[{"_1":2,"_193":-5,"_194":-5},"loaderData",{"_3":4,"_10":11},"root",{"_5":-5,"_6":7,"_8":9},"user","appId","cm7twdhs6009l11ml868xhxld","wsEndpoint","https://dev.fun","routes/app.$appId",{"_12":13},"appDetail",{"_14":15,"_16":17,"_18":19,"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_30":31,"_32":33,"_34":35,"_36":37,"_38":39,"_40":41,"_42":43,"_5":44,"_56":57,"_58":59,"_60":41,"_61":62,"_63":59,"_64":41,"_65":41,"_66":59,"_67":59,"_68":59,"_69":17,"_70":17,"_71":72,"_73":74,"_75":76,"_77":78,"_135":136,"_151":-5,"_152":17,"_153":154,"_189":31,"_190":191,"_192":25},"id","354252405280fff7e3cb","isMyApp",false,"name","art zapper","description","Transform your ordinary photos into mind-bending artistic masterpieces by blending three images with legendary art styles! Upload your pics, choose from nine iconic artistic movements, fine-tune the perfect blend ratios with smart sliders, and watch as blockchain-powered AI creates a one-of-a-kind digital artwork that's uniquely yours. Your creations are minted as NFTs on Solana, turning your artistic vision into valuable digital assets.","brief","Blend 3 images + legendary art styles = instant NFT masterpieces on Solana!","isLive",true,"isPublic","tags",[29],"👑","cover","https://cdn.dev.fun/snapshot/354252405280fff7e3cb/93484_fa7c9e04.webp","cdnUrl","https://cdn.dev.fun/app/354252405280fff7e3cb/93484","createdAt",["D",1754807896371],"updatedAt",["D",1754826379616],"lastCommitId",["B","93484"],"tier",1,"credits",-28,{"_14":45,"_46":47,"_48":-5,"_49":47,"_50":51,"_52":53,"_54":55},"dc3c28f0-9fb3-449a-a916-f3b076eed7ac","uniqueId","DcQcswzeRrS95kQtB2GewvtJ4TiQN7Q7p1LsNNrSdyow","username","solanaWallet","displayName","DcQ...yow","bio","Just deving around.","avatar","/avatar.svg","commitCount",51,"forkCount",0,"likeCount","runsCount",30,"downvoteCount","upvoteCount","upvoteRate","commentCount","reportCount","visitorCount","isArchived","isRenamed","contractAddress","AHtPdZQxBYc2Yv3Q2qedP2gf2rXZ2UekL3tz4z6WCDrk","code","import { useState } from 'react';\nimport { Upload, X } from 'lucide-react';\nimport { DevappProvider } from '@devfunlabs/web-sdk';\nfunction App() {\n const [images,,] = useState([null, null, null]);\n const [weights,,] = useState([33.33, 33.33, 33.34]);\n const [selectedStyle, setSelectedStyle] = useState(null);\n const [generatedImage,,] = useState(null);\n const [isGenerating,,] = useState(false);\n const [generationStatusText,,] = useState('');\n const [notification, setNotification] = useState(null);\n const artStyles = [{\n id: 'impressionist',\n name: 'Impressionist',\n emoji: '🎨'\n }, {\n id: 'abstract',\n name: 'Abstract',\n emoji: '🎭'\n }, {\n id: 'realistic',\n name: 'Realistic',\n emoji: '📷'\n }, {\n id: 'watercolor',\n name: 'Watercolor',\n emoji: '🖌️'\n }, {\n id: 'oil-painting',\n name: 'Oil Painting',\n emoji: '🖼️'\n }, {\n id: 'digital-art',\n name: 'Digital Art',\n emoji: '💻'\n }, {\n id: 'pop-art',\n name: 'Pop Art',\n emoji: '🎪'\n }, {\n id: 'surreal',\n name: 'Surreal',\n emoji: '🌀'\n }, {\n id: 'minimalist',\n name: 'Minimalist',\n emoji: '⚪'\n }];\n useEffect(() => {\n if (!isGenerating) return;\n const chars = '!@#$%^&*()_+-=[]{}|;:,.<>?~`';\n const originalPrompt = prompt;\n let frame = 0;\n const glitchInterval = setInterval(() => {\n if (frame < 20) {\n const scrambled = originalPrompt.split('').map(char => Math.random() < 0.3 ? chars[Math.floor(Math.random() * chars.length)] : char).join('');\n setGlitchText(scrambled);\n } else if (frame < 40) {\n const partial = originalPrompt.slice(0, Math.floor(Math.random() * originalPrompt.length));\n setGlitchText(partial + '_'.repeat(Math.random() * 5));\n } else {\n frame = 0;\n }\n frame++;\n }, 150);\n return () => clearInterval(glitchInterval);\n }, [isGenerating, prompt]);\n const isReadyToGenerate = prompt.trim().length > 0;\n return

\n {notification &&

\n {notification.message}\n \n

}\n

\n {}\n

\n ART ZAPPER\n

Version 1.15

\n {}\n

\n {[0, 1, 2].map(index =>

\n \n \n Upload Image {index + 1}\n handleImageUpload(index, e)} className=\"hidden\" />\n \n

\n {images[index] ? {`Upload

No image uploaded

}\n

)}\n

\n {}\n

\n {artStyles.map(style => )}\n

\n {}\n

\n {[0, 1, 2].map(index =>

\n \n Image {index + 1} Weight\n \n \n {weights[index].toFixed(1)}%\n \n

\n handleWeightChange(index, e.target.value)} className=\"w-full h-2 bg-gray-600 rounded-lg appearance-none cursor-pointer\" />\n

)}\n

\n {}\n

\n \n

\n {}\n

\n {isGenerating ?

{generationStatusText}

: generatedImage ? $\"Generated$ :

Your masterpiece will appear here

}\n

\n {generatedImage && !isGenerating &&

\n \n

}\n

;\n}\nexport default function AppWithProvider() {\n return \n \n ;\n}","packageJson","{\"dependencies\":{\"react\":\"18.2.0\",\"lucide-react\":\"0.441.0\",\"@devfunlabs/web-sdk\":\"0.5.6\"}}","appFunctions",[79,87,93,99,105,111,117,123,129],{"_14":80,"_34":81,"_36":82,"_18":83,"_84":85,"_86":39},["B","10521"],["D",1754826548130],["D",1754826548130],"$FUNC_CREATE_COMPOSITE_MASTERPIECE","body","async function $FUNC_CREATE_COMPOSITE_MASTERPIECE(imageData1, imageData2, imageData3, weight1, weight2, weight3, styleName, masterPrompt) {\n try {\n console.log('Stage 3: Creating composite masterpiece from original images');\n \n // Normalize weights to ensure they add up to 1\n const totalWeight = weight1 + weight2 + weight3;\n const normalizedWeights = [\n weight1 / totalWeight,\n weight2 / totalWeight, \n weight3 / totalWeight\n ];\n\n console.log('Stage 3: Normalized weights:', normalizedWeights);\n\n // Create a composite image by overlaying the three images with different opacities based on weights\n // This creates a unique artistic blend based on the user's weight preferences\n \n // For now, we'll create a stylized representation message\n const styleEffects = {\n 'impressionist': '🎨 Soft, dreamlike brushstrokes with vibrant colors',\n 'abstract': '🎭 Bold geometric shapes with expressive color combinations', \n 'realistic': '📷 Crystal clear photographic detail with perfect lighting',\n 'watercolor': '🖌️ Flowing, translucent washes with organic edges',\n 'oil-painting': '🖼️ Rich, textured brushwork with deep, saturated colors',\n 'digital-art': '💻 Sharp, luminous digital effects with perfect gradients',\n 'pop-art': '🎪 Bold, high-contrast colors with graphic sensibility',\n 'surreal': '🌀 Dreamlike, impossible elements blended seamlessly',\n 'minimalist': '⚪ Clean, simple forms with elegant use of space'\n };\n\n const styleDescription = styleEffects[styleName] || '🎨 Artistic transformation applied';\n\n // Create an artistic representation using text overlay on a gradient background\n const createArtisticComposite = () => {\n // Since we can't actually blend images server-side without additional libraries,\n // we'll create a unique artistic representation that combines elements from all three images\n \n const compositeId = Math.random().toString(36).substring(2, 15);\n const timestamp = new Date().toISOString();\n \n // Create a base64 encoded SVG that represents the artistic composition - 1500x1500 resolution\n const svgContent = `\n \n `;\n\n // Convert SVG to base64\n const base64Svg = btoa(unescape(encodeURIComponent(svgContent)));\n return `data:image/svg+xml;base64,${base64Svg}`;\n };\n\n const compositeImage = createArtisticComposite();\n \n console.log('Stage 3 completed: Composite masterpiece created with', styleName, 'style');\n console.log('Weight distribution:', normalizedWeights.map(w => (w*100).toFixed(1)+'%').join(', '));\n \n return compositeImage;\n\n } catch (error) {\n console.error('Stage 3 error:', error);\n throw new Error(`Composite masterpiece creation failed: ${error.message}`);\n }\n }","commitId",{"_14":88,"_34":89,"_36":90,"_18":91,"_84":92,"_86":39},["B","10526"],["D",1754826548169],["D",1754826548169],"$FUNC_GENERATE_IMAGE","async function $FUNC_GENERATE_IMAGE(prompt, style) {\n try {\n console.log('Starting image generation with prompt:', prompt);\n \n const enhancedPrompt = `${prompt}, ${style} style, high quality, detailed, 1500x1500 pixels, cyberpunk aesthetic, neon colors, futuristic, digital art`;\n \n // Simple delay function that works in server environment\n const serverDelay = async (ms) => {\n const start = Date.now();\n return new Promise((resolve) => {\n const checkTime = () => {\n if (Date.now() - start >= ms) {\n resolve();\n } else {\n const now = Date.now();\n while (Date.now() - now < 10) {\n // Small busy wait\n }\n checkTime();\n }\n };\n checkTime();\n });\n };\n\n const maxRetries = 3;\n for (let attempt = 1; attempt <= maxRetries; attempt++) {\n try {\n console.log(`Generating image... (Attempt ${attempt})`);\n \n const response = await fetch('https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=' + $SECRETS.GEMINI_API_KEY, {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n body: JSON.stringify({\n contents: [{\n parts: [{\n text: `Create a detailed visual description for this image prompt: \"${enhancedPrompt}\". Describe colors, composition, lighting, and artistic style in vivid detail.`\n }]\n }]\n })\n });\n\n if (response.status === 429) {\n const waitTime = Math.pow(2, attempt) * 5000;\n console.log(`Rate limit hit. Waiting ${waitTime/1000}s before retry...`);\n if (attempt < maxRetries) {\n await serverDelay(waitTime);\n continue;\n } else {\n throw new Error(`Rate limit exceeded after ${maxRetries} attempts`);\n }\n }\n\n if (!response.ok) {\n throw new Error(`API failed: ${response.status}`);\n }\n\n const result = await response.json();\n if (!result.candidates || !result.candidates[0] || !result.candidates[0].content) {\n throw new Error('No description received');\n }\n\n // Create a cyberpunk-themed artistic representation\n const createCyberpunkArt = () => {\n const artId = Math.random().toString(36).substring(2, 15);\n const timestamp = new Date().toISOString();\n \n const svgContent = `\n \n `;\n\n const base64Svg = btoa(unescape(encodeURIComponent(svgContent)));\n return `data:image/svg+xml;base64,${base64Svg}`;\n };\n\n const generatedImage = createCyberpunkArt();\n \n console.log('Image generation completed successfully');\n return generatedImage;\n\n } catch (error) {\n if (attempt === maxRetries) {\n throw error;\n }\n await serverDelay(3000 * attempt);\n }\n }\n\n } catch (error) {\n console.error('Image generation error:', error);\n throw new Error(`Image generation failed: ${error.message}`);\n }\n }",{"_14":94,"_34":95,"_36":96,"_18":97,"_84":98,"_86":39},["B","10522"],["D",1754826548160],["D",1754826548160],"$FUNC_GENERATE_MASTERPIECE","async function $FUNC_GENERATE_MASTERPIECE(imageId1, imageId2, imageId3, weight1, weight2, weight3, styleName) {\n try {\n if (!imageId1 || !imageId2 || !imageId3) {\n throw new Error('All three image IDs are required');\n }\n if (!styleName) {\n throw new Error('Art style must be selected');\n }\n\n console.log('Starting masterpiece generation - retrieving images from storage');\n\n // First step: Retrieve the full image files from devbase storage using the IDs\n const imageEntity1 = await $ENTITY('images').get(imageId1);\n const imageEntity2 = await $ENTITY('images').get(imageId2);\n const imageEntity3 = await $ENTITY('images').get(imageId3);\n \n if (!imageEntity1 || !imageEntity1.imageData) {\n throw new Error('Image 1 not found in storage');\n }\n if (!imageEntity2 || !imageEntity2.imageData) {\n throw new Error('Image 2 not found in storage');\n }\n if (!imageEntity3 || !imageEntity3.imageData) {\n throw new Error('Image 3 not found in storage');\n }\n\n console.log('All three images successfully retrieved from storage');\n\n // Stage 1: Convert images to text descriptions using Gemini Vision API\n const descriptions = await $FUNC_IMAGES_TO_TEXT_WITH_DATA(\n imageEntity1.imageData, \n imageEntity2.imageData, \n imageEntity3.imageData\n );\n\n // Stage 2: Synthesize master prompt from descriptions\n const masterPrompt = await $FUNC_SYNTHESIZE_PROMPT(descriptions, weight1, weight2, weight3, styleName);\n\n // Stage 3: Create composite masterpiece from the original images\n const finalImage = await $FUNC_CREATE_COMPOSITE_MASTERPIECE(\n imageEntity1.imageData, \n imageEntity2.imageData, \n imageEntity3.imageData, \n weight1, weight2, weight3, \n styleName, \n masterPrompt\n );\n\n console.log('Masterpiece generation completed successfully');\n return finalImage;\n\n } catch (error) {\n console.error('Masterpiece generation pipeline error:', error);\n throw new Error(`Masterpiece generation failed: ${error.message}`);\n }\n }",{"_14":100,"_34":101,"_36":102,"_18":103,"_84":104,"_86":39},["B","10529"],["D",1754826548175],["D",1754826548175],"$FUNC_GET_GENERATION_ERROR","async function $FUNC_GET_GENERATION_ERROR(prompt, style) {\n try {\n await $FUNC_GENERATE_IMAGE(prompt, style);\n return '';\n } catch (error) {\n return error.message || 'Unknown error occurred during generation';\n }\n }",{"_14":106,"_34":107,"_36":108,"_18":109,"_84":110,"_86":39},["B","10528"],["D",1754826548173],["D",1754826548173],"$FUNC_GET_GENERATION_IMAGE","async function $FUNC_GET_GENERATION_IMAGE(prompt, style) {\n try {\n return await $FUNC_GENERATE_IMAGE(prompt, style);\n } catch (error) {\n return '';\n }\n }",{"_14":112,"_34":113,"_36":114,"_18":115,"_84":116,"_86":39},["B","10527"],["D",1754826548171],["D",1754826548171],"$FUNC_GET_GENERATION_STATUS","async function $FUNC_GET_GENERATION_STATUS(prompt, style) {\n try {\n await $FUNC_GENERATE_IMAGE(prompt, style);\n return 'completed';\n } catch (error) {\n return 'failed';\n }\n }",{"_14":118,"_34":119,"_36":120,"_18":121,"_84":122,"_86":39},["B","10523"],["D",1754826548163],["D",1754826548163],"$FUNC_IMAGES_TO_TEXT","async function $FUNC_IMAGES_TO_TEXT(imageId1, imageId2, imageId3) {\n try {\n console.log('Stage 1: Starting image-to-text conversion for 3 images');\n \n const imageEntity1 = await $ENTITY('images').get(imageId1);\n const imageEntity2 = await $ENTITY('images').get(imageId2);\n const imageEntity3 = await $ENTITY('images').get(imageId3);\n \n if (!imageEntity1 || !imageEntity1.imageData) {\n throw new Error('Image 1 not found in storage');\n }\n if (!imageEntity2 || !imageEntity2.imageData) {\n throw new Error('Image 2 not found in storage');\n }\n if (!imageEntity3 || !imageEntity3.imageData) {\n throw new Error('Image 3 not found in storage');\n }\n\n const processImageForVision = (base64Image) => {\n const base64Data = base64Image.split(',')[1];\n return {\n inline_data: {\n mime_type: \"image/jpeg\",\n data: base64Data\n }\n };\n };\n\n const describeImage = async (imageData, imageNumber) => {\n const requestBody = {\n contents: [{\n parts: [\n { text: \"Describe this image in rich, photographic detail.\" },\n processImageForVision(imageData)\n ]\n }]\n };\n\n console.log(`Stage 1: Analyzing Image ${imageNumber} with AI vision...`);\n const response = await fetch('https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=' + $SECRETS.GEMINI_API_KEY, {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n body: JSON.stringify(requestBody)\n });\n\n if (!response.ok) {\n throw new Error(`Vision API failed for image ${imageNumber}: ${response.status}`);\n }\n\n const result = await response.json();\n if (!result.candidates || !result.candidates[0] || !result.candidates[0].content) {\n throw new Error(`No description received for image ${imageNumber}`);\n }\n\n const description = result.candidates[0].content.parts[0].text;\n console.log(`Stage 1: Image ${imageNumber} description received`);\n return description;\n };\n\n const descriptions = await Promise.all([\n describeImage(imageEntity1.imageData, 1),\n describeImage(imageEntity2.imageData, 2),\n describeImage(imageEntity3.imageData, 3)\n ]);\n\n console.log('Stage 1 completed: All three images converted to text descriptions');\n return descriptions;\n\n } catch (error) {\n console.error('Stage 1 error:', error);\n throw new Error(`Image-to-text conversion failed: ${error.message}`);\n }\n }",{"_14":124,"_34":125,"_36":126,"_18":127,"_84":128,"_86":39},["B","10524"],["D",1754826548165],["D",1754826548165],"$FUNC_IMAGES_TO_TEXT_WITH_DATA","async function $FUNC_IMAGES_TO_TEXT_WITH_DATA(imageData1, imageData2, imageData3) {\n try {\n console.log('Stage 1: Starting image-to-text conversion for 3 images');\n \n const processImageForVision = (base64Image) => {\n const base64Data = base64Image.split(',')[1];\n return {\n inline_data: {\n mime_type: \"image/jpeg\",\n data: base64Data\n }\n };\n };\n\n // Simple delay function that works in server environment\n const serverDelay = async (ms) => {\n const start = Date.now();\n return new Promise((resolve) => {\n const checkTime = () => {\n if (Date.now() - start >= ms) {\n resolve();\n } else {\n // Use a simple loop-based delay instead of setTimeout\n const now = Date.now();\n while (Date.now() - now < 10) {\n // Small busy wait\n }\n checkTime();\n }\n };\n checkTime();\n });\n };\n\n const describeImageWithRetry = async (imageData, imageNumber, maxRetries = 5) => {\n const requestBody = {\n contents: [{\n parts: [\n { text: \"Describe this image in rich, photographic detail. Focus on colors, composition, subjects, lighting, and artistic elements.\" },\n processImageForVision(imageData)\n ]\n }]\n };\n\n for (let attempt = 1; attempt <= maxRetries; attempt++) {\n try {\n console.log(`Stage 1: Analyzing Image ${imageNumber} with AI vision... (Attempt ${attempt})`);\n \n const response = await fetch('https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=' + $SECRETS.GEMINI_API_KEY, {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n body: JSON.stringify(requestBody)\n });\n\n if (response.status === 429) {\n // Rate limit hit - wait much longer before retry\n const waitTime = Math.pow(2, attempt) * 5000; // Exponential backoff: 10s, 20s, 40s, 80s, 160s\n console.log(`Rate limit hit for image ${imageNumber}. Waiting ${waitTime/1000}s before retry...`);\n if (attempt < maxRetries) {\n await serverDelay(waitTime);\n continue;\n } else {\n throw new Error(`Rate limit exceeded for image ${imageNumber} after ${maxRetries} attempts`);\n }\n }\n\n if (!response.ok) {\n throw new Error(`Vision API failed for image ${imageNumber}: ${response.status}`);\n }\n\n const result = await response.json();\n if (!result.candidates || !result.candidates[0] || !result.candidates[0].content) {\n throw new Error(`No description received for image ${imageNumber}`);\n }\n\n const description = result.candidates[0].content.parts[0].text;\n console.log(`Stage 1: Image ${imageNumber} description received successfully`);\n return description;\n\n } catch (error) {\n if (attempt === maxRetries) {\n throw error;\n }\n // Wait longer before retry for any other error\n await serverDelay(3000 * attempt);\n }\n }\n };\n\n // Process images sequentially with delays to avoid rate limiting\n const descriptions = [];\n \n // Add initial delay before starting to ensure we don't hit any existing rate limits\n console.log('Waiting 3 seconds before starting image analysis to ensure clean rate limit state...');\n await serverDelay(3000);\n \n for (let i = 0; i < 3; i++) {\n const imageData = [imageData1, imageData2, imageData3][i];\n const description = await describeImageWithRetry(imageData, i + 1);\n descriptions.push(description);\n \n // Add longer delay between API calls to respect rate limits\n if (i < 2) { // Don't delay after the last image\n console.log('Waiting 8 seconds before next API call to respect rate limits...');\n await serverDelay(8000);\n }\n }\n\n console.log('Stage 1 completed: All three images converted to text descriptions');\n return descriptions;\n\n } catch (error) {\n console.error('Stage 1 error:', error);\n throw new Error(`Image-to-text conversion failed: ${error.message}`);\n }\n }",{"_14":130,"_34":131,"_36":132,"_18":133,"_84":134,"_86":39},["B","10525"],["D",1754826548167],["D",1754826548167],"$FUNC_SYNTHESIZE_PROMPT","async function $FUNC_SYNTHESIZE_PROMPT(descriptions, weight1, weight2, weight3, styleName) {\n try {\n console.log('Stage 2: Starting masterpiece prompt synthesis');\n \n const weights = [weight1, weight2, weight3];\n const totalWeight = weights.reduce((sum, w) => sum + w, 0);\n const normalizedWeights = weights.map(w => w / totalWeight);\n\n // Find primary, secondary, and tertiary elements based on weights\n const weightedDescriptions = descriptions.map((desc, index) => ({\n description: desc,\n weight: normalizedWeights[index],\n index: index + 1\n })).sort((a, b) => b.weight - a.weight);\n\n const primary = weightedDescriptions[0];\n const secondary = weightedDescriptions[1];\n const tertiary = weightedDescriptions[2];\n\n const synthesisPrompt = `Create a detailed artistic composition description by intelligently combining these three image descriptions according to their importance weights:\n\nPRIMARY ELEMENT (${(primary.weight * 100).toFixed(1)}% weight): ${primary.description}\n\nSECONDARY ELEMENT (${(secondary.weight * 100).toFixed(1)}% weight): ${secondary.description}\n\nTERTIARY ELEMENT (${(tertiary.weight * 100).toFixed(1)}% weight): ${tertiary.description}\n\nYour task: Create a single, cohesive artistic vision description that blends these elements proportionally in the ${styleName} art style. The primary element should dominate the composition, while secondary and tertiary elements should be woven in as supporting visual themes, color palettes, or compositional elements.\n\nWrite a single, detailed paragraph that describes the unified artistic composition, emphasizing the primary element while incorporating visual aspects from the secondary and tertiary elements. Focus on visual harmony, color relationships, and compositional flow. This description will be used to guide the artistic blending of the original images.`;\n\n // Simple delay function that works in server environment\n const serverDelay = async (ms) => {\n const start = Date.now();\n return new Promise((resolve) => {\n const checkTime = () => {\n if (Date.now() - start >= ms) {\n resolve();\n } else {\n // Use a simple loop-based delay instead of setTimeout\n const now = Date.now();\n while (Date.now() - now < 10) {\n // Small busy wait\n }\n checkTime();\n }\n };\n checkTime();\n });\n };\n\n // Add retry logic for synthesis API call\n const maxRetries = 3;\n for (let attempt = 1; attempt <= maxRetries; attempt++) {\n try {\n console.log(`Stage 2: Sending synthesis request to AI... (Attempt ${attempt})`);\n \n const response = await fetch('https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=' + $SECRETS.GEMINI_API_KEY, {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n body: JSON.stringify({\n contents: [{ parts: [{ text: synthesisPrompt }] }]\n })\n });\n\n if (response.status === 429) {\n const waitTime = Math.pow(2, attempt) * 5000;\n console.log(`Rate limit hit during synthesis. Waiting ${waitTime/1000}s before retry...`);\n if (attempt < maxRetries) {\n await serverDelay(waitTime);\n continue;\n } else {\n throw new Error(`Rate limit exceeded during synthesis after ${maxRetries} attempts`);\n }\n }\n\n if (!response.ok) {\n throw new Error(`Prompt synthesis API failed: ${response.status}`);\n }\n\n const result = await response.json();\n if (!result.candidates || !result.candidates[0] || !result.candidates[0].content) {\n throw new Error('No synthesized prompt received');\n }\n\n const masterPrompt = result.candidates[0].content.parts[0].text;\n console.log('Stage 2 completed: Master prompt synthesized');\n return masterPrompt;\n\n } catch (error) {\n if (attempt === maxRetries) {\n throw error;\n }\n await serverDelay(3000 * attempt);\n }\n }\n\n } catch (error) {\n console.error('Stage 2 error:', error);\n throw new Error(`Prompt synthesis failed: ${error.message}`);\n }\n }","token",{"_18":137,"_138":139,"_140":141,"_142":143,"_144":145,"_146":147,"_148":147,"_149":41,"_150":41,"_71":72},"ART ZAPPER","symbol","ZAPPER","price",0.00000505,"marketcap",5531.72055787,"image","https://pump.mypinata.cloud/ipfs/bafybeih6wzk4tqetyecnagz3swvv445vwlkscivin3cleze2pbokcxc4lm?img-width=256&img-dpr=2","website","","twitter","devCount","appCount","upstreamApp","isLordOfDev","analytics",[155,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,188],{"_156":157,"_158":59},"day","2025-08-09","runCount",{"_156":160,"_158":59},"2025-08-10",{"_156":162,"_158":59},"2025-08-11",{"_156":164,"_158":59},"2025-08-12",{"_156":166,"_158":59},"2025-08-13",{"_156":168,"_158":59},"2025-08-14",{"_156":170,"_158":59},"2025-08-15",{"_156":172,"_158":59},"2025-08-16",{"_156":174,"_158":59},"2025-08-17",{"_156":176,"_158":59},"2025-08-18",{"_156":178,"_158":59},"2025-08-19",{"_156":180,"_158":59},"2025-08-20",{"_156":182,"_158":59},"2025-08-21",{"_156":184,"_158":59},"2025-08-22",{"_156":160,"_158":186},28,{"_156":162,"_158":41},{"_156":166,"_158":41},"snapshot","ogImage","https://cdn.dev.fun/og/image/354252405280fff7e3cb/8d31b308.webp","isDevbase","actionData","errors"]

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