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"./components/AuthCheck" import SignOutButton from "./components/SignOutButton" import RoomCapture from "./components/RoomCapture" import ArtSuggestions from "./components/ArtSuggestions" import type { RoomAnalysis } from "./types/roomAnalysis" export default function Home() { const [roomAnalysis, setRoomAnalysis] = useState(null) const handleRoomAnalysis = (analysis: RoomAnalysis) => { setRoomAnalysis(analysis) } return (

AI Art Recommendation

Loading...}>

) } [V0_FILE]typescriptreact:file="app/components/ArtworkGrid.tsx" isMerged="true" isEdit="true" "use client" import { useState, useEffect, useCallback } from "react" import { useInView } from "react-intersection-observer" import ArtworkCard from "./ArtworkCard" import { fetchArtworks } from "../services/artwork" import type { Artwork } from "../types/artwork" export default function ArtworkGrid() { const [artworks, setArtworks] = useState([]) const [page, setPage] = useState(1) const [loading, setLoading] = useState(false) const [hasMore, setHasMore] = useState(true) const [error, setError] = useState(null) const { ref, inView } = useInView({ threshold: 0, }) const loadMoreArtworks = useCallback(async () => { if (loading || !hasMore) return setLoading(true) try { const newArtworks = await fetchArtworks(page, 20) if (newArtworks.length === 0) { setHasMore(false) } else { setArtworks((prevArtworks) => [...prevArtworks, ...newArtworks]) setPage((prevPage) => prevPage + 1) } } catch (error) { console.error("Error loading artworks:", error) setError("Failed to load artworks. Please try again later.") } finally { setLoading(false) } }, [loading, hasMore, page]) useEffect(() => { if (inView) { loadMoreArtworks() } }, [inView, loadMoreArtworks]) if (error) { return

{error}

} return (

{artworks.map((artwork) => ( ))}

{loading &&

Loading more artworks...

} {!loading && hasMore &&

} {!hasMore &&

No more artworks to load

}

) } [V0_FILE]typescriptreact:file="app/components/AuthCheck.tsx" isMerged="true" isEdit="true" "use client" import { useEffect, useState } from "react" import { onAuthStateChanged, type User } from "firebase/auth" import type React from "react" import { auth } from "../lib/firebase" import SignIn from "./SignIn" export function AuthCheck({ children }: { children: React.ReactNode }) { const [user, setUser] = useState(null) const [loading, setLoading] = useState(true) useEffect(() => { const unsubscribe = onAuthStateChanged(auth, (user) => { setUser(user) setLoading(false) }) return () => unsubscribe() }, []) if (loading) { return

} if (!user) { return } return <>{children} } [V0_FILE]typescript:file="app/types/artwork.ts" isEdit="true" isMerged="true" export interface ArtworkImage { servingUrl: string fullPath?: string height?: number id?: string order?: number ratio?: string url?: string width?: number } export interface ArtworkAnalysis { basicDetails: { orientation: string } colorAnalysis: { dominantColors: Array<{ name: string; hex: string; rgb: string }> secondaryColors: Array<{ name: string; hex: string; rgb: string }> overallTemperature: string colorRelationships: string } textureAnalysis: { surfaceQualities: string techniqueRelatedTextures: string uniqueTexturalElements: string types: string[] } styleAndGenre: { artisticMovements: string[] genre: string hybridStyles: string } subjectMatter: { primarySubject: string secondarySubjects: string[] keyElements: string[] contextInterpretation: string } mediumAndTechnique: { materialsUsed: string[] applicationTechniques: string uniqueApproaches: string } composition: { type: string focalPoints: string[] description: string } spaceAndDisplay: { optimalViewingConditions: string recommendedEnvironments: string[] lightingRecommendations: string viewingDistance: string } tags: { style: string[] medium: string[] color: string[] genre: string[] subject: string[] texture: string[] } } export interface Artwork { id: string title: string infoText?: string studioName: string images: ArtworkImage[] price: number currency: string techniques: string[] year: number width: number height: number createdAt: Date status: string analysis?: ArtworkAnalysis hasAiAnalysis: boolean explanation?: string } [V0_FILE]typescriptreact:file="app/services/linkAnalysisToArtwork.ts" isQuickEdit="true" isMerged="true" isEdit="true" import { db } from "../lib/firebase" import { doc, updateDoc } from "firebase/firestore" export async function linkAnalysisToArtwork(artworkId: string, analysisId: string): Promise { try { const artworkRef = doc(db, "artworks", artworkId) await updateDoc(artworkRef, { analysisId: analysisId, }) } catch (error) { console.error("Error linking analysis to artwork:", error) throw error } } [V0_FILE]typescript:file="app/lib/firebase.ts" isMerged="true" isEdit="true" import { initializeApp, getApps, getApp } from "firebase/app" import { getFirestore } from "firebase/firestore" import { getAuth } from "firebase/auth" const firebaseConfig = { apiKey: process.env.NEXT_PUBLIC_DEST_FIREBASE_API_KEY, authDomain: process.env.NEXT_PUBLIC_DEST_FIREBASE_AUTH_DOMAIN, projectId: process.env.NEXT_PUBLIC_DEST_FIREBASE_PROJECT_ID, storageBucket: process.env.NEXT_PUBLIC_DEST_FIREBASE_STORAGE_BUCKET, messagingSenderId: process.env.NEXT_PUBLIC_DEST_FIREBASE_MESSAGING_SENDER_ID, appId: process.env.NEXT_PUBLIC_DEST_FIREBASE_APP_ID, } // Initialize Firebase const app = getApps().length ? getApp() : initializeApp(firebaseConfig) const db = getFirestore(app) const auth = getAuth(app) export { app, db, auth } [V0_FILE]typescriptreact:file="app/components/SignIn.tsx" isFixed="true" isQuickEdit="true" isMerged="true" isEdit="true" "use client" import type React from "react" import { useState } from "react" import { signInWithEmailAndPassword } from "firebase/auth" import { auth } from "../lib/firebase" import Link from "next/link" export default function SignIn() { const [email, setEmail] = useState("") const [password, setPassword] = useState("") const [error, setError] = useState(null) const handleSignIn = async (e: React.FormEvent) => { e.preventDefault() setError(null) try { await signInWithEmailAndPassword(auth, email, password) } catch (error) { setError("Failed to sign in. Please check your email and password.") console.error("Sign-in error:", error) } } return (

Sign In

{error &&

{error}

}

Don't have an account?{" "} Sign Up

) } [V0_FILE]typescriptreact:file="app/components/SignOutButton.tsx" isMerged="true" isEdit="true" "use client" import { signOut } from "firebase/auth" import { auth } from "../lib/firebase" export default function SignOutButton() { const handleSignOut = async () => { try { await signOut(auth) } catch (error) { console.error("Sign-out error:", error) } } return ( ) } [V0_FILE]typescriptreact:file="app/components/SignUp.tsx" isFixed="true" isQuickEdit="true" isMerged="true" isEdit="true" "use client" import type React from "react" import { useState } from "react" import { createUserWithEmailAndPassword } from "firebase/auth" import { auth } from "../lib/firebase" import Link from "next/link" export default function SignUp() { const [email, setEmail] = useState("") const [password, setPassword] = useState("") const [error, setError] = useState(null) const handleSignUp = async (e: React.FormEvent) => { e.preventDefault() setError(null) try { await createUserWithEmailAndPassword(auth, email, password) } catch (error) { setError("Failed to create an account. Please try again.") console.error("Sign-up error:", error) } } return (

Sign Up

{error &&

{error}

}

Already have an account?{" "} Sign In

) } [V0_FILE]typescriptreact:file="app/signin/page.tsx" isMerged="true" isEdit="true" import SignIn from "../components/SignIn" export default function SignInPage() { return } [V0_FILE]typescriptreact:file="app/signup/page.tsx" isMerged="true" isEdit="true" import SignUp from "../components/SignUp" export default function SignUpPage() { return } [V0_FILE]typescript:file="app/services/updateExistingAnalyzedArtworks.ts" isMerged="true" isEdit="true" import { db } from "../lib/firebase" import { collection, query, where, getDocs } from "firebase/firestore" export async function updateExistingAnalyzedArtworks() { const artworksRef = collection(db, "artworks") const q = query(artworksRef, where("analysis", "!=", null)) try { const querySnapshot = await getDocs(q) const batch = db.batch() querySnapshot.forEach((doc) => { const artworkRef = doc.ref batch.update(artworkRef, { hasAiAnalysis: true }) }) await batch.commit() console.log(`Updated ${querySnapshot.size} artworks`) } catch (error) { console.error("Error updating existing analyzed artworks:", error) } } [V0_FILE]typescriptreact:file="app/components/ArtworkCard.tsx" isQuickEdit="true" isMerged="true" isEdit="true" "use client" import Image from "next/image" import { useState } from "react" import type { Artwork } from "../types/artwork" import { analyzeArtwork } from "../services/artAnalysis" interface ArtworkCardProps { artwork: Artwork } export default function ArtworkCard({ artwork }: ArtworkCardProps) { const [analysis, setAnalysis] = useState(artwork.analysis || null) const [analyzing, setAnalyzing] = useState(false) const [error, setError] = useState(null) const handleAnalyze = async () => { setAnalyzing(true) setError(null) try { const result = await analyzeArtwork(artwork) setAnalysis(result) // Update the artwork's hasAiAnalysis field in the component state artwork.hasAiAnalysis = true } catch (error) { console.error("Error analyzing artwork:", error) setError("Failed to analyze artwork. Please try again.") } finally { setAnalyzing(false) } } const previewImage = artwork.images[0]?.servingUrl || "/placeholder.svg" return (

{artwork.title}

{artwork.studioName}

{artwork.width}cm x {artwork.height}cm, {artwork.year}

{artwork.techniques.join(", ")}

{artwork.price} {artwork.currency}

{error &&

{error}

} {analysis ? (

Analysis:

) : ( )}

) } function AnalysisDisplay({ analysis }: { analysis: any }) { if (!analysis) { return

No analysis available.

} const formatArray = (arr: any[] | string | undefined) => { if (Array.isArray(arr)) { return arr.join(", ") } if (typeof arr === "string") { return arr } return "Not specified" } return (

{analysis.basicDetails && ( <>

Basic Details:

Orientation: {analysis.basicDetails.orientation || "Not specified"}

)} {analysis.colorAnalysis && ( <>

Color Analysis:

Dominant Colors:{" "} {Array.isArray(analysis.colorAnalysis.dominantColors) ? analysis.colorAnalysis.dominantColors .map((c: any) => `${c.name || "Unnamed"} (${c.hex || "No hex"})`) .join(", ") : "Not specified"}

Overall Temperature: {analysis.colorAnalysis.overallTemperature || "Not specified"}

)} {analysis.textureAnalysis && ( <>

Texture:

{analysis.textureAnalysis.surfaceQualities || "Not specified"}

Types: {formatArray(analysis.textureAnalysis.types)}

)} {analysis.styleAndGenre && ( <>

Style and Genre:

Genre: {analysis.styleAndGenre.genre || "Not specified"}

Movements: {formatArray(analysis.styleAndGenre.artisticMovements)}

)} {analysis.subjectMatter && ( <>

Subject Matter:

Primary: {analysis.subjectMatter.primarySubject || "Not specified"}

Key Elements: {formatArray(analysis.subjectMatter.keyElements)}

)} {analysis.mediumAndTechnique && ( <>

Medium and Technique:

Materials: {formatArray(analysis.mediumAndTechnique.materialsUsed)}

)} {analysis.composition && ( <>

Composition:

Type: {analysis.composition.type || "Not specified"}

Focal Points: {formatArray(analysis.composition.focalPoints)}

)} {analysis.spaceAndDisplay && ( <>

Display Recommendations:

Environments: {formatArray(analysis.spaceAndDisplay.recommendedEnvironments)}

Viewing Distance: {analysis.spaceAndDisplay.viewingDistance || "Not specified"}

)} {analysis.tags && ( <>

Tags:

Style: {formatArray(analysis.tags.style)}

Color: {formatArray(analysis.tags.color)}

Subject: {formatArray(analysis.tags.subject)}

)}

) } [V0_FILE]typescriptreact:file="app/services/artwork.ts" isQuickEdit="true" isMerged="true" isEdit="true" import { db } from "../lib/firebase" import { collection, query, orderBy, limit, startAfter, getDocs, type DocumentData, type QueryDocumentSnapshot, } from "firebase/firestore" import type { Artwork } from "../types/artwork" let lastVisible: QueryDocumentSnapshot | null = null export async function fetchArtworks(page: number, pageSize: number): Promise { const artworksRef = collection(db, "artworks") let q = query(artworksRef, orderBy("createdAt", "desc"), limit(pageSize)) if (page > 1 && lastVisible) { q = query(q, startAfter(lastVisible)) } try { const snapshot = await getDocs(q) lastVisible = snapshot.docs[snapshot.docs.length - 1] || null return snapshot.docs.map((doc) => { const data = doc.data() return { id: doc.id, title: data.title, studioName: data.studioName, images: data.images, price: data.price, currency: data.currency, techniques: data.techniques, year: data.year, width: data.width, height: data.height, createdAt: data.createdAt.toDate(), status: data.status, analysis: data.analysis, hasAiAnalysis: data.hasAiAnalysis || false, } as Artwork }) } catch (error) { console.error("Error fetching artworks:", error) throw error } } [V0_FILE]typescript:file="app/types/roomAnalysis.ts" isMerged="true" isEdit="true" export interface RoomAnalysis { style: string[] colors: { dominant: string[] accent: string[] } lighting: string size: string furnishings: string[] mood: string[] artPlacement: string[] } [V0_FILE]typescript:file="app/services/roomAnalysis.ts" isEdit="true" isMerged="true" "use server" import { generateText } from "ai" import { openai } from "@ai-sdk/openai" import type { RoomAnalysis } from "../types/roomAnalysis" import { compressImage } from "../utils/imageProcessing" export async function analyzeRoom(image: File): Promise { try { const compressedImage = await compressImage(image) const prompt = `Analyze this room image and return ONLY a JSON object without any explanation or markdown formatting. Provide detailed and varied responses for each category: { "style": { "primary": ["string"], "secondary": ["string"], "era": "string", "description": "string" }, "colors": { "dominant": [{"name": "string", "hex": "string"}], "accent": [{"name": "string", "hex": "string"}], "overall_palette": "string" }, "lighting": { "type": "string", "intensity": "number (1-10)", "temperature": "string", "description": "string" }, "space": { "size": "string", "proportions": "string", "ceiling_height": "string" }, "furnishings": ["string"], "textures": ["string"], "mood": { "primary": ["string"], "secondary": ["string"], "description": "string" }, "art_considerations": { "suitable_styles": ["string"], "recommended_sizes": ["string"], "potential_themes": ["string"], "placement_suggestions": ["string"] }, "overall_impression": "string" } Provide varied and nuanced analysis, considering subtle details and potential artistic interpretations of the space. Aim for a rich, descriptive response that captures the essence of the room and its potential for art display. Image: ${compressedImage}` const { text } = await generateText({ model: openai("gpt-4o-mini"), prompt: prompt, maxTokens: 500, }) // Extract JSON from the response let jsonString = text // If the response contains markdown code blocks, extract the JSON if (text.includes("```")) { const match = text.match(/```(?:json)?\s*([\s\S]*?)\s*```/) if (match && match[1]) { jsonString = match[1].trim() } } try { const analysis: RoomAnalysis = JSON.parse(jsonString) return analysis } catch (parseError) { console.error("JSON Parse Error:", parseError) throw new Error("Failed to parse room analysis results. Please try again.") } } catch (error) { console.error("Error analyzing room:", error) // Handle specific error cases if (error instanceof Error) { if (error.message.includes("503")) { throw new Error("Service temporarily unavailable. Please try again in a few moments.") } if (error.message.includes("context_length_exceeded")) { throw new Error("Image is too large to process. Please try a smaller image.") } throw error } throw new Error("Failed to analyze room. Please try again.") } } [V0_FILE]typescript:file="app/components/ArtSuggestions.tsx" isEdit="true" isMerged="true" "use client" import { useState, useEffect } from "react" import { fetchMatchingArtworks } from "../services/artworkRecommendation" import type { Artwork } from "../types/artwork" import type { RoomAnalysis } from "../types/roomAnalysis" import Image from "next/image" interface ArtSuggestionsProps { roomAnalysis: RoomAnalysis | null } export default function ArtSuggestions({ roomAnalysis }: ArtSuggestionsProps) { const [matchingArtworks, setMatchingArtworks] = useState([]) const [loading, setLoading] = useState(false) const [error, setError] = useState(null) useEffect(() => { if (roomAnalysis) { fetchArtworks() } }, [roomAnalysis]) const fetchArtworks = async () => { if (!roomAnalysis) return setLoading(true) setError(null) try { console.log("Fetching artworks with room analysis:", roomAnalysis) const artworks = await fetchMatchingArtworks(roomAnalysis) console.log("Fetched artworks:", artworks) setMatchingArtworks(artworks) } catch (err) { console.error("Error fetching matching artworks:", err) setError("Failed to fetch matching artworks. Please try again.") } finally { setLoading(false) } } if (!roomAnalysis) { return null } return (

Art Suggestions

{loading &&

Loading suggestions...

} {error &&

{error}

}

{matchingArtworks.map((artwork) => (

{artwork.title}

{artwork.studioName}

{artwork.width}cm x {artwork.height}cm, {artwork.year}

{artwork.price} {artwork.currency}

{artwork.explanation && (

Why this artwork?

{artwork.explanation}

)} {artwork.analysis && (

AI Art Analysis

Style: {artwork.analysis.styleAndGenre.artisticMovements.join(", ")}

Colors:{" "} {artwork.analysis.colorAnalysis.dominantColors.map((c) => c.name).join(", ")}

Mood: {artwork.analysis.subjectMatter.contextInterpretation}

Composition: {artwork.analysis.composition.type}

Texture: {artwork.analysis.textureAnalysis.surfaceQualities}

)}

))}

{matchingArtworks.length === 0 && !loading && (

No matching artworks found. Try analyzing a different room.

)}

) } [V0_FILE]typescript:file="app/services/artworkRecommendation.ts" isFixed="true" isEdit="true" isQuickEdit="true" isMerged="true" import { db } from "../lib/firebase" import { collection, query, where, getDocs, limit } from "firebase/firestore" import type { Artwork } from "../types/artwork" import type { RoomAnalysis } from "../types/roomAnalysis" import { generateArtworkExplanations } from "../actions/artworkActions" export async function fetchMatchingArtworks(roomAnalysis: RoomAnalysis): Promise { console.log("Starting fetchMatchingArtworks with roomAnalysis:", roomAnalysis) const artworksRef = collection(db, "artworks") // Create a base query const q = query( artworksRef, where("hasAiAnalysis", "==", true), limit(100), // Fetch more artworks to allow for better filtering ) try { console.log("Executing Firestore query") const snapshot = await getDocs(q) console.log("Query executed, document count:", snapshot.size) const artworks: Artwork[] = [] snapshot.forEach((doc) => { const data = doc.data() artworks.push({ id: doc.id, title: data.title, studioName: data.studioName, images: data.images, price: data.price, currency: data.currency, techniques: data.techniques, year: data.year, width: data.width, height: data.height, createdAt: data.createdAt.toDate(), status: data.status, analysis: data.analysis, hasAiAnalysis: data.hasAiAnalysis, } as Artwork) }) console.log("Artworks fetched:", artworks.length) // Implement more sophisticated matching logic const matchedArtworks = artworks.filter((artwork) => { if (!artwork.analysis) return false const styleMatch = checkStyleMatch(artwork, roomAnalysis) const colorMatch = checkColorMatch(artwork, roomAnalysis) const moodMatch = checkMoodMatch(artwork, roomAnalysis) const sizeMatch = checkSizeMatch(artwork, roomAnalysis) const textureMatch = checkTextureMatch(artwork, roomAnalysis) const themeMatch = checkThemeMatch(artwork, roomAnalysis) // Require at least two matches for a more stringent filtering return [styleMatch, colorMatch, moodMatch, sizeMatch, textureMatch, themeMatch].filter(Boolean).length >= 2 }) console.log("Matched artworks:", matchedArtworks.length) // Sort matched artworks by relevance score matchedArtworks.sort((a, b) => { const scoreA = calculateRelevanceScore(a, roomAnalysis) const scoreB = calculateRelevanceScore(b, roomAnalysis) return scoreB - scoreA }) // Introduce randomness to avoid suggesting the same pictures each time const shuffledArtworks = shuffleArray(matchedArtworks.slice(0, 20)) const result = shuffledArtworks.slice(0, 8) // Return top 8 artworks console.log("Final result:", result.length, "artworks") // Generate explanations using the server action const explanations = await generateArtworkExplanations(roomAnalysis, result) // Add explanations to the artworks return result.map((artwork) => ({ ...artwork, explanation: explanations[artwork.id] })) } catch (error) { console.error("Error fetching matching artworks:", error) throw error } } function checkStyleMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { return artwork.analysis?.styleAndGenre.artisticMovements.some((style) => roomAnalysis.style.primary.concat(roomAnalysis.style.secondary).includes(style), ) } function checkColorMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { return artwork.analysis?.colorAnalysis.dominantColors.some((color) => roomAnalysis.colors.dominant .concat(roomAnalysis.colors.accent) .some((c) => c.name.toLowerCase().includes(color.name.toLowerCase())), ) } function checkMoodMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { return roomAnalysis.mood.primary .concat(roomAnalysis.mood.secondary) .some((mood) => artwork.analysis?.subjectMatter.contextInterpretation.toLowerCase().includes(mood.toLowerCase())) } function checkSizeMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { const artworkSize = artwork.width * artwork.height switch (roomAnalysis.space.size) { case "small": return artworkSize < 5000 // Less than 50x100 cm case "medium": return artworkSize >= 5000 && artworkSize < 20000 // Between 50x100 cm and 100x200 cm case "large": return artworkSize >= 20000 // Larger than 100x200 cm default: return true } } function checkTextureMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { return artwork.analysis?.textureAnalysis.types.some((texture) => roomAnalysis.textures.includes(texture)) } function checkThemeMatch(artwork: Artwork, roomAnalysis: RoomAnalysis): boolean { return roomAnalysis.art_considerations.potential_themes.some((theme) => artwork.analysis?.subjectMatter.keyElements.includes(theme), ) } function calculateRelevanceScore(artwork: Artwork, roomAnalysis: RoomAnalysis): number { let score = 0 if (artwork.analysis) { // Style match score += roomAnalysis.style.primary .concat(roomAnalysis.style.secondary) .filter((style) => artwork.analysis?.styleAndGenre.artisticMovements.includes(style)).length * 2 // Color match score += roomAnalysis.colors.dominant .concat(roomAnalysis.colors.accent) .filter((color) => artwork.analysis?.colorAnalysis.dominantColors.some((c) => c.name.toLowerCase().includes(color.name.toLowerCase()), ), ).length * 2 // Mood match score += roomAnalysis.mood.primary .concat(roomAnalysis.mood.secondary) .filter((mood) => artwork.analysis?.subjectMatter.contextInterpretation.toLowerCase().includes(mood.toLowerCase()), ).length * 1.5 // Size match const artworkSize = artwork.width * artwork.height const recommendedSizes = roomAnalysis.art_considerations.recommended_sizes if (recommendedSizes.includes("small") && artworkSize < 5000) score += 1 if (recommendedSizes.includes("medium") && artworkSize >= 5000 && artworkSize < 20000) score += 1 if (recommendedSizes.includes("large") && artworkSize >= 20000) score += 1 // Lighting match if (roomAnalysis.lighting.temperature === "warm" && artwork.analysis.colorAnalysis.overallTemperature === "warm") { score += 1 } else if ( roomAnalysis.lighting.temperature === "cool" && artwork.analysis.colorAnalysis.overallTemperature === "cool" ) { score += 1 } // Style suitability score += roomAnalysis.art_considerations.suitable_styles.filter((style) => artwork.analysis?.styleAndGenre.artisticMovements.includes(style), ).length * 1.5 // Theme match score += roomAnalysis.art_considerations.potential_themes.filter((theme) => artwork.analysis?.subjectMatter.keyElements.includes(theme), ).length * 1.5 // Texture match score += roomAnalysis.textures.filter((texture) => artwork.analysis?.textureAnalysis.types.includes(texture)).length // Era match if (artwork.analysis.styleAndGenre.artisticMovements.includes(roomAnalysis.style.era)) { score += 2 } } return score } function shuffleArray(array: T[]): T[] { for (let i = array.length - 1; i > 0; i--) { const j = Math.floor(Math.random() * (i + 1)) ;[array[i], array[j]] = [array[j], array[i]] } return array } [V0_FILE]typescript:file="app/actions/artworkActions.ts" isMerged="true" isEdit="true" "use server" import { generateText } from "ai" import { openai } from "@ai-sdk/openai" import type { Artwork } from "../types/artwork" import type { RoomAnalysis } from "../types/roomAnalysis" export async function generateArtworkExplanations( roomAnalysis: RoomAnalysis, matchedArtworks: Artwork[], ): Promise<{ [key: string]: string }> { const prompt = ` Given the following room analysis and a list of artworks that have been matched to this room, provide a short but informative explanation for why each artwork is suitable for the room. Consider factors such as style, color, mood, size, texture, and theme in your explanations. Be specific and highlight the unique aspects of each artwork that make it a good fit for the room. Room Analysis: ${JSON.stringify(roomAnalysis, null, 2)} Matched Artworks: ${matchedArtworks .map( (artwork, index) => ` Artwork ${index + 1}: Title: ${artwork.title} Artist: ${artwork.studioName} Size: ${artwork.width}cm x ${artwork.height}cm Year: ${artwork.year} Analysis: ${JSON.stringify(artwork.analysis, null, 2)} `, ) .join("\n")} Provide a concise explanation (2-3 sentences) for each artwork, starting with "Artwork 1:" and so on. ` try { const { text } = await generateText({ model: openai("gpt-4o-mini"), prompt: prompt, maxTokens: 1000, }) const explanations: { [key: string]: string } = {} const explanationRegex = /Artwork (\d+):\s*([\s\S]*?)(?=Artwork \d+:|$)/g let match while ((match = explanationRegex.exec(text)) !== null) { const artworkIndex = Number.parseInt(match[1]) - 1 const explanation = match[2].trim() if (artworkIndex >= 0 && artworkIndex < matchedArtworks.length) { explanations[matchedArtworks[artworkIndex].id] = explanation } } return explanations } catch (error) { console.error("Error generating artwork explanations:", error) throw error } } [V0_FILE]typescript:file="app/actions/imageActions.ts" isEdit="true" isMerged="true" "use server" import { generateText } from "ai" import { openai } from "@ai-sdk/openai" import type { RoomAnalysis } from "../types/roomAnalysis" export async function analyzeRoomImage(imageBase64: string): Promise { try { console.log("Starting room analysis with base64 image") if (!imageBase64 || typeof imageBase64 !== "string") { console.error("Invalid image data:", typeof imageBase64) throw new Error("Invalid image data provided") } // Log image size console.log("Image base64 length:", imageBase64.length) // Check if OpenAI API key is available if (!process.env.OPENAI_API_KEY) { console.error("OpenAI API key is missing") throw new Error("OpenAI API key is not configured") } const prompt = `Analyze this room image and return ONLY a JSON object without any explanation or markdown formatting. Provide detailed and varied responses for each category: { "style": { "primary": ["string"], "secondary": ["string"], "era": "string", "description": "string" }, "colors": { "dominant": [{"name": "string", "hex": "string"}], "accent": [{"name": "string", "hex": "string"}], "overall_palette": "string" }, "lighting": { "type": "string", "intensity": "number (1-10)", "temperature": "string", "description": "string" }, "space": { "size": "string", "proportions": "string", "ceiling_height": "string" }, "furnishings": ["string"], "textures": ["string"], "mood": { "primary": ["string"], "secondary": ["string"], "description": "string" }, "art_considerations": { "suitable_styles": ["string"], "recommended_sizes": ["string"], "potential_themes": ["string"], "placement_suggestions": ["string"] }, "overall_impression": "string" } Provide varied and nuanced analysis, considering subtle details and potential artistic interpretations of the space. Aim for a rich, descriptive response that captures the essence of the room and its potential for art display. Image: ${imageBase64}` console.log("Calling OpenAI API") try { const { text } = await generateText({ model: openai("gpt-4o-mini"), prompt: prompt, maxTokens: 500, }) console.log("Received response from OpenAI") // Extract JSON from the response let jsonString = text // If the response contains markdown code blocks, extract the JSON if (text.includes("```")) { const match = text.match(/```(?:json)?\s*([\s\S]*?)\s*```/) if (match && match[1]) { jsonString = match[1].trim() } } try { console.log("Parsing JSON response") const analysis: RoomAnalysis = JSON.parse(jsonString) console.log("Room analysis completed successfully") return analysis } catch (parseError) { console.error("JSON Parse Error:", parseError) throw new Error("Failed to parse room analysis results. Please try again.") } } catch (openaiError) { console.error("OpenAI API Error:", openaiError) throw new Error(`OpenAI API error: ${openaiError instanceof Error ? openaiError.message : "Unknown error"}`) } } catch (error) { console.error("Error analyzing room:", error) throw new Error(`Room analysis failed: ${error instanceof Error ? error.message : "Unknown error"}`) } } [V0_FILE]typescript:file="app/utils/imageProcessing.ts" isEdit="true" "use client" // This file should only be used on the client side export async function compressImage(file: File): Promise { return new Promise((resolve, reject) => { const reader = new FileReader() reader.onload = (e) => { const img = new Image() img.onload = () => { const canvas = document.createElement("canvas") // Calculate new dimensions while maintaining aspect ratio let width = img.width let height = img.height const maxDimension = 800 if (width > height && width > maxDimension) { height = (height * maxDimension) / width width = maxDimension } else if (height > maxDimension) { width = (width * maxDimension) / height height = maxDimension } canvas.width = width canvas.height = height const ctx = canvas.getContext("2d") if (!ctx) { reject(new Error("Could not get canvas context")) return } ctx.drawImage(img, 0, 0, width, height) // Convert to JPEG with reduced quality const compressedImage = canvas.toDataURL("image/jpeg", 0.6) resolve(compressedImage) } img.onerror = () => { reject(new Error("Failed to load image")) } img.src = e.target?.result as string } reader.onerror = () => { reject(new Error("Failed to read file")) } reader.readAsDataURL(file) }) } [V0_FILE]typescript:file="app/components/RoomCapture.tsx" isEdit="true" "use client" import type React from "react" import { useState } from "react" import Image from "next/image" import { analyzeRoomImage } from "../actions/roomActions" import type { RoomAnalysis } from "../types/roomAnalysis" import { compressImage } from "../utils/imageProcessing" interface RoomCaptureProps { onAnalysis: (analysis: RoomAnalysis) => void } export default function RoomCapture({ onAnalysis }: RoomCaptureProps) { const [image, setImage] = useState(null) const [imageUrl, setImageUrl] = useState(null) const [analyzing, setAnalyzing] = useState(false) const [error, setError] = useState(null) const [retryCount, setRetryCount] = useState(0) const [roomAnalysisResult, setRoomAnalysisResult] = useState(null) const [isCollapsed, setIsCollapsed] = useState(true) const MAX_RETRIES = 3 const handleImageChange = (event: React.ChangeEvent) => { const file = event.target.files?.[0] if (file) { if (file.size > 10 * 1024 * 1024) { setError("Image file is too large. Please choose an image under 10MB.") return } setImage(file) setImageUrl(URL.createObjectURL(file)) setError(null) setRetryCount(0) } else { setImage(null) setImageUrl(null) } } const handleAnalyze = async () => { if (!image) return setAnalyzing(true) setError(null) try { // Compress the image on the client side const compressedImageBase64 = await compressImage(image) // Send the compressed image to the server action const analysis = await analyzeRoomImage(compressedImageBase64) setRoomAnalysisResult(analysis) onAnalysis(analysis) setRetryCount(0) } catch (err) { const errorMessage = err instanceof Error ? err.message : "Failed to analyze the room. Please try again." if (errorMessage.includes("Service temporarily unavailable") && retryCount < MAX_RETRIES) { setRetryCount((prev) => prev + 1) setTimeout(() => { handleAnalyze() }, 2000) return } setError(errorMessage) console.error("Error analyzing room:", err) } finally { setAnalyzing(false) } } return (

Room Capture

Upload a room photo

Maximum file size: 10MB

{imageUrl && (

{roomAnalysisResult && (

Room Analysis

Style

Primary: {roomAnalysisResult.style.primary.join(", ")}

Secondary: {roomAnalysisResult.style.secondary.join(", ")}

Era: {roomAnalysisResult.style.era}

Description: {roomAnalysisResult.style.description}

Colors

Dominant: {roomAnalysisResult.colors.dominant.map((c) => `${c.name} (${c.hex})`).join(", ")}

Accent: {roomAnalysisResult.colors.accent.map((c) => `${c.name} (${c.hex})`).join(", ")}

Overall Palette: {roomAnalysisResult.colors.overall_palette}

Lighting

Type: {roomAnalysisResult.lighting.type}

Intensity: {roomAnalysisResult.lighting.intensity}/10

Temperature: {roomAnalysisResult.lighting.temperature}

Description: {roomAnalysisResult.lighting.description}

Space

Size: {roomAnalysisResult.space.size}

Proportions: {roomAnalysisResult.space.proportions}

Ceiling Height: {roomAnalysisResult.space.ceiling_height}

Furnishings

{roomAnalysisResult.furnishings.join(", ")}

Textures

{roomAnalysisResult.textures.join(", ")}

Mood

Primary: {roomAnalysisResult.mood.primary.join(", ")}

Secondary: {roomAnalysisResult.mood.secondary.join(", ")}

Description: {roomAnalysisResult.mood.description}

Art Considerations

Suitable Styles: {roomAnalysisResult.art_considerations.suitable_styles.join(", ")}

Recommended Sizes: {roomAnalysisResult.art_considerations.recommended_sizes.join(", ")}

Potential Themes: {roomAnalysisResult.art_considerations.potential_themes.join(", ")}

Placement Suggestions: {roomAnalysisResult.art_considerations.placement_suggestions.join(", ")}

Overall Impression

{roomAnalysisResult.overall_impression}

)}

)} {error &&

{error}

}

AI Art Recommendation

}> ) } [V0_FILE]typescriptreact:file="app/components/ArtworkGrid.tsx" isMerged="true" isEdit="true" "use client" import { useState, useEffect, useCallback } from "react" import { useInView } from "react-intersection-observer" import ArtworkCard from "./ArtworkCard" import { fetchArtworks } from "../services/artwork" import type { Artwork } from "../types/artwork" export default function ArtworkGrid() { const [artworks, setArtworks] = useState([]) const [page, setPage] = useState(1) const [loading, setLoading] = useState(false) const [hasMore, setHasMore] = useState(true) const [error, setError] = useState(null) const { ref, inView } = useInView({ threshold: 0, }) const loadMoreArtworks = useCallback(async () => { if (loading || !hasMore) return setLoading(true) try { const newArtworks = await fetchArtworks(page, 20) if (newArtworks.length === 0) { setHasMore(false) } else { setArtworks((prevArtworks) => [...prevArtworks, ...newArtworks]) setPage((prevPage) => prevPage + 1) } } catch (error) { console.error("Error loading artworks:", error) setError("Failed to load artworks. Please try again later.") } finally { setLoading(false) } }, [loading, hasMore, page]) useEffect(() => { if (inView) { loadMoreArtworks() } }, [inView, loadMoreArtworks]) if (error) { return

{error}

} return (

{artworks.map((artwork) => ( ))}

{loading &&

Loading more artworks...

} {!loading && hasMore &&

} {!hasMore &&

No more artworks to load

}

Sign In

{error &&

{error}

}

Don't have an account?{" "} Sign Up

Sign Up

{error &&

{error}

}

Already have an account?{" "} Sign In

{artwork.title}

{artwork.studioName}

{artwork.width}cm x {artwork.height}cm, {artwork.year}

{artwork.techniques.join(", ")}

{artwork.price} {artwork.currency}

{error &&

{error}

} {analysis ? (

Analysis:

) : ( )}

) } function AnalysisDisplay({ analysis }: { analysis: any }) { if (!analysis) { return

No analysis available.

} const formatArray = (arr: any[] | string | undefined) => { if (Array.isArray(arr)) { return arr.join(", ") } if (typeof arr === "string") { return arr } return "Not specified" } return (

{analysis.basicDetails && ( <>

Basic Details:

Orientation: {analysis.basicDetails.orientation || "Not specified"}

)} {analysis.colorAnalysis && ( <>

Color Analysis:

Overall Temperature: {analysis.colorAnalysis.overallTemperature || "Not specified"}

)} {analysis.textureAnalysis && ( <>

Texture:

{analysis.textureAnalysis.surfaceQualities || "Not specified"}

Types: {formatArray(analysis.textureAnalysis.types)}

)} {analysis.styleAndGenre && ( <>

Style and Genre:

Genre: {analysis.styleAndGenre.genre || "Not specified"}

Movements: {formatArray(analysis.styleAndGenre.artisticMovements)}

)} {analysis.subjectMatter && ( <>

Subject Matter:

Primary: {analysis.subjectMatter.primarySubject || "Not specified"}

Key Elements: {formatArray(analysis.subjectMatter.keyElements)}

)} {analysis.mediumAndTechnique && ( <>

Medium and Technique:

Materials: {formatArray(analysis.mediumAndTechnique.materialsUsed)}

)} {analysis.composition && ( <>

Composition:

Type: {analysis.composition.type || "Not specified"}

Focal Points: {formatArray(analysis.composition.focalPoints)}

)} {analysis.spaceAndDisplay && ( <>

Display Recommendations:

Environments: {formatArray(analysis.spaceAndDisplay.recommendedEnvironments)}

Viewing Distance: {analysis.spaceAndDisplay.viewingDistance || "Not specified"}

)} {analysis.tags && ( <>

Tags:

Style: {formatArray(analysis.tags.style)}

Color: {formatArray(analysis.tags.color)}

Subject: {formatArray(analysis.tags.subject)}

)}

Art Suggestions

{loading &&

Loading suggestions...

} {error &&

{error}

}

{matchingArtworks.map((artwork) => (

{artwork.title}

{artwork.studioName}

{artwork.width}cm x {artwork.height}cm, {artwork.year}

{artwork.price} {artwork.currency}

{artwork.explanation && (

Why this artwork?

{artwork.explanation}

)} {artwork.analysis && (

AI Art Analysis

Style: {artwork.analysis.styleAndGenre.artisticMovements.join(", ")}

Colors:{" "} {artwork.analysis.colorAnalysis.dominantColors.map((c) => c.name).join(", ")}

Mood: {artwork.analysis.subjectMatter.contextInterpretation}

Composition: {artwork.analysis.composition.type}

Texture: {artwork.analysis.textureAnalysis.surfaceQualities}

)}

))}

{matchingArtworks.length === 0 && !loading && (

No matching artworks found. Try analyzing a different room.

)}

Room Capture

Upload a room photo

Maximum file size: 10MB

{imageUrl && (

{roomAnalysisResult && (

Room Analysis

Style

Primary: {roomAnalysisResult.style.primary.join(", ")}

Secondary: {roomAnalysisResult.style.secondary.join(", ")}

Era: {roomAnalysisResult.style.era}

Description: {roomAnalysisResult.style.description}

Colors

Dominant: {roomAnalysisResult.colors.dominant.map((c) => `${c.name} (${c.hex})`).join(", ")}

Accent: {roomAnalysisResult.colors.accent.map((c) => `${c.name} (${c.hex})`).join(", ")}

Overall Palette: {roomAnalysisResult.colors.overall_palette}

Lighting

Type: {roomAnalysisResult.lighting.type}

Intensity: {roomAnalysisResult.lighting.intensity}/10

Temperature: {roomAnalysisResult.lighting.temperature}

Description: {roomAnalysisResult.lighting.description}

Space

Size: {roomAnalysisResult.space.size}

Proportions: {roomAnalysisResult.space.proportions}

Ceiling Height: {roomAnalysisResult.space.ceiling_height}

Furnishings

{roomAnalysisResult.furnishings.join(", ")}

Textures

{roomAnalysisResult.textures.join(", ")}

Mood

Primary: {roomAnalysisResult.mood.primary.join(", ")}

Secondary: {roomAnalysisResult.mood.secondary.join(", ")}

Description: {roomAnalysisResult.mood.description}

Art Considerations

Suitable Styles: {roomAnalysisResult.art_considerations.suitable_styles.join(", ")}

Recommended Sizes: {roomAnalysisResult.art_considerations.recommended_sizes.join(", ")}

Potential Themes: {roomAnalysisResult.art_considerations.potential_themes.join(", ")}

Placement Suggestions: {roomAnalysisResult.art_considerations.placement_suggestions.join(", ")}

Overall Impression

{roomAnalysisResult.overall_impression}

)}

)} {error &&

{error}

}

) } [V0_FILE]typescript:file="app/services/artAnalysis.ts" isEdit="true" "use server" import { db } from "../lib/firebase" import { doc, updateDoc } from "firebase/firestore" import type { Artwork } from "../types/artwork" import { generateText } from "ai" import { openai } from "@ai-sdk/openai" export async function analyzeArtwork(artwork: Artwork) { try { const prompt = `Analyze the following artwork and provide a detailed analysis in JSON format: Image URL: ${artwork.images[0]?.servingUrl} Title: ${artwork.title} Artist: ${artwork.studioName} Year: ${artwork.year} Techniques: ${artwork.techniques.join(", ")} Please provide analysis in the following structure: { "basicDetails": { "orientation": "" }, "colorAnalysis": { "dominantColors": [{"name": "", "hex": "", "rgb": ""}], "secondaryColors": [{"name": "", "hex": "", "rgb": ""}], "overallTemperature": "", "colorRelationships": "" }, "textureAnalysis": { "surfaceQualities": "", "techniqueRelatedTextures": "", "uniqueTexturalElements": "", "types": [] }, "styleAndGenre": { "artisticMovements": [], "genre": "", "hybridStyles": "" }, "subjectMatter": { "primarySubject": "", "secondarySubjects": [], "keyElements": [], "contextInterpretation": "" }, "mediumAndTechnique": { "materialsUsed": [], "applicationTechniques": "", "uniqueApproaches": "" }, "composition": { "type": "", "focalPoints": [], "description": "" }, "spaceAndDisplay": { "optimalViewingConditions": "", "recommendedEnvironments": [], "lightingRecommendations": "", "viewingDistance": "" }, "tags": { "style": [], "medium": [], "color": [], "genre": [], "subject": [], "texture": [] } }` const { text } = await generateText({ model: openai("gpt-4o-mini"), prompt: prompt, }) // Remove markdown code block syntax if present const jsonString = text.replace(/^```json\n|\n```$/g, "").trim() const analysis = JSON.parse(jsonString) // Save analysis to Firebase const artworkRef = doc(db, "artworks", artwork.id) await updateDoc(artworkRef, { analysis: analysis, hasAiAnalysis: true, // Set the new field }) return analysis } catch (error) { console.error("Error analyzing artwork:", error) throw error } } [V0_FILE]typescript:file="app/actions/roomActions.ts" isQuickEdit="true" "use server" import { analyzeRoomImage } from "./imageActions" export { analyzeRoomImage } 3c:o56f7,106:["$","$L112",null,{"value":{"cacheData":{}},"children":["$","$L108",null,{"params":{"chatId":"iUsiPzinbs5"},"promises":{"attributes":"$@113","externalMeta":"$@114","latest":"$@115","messagesRange":"$@116","messages":"$@117","template":"$@118","gitStatus":"$@119"},"children":["$","$L11b",null,{"chatId":"iUsiPzinbs5","children":["$","$L11c",null,{"seedOnlyKeys":["@\"chat:state\",\"iUsiPzinbs5\","],"data":{"block:output:initial":{"id":"b_kbLkElD7oFS","result":"$@11d"},"@\"block:mappings\",\"iUsiPzinbs5\",":{"sqlBlockExecutionStates":"$Q11e","blocksForkedFrom":"$Q11f","blocksTemplateIds":"$Q120","blocksCreatedAt":"$Q121","blocksShared":"$Q122","blocksDeployed":"$Q123","blocksSubdomains":"$Q124","brandedBlocks":"$Q125","unlistedBlocks":"$Q126","blockOutputs":"$Q127"},"@\"chat:state\",\"iUsiPzinbs5\",":{"messages":[{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"QPB9W67LU75fK3Wr6BXymRGIdT7iACGZ","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:00:59.678Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"llAhQqSXLAI","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"eZ6xqmkGeSP2u7Dsc53pLVBilLTAhmmS","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"QPB9W67LU75fK3Wr6BXymRGIdT7iACGZ","type":"message","createdAt":"$D2025-02-04T11:01:00.678Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"yuiVvgyUANrfOGmgZEoLIBDKsO2m8t06","parentId":"eZ6xqmkGeSP2u7Dsc53pLVBilLTAhmmS","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:05:52.399Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"b_Qif2nkT8CYQ","type":"code-project","isVersioned":false},{"blockId":"b_UP6uqbaxprg","type":"code-project","isVersioned":false},{"blockId":"b_2M5b3qumYmK","type":"code-project","isVersioned":false},{"blockId":"b_EGwib8tbB1x","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"StMaGHrymYFmnTQzO7CLHBSVXg0S6w8K","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"yuiVvgyUANrfOGmgZEoLIBDKsO2m8t06","type":"message","createdAt":"$D2025-02-04T11:05:52.400Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"GoUiIYZOfmw8JVDiEzwbJGBVoQQHqTjE","parentId":"StMaGHrymYFmnTQzO7CLHBSVXg0S6w8K","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:09:21.417Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"b_yMa3nO9ssfG","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"VpUEEzQ7Zf7tOsCAsuwKUDfHmMEqH6g1","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"GoUiIYZOfmw8JVDiEzwbJGBVoQQHqTjE","type":"message","createdAt":"$D2025-02-04T11:09:21.418Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"wLnUfqn2I0Ln8hg1f5z76h3fkKDocd7r","parentId":"VpUEEzQ7Zf7tOsCAsuwKUDfHmMEqH6g1","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:11:16.070Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"b_y6Ha6ZrWWKr","type":"code-project","isVersioned":false},{"blockId":"b_u0qN38gPPZ8","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"JDnHKo7qyVNoHuMMgiK2Oe1V0Rp2URYL","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"wLnUfqn2I0Ln8hg1f5z76h3fkKDocd7r","type":"message","createdAt":"$D2025-02-04T11:11:16.071Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"8UZmW23SlrJkQqSae0y543yaxT4rNAAW","parentId":"JDnHKo7qyVNoHuMMgiK2Oe1V0Rp2URYL","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:12:58.275Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading 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message"}]]},"loadingBlocks":[{"blockId":"b_DunIogFRBVT","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"sj10gFbqYprQ6ozeVxbeaUnEinNMcaNe","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"aUvvdoRcjr9z4n5wWYfnpUnoaDJY6ZvZ","type":"message","createdAt":"$D2025-02-04T11:18:44.005Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"gt3uGqJPXBlwdSMLNi5Mb1AUmwcv4zPv","parentId":"sj10gFbqYprQ6ozeVxbeaUnEinNMcaNe","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:36:49.789Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading message"}]]},"loadingBlocks":[{"blockId":"b_MCtd1Ypu2dT","type":"code-project","isVersioned":false},{"blockId":"b_G50XDe7ZMt5","type":"code-project","isVersioned":true}],"isContentLoading":true,"role":"assistant","id":"youl0hQz7jqh9dxTwsXDJ13DGJ59IcCc","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"gt3uGqJPXBlwdSMLNi5Mb1AUmwcv4zPv","type":"message","createdAt":"$D2025-02-04T11:36:49.790Z"},{"content":{"type":"parts","parts":[],"version":1},"role":"user","id":"IZC05Yk8gAAD1ysnTRhwd6BnVSYDtj4C","parentId":"youl0hQz7jqh9dxTwsXDJ13DGJ59IcCc","type":"message","isContentLoading":true,"authorId":"","createdAt":"$D2025-02-04T11:41:09.872Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"Loading..."]]]],[1,{"title":"Loading 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error."}],"version":1},"role":"user","id":"8LrVTlKtwKErbjSDMDs6ZuExFlIi7n50","attachments":[],"parentId":"9fjQMHWJiDJR4wFH32bdKgAgG33gXEgC","type":"fix-with-v0","authorId":"pqpc5zNlmrSXIWsTeoZGqcHp","createdAt":"$D2025-03-12T17:26:25.687Z"},{"content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-0","parts":[{"type":"thinking-end","duration":1.988235294117647,"thought":"The error is occurring because we've moved the `generateArtworkExplanations` function from `app/services/artworkRecommendation.ts` to `app/actions/artworkActions.ts`, but we're still trying to import it from the original location. 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The application should now properly handle the image processing on the client side and use server actions for the OpenAI API calls."]]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"fvSDqXLNnaWuhuy8vucGUg9Wq1dEQqFb","gitRevision":null,"streaming":false,"finishReason":"stop","parentId":"8LrVTlKtwKErbjSDMDs6ZuExFlIi7n50","type":"message","creditCost":0.1119735,"workMetrics":{"diffMap":{}},"createdAt":"$D2025-03-12T17:26:25.688Z","updatedAt":"$D2025-03-12T17:27:21.854Z"},{"content":{"type":"parts","parts":[{"type":"mdx","content":"The user used the v0 interface to revert back to version 22. It has been copied into a new version, v36."}],"version":1},"role":"user","id":"4CHpQXilnEJGxyUM8hBbmH5C7Id3CXxQ","parentId":"fvSDqXLNnaWuhuy8vucGUg9Wq1dEQqFb","type":"reverted-block","authorId":"pqpc5zNlmrSXIWsTeoZGqcHp","createdAt":"$D2025-03-12T17:33:25.665Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"v36 was created by restoring "],["a",{"href":"#mY2gopLHygyqXdjS7gXeBof9LQkXLIhw"},["text",{},"v22"]]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_ipVdYMXn0kS","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[],"source":"[V0_FILE]tsx:file=\"empty.tsx\"\n...","needsBlockSource":true,"isAllImages":false,"blockId":"b_ipVdYMXn0kS","meta":{"type":"code-project","id":"ArtGalleryAnalysis","project":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"block-b_ipVdYMXn0kS"}]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"ieynUO5ugysLNVfuqCkiBHz57Yq4Cz6p","gitRevision":null,"streaming":false,"finishReason":null,"parentId":"4CHpQXilnEJGxyUM8hBbmH5C7Id3CXxQ","type":"reverted-block","workMetrics":{"diffMap":{}},"createdAt":"$D2025-03-12T17:33:25.666Z","updatedAt":"$D2025-03-12T17:33:25.694Z"},{"content":{"type":"parts","parts":[{"type":"mdx","content":"I found this error in vercel logs that seems relevant\nError analyzing room: ReferenceError: FileReader is not defined\n at (.next/server/app/page.js:22:19971)\n at new Promise ()\n at oi (.next/server/app/page.js:22:19945)\n at ou (.next/server/app/page.js:22:20494)\n\n⨯ ReferenceError: FileReader is not defined\n at (.next/server/app/page.js:22:19971)\n at new Promise ()\n at oi (.next/server/app/page.js:22:19945)\n at ou (.next/server/app/page.js:22:20494) {\n digest: '532118214'\n}"}],"version":1},"role":"user","id":"79OIVWWnqRcqgpgxXa8iz2HemtzMxze4","attachments":[],"parentId":"ieynUO5ugysLNVfuqCkiBHz57Yq4Cz6p","type":"message","authorId":"pqpc5zNlmrSXIWsTeoZGqcHp","createdAt":"$D2025-03-12T17:33:56.141Z"},{"content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-0","parts":[{"type":"thinking-end","duration":4.758823529411765,"thought":"The error is occurring because the `FileReader` API is only available in browser environments, but it's being called on the server. 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