\n send(\"text\", { text: `🚀 Processing with Gemini 2.5 Pro...` });\n\n let parts: any[] = [{ text: prompt }];\n if (imageUrl) {\n const { data: imageBase64, mimeType } = await imageToBase64(imageUrl);\n parts.push({\n inline_data: {\n mime_type: mimeType,\n data: imageBase64\n }\n });\n }\n\n const payload = {\n contents: [{\n role: \"user\",\n parts: parts\n }],\n generationConfig: {\n temperature: 0.3,\n maxOutputTokens: 2048,\n candidateCount: 1\n }\n };\n\n const url = `${GEMINI_PRO_CONFIG.endpoint}?key=${process.env.GEMINI_API_KEY}`;\n const response = await fetch(url, {\n method: \"POST\",\n headers: GEMINI_PRO_CONFIG.headers,\n body: JSON.stringify(payload),\n timeout: 20000\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`Gemini 2.5 Pro API error: ${response.status} - ${errorText}`);\n }\n\n const result = await response.json();\n const content = result.candidates?.[0]?.content?.parts?.[0]?.text;\n\n if (content && content.trim().length > 0) {\n send(\"replace_response\", { text: `🚀 **${taskType} completed using Gemini 2.5 Pro**\\n\\n${content}` });\n return content;\n } else {\n throw new Error(\"No content from Gemini 2.5 Pro\");\n }\n } catch (error) {\n console.error(\"Gemini 2.5 Pro attempt failed:\", error);\n send(\"text\", { text: `⚠️ Gemini 2.5 Pro failed, trying fallback models...` });\n return null;\n }\n}\n\nasync function callGeminiVision(\n prompt: string,\n imageUrl: string,\n send: SendEventFn,\n taskType: string\n): Promise {\n try {\n console.log(`Trying ${taskType} with Gemini Vision`);\n send(\"text\", { text: `🔍 Processing image with Gemini Vision...` });\n\n const { data: imageBase64, mimeType } = await imageToBase64(imageUrl);\n\n const payload = {\n contents: [{\n role: \"user\",\n parts: [\n { text: prompt },\n {\n inline_data: {\n mime_type: mimeType,\n data: imageBase64\n }\n }\n ]\n }],\n generationConfig: {\n temperature: 0.3,\n maxOutputTokens: 2048,\n candidateCount: 1\n }\n };\n\n const url = `${GEMINI_VISION_CONFIG.endpoint}?key=${process.env.GEMINI_API_KEY}`;\n const response = await fetch(url, {\n method: \"POST\",\n headers: GEMINI_VISION_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(`Vision API error: ${response.status} - ${errorText}`);\n }\n\n const result = await response.json();\n const content = result.candidates?.[0]?.content?.parts?.[0]?.text;\n\n if (content && content.trim().length > 0) {\n send(\"replace_response\", { text: `🔍 **${taskType} completed using Gemini Vision**\\n\\n${content}` });\n return content;\n } else {\n throw new Error(\"No content from Gemini Vision\");\n }\n } catch (error) {\n console.error(\"Gemini Vision attempt failed:\", error);\n send(\"text\", { text: `❌ Gemini Vision failed: ${error.message}` });\n return null;\n }\n}\n\nasync function callGeminiFirst(\n prompt: string,\n send: SendEventFn,\n taskType: string\n): Promise {\n try {\n console.log(`Trying ${taskType} with Gemini API`);\n send(\"text\", { text: `🔍 Processing with Gemini...` });\n\n const payload = {\n contents: [{\n role: \"user\",\n parts: [{ text: prompt }]\n }],\n generationConfig: {\n temperature: 0.3,\n maxOutputTokens: 2048,\n candidateCount: 1\n }\n };\n\n const url = `${GEMINI_FALLBACK.endpoint}?key=${process.env.GEMINI_API_KEY}`;\n const response = await fetch(url, {\n method: \"POST\",\n headers: GEMINI_FALLBACK.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(`Gemini API error: ${response.status} - ${errorText}`);\n }\n\n const result = await response.json();\n const content = result.candidates?.[0]?.content?.parts?.[0]?.text;\n\n if (content && content.trim().length > 0) {\n send(\"replace_response\", { text: `🔍 **${taskType} completed **\\n\\n${content}` });\n return content;\n } else {\n throw new Error(\"No content from Gemini\");\n }\n } catch (error) {\n console.error(\"Gemini first attempt failed:\", error);\n send(\"text\", { text: `❌ Gemini API failed: ${error.message}` });\n return null;\n }\n}\n\nasync function fallbackToGemini(\n prompt: string,\n send: SendEventFn,\n taskType: string\n): Promise {\n try {\n console.log(`Falling back to Gemini for ${taskType}`);\n send(\"text\", { text: `🔄 Trying Gemini fallback...` });\n\n const payload = {\n contents: [{\n role: \"user\",\n parts: [{ text: prompt }]\n }],\n generationConfig: {\n temperature: 0.7,\n maxOutputTokens: 2048,\n candidateCount: 1\n }\n };\n\n const url = `${GEMINI_FALLBACK.endpoint}?key=${process.env.GEMINI_API_KEY}`;\n const response = await fetch(url, {\n method: \"POST\",\n headers: GEMINI_FALLBACK.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(`Gemini API error: ${response.status} - ${errorText}`);\n }\n\n const result = await response.json();\n const content = result.candidates?.[0]?.content?.parts?.[0]?.text;\n\n if (content && content.trim().length > 0) {\n send(\"replace_response\", { text: `🔄 **${taskType} completed using Gemini fallback**\\n\\n${content}` });\n } else {\n throw new Error(\"No content from Gemini fallback\");\n }\n } catch (error) {\n console.error(\"Gemini fallback failed:\", error);\n send(\"error\", {\n text: `❌ ${taskType} failed: All models exhausted including Gemini fallback. Error: ${error.message}`,\n allow_retry: true\n });\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 send(\"text\", { text: `🎨 Generating image...` });\n\n const result = await callPoeModel(\n POE_MODELS.imageGeneration,\n imagePrompt,\n send,\n \"Image Generation\",\n 20000,\n true // This is the key change - telling the function this is image generation\n );\n\n if (!result) {\n await fallbackToGemini(imagePrompt, send, \"Image Generation\");\n }\n}\n\nasync function generateText(prompt: string, send: SendEventFn, useClaudeModels: boolean = false): Promise {\n let models = POE_MODELS.textGeneration;\n if (useClaudeModels) {\n models = POE_MODELS.claudeSpecific;\n }\n\n send(\"text\", { text: `💬 Generating response...` });\n\n const geminiProResult = await callGeminiPro(prompt, send, \"Text Generation\");\n if (!geminiProResult) {\n const result = await callPoeModel(\n models,\n prompt,\n send,\n \"Text Generation\",\n 25000\n );\n\n if (!result) {\n await fallbackToGemini(prompt, send, \"Text Generation\");\n }\n }\n}\n\nasync function analyzeFile(fileContent: string, fileName: string, userPrompt: string, send: SendEventFn): Promise {\n if (!fileContent || fileContent.trim().length === 0) {\n send(\"error\", {\n text: `❌ File analysis failed: No content found in ${fileName}. Please ensure the file is not empty and is in a supported format.`,\n allow_retry: true\n });\n return;\n }\n\n const maxContentLength = 10000;\n let processedContent = fileContent;\n if (fileContent.length > maxContentLength) {\n processedContent = fileContent.substring(0, maxContentLength) + \"\\n\\n[Content truncated for analysis...]\";\n }\n\n let analysisPrompt: string;\n if (!userPrompt || userPrompt.trim().length === 0) {\n analysisPrompt = `Analyze file (${fileName}). Provide comprehensive analysis including key insights, summary and any relevant observations:\\n\\n${processedContent}`;\n } else {\n analysisPrompt = `${userPrompt}\\n\\nFile (${fileName}):\\n${processedContent}`;\n }\n\n send(\"text\", { text: `📄 Processing file: ${fileName}...` });\n\n const geminiProResult = await callGeminiPro(analysisPrompt, send, \"File Analysis\");\n if (!geminiProResult) {\n const geminiResult = await callGeminiFirst(analysisPrompt, send, \"File Analysis\");\n if (!geminiResult) {\n const poeResult = await callPoeModel(\n POE_MODELS.fileAnalysis,\n analysisPrompt,\n send,\n \"File Analysis\",\n 45000\n );\n\n if (!poeResult) {\n await fallbackToGemini(analysisPrompt, send, \"File Analysis\");\n }\n }\n }\n}\n\nasync function analyzeImage(imageUrl: string, userPrompt: string, send: SendEventFn): Promise {\n let analysisPrompt: string;\n if (!userPrompt || userPrompt.trim().length === 0) {\n analysisPrompt = `Analyze this image in detail. Provide a comprehensive analysis including what you see, colors, objects, people, text, context, and any other relevant details.`;\n } else {\n analysisPrompt = `${userPrompt}`;\n }\n\n const geminiProResult = await callGeminiPro(analysisPrompt, send, \"Image Analysis\", imageUrl);\n if (!geminiProResult) {\n const geminiResult = await callGeminiVision(analysisPrompt, imageUrl, send, \"Image Analysis\");\n if (!geminiResult) {\n const fallbackPrompt = `Analyze image: ${imageUrl}\\n\\nUser's request: ${userPrompt || \"Provide detailed analysis\"}\\n\\n`;\n \n const poeResult = await callPoeModel(\n POE_MODELS.imageAnalysis,\n fallbackPrompt,\n send,\n \"Image Analysis\",\n 25000\n );\n\n if (!poeResult) {\n await fallbackToGemini(fallbackPrompt, send, \"Image Analysis\");\n }\n }\n }\n}\n\nasync function performSearch(query: string, send: SendEventFn): Promise {\n const searchPrompt = `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 for: ${query}...` });\n\n const geminiProResult = await callGeminiPro(searchPrompt, send, \"Web Search\");\n if (!geminiProResult) {\n const geminiResult = await callGeminiFirst(searchPrompt, send, \"Web Search\");\n if (!geminiResult) {\n const poeResult = await callPoeModel(\n POE_MODELS.search,\n searchPrompt,\n send,\n \"Web Search\",\n 20000\n );\n\n if (!poeResult) {\n await fallbackToGemini(searchPrompt, send, \"Web Search\");\n }\n }\n }\n}\n\nfunction detectIntent(content: string, hasAttachments: boolean): { 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 (cleanContent.startsWith('search for ') || cleanContent.endsWith('--search')) {\n return { intent: 'search', useClaudeModels };\n }\n\n if (hasAttachments) {\n if (lowerContent.includes('image') || lowerContent.includes('picture') || lowerContent.includes('photo') || \n lowerContent.includes('analyze') || lowerContent.includes('what') || lowerContent.includes('describe')) {\n return { intent: 'image_analysis', useClaudeModels };\n }\n return { intent: 'file_analysis', useClaudeModels };\n }\n\n if (lowerContent.includes('generate image') || lowerContent.includes('create image') || \n lowerContent.includes('draw') || lowerContent.includes('picture of') || lowerContent.includes('image of')) {\n return { intent: 'image_generation', useClaudeModels };\n }\n\n if (lowerContent.includes('search') || lowerContent.includes('find information') || \n lowerContent.includes('look up') || lowerContent.includes('what is') || lowerContent.includes('do a search')) {\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\" ? \"assistant\" : 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(userContent, attachments.length > 0);\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 = `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: `❌ 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(7).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 = \"| Task Type | Price |\\n\" +\n \"|-----------|-------|\\n\" +\n `| Input (image) | ${IMAGE_COST} points |\\n` +\n `| Input (file) | ${FILE_ANALYSIS_COST} points |\\n` +\n `| Input (text) | ${TEXT_COST} points |\\n` +\n `| Output (search) | ${SEARCH_COST} points |\\n`+\n `| Output (image) | ${IMAGE_COST} points |\\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: \"200 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 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: ${e.message}`,\n allow_retry: true\n });\n })\n .finally(() => controller.close());\n },\n });\n\n return new Response(body, {\n status: 200,\n headers: {\n \"Content-Type\": \"text/event-stream\",\n },\n });\n}",[],[99],"files",[152,173],"counts",{"_186":32,"_187":160,"_188":189,"_190":191},"pulls","branches","forks",2,"envVars",7,"contents",[],{"_104":174,"_95":99,"_155":175,"_157":-5,"_158":140,"_159":101,"_161":22,"_106":195,"_100":177,"_164":178,"_166":179,"_168":22,"_169":-5,"_98":196},["D",1758101065299],[99],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_93":94,"_95":96,"_97":43,"_100":101,"_98":99,"_102":205,"_150":210,"_182":217,"_165":218,"_192":220,"_221":-5,"_222":22,"_223":-5,"_224":-5,"_225":-5,"_226":227},{"_104":105,"_95":96,"_106":206,"_108":-5,"_109":22,"_110":111,"_112":113,"_114":115,"_14":116,"_117":-5,"_118":22,"_119":-5,"_120":32,"_16":121,"_122":123,"_124":123,"_125":126,"_127":126,"_128":126,"_129":123,"_130":123,"_23":207,"_134":208,"_137":209,"_149":22},["D",1758097966548],{"_93":94,"_132":133},{"_93":94,"_136":133},{"_100":139,"_95":43,"_104":140,"_141":105,"_142":-5,"_143":-5,"_144":-5,"_145":-5,"_146":-5,"_147":96,"_148":94},[211,214],{"_104":153,"_95":154,"_155":156,"_157":-5,"_158":140,"_159":160,"_161":162,"_106":163,"_100":32,"_164":165,"_166":167,"_168":22,"_169":-5,"_170":212,"_98":213},[],[154],{"_104":174,"_95":99,"_155":175,"_157":-5,"_158":140,"_159":101,"_161":22,"_106":176,"_100":177,"_164":178,"_166":179,"_168":22,"_169":-5,"_170":215,"_98":216},[],[99],[211,214],{"_104":174,"_95":99,"_155":175,"_157":-5,"_158":140,"_159":101,"_161":22,"_106":195,"_100":177,"_164":178,"_166":179,"_168":22,"_169":-5,"_98":219},[99],[],"upstream","showCompareCallout","pull","ahead","behind","templates",[],"actionData","errors"]