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3c:o25e,121:[["b_s5ituBGoRjm",false],["b_575jXfWR3sY",false],["b_yMSwo0Hw0yO",false]] 122:[["b_s5ituBGoRjm","$undefined"],["b_575jXfWR3sY",{"aliasUrl":"nycerebro.vercel.app","readyState":"READY","deploymentId":"dpl_CuCPWZghH6c5psJjSMJubgFWWhmP"}],["b_yMSwo0Hw0yO",{"aliasUrl":"nycerebro.vercel.app","readyState":"READY","deploymentId":"dpl_C4kNoZYhKqQGPsum8RYwanq9iRxf"}]] 123:[["b_s5ituBGoRjm",null],["b_575jXfWR3sY",null],["b_yMSwo0Hw0yO",null]] 124:[["b_s5ituBGoRjm",true],["b_575jXfWR3sY",true],["b_yMSwo0Hw0yO",true]] 125:[["b_s5ituBGoRjm",true],["b_575jXfWR3sY",true],["b_yMSwo0Hw0yO",true]] 126:[] 127:Te48,3c:oe48,[V0_FILE]typescriptreact:file="app/page.tsx" "use client" import { useState } from "react" import Link from "next/link" import { Input } from "@/components/ui/input" import { Button } from "@/components/ui/button" import { InfoIcon as InfoCircle } from 'lucide-react' export default function Home() { const [query, setQuery] = useState("") return (

{/* Background image with overlay */}

{/* Content */}

{/* Title */}

NYCerebro

{/* Search form */} {/* Quick search links */}

{["brooklyn bridge", "construction", "time square", "tunnel", "weird"].map((term) => ( {term} ))}

{/* Info call-out */}

NYCerebro is a CLIP-powered search engine for NYC traffic cameras. It uses AI to find camera views matching your text descriptions.

Learn more about how it works in our{" "} blog post .

) }3c:o9,128:Te4d,3c:oe4d,[V0_FILE]typescriptreact:file="app/page.tsx" "use client" import { useState } from "react" import Link from "next/link" import { Input } from "@/components/ui/input" import { Button } from "@/components/ui/button" import { InfoIcon as InfoCircle } from 'lucide-react' export default function Home() { const [query, setQuery] = useState("") return (

{/* Background image with overlay */}

{/* Content */}

{/* Title */}

NYCerebro

{/* Search form */} {/* Quick search links */}

{["brooklyn bridge", "construction zone", "time square", "tunnel", "weird"].map((term) => ( {term} ))}

{/* Info call-out */}

NYCerebro is a CLIP-powered search engine for NYC traffic cameras. It uses AI to find camera views matching your text descriptions.

Learn more about how it works in our{" "} blog post .

) }3c:oa,129:T7fc7,3c:o7fc7,[V0_FILE]typescriptreact:file="app/layout.tsx" isEdit="true" isMerged="true" import './globals.css' import type { Metadata } from 'next' import { JetBrains_Mono } from 'next/font/google' const jetBrainsMono = JetBrains_Mono({ subsets: ['latin'] }) export const metadata: Metadata = { title: 'NYCerebro', description: 'Search NYC traffic cameras using CLIP', } export default function RootLayout({ children, }: { children: React.ReactNode }) { return ( {children} ) } [V0_FILE]typescriptreact:file="app/search/loading.tsx" isEdit="true" isMerged="true" export default function LoadingScreen() { return (

) } [V0_FILE]typescriptreact:file="components/LiveWebcam.tsx" isEdit="true" isMerged="true" 'use client' import { useState, useEffect } from 'react' import Image from 'next/image' import { AlertCircle, RefreshCcw } from 'lucide-react' import { Button } from "@/components/ui/button" interface LiveWebcamProps { cameraId: string } export default function LiveWebcam({ cameraId }: LiveWebcamProps) { const [imageUrl, setImageUrl] = useState('') const [error, setError] = useState(null) const [loading, setLoading] = useState(true) const updateImage = async () => { setLoading(true) setError(null) const epochTime = Math.floor(Date.now() / 1000) const originalUrl = `https://webcams.nyctmc.org/api/cameras/${cameraId}/image?t=${epochTime}` const proxyUrl = `/api/proxy-image?url=${encodeURIComponent(originalUrl)}` try { const response = await fetch(proxyUrl) if (!response.ok) { throw new Error(`Failed to load webcam image: ${response.status} ${response.statusText}`) } const blob = await response.blob() setImageUrl(URL.createObjectURL(blob)) } catch (err: any) { console.error('Error loading webcam image:', err) setError(err.message || 'Failed to load webcam image') } finally { setLoading(false) } } useEffect(() => { updateImage() const interval = setInterval(updateImage, 2000) return () => clearInterval(interval) }, [cameraId]) return (

Live Webcam

{loading && (

)} {error ? (

{error}

) : ( imageUrl && ( Live webcam

) )}

) } [V0_FILE]typescript:file="app/api/getResults/route.ts" isMerged="true" import { createClient } from '@supabase/supabase-js' import { NextResponse } from 'next/server' const supabase = createClient(process.env.SUPABASE_URL!, process.env.SUPABASE_ANON_KEY!) export async function GET(request: Request) { const { searchParams } = new URL(request.url) const query = searchParams.get('q') // For now, we'll just return random results const { data: cameras, error } = await supabase .from('cameras') .select('camera_id, latitude, longitude') .limit(50) if (error) { return NextResponse.json({ error: error.message }, { status: 500 }) } const bestMatch = cameras[Math.floor(Math.random() * cameras.length)].camera_id const heatmapData = cameras.map(camera => ({ latitude: camera.latitude, longitude: camera.longitude, intensity: Math.random() // Random intensity for now })) return NextResponse.json({ bestMatch, heatmapData }) } [V0_FILE]css:file="app/globals.css" isEdit="true" isMerged="true" @tailwind base; @tailwind components; @tailwind utilities; @layer base { :root { --background: 0 0% 100%; --foreground: 222.2 84% 4.9%; --card: 0 0% 100%; --card-foreground: 222.2 84% 4.9%; --popover: 0 0% 100%; --popover-foreground: 222.2 84% 4.9%; --primary: 222.2 47.4% 11.2%; --primary-foreground: 210 40% 98%; --secondary: 210 40% 96.1%; --secondary-foreground: 222.2 47.4% 11.2%; --muted: 210 40% 96.1%; --muted-foreground: 215.4 16.3% 46.9%; --accent: 210 40% 96.1%; --accent-foreground: 222.2 47.4% 11.2%; --destructive: 0 84.2% 60.2%; --destructive-foreground: 210 40% 98%; --border: 214.3 31.8% 91.4%; --input: 214.3 31.8% 91.4%; --ring: 222.2 84% 4.9%; --radius: 0.5rem; } .dark { --background: 222.2 84% 4.9%; --foreground: 210 40% 98%; --card: 222.2 84% 4.9%; --card-foreground: 210 40% 98%; --popover: 222.2 84% 4.9%; --popover-foreground: 210 40% 98%; --primary: 210 40% 98%; --primary-foreground: 222.2 47.4% 11.2%; --secondary: 217.2 32.6% 17.5%; --secondary-foreground: 210 40% 98%; --muted: 217.2 32.6% 17.5%; --muted-foreground: 215 20.2% 65.1%; --accent: 217.2 32.6% 17.5%; --accent-foreground: 210 40% 98%; --destructive: 0 62.8% 30.6%; --destructive-foreground: 210 40% 98%; --border: 217.2 32.6% 17.5%; --input: 217.2 32.6% 17.5%; --ring: 212.7 26.8% 83.9%; } } @layer base { * { @apply border-border; } body { @apply bg-background text-foreground; } } @layer utilities { .animate-text-shine { background-size: 200% auto; animation: text-shine 3s linear infinite; } @keyframes text-shine { from { background-position: 0% center; } to { background-position: 200% center; } } } [V0_FILE]typescriptreact:file="components/ErrorDisplay.tsx" isEdit="true" isQuickEdit="true" isMerged="true" import { AlertCircle } from 'lucide-react' interface ErrorDisplayProps { message: string details?: any } export default function ErrorDisplay({ message, details }: ErrorDisplayProps) { return (

Error:

{message} {process.env.NODE_ENV === 'development' && details && (

            {JSON.stringify(details, null, 2)}

)}

) } [V0_FILE]typescript:file="app/api/proxy-image/route.ts" isMerged="true" import { NextResponse } from 'next/server' export async function GET(request: Request) { const { searchParams } = new URL(request.url) const imageUrl = searchParams.get('url') if (!imageUrl) { return NextResponse.json({ error: 'Image URL is required' }, { status: 400 }) } try { const response = await fetch(imageUrl) if (!response.ok) { throw new Error(`Failed to fetch image: ${response.status} ${response.statusText}`) } const imageBuffer = await response.arrayBuffer() const headers = new Headers(response.headers) headers.set('Access-Control-Allow-Origin', '*') return new NextResponse(imageBuffer, { status: 200, headers: headers, }) } catch (error: any) { console.error('Proxy error:', error) return NextResponse.json({ error: error.message || 'Failed to fetch image' }, { status: 500 }) } } [V0_FILE]typescriptreact:file="app/search/page.tsx" isEdit="true" isQuickEdit="true" isMerged="true" "use client" import { useSearchParams, useRouter } from "next/navigation" import { useEffect, useState, useCallback } from "react" import LoadingScreen from "./loading" import LiveWebcam from "@/components/LiveWebcam" import HeatMap from "@/components/HeatMap" import ErrorDisplay from "@/components/ErrorDisplay" import Link from "next/link" import debounce from "lodash/debounce" interface SearchResult { bestMatch: string heatmapData: Array<{ latitude: number longitude: number intensity: number camera_id: string last_updated: string }> } export default function SearchResults() { const searchParams = useSearchParams() const router = useRouter() const query = searchParams.get("q") const [results, setResults] = useState(null) const [loading, setLoading] = useState(true) const [error, setError] = useState(null) const [activeCameraId, setActiveCameraId] = useState(null) const fetchResults = useCallback( debounce(async (searchQuery: string) => { setLoading(true) setError(null) try { const response = await fetch(`/api/search?q=${encodeURIComponent(searchQuery)}`) const data = await response.json() if (!response.ok) { throw new Error(data.error || "An unknown error occurred") } setResults(data) setActiveCameraId((prevId) => prevId || data.bestMatch) } catch (error: any) { console.error("Search error:", error) setError(error.message || "An unknown error occurred") } finally { setLoading(false) } }, 300), [], ) useEffect(() => { if (query) { fetchResults(query) } }, [query, fetchResults]) useEffect(() => { const cameraFromUrl = searchParams.get("camera") if (cameraFromUrl) { setActiveCameraId(cameraFromUrl) } }, [searchParams]) const handleCameraClick = (cameraId: string) => { setActiveCameraId(cameraId) router.push(`/search?q=${encodeURIComponent(query || "")}&camera=${cameraId}`, { scroll: false }) } if (loading) { return } if (error) { return } const activeCamera = results?.heatmapData.find((camera) => camera.camera_id === activeCameraId) const formatTimeAgo = (dateString: string) => { if (!dateString) return "Unknown" const lastUpdated = new Date(dateString) if (isNaN(lastUpdated.getTime())) return "Invalid date" const now = new Date() const diffInSeconds = Math.floor((now.getTime() - lastUpdated.getTime()) / 1000) if (diffInSeconds < 60) return `${diffInSeconds} seconds ago` if (diffInSeconds < 3600) return `${Math.floor(diffInSeconds / 60)} minutes ago` if (diffInSeconds < 86400) return `${Math.floor(diffInSeconds / 3600)} hours ago` return `${Math.floor(diffInSeconds / 86400)} days ago` } return (

Search Results for: {query}

← Back to Homepage {results && ( <> {results.heatmapData.length > 0 ? ( ) : (

No heatmap data available

)} {activeCamera && (

Active Camera Information

Camera ID: {activeCamera.camera_id}

Lat/Lng: {activeCamera.latitude.toFixed(6)}, {activeCamera.longitude.toFixed(6)}

CLIP Vector Last Indexed:{" "} {activeCamera.last_updated ? formatTimeAgo(activeCamera.last_updated) : "Unknown"}

)} {results && (

Top Matching Cameras

{camera.camera_id === activeCameraId ? "★ " : ""} Camera {camera.camera_id}

)} )}

) } [V0_FILE]typescriptreact:file="components/HeatMap.tsx" isEdit="true" isMerged="true" "use client" import { useEffect, useRef, useState, useCallback } from "react" import mapboxgl from "mapbox-gl" import "mapbox-gl/dist/mapbox-gl.css" interface HeatMapProps { data: Array<{ latitude: number; longitude: number; intensity: number; camera_id: string }> activeCameraId: string onCameraClick: (cameraId: string) => void } export default function HeatMap({ data, activeCameraId, onCameraClick }: HeatMapProps) { const mapContainer = useRef(null) const map = useRef(null) const markersRef = useRef([]) const [error, setError] = useState(null) // Cleanup function for markers const clearMarkers = useCallback(() => { markersRef.current.forEach((marker) => marker.remove()) markersRef.current = [] }, []) const updateMapData = useCallback(() => { if (!map.current) return try { const source = map.current.getSource("points") as mapboxgl.GeoJSONSource if (!source) return const features = data.map((point) => ({ type: "Feature" as const, properties: { intensity: point.intensity, camera_id: point.camera_id, isActive: point.camera_id === activeCameraId, }, geometry: { type: "Point" as const, coordinates: [point.longitude, point.latitude], }, })) const featureCollection = { type: "FeatureCollection", features, } source.setData(featureCollection) } catch (err) { console.error("Error updating map data:", err) } }, [data, activeCameraId]) useEffect(() => { if (map.current) return try { mapboxgl.accessToken = "pk.eyJ1Ijoicm9ib2Zsb3dicmFkIiwiYSI6ImNtNjA1cXZnOTA1bHUya284OHB4Z25lYTEifQ.Y8nuCyyyE_nKDEK4TaHduA" map.current = new mapboxgl.Map({ container: mapContainer.current!, style: "mapbox://styles/mapbox/dark-v10", center: [-73.98, 40.75], zoom: 11, }) map.current.on("load", () => { if (!map.current) return map.current.addSource("points", { type: "geojson", data: { type: "FeatureCollection", features: [], }, }) map.current.addLayer({ id: "points-heat", type: "heatmap", source: "points", minzoom: 0, maxzoom: 22, paint: { "heatmap-weight": ["get", "intensity"], "heatmap-intensity": ["interpolate", ["linear"], ["zoom"], 0, 1, 9, 3], "heatmap-color": [ "interpolate", ["linear"], ["heatmap-density"], 0, "rgba(0, 0, 255, 0)", 0.2, "rgba(0, 255, 255, 0.5)", 0.4, "rgba(0, 255, 0, 0.7)", 0.6, "rgba(255, 255, 0, 0.8)", 0.8, "rgba(255, 0, 0, 0.9)", 1, "rgba(255, 0, 255, 1)", ], "heatmap-radius": ["interpolate", ["linear"], ["zoom"], 0, 2, 9, 20], "heatmap-opacity": 0.75, }, }) map.current.addLayer({ id: "points-circle", type: "circle", source: "points", paint: { "circle-radius": ["case", ["boolean", ["get", "isActive"], false], 12, 8], "circle-color": ["case", ["boolean", ["get", "isActive"], false], "#ff0000", "#ffffff"], "circle-opacity": 0.7, "circle-stroke-width": 2, "circle-stroke-color": "#000000", }, }) updateMapData() map.current.on("click", "points-circle", (e) => { if (e.features && e.features[0].properties) { const cameraId = e.features[0].properties.camera_id onCameraClick(cameraId) } }) map.current.on("mouseenter", "points-circle", () => { if (map.current) map.current.getCanvas().style.cursor = "pointer" }) map.current.on("mouseleave", "points-circle", () => { if (map.current) map.current.getCanvas().style.cursor = "" }) }) } catch (err) { console.error("Error initializing map:", err) setError("Failed to initialize map") } return () => { clearMarkers() if (map.current) { map.current.remove() map.current = null } } }, [updateMapData, onCameraClick, clearMarkers]) useEffect(() => { if (!map.current) return try { updateMapData() } catch (err) { console.error("Error in data update effect:", err) } }, [data, activeCameraId, updateMapData]) useEffect(() => { if (!map.current) return try { clearMarkers() const activeCamera = data.find((camera) => camera.camera_id === activeCameraId) if (activeCamera) { const el = document.createElement("div") el.className = "camera-marker" el.innerHTML = '' const marker = new mapboxgl.Marker(el) .setLngLat([activeCamera.longitude, activeCamera.latitude]) .addTo(map.current) markersRef.current.push(marker) } } catch (err) { console.error("Error adding camera marker:", err) } }, [data, activeCameraId, clearMarkers]) if (error) { return

{error}

} return (

) } [V0_FILE]sql:file="supabase/functions/match_cameras.sql" isEdit="true" isMerged="true" -- First, drop the existing function DROP FUNCTION IF EXISTS match_cameras(vector, float, int); -- Now, create the function with the correct structure CREATE OR REPLACE FUNCTION match_cameras(query_embedding vector(512), match_threshold float, match_count int) RETURNS TABLE ( camera_id uuid, last_updated timestamp with time zone, latitude numeric, longitude numeric, intensity float ) LANGUAGE plpgsql AS $$ BEGIN RETURN QUERY SELECT c.camera_id, c.last_updated, c.latitude, c.longitude, 1 - (c.embedding <=> query_embedding) AS intensity FROM cameras c WHERE 1 - (c.embedding <=> query_embedding) > match_threshold ORDER BY c.embedding <=> query_embedding LIMIT match_count; END; $$; [V0_FILE]sql:file="supabase/check_table_structure.sql" isMerged="true" SELECT column_name, data_type, is_nullable FROM information_schema.columns WHERE table_name = 'cameras' ORDER BY ordinal_position; [V0_FILE]sql:file="supabase/migrations/01_create_embedding_cache.sql" isEdit="true" isMerged="true" -- Create a new table for caching text/embedding pairs CREATE TABLE IF NOT EXISTS embedding_cache ( id SERIAL PRIMARY KEY, normalized_text TEXT UNIQUE NOT NULL, embedding vector(512) NOT NULL, created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP, last_used TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP ); -- Create an index on the normalized_text column for faster lookups CREATE INDEX IF NOT EXISTS idx_embedding_cache_normalized_text ON embedding_cache (normalized_text); -- Create a function to normalize text CREATE OR REPLACE FUNCTION normalize_text(input_text TEXT) RETURNS TEXT AS $$ BEGIN RETURN lower(regexp_replace(trim(input_text), '\s+', ' ', 'g')); END; $$ LANGUAGE plpgsql IMMUTABLE; [V0_FILE]typescriptreact:file="app/api/search/route.ts" isEdit="true" isQuickEdit="true" isMerged="true" import { createClient } from "@supabase/supabase-js" import { NextResponse } from "next/server" const supabase = createClient(process.env.SUPABASE_URL!, process.env.SUPABASE_ANON_KEY!) async function normalizeText(text: string): Promise { const { data, error } = await supabase.rpc("normalize_text", { input_text: text }) if (error) throw error return data } async function getEmbedding(normalizedText: string): Promise { // Check if embedding exists in cache const { data: cachedEmbedding, error: cacheError } = await supabase .from("embedding_cache") .select("embedding") .eq("normalized_text", normalizedText) .single() if (cacheError && cacheError.code !== "PGRST116") { console.error("Error checking embedding cache:", cacheError) throw cacheError } if (cachedEmbedding) { console.log("Using cached embedding for:", normalizedText) await supabase .from("embedding_cache") .update({ last_used: new Date().toISOString() }) .eq("normalized_text", normalizedText) return cachedEmbedding.embedding } // If not in cache, calculate embedding using Roboflow API console.log("Calculating new embedding for:", normalizedText) const clipResponse = await fetch("https://detect.roboflow.com/infer/workflows/shortest-hackathon/embed-text", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ api_key: process.env.ROBOFLOW_API_KEY, inputs: { image: { type: "url", value: "https://source.roboflow.com/c8QoUtY71EUIn6gsXQMkSt8K0fC3/lUis6HgjW32zZv1WYVKQ/thumb.jpg", }, query: normalizedText, }, }), }) if (!clipResponse.ok) { const errorText = await clipResponse.text() console.error("Roboflow API error response:", errorText) throw new Error(`Roboflow API error: ${clipResponse.status} ${clipResponse.statusText} - ${errorText}`) } const clipResult = await clipResponse.json() if (!clipResult.outputs || !Array.isArray(clipResult.outputs) || clipResult.outputs.length === 0) { console.error("Invalid Roboflow API response structure:", clipResult) throw new Error("Invalid response structure from Roboflow API: Missing or empty outputs array") } const embedding = clipResult.outputs[0].embedding if (!Array.isArray(embedding) || embedding.length === 0) { console.error("Invalid embedding in Roboflow API response:", clipResult.outputs[0]) throw new Error("Invalid embedding in Roboflow API response: Expected non-empty array") } // Store the new embedding in the cache or update if it already exists const { error: upsertError } = await supabase.from("embedding_cache").upsert( { normalized_text: normalizedText, embedding, last_used: new Date().toISOString(), }, { onConflict: "normalized_text", }, ) if (upsertError) { console.error("Error upserting embedding into cache:", upsertError) } return embedding } async function getCameras(embedding: number[], matchThreshold: number, matchCount: number) { console.log("Querying Supabase with parameters:", { matchThreshold, matchCount, embedding }) const { data: matchedCameras, error } = await supabase.rpc("match_cameras", { query_embedding: embedding, match_threshold: matchThreshold, match_count: matchCount, }) if (error) { console.error("Supabase error:", error) throw new Error(`Supabase error: ${error.message}`) } console.log("Supabase query result:", JSON.stringify(matchedCameras, null, 2)) if (!matchedCameras || matchedCameras.length === 0 || !Array.isArray(matchedCameras)) { console.log("No matching cameras found or invalid result structure. Returning all cameras.") const { data: allCameras, error: allCamerasError } = await supabase .from("cameras") .select("camera_id, latitude, longitude, last_updated") .limit(10) if (allCamerasError) { console.error("Error fetching all cameras:", allCamerasError) throw new Error(`Error fetching all cameras: ${allCamerasError.message}`) } if (!allCameras || allCameras.length === 0) { throw new Error("No cameras found in the database") } return allCameras.map((camera) => ({ ...camera, intensity: 0.5, // Assign a default intensity })) } // Check if the returned data has the expected structure if (!matchedCameras[0].hasOwnProperty("camera_id") || !matchedCameras[0].hasOwnProperty("intensity")) { console.error("Unexpected data structure returned from match_cameras:", matchedCameras[0]) throw new Error("Unexpected data structure returned from match_cameras") } return matchedCameras } export async function GET(request: Request) { const { searchParams } = new URL(request.url) const query = searchParams.get("q") if (!query) { return NextResponse.json({ error: "Query parameter is required" }, { status: 400 }) } try { const normalizedQuery = await normalizeText(query) const embedding = await getEmbedding(normalizedQuery) const cameras = await getCameras(embedding, 0.2, 20) // Lowered threshold from 0.5 to 0.3 const heatmapData = cameras.map((camera: any) => ({ latitude: camera.latitude, longitude: camera.longitude, intensity: camera.intensity, camera_id: camera.camera_id, last_updated: camera.last_updated, })) console.log("Processed heatmap data:", heatmapData) const bestMatch = cameras[0].camera_id const result = { bestMatch, heatmapData } console.log("Returning result:", result) return NextResponse.json(result) } catch (error: any) { console.error("Search error:", error) console.error("Error stack:", error.stack) return NextResponse.json( { error: error.message || "An unknown error occurred", stack: process.env.NODE_ENV === "development" ? error.stack : undefined, }, { status: 500 }, ) } } [V0_FILE]typescriptreact:file="app/page.tsx" isEdit="true" "use client" import { useState } from "react" import Link from "next/link" import { Input } from "@/components/ui/input" import { Button } from "@/components/ui/button" import { InfoIcon as InfoCircle, Github } from "lucide-react" export default function Home() { const [query, setQuery] = useState("") return (

{/* Background image with overlay */}

{/* Content */}

{/* Title */}

NYCerebro

{/* Search form */} {/* Quick search links */}

{["brooklyn bridge", "construction zone", "time square", "tunnel", "weird"].map((term) => ( {term} ))}

{/* Info call-out */}

NYCerebro is a CLIP-powered search engine for NYC traffic cameras. It uses AI to find camera views matching your text descriptions.

Learn more about how it works in our{" "} blog post .

{/* GitHub repo call-out */}

Check out the source code on GitHub:{" "} yeldarby/nycerebro

) } 3c:oa,12a:T7fc7,3c:o7fc7,[V0_FILE]typescriptreact:file="app/layout.tsx" isEdit="true" isMerged="true" import './globals.css' import type { Metadata } from 'next' import { JetBrains_Mono } from 'next/font/google' const jetBrainsMono = JetBrains_Mono({ subsets: ['latin'] }) export const metadata: Metadata = { title: 'NYCerebro', description: 'Search NYC traffic cameras using CLIP', } export default function RootLayout({ children, }: { children: React.ReactNode }) { return ( {children} ) } [V0_FILE]typescriptreact:file="app/search/loading.tsx" isEdit="true" isMerged="true" export default function LoadingScreen() { return (

Live Webcam

{loading && (

)} {error ? (

{error}

) : ( imageUrl && ( Live webcam

) )}

Error:

{message} {process.env.NODE_ENV === 'development' && details && (

            {JSON.stringify(details, null, 2)}

)}

Search Results for: {query}

← Back to Homepage {results && ( <> {results.heatmapData.length > 0 ? ( ) : (

No heatmap data available

)} {activeCamera && (

Active Camera Information

Camera ID: {activeCamera.camera_id}

Lat/Lng: {activeCamera.latitude.toFixed(6)}, {activeCamera.longitude.toFixed(6)}

CLIP Vector Last Indexed:{" "} {activeCamera.last_updated ? formatTimeAgo(activeCamera.last_updated) : "Unknown"}

)} {results && (

Top Matching Cameras

{camera.camera_id === activeCameraId ? "★ " : ""} Camera {camera.camera_id}

)} )}

{error}

} return (

{/* Background image with overlay */}

{/* Content */}

{/* Title */}

NYCerebro

{/* Search form */} {/* Quick search links */}

{["brooklyn bridge", "construction zone", "time square", "tunnel", "weird"].map((term) => ( {term} ))}

{/* Info call-out */}

NYCerebro is a CLIP-powered search engine for NYC traffic cameras. It uses AI to find camera views matching your text descriptions.

Learn more about how it works in our{" "} blog post .

{/* GitHub repo call-out */}

Check out the source code on GitHub:{" "} yeldarby/nycerebro

) } 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another callout on the hompage for the GitHub repo: `https://github.com/yeldarby/nycerebro`"}],"version":1},"role":"user","id":"hGYrSvPLnoi1R0726rNTRpJFaiNQnS25","attachments":[],"parentId":"II7HwIhPr2OYSEETIyBr9DxvAbTXeuuK","type":"message","authorId":"jTqndOGhkzL6ENdJcE8GrgM8","createdAt":"$D2025-01-22T07:05:27.757Z"},{"content":{"type":"message-binary-format","value":[[0,[["p",{},["text",{},"I'll add another callout on the homepage for the GitHub repo. 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