\n \n \n \n `;\n\n return Response.json({\n canvasHtml: canvasScript,\n filename: filename.replace(/\\.[^/.]+$/, '') + '_instructions.html'\n });\n\n } catch (error) {\n console.error('Error generating composite image:', error);\n return Response.json({ error: 'Failed to generate composite image' }, { status: 500 });\n }\n}",[],[171],{"_102":179,"_95":180,"_151":181,"_153":-5,"_154":136,"_155":182,"_157":158,"_104":183,"_99":174,"_160":161,"_162":184,"_164":22,"_165":-5,"_166":185,"_98":186},"bcbd419a-b7a5-11f0-9d8c-0224a6c84d84","fue26ff98739f748ses23q9ff73s44339q.ts","9abe69c0-b764-11f0-9782-0224a6c84d84",13,["D",1762058291230],"import { connect } from \"npm:@tursodatabase/serverless\";\n\nexport default async function(req: Request): Promise {\n // Read secrets from x-secrets header\n const __secrets = JSON.parse(req.headers.get('x-secrets') || '{}');\n\n try {\n const { promptId } = await req.json();\n \n if (!promptId) {\n return Response.json({ error: \"promptId is required\" }, { status: 400 });\n }\n\n const userUuid = req.headers.get(\"x-user-uuid\");\n if (!userUuid) {\n return Response.json({ error: \"Unauthorized\" }, { status: 401 });\n }\n\n const apiKey = __secrets['openai_api_key'];\n if (!apiKey) {\n return Response.json({ error: \"OpenAI API key not configured\" }, { status: 500 });\n }\n\n // Import platform SDK\n const sdkUrl = req.headers.get(\"x-sdk-url\");\n const { PlatformClient } = await import(sdkUrl);\n const platform = new PlatformClient(\n req.headers.get(\"x-platform-token\"),\n req.headers.get(\"x-api-base-url\")\n );\n\n // Connect to database\n const conn = connect({\n url: req.headers.get(\"x-database-url\"),\n authToken: req.headers.get(\"x-database-token\")\n });\n\n // Get prompt details including reference image - Fixed: include userUuid parameter\n const promptStmt = conn.prepare(\"SELECT * FROM prompts WHERE _row_id = ? AND _created_by = ?\");\n const promptData = await promptStmt.get([promptId, userUuid]);\n\n if (!promptData) {\n return Response.json({ error: \"Prompt not found\" }, { status: 404 });\n }\n\n // Count existing images for this prompt\n const countStmt = conn.prepare(\"SELECT COUNT(*) as count FROM images WHERE prompt_id = ?\");\n const { count } = await countStmt.get([promptId]);\n\n // Only allow generating more images if we haven't exceeded the target count\n // or if we're generating additional images beyond the original target\n const nextImageNumber = count + 1;\n const shouldGenerateMore = nextImageNumber <= promptData.image_count || \n promptData.status === \"completed\" || \n promptData.status === \"processing\";\n\n if (!shouldGenerateMore) {\n return Response.json({ \n success: true, \n message: \"All target images already generated\",\n status: promptData.status,\n existingImages: await getExistingImages(conn, promptId)\n });\n }\n\n // Update prompt status to processing if it's not already\n if (promptData.status === \"pending\" || promptData.status === \"completed\") {\n const updateStmt = conn.prepare(\"UPDATE prompts SET status = ?, image_count = ? WHERE _row_id = ?\");\n // Increase image_count if we're generating beyond the original target\n const newImageCount = nextImageNumber > promptData.image_count ? nextImageNumber : promptData.image_count;\n await updateStmt.run([\"processing\", newImageCount, promptId]);\n }\n\n // Analyze reference image if available and no analysis exists yet\n let styleAnalysis = null;\n if (promptData.reference_image_path && !promptData.style_analysis) {\n console.log(\"Analyzing reference image for style guidance...\");\n \n try {\n // Call the analysis function using HTTP request\n const functionUrl = `${req.headers.get(\"x-api-base-url\")}/functions/analyze-reference-image`;\n \n const analysisResponse = await fetch(functionUrl, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${req.headers.get(\"x-platform-token\")}`,\n \"X-User-UUID\": userUuid\n },\n body: JSON.stringify({\n referenceImagePath: promptData.reference_image_path\n })\n });\n \n const analysisData = await analysisResponse.json();\n \n if (analysisData.success) {\n styleAnalysis = analysisData.analysis;\n \n // Store the analysis in the database for future use\n const updateAnalysisStmt = conn.prepare(\"UPDATE prompts SET style_analysis = ? WHERE _row_id = ?\");\n await updateAnalysisStmt.run([styleAnalysis, promptId]);\n \n console.log(\"Reference image analysis completed and stored\");\n } else {\n console.warn(\"Failed to analyze reference image:\", analysisData.error);\n }\n } catch (analysisError) {\n console.warn(\"Error during reference image analysis:\", analysisError.message);\n }\n } else if (promptData.style_analysis) {\n styleAnalysis = promptData.style_analysis;\n console.log(\"Using stored style analysis\");\n }\n\n // Generate one image\n try {\n console.log(`Generating image ${nextImageNumber} for prompt ${promptId}`);\n \n // Prepare OpenAI request\n let enhancedPrompt = promptData.prompt_text;\n\n // Add style guidance if available\n if (styleAnalysis) {\n enhancedPrompt = `${promptData.prompt_text}\\n\\nStyle Guidance: Based on the reference image analysis, create images with these specific characteristics:\\n${styleAnalysis}`;\n } else if (promptData.reference_image_url) {\n // Fallback to basic guidance if analysis failed\n enhancedPrompt = `${promptData.prompt_text}\\n\\nStyle reference: Create images in a similar style to the provided reference image, matching its aesthetic, composition, color palette, and overall mood.`;\n }\n\n const openaiRequestBody: any = {\n model: \"dall-e-3\",\n prompt: enhancedPrompt,\n n: 1,\n size: \"1024x1024\",\n quality: \"standard\"\n };\n\n console.log(`Using enhanced prompt with style guidance`);\n\n // Call OpenAI API\n const openaiResponse = await fetch(\"https://api.openai.com/v1/images/generations\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${apiKey}`\n },\n body: JSON.stringify(openaiRequestBody)\n });\n\n if (!openaiResponse.ok) {\n const error = await openaiResponse.text();\n console.error(\"OpenAI API error:\", error);\n return Response.json({ \n success: false, \n error: \"OpenAI API error\",\n details: error \n }, { status: 500 });\n }\n\n const data = await openaiResponse.json();\n const imageUrl = data.data[0]?.url;\n\n if (!imageUrl) {\n return Response.json({ \n success: false, \n error: \"No image URL in OpenAI response\" \n }, { status: 500 });\n }\n\n console.log(`Downloading image from: ${imageUrl}`);\n \n // Download the image\n const imageResponse = await fetch(imageUrl);\n const imageBlob = await imageResponse.arrayBuffer();\n \n // Generate unique filename\n const timestamp = Date.now();\n const filename = `${timestamp}_${count}.png`;\n const filePath = `/content/generated-images/${userUuid}/${promptId}/${filename}`;\n\n console.log(`Uploading image to: ${filePath}`);\n\n // Upload to content filesystem\n await platform.uploadContentFile({\n path: filePath,\n content: new Uint8Array(imageBlob),\n contentType: \"image/png\"\n });\n\n // Save to database\n const insertStmt = conn.prepare(\n \"INSERT INTO images (prompt_id, image_url, image_path, sort_order, _created_by) VALUES (?, ?, ?, ?, ?)\"\n );\n await insertStmt.run([promptId, filePath, filePath, count, userUuid]);\n\n console.log(`Successfully generated and saved image ${nextImageNumber}`);\n\n // Check if we've reached the new target count\n const newCountStmt = conn.prepare(\"SELECT COUNT(*) as newCount FROM images WHERE prompt_id = ?\");\n const { newCount } = await newCountStmt.get([promptId]);\n\n // Get updated prompt data to see the current target\n const updatedPromptStmt = conn.prepare(\"SELECT image_count FROM prompts WHERE _row_id = ?\");\n const { image_count: newTargetCount } = await updatedPromptStmt.get([promptId]);\n\n if (newCount >= newTargetCount) {\n // Update prompt status to completed\n const completeStmt = conn.prepare(\"UPDATE prompts SET status = ? WHERE _row_id = ?\");\n await completeStmt.run([\"completed\", promptId]);\n \n return Response.json({\n success: true,\n message: \"All images generated successfully\",\n promptId,\n imageUrl: filePath,\n imageNumber: nextImageNumber,\n totalImages: newTargetCount,\n status: \"completed\",\n allImages: await getExistingImages(conn, promptId)\n });\n }\n\n return Response.json({\n success: true,\n message: \"Image generated successfully\",\n promptId,\n imageUrl: filePath,\n imageNumber: nextImageNumber,\n totalImages: newTargetCount,\n status: \"processing\",\n existingImages: await getExistingImages(conn, promptId)\n });\n\n } catch (error) {\n console.error(\"Error generating image:\", error);\n return Response.json({\n success: false,\n error: \"Failed to generate image\",\n details: error.message\n }, { status: 500 });\n }\n\n } catch (error) {\n console.error(\"Error in generate-single-image function:\", error);\n \n // Try to update status to failed\n try {\n const { promptId } = await req.json();\n if (promptId) {\n const conn = connect({\n url: req.headers.get(\"x-database-url\"),\n authToken: req.headers.get(\"x-database-token\")\n });\n const failStmt = conn.prepare(\"UPDATE prompts SET status = ? WHERE _row_id = ?\");\n await failStmt.run([\"failed\", promptId]);\n }\n } catch (dbError) {\n console.error(\"Failed to update status to failed:\", dbError);\n }\n \n return Response.json({\n error: \"Failed to process image generation\",\n details: error.message\n }, { status: 500 });\n }\n}\n\nasync function getExistingImages(conn: any, promptId: number) {\n const imagesStmt = conn.prepare(\"SELECT * FROM images WHERE prompt_id = ? ORDER BY sort_order\");\n return await imagesStmt.all([promptId]);\n}",[],[180],{"_102":188,"_95":189,"_151":190,"_153":-5,"_154":136,"_155":174,"_157":158,"_104":191,"_99":32,"_160":161,"_162":192,"_164":22,"_165":-5,"_166":193,"_98":194},"9e5de674-b763-11f0-9d3c-0224a6c84d84","fuf816se755st042se89eq2949968s5f41.ts","9e5de926-b763-11f0-9d3c-0224a6c84d84",["D",1762029893488],"import { connect } from \"npm:@tursodatabase/serverless\";\n\nexport default async function(req: Request): Promise {\n // Read secrets from x-secrets header\n const __secrets = JSON.parse(req.headers.get('x-secrets') || '{}');\n\n try {\n const { promptId } = await req.json();\n \n if (!promptId) {\n return Response.json({ error: \"promptId is required\" }, { status: 400 });\n }\n\n const userUuid = req.headers.get(\"x-user-uuid\");\n if (!userUuid) {\n return Response.json({ error: \"Unauthorized\" }, { status: 401 });\n }\n\n const apiKey = __secrets['openai_api_key'];\n if (!apiKey) {\n return Response.json({ error: \"OpenAI API key not configured\" }, { status: 500 });\n }\n\n // Import platform SDK\n const sdkUrl = req.headers.get(\"x-sdk-url\");\n const { PlatformClient } = await import(sdkUrl);\n const platform = new PlatformClient(\n req.headers.get(\"x-platform-token\"),\n req.headers.get(\"x-api-base-url\")\n );\n\n // Connect to database\n const conn = connect({\n url: req.headers.get(\"x-database-url\"),\n authToken: req.headers.get(\"x-database-token\")\n });\n\n // Get prompt details\n const promptStmt = conn.prepare(\"SELECT * FROM prompts WHERE _row_id = ? AND _created_by = ?\");\n const promptData = await promptStmt.get([promptId, userUuid]);\n\n if (!promptData) {\n return Response.json({ error: \"Prompt not found\" }, { status: 404 });\n }\n\n // Check if already processing or completed\n if (promptData.status !== \"pending\" && promptData.status !== \"processing\") {\n return Response.json({ \n success: true, \n message: \"Prompt already processed\",\n status: promptData.status \n });\n }\n\n // Update status to processing\n const updateStmt = conn.prepare(\"UPDATE prompts SET status = ? WHERE _row_id = ?\");\n await updateStmt.run([\"processing\", promptId]);\n\n const generatedImages = [];\n \n // Generate images one by one\n for (let i = 0; i < promptData.image_count; i++) {\n try {\n console.log(`Generating image ${i + 1}/${promptData.image_count} for prompt ${promptId}`);\n \n // Call OpenAI API\n const openaiResponse = await fetch(\"https://api.openai.com/v1/images/generations\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${apiKey}`\n },\n body: JSON.stringify({\n model: \"dall-e-3\",\n prompt: promptData.prompt_text,\n n: 1,\n size: \"1024x1024\",\n quality: \"standard\"\n })\n });\n\n if (!openaiResponse.ok) {\n const error = await openaiResponse.text();\n console.error(\"OpenAI API error:\", error);\n continue;\n }\n\n const data = await openaiResponse.json();\n const imageUrl = data.data[0]?.url;\n\n if (!imageUrl) {\n console.error(\"No image URL in response\");\n continue;\n }\n\n console.log(`Downloading image from: ${imageUrl}`);\n \n // Download the image\n const imageResponse = await fetch(imageUrl);\n const imageBlob = await imageResponse.arrayBuffer();\n \n // Generate unique filename\n const timestamp = Date.now();\n const filename = `${timestamp}_${i}.png`;\n const filePath = `/content/generated-images/${userUuid}/${promptId}/${filename}`;\n\n console.log(`Uploading image to: ${filePath}`);\n\n // Upload to content filesystem\n await platform.uploadContentFile({\n path: filePath,\n content: new Uint8Array(imageBlob),\n contentType: \"image/png\"\n });\n\n // Save to database\n const insertStmt = conn.prepare(\n \"INSERT INTO images (prompt_id, image_url, image_path, sort_order, _created_by) VALUES (?, ?, ?, ?, ?)\"\n );\n await insertStmt.run([promptId, filePath, filePath, i, userUuid]);\n\n generatedImages.push({\n url: filePath,\n order: i\n });\n\n console.log(`Successfully generated and saved image ${i + 1}`);\n\n } catch (error) {\n console.error(`Error generating image ${i}:`, error);\n }\n }\n\n // Update prompt status to completed\n const completeStmt = conn.prepare(\"UPDATE prompts SET status = ? WHERE _row_id = ?\");\n await completeStmt.run([\"completed\", promptId]);\n\n console.log(`Generation completed for prompt ${promptId}. Generated ${generatedImages.length} images.`);\n\n return Response.json({\n success: true,\n promptId,\n images: generatedImages,\n count: generatedImages.length,\n status: \"completed\"\n });\n\n } catch (error) {\n console.error(\"Error in process-image-generation function:\", error);\n \n // Try to update status to failed\n try {\n const { promptId } = await req.json();\n if (promptId) {\n const conn = connect({\n url: req.headers.get(\"x-database-url\"),\n authToken: req.headers.get(\"x-database-token\")\n });\n const failStmt = conn.prepare(\"UPDATE prompts SET status = ? WHERE _row_id = ?\");\n await failStmt.run([\"failed\", promptId]);\n }\n } catch (dbError) {\n console.error(\"Failed to update status to failed:\", dbError);\n }\n \n return Response.json({\n error: \"Failed to process image generation\",\n details: error.message\n }, { status: 500 });\n }\n}",[],[189],{"_102":196,"_95":197,"_151":198,"_153":-5,"_154":136,"_155":199,"_157":158,"_104":200,"_99":201,"_160":161,"_162":202,"_164":22,"_165":-5,"_166":203,"_98":204},"ad0d2654-b7c6-11f0-a9d5-0224a6c84d84","fuq7f17s5s5q9t4eq0sq9200tf3tvq8eqs.ts","c65121c0-b7c5-11f0-8824-0224a6c84d84",15,["D",1762072438305],1,"export default async function(req: Request): Promise {\n // Read secrets from x-secrets header\n const __secrets = JSON.parse(req.headers.get('x-secrets') || '{}');\n\n try {\n const { imageUrl, imageId } = await req.json();\n \n if (!imageUrl || !imageId) {\n return Response.json({ error: \"Missing imageUrl or imageId\" }, { status: 400 });\n }\n\n const apiKey = __secrets['openai_api_key'];\n if (!apiKey) {\n return Response.json({ error: \"OpenAI API key not configured\" }, { status: 500 });\n }\n\n // Call OpenAI Vision API with manufacturing-focused prompt\n const openaiResponse = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${apiKey}`\n },\n body: JSON.stringify({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: \"You are a manufacturing specification expert. Analyze this product image and create detailed production instructions for manufacturing partners. Focus on:\\n\\n1. COMPONENTS & MATERIALS: List all visible components, materials, and parts needed\\n2. DIMENSIONS & SPECIFICATIONS: Provide measurements and technical specifications\\n3. ASSEMBLY STEPS: Give step-by-step assembly or manufacturing instructions\\n4. QUALITY CONTROL: Include quality checks and inspection points\\n5. TOOLS REQUIRED: List any tools or equipment needed\\n\\nFormat as numbered sections with clear, actionable instructions. Be specific about materials, dimensions, tolerances, and manufacturing processes. Assume this is for a production partner who needs to replicate or assemble this product exactly.\"\n },\n {\n type: \"image_url\",\n image_url: {\n url: imageUrl\n }\n }\n ]\n }\n ],\n max_tokens: 1500\n })\n });\n\n if (!openaiResponse.ok) {\n const errorText = await openaiResponse.text();\n console.error(\"OpenAI API error:\", errorText);\n return Response.json({ \n error: \"Failed to generate production specifications\",\n details: errorText \n }, { status: 500 });\n }\n\n const data = await openaiResponse.json();\n const instructions = data.choices[0].message.content;\n\n // Update the database with instructions\n const dbUrl = req.headers.get(\"x-database-url\");\n const dbToken = req.headers.get(\"x-database-token\");\n \n if (dbUrl && dbToken) {\n const { connect } = await import(\"npm:@tursodatabase/serverless\");\n const conn = connect({ url: dbUrl, authToken: dbToken });\n \n const updateStmt = conn.prepare(\n \"UPDATE images SET instructions = ?, status = ? WHERE _row_id = ?\"\n );\n await updateStmt.run([instructions, \"completed\", imageId]);\n }\n\n return Response.json({ \n success: true, \n instructions,\n imageId \n });\n \n } catch (error) {\n console.error(\"Error generating production specifications:\", error);\n return Response.json({ \n error: \"Internal server error\",\n message: error.message \n }, { status: 500 });\n }\n}",[],[197],{"_102":206,"_95":207,"_151":208,"_153":-5,"_154":136,"_155":209,"_157":158,"_104":210,"_99":32,"_160":161,"_162":211,"_164":22,"_165":-5,"_166":212,"_98":213},"d42dda02-b7a4-11f0-87d6-0224a6c84d84","fuq8730qev56vq4sq689v8f5359s23qq7t.ts","d42ddaac-b7a4-11f0-87d6-0224a6c84d84",10,["D",1762057901065],"import { connect } from \"npm:@tursodatabase/serverless\";\n\nexport default async function(req: Request): Promise {\n // Read secrets from x-secrets header\n const __secrets = JSON.parse(req.headers.get('x-secrets') || '{}');\n\n try {\n const { referenceImagePath } = await req.json();\n \n if (!referenceImagePath) {\n return Response.json({ error: \"referenceImagePath is required\" }, { status: 400 });\n }\n\n const userUuid = req.headers.get(\"x-user-uuid\");\n if (!userUuid) {\n return Response.json({ error: \"Unauthorized\" }, { status: 401 });\n }\n\n const apiKey = __secrets['openai_api_key'];\n if (!apiKey) {\n return Response.json({ error: \"OpenAI API key not configured\" }, { status: 500 });\n }\n\n // Import platform SDK\n const sdkUrl = req.headers.get(\"x-sdk-url\");\n const { PlatformClient } = await import(sdkUrl);\n const platform = new PlatformClient(\n req.headers.get(\"x-platform-token\"),\n req.headers.get(\"x-api-base-url\")\n );\n\n // Get the reference image file\n const imageContent = await platform.getContentFile(referenceImagePath);\n \n // Convert to base64 for OpenAI Vision API\n const base64Image = btoa(String.fromCharCode(...new Uint8Array(imageContent)));\n const dataUrl = `data:image/png;base64,${base64Image}`;\n\n console.log(`Analyzing reference image: ${referenceImagePath}`);\n\n // Use OpenAI Vision API to analyze the image\n const visionResponse = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${apiKey}`\n },\n body: JSON.stringify({\n model: \"gpt-4-vision-preview\",\n messages: [\n {\n role: \"system\",\n content: \"You are an art and image analysis expert. Analyze the provided image and extract detailed visual characteristics that would be useful for generating similar images. Focus on: artistic style, color palette, composition, lighting, mood, textures, and key visual elements. Provide a comprehensive analysis that can be used as style guidance for image generation.\"\n },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: \"Analyze this image and provide detailed style guidance for generating similar images. Include specific details about artistic style, color scheme, composition, lighting, mood, and visual techniques.\"\n },\n {\n type: \"image_url\",\n image_url: {\n url: dataUrl\n }\n }\n ]\n }\n ],\n max_tokens: 500\n })\n });\n\n if (!visionResponse.ok) {\n const error = await visionResponse.text();\n console.error(\"OpenAI Vision API error:\", error);\n return Response.json({ \n success: false, \n error: \"Failed to analyze reference image\",\n details: error \n }, { status: 500 });\n }\n\n const visionData = await visionResponse.json();\n const analysis = visionData.choices[0]?.message?.content;\n\n if (!analysis) {\n return Response.json({ \n success: false, \n error: \"No analysis generated from reference image\" \n }, { status: 500 });\n }\n\n console.log(\"Reference image analysis completed\");\n\n return Response.json({\n success: true,\n analysis,\n referenceImagePath\n });\n\n } catch (error) {\n console.error(\"Error analyzing reference image:\", error);\n return Response.json({\n success: false,\n error: \"Failed to analyze reference image\",\n details: error.message\n }, { status: 500 });\n }\n}",[],[207],{"_102":215,"_95":216,"_151":217,"_153":-5,"_154":136,"_155":218,"_157":158,"_104":219,"_99":156,"_160":161,"_162":220,"_164":22,"_165":-5,"_166":221,"_98":222},"df384694-b763-11f0-b25e-0224a6c84d84","fus840v570v9244e97s43721tt15ss5v95.ts","2f8c6b40-b762-11f0-a2e2-0224a6c84d84",6,["D",1762030002295],"import { connect } from \"npm:@tursodatabase/serverless\";\n\nexport default async function(req: Request): Promise {\n // Read secrets from x-secrets header\n const __secrets = JSON.parse(req.headers.get('x-secrets') || '{}');\n\n try {\n const { prompt, imageCount = 5, promptId } = await req.json();\n \n if (!prompt || !promptId) {\n return Response.json({ error: \"Prompt and promptId are required\" }, { status: 400 });\n }\n\n const userUuid = req.headers.get(\"x-user-uuid\");\n if (!userUuid) {\n return Response.json({ error: \"Unauthorized\" }, { status: 401 });\n }\n\n // Connect to database to verify the prompt exists and is pending\n const conn = connect({\n url: req.headers.get(\"x-database-url\"),\n authToken: req.headers.get(\"x-database-token\")\n });\n\n // Verify prompt exists and is in pending status\n const verifyStmt = conn.prepare(\"SELECT * FROM prompts WHERE _row_id = ? AND _created_by = ?\");\n const promptData = await verifyStmt.get([promptId, userUuid]);\n\n if (!promptData) {\n return Response.json({ error: \"Prompt not found\" }, { status: 404 });\n }\n\n // Return immediately with success, keeping status as pending\n // The actual processing will be triggered by the frontend\n return Response.json({\n success: true,\n message: \"Image generation queued\",\n promptId,\n status: \"pending\"\n });\n\n } catch (error) {\n console.error(\"Error in generate-images function:\", error);\n return Response.json({\n error: \"Failed to queue image generation\",\n details: error.message\n }, { status: 500 });\n }\n}",[],[216],"files",[148,169,178,187,195,205,214],"counts",{"_227":32,"_228":201,"_229":32,"_230":231},"pulls","branches","forks","envVars",{"_232":32,"_233":32},"count","emptyCount","contents",[236,239,242,245,248,251,254],{"_102":149,"_95":150,"_151":152,"_153":-5,"_154":136,"_155":156,"_157":158,"_104":237,"_99":32,"_160":161,"_162":163,"_164":22,"_165":-5,"_98":238},["D",1762029754693],[150],{"_102":170,"_95":171,"_151":172,"_153":-5,"_154":136,"_155":135,"_157":158,"_104":240,"_99":174,"_160":161,"_162":175,"_164":22,"_165":-5,"_98":241},["D",1762080480634],[171],{"_102":179,"_95":180,"_151":181,"_153":-5,"_154":136,"_155":182,"_157":158,"_104":243,"_99":174,"_160":161,"_162":184,"_164":22,"_165":-5,"_98":244},["D",1762058291230],[180],{"_102":188,"_95":189,"_151":190,"_153":-5,"_154":136,"_155":174,"_157":158,"_104":246,"_99":32,"_160":161,"_162":192,"_164":22,"_165":-5,"_98":247},["D",1762029893488],[189],{"_102":196,"_95":197,"_151":198,"_153":-5,"_154":136,"_155":199,"_157":158,"_104":249,"_99":201,"_160":161,"_162":202,"_164":22,"_165":-5,"_98":250},["D",1762072438305],[197],{"_102":206,"_95":207,"_151":208,"_153":-5,"_154":136,"_155":209,"_157":158,"_104":252,"_99":32,"_160":161,"_162":211,"_164":22,"_165":-5,"_98":253},["D",1762057901065],[207],{"_102":215,"_95":216,"_151":217,"_153":-5,"_154":136,"_155":218,"_157":158,"_104":255,"_99":156,"_160":161,"_162":220,"_164":22,"_165":-5,"_98":256},["D",1762030002295],[216],"file","upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.pulls",{"_93":94,"_95":96,"_266":267,"_100":270,"_227":275,"_276":-5,"_277":22,"_278":-5},"filters",{"_268":269},"state","open",{"_102":103,"_95":96,"_104":271,"_106":-5,"_107":22,"_108":109,"_110":-5,"_111":-5,"_14":112,"_113":-5,"_114":22,"_115":-5,"_116":32,"_16":117,"_118":119,"_120":119,"_121":122,"_123":122,"_124":122,"_125":119,"_126":119,"_23":272,"_130":273,"_133":274,"_145":22},["D",1762029277999],{"_93":94,"_128":129},{"_93":94,"_132":129},{"_99":135,"_95":43,"_102":136,"_137":103,"_138":-5,"_139":-5,"_140":-5,"_141":-5,"_142":-5,"_143":96,"_144":94},[],"latestBranch","isForkOwner","ownProjectForks","actionData","errors"]

kliv

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Pull requests