\n \n \n \n

\n {formatPrice(property.price)}\n

\n\n

\n {property.address}\n

\n\n

\n \n \n \n \n \n {property.beds} bed{property.beds !== 1 ? \"s\" : \"\"}\n \n \n \n \n \n {property.baths} bath{property.baths !== 1 ? \"s\" : \"\"}\n \n \n \n \n \n {property.sqft.toLocaleString()} sqft\n \n

\n\n

\n \n {property.propertyType}\n \n {property.features.slice(0, 2).map((feature) => (\n \n {feature}\n \n ))}\n {property.features.length > 2 && (\n \n +{property.features.length - 2} more\n \n )}\n

\n );\n\n return (\n

\n Find Your Dream Home\n

\n Type naturally - \"2 bed 1 bath under 750k in Williamsburg with\n laundry\"\n

\n\n {/* Main Search Input */}\n

\n setQuery(e.target.value)}\n placeholder=\"2 bed 1 bath under 750k in Williamsburg with laundry\"\n className=\"w-full px-6 py-4 text-lg border-2 border-gray-200 rounded-xl focus:border-blue-500 focus:outline-none search-input\"\n style={{\n background: \"transparent\",\n color: \"transparent\",\n caretColor: \"#374151\",\n }}\n />\n \n {renderHighlightedText()}\n

\n\n {/* Filter Dropdowns */}\n

\n {/* Beds */}\n \n handleManualFilterChange(\n \"beds\",\n e.target.value ? parseInt(e.target.value) : undefined,\n )}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-blue-500 filter-dropdown ${\n updatedFilters.has(\"beds\") ? \"updated\" : \"\"\n }`}\n >\n \n {[1, 2, 3, 4, 5].map((num) => (\n \n ))}\n \n\n {/* Baths */}\n \n handleManualFilterChange(\n \"baths\",\n e.target.value ? parseFloat(e.target.value) : undefined,\n )}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-green-500 filter-dropdown ${\n updatedFilters.has(\"baths\") ? \"updated\" : \"\"\n }`}\n >\n \n {[1, 1.5, 2, 2.5, 3, 3.5, 4].map((num) => (\n \n ))}\n \n\n {/* Price Min */}\n \n handleManualFilterChange(\n \"priceMin\",\n e.target.value ? parseInt(e.target.value) : undefined,\n )}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-yellow-500 filter-dropdown ${\n updatedFilters.has(\"priceMin\") ? \"updated\" : \"\"\n }`}\n >\n \n {[200000, 300000, 400000, 500000, 750000, 1000000, 1500000].map(\n (price) => (\n \n ),\n )}\n \n\n {/* Price Max */}\n \n handleManualFilterChange(\n \"priceMax\",\n e.target.value ? parseInt(e.target.value) : undefined,\n )}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-red-500 filter-dropdown ${\n updatedFilters.has(\"priceMax\") ? \"updated\" : \"\"\n }`}\n >\n \n {[500000, 750000, 1000000, 1500000, 2000000, 3000000].map((price) => (\n \n ))}\n \n\n {/* Neighborhoods */}\n {\n if (\n e.target.value && !filters.neighborhoods.includes(e.target.value)\n ) {\n handleManualFilterChange(\"neighborhoods\", [\n ...filters.neighborhoods,\n e.target.value,\n ]);\n }\n }}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-purple-500 filter-dropdown ${\n updatedFilters.has(\"neighborhoods\") ? \"updated\" : \"\"\n }`}\n >\n \n {NEIGHBORHOODS.filter((n) => !filters.neighborhoods.includes(n)).map(\n (neighborhood) => (\n \n ),\n )}\n \n\n {/* Property Type */}\n {\n if (\n e.target.value && !filters.propertyTypes.includes(e.target.value)\n ) {\n handleManualFilterChange(\"propertyTypes\", [\n ...filters.propertyTypes,\n e.target.value,\n ]);\n }\n }}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-cyan-500 filter-dropdown ${\n updatedFilters.has(\"propertyTypes\") ? \"updated\" : \"\"\n }`}\n >\n \n {PROPERTY_TYPES.filter((t) => !filters.propertyTypes.includes(t)).map(\n (type) => ,\n )}\n \n\n {/* Features */}\n {\n if (e.target.value && !filters.features.includes(e.target.value)) {\n handleManualFilterChange(\"features\", [\n ...filters.features,\n e.target.value,\n ]);\n }\n }}\n className={`px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:border-lime-500 filter-dropdown ${\n updatedFilters.has(\"features\") ? \"updated\" : \"\"\n }`}\n >\n \n {FEATURES.filter((f) => !filters.features.includes(f)).map(\n (feature) => (\n \n ),\n )}\n \n

\n\n {/* Filter Chips */}\n {getChips().length > 0 && (\n

\n {getChips().map((chip, index) => (\n \n {chip.label}\n removeChip(chip.type, chip.value)}\n className=\"ml-2 hover:bg-black hover:bg-opacity-10 rounded-full p-0.5\"\n >\n \n \n \n \n \n ))}\n

\n )}\n\n {/* Search Results */}\n {loading\n ? (\n

Searching properties...

\n )\n : properties.length > 0\n ? (\n

\n {properties.length} Properties Found\n

\n Showing results for your search criteria\n

\n\n

\n {properties.map((property) => (\n \n ))}\n

\n )\n : (\n

\n \n \n \n

\n {Object.values(filters).some((v) =>\n v !== undefined && (Array.isArray(v) ? v.length > 0 : true)\n )\n ? \"No Properties Found\"\n : \"Search Results\"}\n

\n {Object.values(filters).some((v) =>\n v !== undefined && (Array.isArray(v) ? v.length > 0 : true)\n )\n ? \"Try adjusting your search criteria to find more properties\"\n : \"Start typing to see the magic happen!\"}\n

\n )}\n \n );\n}","deleted",false,"label",[58,59,36],"frontend","components","e0e74cd6-ea73-11f0-847c-42dde27851f2","af93c481-5dac-4ae3-a3ea-2e92fff478d2",7,["D",1767644386375],0,"directory",[58,59],{"_34":145,"_13":146,"_37":147,"_39":-5,"_41":42,"_43":148,"_45":46,"_47":149,"_17":64,"_50":65,"_52":-5,"_54":55,"_56":-5,"_80":150,"_18":151},{"_34":115,"_13":58,"_37":61,"_39":-5,"_41":42,"_43":116,"_45":46,"_47":117,"_17":64,"_50":65,"_52":-5,"_54":55,"_56":-5,"_80":118,"_18":119},{"_34":91,"_13":92,"_37":93,"_39":-5,"_41":42,"_43":94,"_45":46,"_47":95,"_17":64,"_50":65,"_52":-5,"_54":55,"_56":-5,"_80":96,"_18":97},{"_34":83,"_13":84,"_37":85,"_39":-5,"_41":42,"_43":86,"_45":46,"_47":87,"_17":77,"_50":24,"_52":88,"_54":55,"_56":-5,"_80":89,"_18":90},{"_34":72,"_13":73,"_37":74,"_39":-5,"_41":42,"_43":75,"_45":46,"_47":76,"_17":77,"_50":78,"_52":79,"_54":55,"_56":-5,"_80":81,"_18":82},"4e943892-ea75-11f0-ab5e-42dde27851f2","main.ts","4a559278-ea69-11f0-a5d8-42dde27851f2",10,["D",1767644999878],2,"http","export { default } from './backend/index.ts';","children",[],[73],"d1787098-ea75-11f0-9995-42dde27851f2","README.md","d22fc82c-ea72-11f0-9163-42dde27851f2",18,["D",1767645219476],"# Real Estate Search Bar\n\nA magical real estate search interface that parses natural language queries in real-time as you type.\n\n## 🚀 Quick Start\n\n### Launch the Application\n\n1. **Set HTTP Trigger**: The `main.ts` file is already configured as an HTTP trigger\n2. **Access the App**: Visit your Val Town URL to see the live application\n3. **Start Searching**: Type natural language queries in the search bar\n\n### Testing the Application\n\n#### 🌐 **Web Interface Testing**\nVisit the main URL and try these example searches:\n- `\"2 bed 2 bath under 800k in Williamsburg\"`\n- `\"3 bedroom townhouse with yard in Park Slope\"`\n- `\"condo with doorman and gym under 1M\"`\n- `\"apartment in Bed Stuy with parking above 400k\"`\n\n#### 🔧 **API Testing**\nTest the backend endpoints directly:\n\n**Search Properties:**\n```\nGET /api/search?beds=2&priceMax=800000&neighborhoods=Williamsburg\n```\n\n**Health Check:**\n```\nGET /api/health\n```\n\n**Example API Response:**\n```json\n{\n \"properties\": [\n {\n \"id\": 1,\n \"address\": \"123 Main St, Williamsburg, Brooklyn\",\n \"price\": 750000,\n \"beds\": 2,\n \"baths\": 2,\n \"sqft\": 1200,\n \"propertyType\": \"condo\",\n \"features\": [\"parking\", \"laundry\"],\n \"neighborhood\": \"Williamsburg\"\n }\n ],\n \"total\": 1,\n \"filters\": {\n \"beds\": \"2\",\n \"priceMax\": \"800000\",\n \"neighborhoods\": \"Williamsburg\"\n }\n}\n```\n\n## ✨ Features\n\n- **Live Parsing**: Filters update instantly as you type, character by character\n- **Natural Language**: Type \"2 bed 3 bath under 500k in Brooklyn with yard\" and watch the magic happen\n- **Visual Feedback**: Colored underlines show what's been parsed\n- **Bidirectional**: Manual filter changes don't get overwritten\n- **Smart Parsing**: Handles various formats like \"2br\", \"1.5ba\", \"500k\", \"$1M\"\n- **Real Results**: Displays actual property listings based on your search criteria\n- **Responsive Design**: Works beautifully on desktop and mobile\n\n## 📁 Project Structure\n\n```\n├── backend/\n│ └── index.ts # Hono API server with search endpoints\n├── frontend/\n│ ├── components/\n│ │ └── SearchApp.tsx # Main React search interface\n│ ├── index.html # HTML template with styling\n│ └── index.tsx # React app entry point\n├── shared/\n│ ├── types.ts # TypeScript interfaces and constants\n│ └── parser.ts # Natural language parsing logic\n├── main.ts # HTTP trigger entry point\n└── README.md\n```\n\n## 🔌 API Endpoints\n\n- `GET /` - Main application interface\n- `GET /api/search` - Property search with filter parameters\n- `GET /api/health` - Health check endpoint\n\n### Search Parameters\n- `beds` - Minimum number of bedrooms\n- `baths` - Minimum number of bathrooms\n- `priceMin` - Minimum price\n- `priceMax` - Maximum price\n- `neighborhoods` - Comma-separated list of neighborhoods\n- `propertyTypes` - Comma-separated list of property types\n- `features` - Comma-separated list of desired features\n\n## 🎯 Usage Guide\n\n### Natural Language Parsing\nType naturally in the search bar and watch filters populate automatically:\n\n**Bedrooms:**\n- \"2 bed\", \"3br\", \"2 bedroom\", \"2 beds\"\n\n**Bathrooms:**\n- \"2 bath\", \"1.5ba\", \"2 bathrooms\", \"1.5 baths\"\n\n**Price Ranges:**\n- \"under 500k\", \"below $1M\", \"above 300k\", \"max 800000\"\n\n**Neighborhoods:**\n- Bed Stuy, Williamsburg, Park Slope, Bushwick, Crown Heights, Greenpoint, Fort Greene, DUMBO, Cobble Hill, Clinton Hill\n\n**Property Types:**\n- house, condo, co-op, townhouse, apartment\n\n**Features:**\n- yard, parking, garage, doorman, laundry, gym, pool, rooftop\n\n### Manual Filters\n- Use dropdown menus to manually set filters\n- Manual selections won't be overwritten by natural language parsing\n- Remove filters using the \"X\" button on filter chips\n\n## 🧪 Demo Examples\n\nTry these search queries to see the magic in action:\n\n1. **Basic Search**: `\"2 bed 2 bath\"`\n2. **Price Range**: `\"under 800k\"`\n3. **Location Specific**: `\"condo in Williamsburg\"`\n4. **Feature Based**: `\"apartment with doorman and gym\"`\n5. **Complex Query**: `\"3 bedroom townhouse with yard in Park Slope under 1.2M\"`\n\n## 🛠️ Technical Implementation\n\n- **Frontend**: React 18 with TypeScript, TailwindCSS for styling\n- **Backend**: Hono framework with mock property data (10 sample properties)\n- **Parsing**: Custom natural language parser with regex patterns\n- **Real-time**: Debounced search with loading states (300ms delay)\n- **Responsive**: Mobile-first design with CSS Grid layouts\n- **State Management**: React hooks with manual override tracking\n\n## 🎨 Visual Features\n\n- **Syntax Highlighting**: Different colors for parsed segments (beds=blue, baths=green, price=yellow/red, etc.)\n- **Filter Animations**: Smooth pulse animations when filters update automatically\n- **Loading States**: Spinner during property searches\n- **Empty States**: Helpful messages when no results are found\n- **Hover Effects**: Interactive property cards with subtle animations",[],[84],"d7326636-ea72-11f0-9163-42dde27851f2","shared","e77da4cc-e5a3-443d-aa03-7dd0d12164a0",3,["D",1767643940599],[98,99],[92],{"_34":107,"_13":108,"_37":109,"_39":93,"_41":42,"_43":110,"_45":46,"_47":111,"_17":64,"_50":51,"_52":112,"_54":55,"_56":-5,"_80":113,"_18":114},{"_34":100,"_13":101,"_37":102,"_39":93,"_41":42,"_43":94,"_45":46,"_47":103,"_17":64,"_50":51,"_52":104,"_54":55,"_56":-5,"_80":105,"_18":106},"d7324fac-ea72-11f0-9163-42dde27851f2","types.ts","d7325092-ea72-11f0-9163-42dde27851f2",["D",1767643940599],"export interface SearchFilters {\n beds?: number;\n baths?: number;\n priceMin?: number;\n priceMax?: number;\n neighborhoods: string[];\n propertyTypes: string[];\n features: string[];\n}\n\nexport interface ParsedSegment {\n text: string;\n start: number;\n end: number;\n type: 'beds' | 'baths' | 'priceMin' | 'priceMax' | 'neighborhood' | 'propertyType' | 'feature';\n value: any;\n}\n\nexport const NEIGHBORHOODS = [\n 'Bed Stuy',\n 'Williamsburg', \n 'Park Slope',\n 'Bushwick',\n 'Crown Heights',\n 'Greenpoint',\n 'Fort Greene',\n 'DUMBO',\n 'Cobble Hill',\n 'Clinton Hill'\n];\n\nexport const PROPERTY_TYPES = [\n 'house',\n 'condo', \n 'co-op',\n 'townhouse',\n 'apartment'\n];\n\nexport const FEATURES = [\n 'yard',\n 'parking',\n 'garage', \n 'doorman',\n 'laundry',\n 'gym',\n 'pool',\n 'rooftop'\n];",[],[92,101],"e34d73ca-ea72-11f0-ab5b-42dde27851f2","parser.ts","e34d74c4-ea72-11f0-ab5b-42dde27851f2",4,["D",1767643960907],"import { SearchFilters, ParsedSegment, NEIGHBORHOODS, PROPERTY_TYPES, FEATURES } from './types.ts';\n\nexport function parseSearchQuery(query: string): { filters: SearchFilters; segments: ParsedSegment[] } {\n const segments: ParsedSegment[] = [];\n const filters: SearchFilters = {\n neighborhoods: [],\n propertyTypes: [],\n features: []\n };\n\n // Beds parsing - matches \"2 bed\", \"3br\", \"2 bedroom\", \"2 beds\"\n const bedsRegex = /(\\d+)\\s*(?:bed(?:room)?s?|br)\\b/gi;\n let match;\n while ((match = bedsRegex.exec(query)) !== null) {\n const beds = parseInt(match[1]);\n if (!filters.beds || beds > filters.beds) { // Take the highest if multiple\n filters.beds = beds;\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'beds',\n value: beds\n });\n }\n }\n\n // Baths parsing - matches \"2 bath\", \"1.5ba\", \"2 bathrooms\"\n const bathsRegex = /(\\d+(?:\\.\\d+)?)\\s*(?:bath(?:room)?s?|ba)\\b/gi;\n while ((match = bathsRegex.exec(query)) !== null) {\n const baths = parseFloat(match[1]);\n if (!filters.baths || baths > filters.baths) { // Take the highest if multiple\n filters.baths = baths;\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'baths',\n value: baths\n });\n }\n }\n\n // Price max parsing - \"under 500k\", \"below $1M\", \"max 600000\"\n const priceMaxRegex = /(?:under|below|max|maximum)\\s*\\$?(\\d+(?:\\.\\d+)?)\\s*([km])?/gi;\n while ((match = priceMaxRegex.exec(query)) !== null) {\n let price = parseFloat(match[1]);\n const suffix = match[2]?.toLowerCase();\n if (suffix === 'k') price *= 1000;\n if (suffix === 'm') price *= 1000000;\n \n if (!filters.priceMax || price < filters.priceMax) { // Take the lowest max\n filters.priceMax = price;\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'priceMax',\n value: price\n });\n }\n }\n\n // Price min parsing - \"above 300k\", \"at least 400k\", \"min 500k\"\n const priceMinRegex = /(?:above|at\\s+least|min|minimum)\\s*\\$?(\\d+(?:\\.\\d+)?)\\s*([km])?/gi;\n while ((match = priceMinRegex.exec(query)) !== null) {\n let price = parseFloat(match[1]);\n const suffix = match[2]?.toLowerCase();\n if (suffix === 'k') price *= 1000;\n if (suffix === 'm') price *= 1000000;\n \n if (!filters.priceMin || price > filters.priceMin) { // Take the highest min\n filters.priceMin = price;\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'priceMin',\n value: price\n });\n }\n }\n\n // Neighborhoods parsing\n NEIGHBORHOODS.forEach(neighborhood => {\n const regex = new RegExp(`\\\\b${neighborhood.replace(/\\s+/g, '\\\\s+')}\\\\b`, 'gi');\n while ((match = regex.exec(query)) !== null) {\n if (!filters.neighborhoods.includes(neighborhood)) {\n filters.neighborhoods.push(neighborhood);\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'neighborhood',\n value: neighborhood\n });\n }\n }\n });\n\n // Property types parsing\n PROPERTY_TYPES.forEach(type => {\n const regex = new RegExp(`\\\\b${type}s?\\\\b`, 'gi');\n while ((match = regex.exec(query)) !== null) {\n if (!filters.propertyTypes.includes(type)) {\n filters.propertyTypes.push(type);\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'propertyType',\n value: type\n });\n }\n }\n });\n\n // Features parsing\n FEATURES.forEach(feature => {\n const regex = new RegExp(`\\\\b${feature}\\\\b`, 'gi');\n while ((match = regex.exec(query)) !== null) {\n if (!filters.features.includes(feature)) {\n filters.features.push(feature);\n segments.push({\n text: match[0],\n start: match.index,\n end: match.index + match[0].length,\n type: 'feature',\n value: feature\n });\n }\n }\n });\n\n // Sort segments by position for proper highlighting\n segments.sort((a, b) => a.start - b.start);\n\n return { filters, segments };\n}\n\nexport function formatPrice(price: number): string {\n if (price >= 1000000) {\n return `$${(price / 1000000).toFixed(price % 1000000 === 0 ? 0 : 1)}M`;\n } else if (price >= 1000) {\n return `$${(price / 1000).toFixed(price % 1000 === 0 ? 0 : 0)}k`;\n } else {\n return `$${price.toLocaleString()}`;\n }\n}",[],[92,108],"ea16efec-ea72-11f0-a178-42dde27851f2",5,["D",1767643972296],[120,121,122],[58],{"_34":60,"_13":59,"_37":40,"_39":61,"_41":42,"_43":62,"_45":46,"_47":138,"_17":64,"_50":65,"_52":-5,"_54":55,"_56":-5,"_80":139,"_18":140},{"_34":131,"_13":132,"_37":133,"_39":61,"_41":42,"_43":116,"_45":46,"_47":134,"_17":64,"_50":24,"_52":135,"_54":55,"_56":-5,"_80":136,"_18":137},{"_34":123,"_13":124,"_37":125,"_39":61,"_41":42,"_43":126,"_45":46,"_47":127,"_17":64,"_50":51,"_52":128,"_54":55,"_56":-5,"_80":129,"_18":130},"ed8f49c6-ea72-11f0-99f3-42dde27851f2","index.tsx","ed8f4a5c-ea72-11f0-99f3-42dde27851f2",6,["D",1767643978118],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'https://esm.sh/react@18.2.0';\nimport { createRoot } from 'https://esm.sh/react-dom@18.2.0/client';\nimport { SearchApp } from './components/SearchApp.tsx';\n\nconst root = createRoot(document.getElementById('root')!);\nroot.render();",[],[58,124],"ea16c58a-ea72-11f0-a178-42dde27851f2","index.html","ea16c63e-ea72-11f0-a178-42dde27851f2",["D",1767643972296],"\n\n\n \n \n Real Estate Search\n \n \n \n\n\n

\n \n\n",[],[58,132],["D",1767644386375],[141],[58,59],{"_34":35,"_13":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":142,"_17":49,"_50":51,"_52":53,"_54":55,"_56":-5,"_80":143,"_18":144},["D",1767646249076],[],[58,59,36],"48325b6e-ea75-11f0-9044-42dde27851f2","backend","ff80babd-14ce-48e0-acd7-7eca31a11ee7",8,["D",1767644989162],[152],[146],{"_34":153,"_13":154,"_37":155,"_39":147,"_41":42,"_43":156,"_45":46,"_47":157,"_17":158,"_50":51,"_52":159,"_54":55,"_56":-5,"_80":160,"_18":161},"87048f38-ea75-11f0-b96e-42dde27851f2","index.ts","483220ea-ea75-11f0-9044-42dde27851f2",16,["D",1767645094567],1,"import { Hono } from 'https://esm.sh/hono@3.11.7';\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err) => Promise.reject(err));\n\n// Serve static files from frontend and shared directories\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Serve the main application\napp.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Add any initial data injection here if needed\n const initialData = {\n timestamp: new Date().toISOString(),\n version: \"1.0.0\"\n };\n \n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n});\n\n// API endpoint for search (placeholder for future property data)\napp.get(\"/api/search\", async c => {\n const searchParams = c.req.query();\n \n // Mock property data - in a real app this would query a database\n const mockProperties = [\n {\n id: 1,\n address: \"123 Main St, Williamsburg, Brooklyn\",\n price: 750000,\n beds: 2,\n baths: 2,\n sqft: 1200,\n propertyType: \"condo\",\n features: [\"parking\", \"laundry\"],\n neighborhood: \"Williamsburg\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 2,\n address: \"456 Oak Ave, Park Slope, Brooklyn\", \n price: 1200000,\n beds: 3,\n baths: 2.5,\n sqft: 1800,\n propertyType: \"townhouse\",\n features: [\"yard\", \"garage\"],\n neighborhood: \"Park Slope\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 3,\n address: \"789 Pine St, Bed Stuy, Brooklyn\",\n price: 450000,\n beds: 1,\n baths: 1,\n sqft: 800,\n propertyType: \"apartment\",\n features: [\"doorman\", \"gym\"],\n neighborhood: \"Bed Stuy\", \n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 4,\n address: \"321 Elm Dr, Bushwick, Brooklyn\",\n price: 650000,\n beds: 2,\n baths: 1.5,\n sqft: 1100,\n propertyType: \"co-op\",\n features: [\"laundry\", \"rooftop\"],\n neighborhood: \"Bushwick\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 5,\n address: \"567 Maple St, Crown Heights, Brooklyn\",\n price: 850000,\n beds: 3,\n baths: 2,\n sqft: 1500,\n propertyType: \"house\",\n features: [\"yard\", \"parking\", \"garage\"],\n neighborhood: \"Crown Heights\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 6,\n address: \"890 Cedar Ave, Greenpoint, Brooklyn\",\n price: 950000,\n beds: 2,\n baths: 2,\n sqft: 1300,\n propertyType: \"condo\",\n features: [\"doorman\", \"gym\", \"pool\"],\n neighborhood: \"Greenpoint\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 7,\n address: \"234 Birch Ln, Fort Greene, Brooklyn\",\n price: 1100000,\n beds: 3,\n baths: 2.5,\n sqft: 1600,\n propertyType: \"townhouse\",\n features: [\"yard\", \"parking\"],\n neighborhood: \"Fort Greene\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 8,\n address: \"678 Spruce St, DUMBO, Brooklyn\",\n price: 1800000,\n beds: 2,\n baths: 2,\n sqft: 1400,\n propertyType: \"condo\",\n features: [\"doorman\", \"gym\", \"pool\", \"rooftop\"],\n neighborhood: \"DUMBO\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 9,\n address: \"345 Willow St, Cobble Hill, Brooklyn\",\n price: 1350000,\n beds: 3,\n baths: 2,\n sqft: 1700,\n propertyType: \"co-op\",\n features: [\"doorman\", \"laundry\"],\n neighborhood: \"Cobble Hill\",\n images: [\"https://via.placeholder.com/400x300\"]\n },\n {\n id: 10,\n address: \"789 Ash St, Clinton Hill, Brooklyn\",\n price: 725000,\n beds: 2,\n baths: 1.5,\n sqft: 1250,\n propertyType: \"apartment\",\n features: [\"laundry\", \"gym\"],\n neighborhood: \"Clinton Hill\",\n images: [\"https://via.placeholder.com/400x300\"]\n }\n ];\n \n // Filter properties based on search parameters\n let filteredProperties = mockProperties;\n \n if (searchParams.beds) {\n const minBeds = parseInt(searchParams.beds);\n filteredProperties = filteredProperties.filter(p => p.beds >= minBeds);\n }\n \n if (searchParams.baths) {\n const minBaths = parseFloat(searchParams.baths);\n filteredProperties = filteredProperties.filter(p => p.baths >= minBaths);\n }\n \n if (searchParams.priceMin) {\n const minPrice = parseInt(searchParams.priceMin);\n filteredProperties = filteredProperties.filter(p => p.price >= minPrice);\n }\n \n if (searchParams.priceMax) {\n const maxPrice = parseInt(searchParams.priceMax);\n filteredProperties = filteredProperties.filter(p => p.price <= maxPrice);\n }\n \n if (searchParams.neighborhoods) {\n const neighborhoods = searchParams.neighborhoods.split(',');\n filteredProperties = filteredProperties.filter(p => \n neighborhoods.includes(p.neighborhood)\n );\n }\n \n if (searchParams.propertyTypes) {\n const types = searchParams.propertyTypes.split(',');\n filteredProperties = filteredProperties.filter(p => \n types.includes(p.propertyType)\n );\n }\n \n if (searchParams.features) {\n const features = searchParams.features.split(',');\n filteredProperties = filteredProperties.filter(p => \n features.some(feature => p.features.includes(feature))\n );\n }\n \n return c.json({\n properties: filteredProperties,\n total: filteredProperties.length,\n filters: 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