\n \n \n\n",[],[281,283],"32047e66-3cbb-11f0-b043-9e149126039e","SearchComponent.tsx","32047f56-3cbb-11f0-b043-9e149126039e","420348fe-490a-49ac-adf7-fdcd5f2e2f5e",["D",1748543514415],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState } from \"https://esm.sh/react@18.2.0\";\nimport { MediaService } from \"../services/MediaService.ts\";\nimport { formatDate } from \"../../shared/utils.ts\";\nimport type { MediaAnalysis } from \"../../shared/types.ts\";\n\nexport default function SearchComponent() {\n const [query, setQuery] = useState('');\n const [mediaType, setMediaType] = useState<'image' | 'video' | 'audio' | ''>('');\n const [results, setResults] = useState([]);\n const [suggestions, setSuggestions] = useState([]);\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState(null);\n const [hasSearched, setHasSearched] = useState(false);\n\n const handleSearch = async (searchQuery?: string) => {\n const searchTerm = searchQuery || query;\n if (!searchTerm.trim()) {\n setError('Please enter a search query');\n return;\n }\n\n setLoading(true);\n setError(null);\n setHasSearched(true);\n\n try {\n const response = await MediaService.searchMedia({\n query: searchTerm.trim(),\n mediaType: mediaType || undefined,\n limit: 20\n });\n\n if (response.success) {\n setResults(response.results || []);\n setSuggestions(response.suggestions || []);\n } else {\n setError(response.message || 'Search failed');\n setResults([]);\n setSuggestions([]);\n }\n } catch (error) {\n setError('Search failed. Please try again.');\n console.error('Search error:', error);\n setResults([]);\n setSuggestions([]);\n } finally {\n setLoading(false);\n }\n };\n\n const handleSuggestionClick = (suggestion: string) => {\n setQuery(suggestion);\n handleSearch(suggestion);\n };\n\n const getMediaTypeIcon = (type: string) => {\n switch (type) {\n case 'image': return '🖼️';\n case 'video': return '🎬';\n case 'audio': return '🎵';\n default: return '📄';\n }\n };\n\n const getConfidenceColor = (confidence: number) => {\n if (confidence >= 0.8) return 'bg-green-100 text-green-800';\n if (confidence >= 0.6) return 'bg-yellow-100 text-yellow-800';\n return 'bg-red-100 text-red-800';\n };\n\n return (\n

\n {/* Search Header */}\n

\n 🔍 Search Your Media\n

\n\n {/* Search Form */}\n

\n \n Search Query\n \n setQuery(e.target.value)}\n onKeyPress={(e) => e.key === 'Enter' && handleSearch()}\n placeholder=\"Describe what you're looking for...\"\n className=\"w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500 focus:border-blue-500\"\n />\n

\n\n

\n \n Media Type (Optional)\n \n setMediaType(e.target.value as any)}\n className=\"w-full px-4 py-3 border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500 focus:border-blue-500\"\n >\n \n \n \n \n \n

\n\n handleSearch()}\n disabled={loading || !query.trim()}\n className=\"w-full bg-blue-600 hover:bg-blue-700 text-white px-6 py-3 rounded-lg font-medium disabled:opacity-50 disabled:cursor-not-allowed transition-colors\"\n >\n {loading ? (\n

\n Searching...\n

\n ) : (\n '🔍 Search'\n )}\n \n

\n\n {/* Search Suggestions */}\n {suggestions.length > 0 && (\n

💡 Related Suggestions

\n {suggestions.map((suggestion, index) => (\n handleSuggestionClick(suggestion)}\n className=\"px-3 py-1 bg-blue-100 text-blue-800 rounded-full text-sm hover:bg-blue-200 transition-colors\"\n >\n {suggestion}\n \n ))}\n

\n )}\n\n {/* Error Display */}\n {error && (\n

\n {error}\n

\n )}\n

\n\n {/* Search Results */}\n {hasSearched && (\n

\n Search Results\n

\n \n {results.length} result{results.length !== 1 ? 's' : ''} found\n \n

\n\n {results.length === 0 ? (\n

🔍

No results found

\n Try adjusting your search terms or upload more media to analyze.\n

Search tips:

• Use descriptive keywords
• Try different synonyms
• Search for objects, colors, or emotions
• Use broader terms for more results

\n ) : (\n

\n {results.map((result) => (\n

\n \n {getMediaTypeIcon(result.media_type)}\n \n

\n {result.file_name}\n

\n {formatDate(result.created_at)}\n

\n\n

\n {result.analysis_result}\n

\n\n {result.tags && result.tags.length > 0 && (\n

\n {result.tags.map((tag, index) => (\n \n {tag}\n \n ))}\n

\n )}\n

\n\n

\n \n {Math.round(result.confidence_score * 100)}% confidence\n \n

\n {(result.file_size / 1024 / 1024).toFixed(2)} MB\n

\n ))}\n

\n )}\n

\n )}\n\n {/* Search Tips */}\n

🎯 Search Tips

What you can search for:

• Objects and subjects in images
• Colors and visual elements
• Emotions and moods
• Text content in media

Example searches:

• \"sunset landscape\"
• \"people smiling\"
• \"text document\"
• \"music recording\"

\n );\n}",[],[281,298,291],"components","0bfae412-3cbb-11f0-9451-9e149126039e","MediaCapture.tsx","0bfae4e4-3cbb-11f0-9451-9e149126039e",22,["D",1748543450599],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState, useRef } from \"https://esm.sh/react@18.2.0\";\nimport { MediaService } from \"../services/MediaService.ts\";\nimport { formatFileSize } from \"../../shared/utils.ts\";\nimport { MEDIA_CONSTRAINTS } from \"../../shared/types.ts\";\n\ninterface MediaCaptureProps {\n onAnalysisComplete: (data: any) => void;\n}\n\nexport default function MediaCapture({ onAnalysisComplete }: MediaCaptureProps) {\n const [selectedFile, setSelectedFile] = useState(null);\n const [previewUrl, setPreviewUrl] = useState(null);\n const [isCapturing, setIsCapturing] = useState(false);\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n const [error, setError] = useState(null);\n const [captureMode, setCaptureMode] = useState<'photo' | 'video' | 'audio' | null>(null);\n \n const fileInputRef = useRef(null);\n const videoRef = useRef(null);\n const canvasRef = useRef(null);\n const mediaRecorderRef = useRef(null);\n const streamRef = useRef(null);\n\n const handleFileSelect = (event: React.ChangeEvent) => {\n const file = event.target.files?.[0];\n if (!file) return;\n\n setError(null);\n\n // Validate file size\n if (file.size > MEDIA_CONSTRAINTS.maxFileSize) {\n setError(`File too large. Maximum size is ${formatFileSize(MEDIA_CONSTRAINTS.maxFileSize)}`);\n return;\n }\n\n // Validate file type\n if (!MEDIA_CONSTRAINTS.allowedTypes.includes(file.type)) {\n setError('Unsupported file type. Please select an image, video, or audio file.');\n return;\n }\n\n setSelectedFile(file);\n\n // Create preview URL\n const url = URL.createObjectURL(file);\n setPreviewUrl(url);\n };\n\n const startCapture = async (mode: 'photo' | 'video' | 'audio') => {\n try {\n setError(null);\n setCaptureMode(mode);\n setIsCapturing(true);\n\n let constraints: MediaStreamConstraints = {};\n\n if (mode === 'photo' || mode === 'video') {\n constraints.video = {\n facingMode: 'environment' // Use back camera by default\n };\n }\n\n if (mode === 'video' || mode === 'audio') {\n constraints.audio = true;\n }\n\n const stream = await navigator.mediaDevices.getUserMedia(constraints);\n streamRef.current = stream;\n\n if (videoRef.current && (mode === 'photo' || mode === 'video')) {\n videoRef.current.srcObject = stream;\n videoRef.current.play();\n }\n\n if (mode === 'video' || mode === 'audio') {\n const mediaRecorder = new MediaRecorder(stream);\n mediaRecorderRef.current = mediaRecorder;\n\n const chunks: BlobPart[] = [];\n mediaRecorder.ondataavailable = (event) => {\n if (event.data.size > 0) {\n chunks.push(event.data);\n }\n };\n\n mediaRecorder.onstop = () => {\n const blob = new Blob(chunks, { \n type: mode === 'video' ? 'video/webm' : 'audio/webm' \n });\n const file = new File([blob], `captured_${mode}_${Date.now()}.webm`, {\n type: blob.type\n });\n \n setSelectedFile(file);\n setPreviewUrl(URL.createObjectURL(blob));\n stopCapture();\n };\n\n mediaRecorder.start();\n }\n } catch (error) {\n setError('Failed to access camera/microphone. Please check permissions.');\n console.error('Capture error:', error);\n setIsCapturing(false);\n setCaptureMode(null);\n }\n };\n\n const capturePhoto = () => {\n if (!videoRef.current || !canvasRef.current) return;\n\n const video = videoRef.current;\n const canvas = canvasRef.current;\n const context = canvas.getContext('2d');\n\n if (!context) return;\n\n canvas.width = video.videoWidth;\n canvas.height = video.videoHeight;\n context.drawImage(video, 0, 0);\n\n canvas.toBlob((blob) => {\n if (blob) {\n const file = new File([blob], `captured_photo_${Date.now()}.jpg`, {\n type: 'image/jpeg'\n });\n setSelectedFile(file);\n setPreviewUrl(URL.createObjectURL(blob));\n stopCapture();\n }\n }, 'image/jpeg', 0.9);\n };\n\n const stopCapture = () => {\n if (streamRef.current) {\n streamRef.current.getTracks().forEach(track => track.stop());\n streamRef.current = null;\n }\n\n if (mediaRecorderRef.current && mediaRecorderRef.current.state !== 'inactive') {\n mediaRecorderRef.current.stop();\n }\n\n setIsCapturing(false);\n setCaptureMode(null);\n };\n\n const analyzeMedia = async () => {\n if (!selectedFile) return;\n\n setIsAnalyzing(true);\n setError(null);\n\n try {\n // Convert file to base64\n const reader = new FileReader();\n reader.onload = async () => {\n const base64Data = reader.result as string;\n \n let mediaType: 'image' | 'video' | 'audio';\n if (selectedFile.type.startsWith('image/')) {\n mediaType = 'image';\n } else if (selectedFile.type.startsWith('video/')) {\n mediaType = 'video';\n } else {\n mediaType = 'audio';\n }\n\n const response = await MediaService.analyzeMedia({\n mediaType,\n fileName: selectedFile.name,\n fileData: base64Data\n });\n\n if (response.success) {\n onAnalysisComplete({\n ...response,\n file: selectedFile,\n previewUrl\n });\n } else {\n setError(response.message || 'Analysis failed');\n }\n };\n\n reader.readAsDataURL(selectedFile);\n } catch (error) {\n setError('Failed to analyze media. Please try again.');\n console.error('Analysis error:', error);\n } finally {\n setIsAnalyzing(false);\n }\n };\n\n const clearSelection = () => {\n setSelectedFile(null);\n setPreviewUrl(null);\n setError(null);\n if (fileInputRef.current) {\n fileInputRef.current.value = '';\n }\n };\n\n return (\n

\n 📸 Capture or Upload Media\n

\n\n {/* Capture Controls */}\n {!isCapturing && !selectedFile && (\n

\n startCapture('photo')}\n className=\"capture-button flex flex-col items-center p-6 rounded-lg\"\n >\n 📷\n Take Photo\n \n \n startCapture('video')}\n className=\"capture-button flex flex-col items-center p-6 rounded-lg\"\n >\n 🎬\n Record Video\n \n \n startCapture('audio')}\n className=\"capture-button flex flex-col items-center p-6 rounded-lg\"\n >\n 🎤\n Record Audio\n \n

\n\n

\n \n fileInputRef.current?.click()}\n className=\"bg-blue-600 hover:bg-blue-700 text-white px-6 py-3 rounded-lg font-medium transition-colors\"\n >\n 📁 Upload File\n \n

\n )}\n\n {/* Capture Interface */}\n {isCapturing && (\n

\n {(captureMode === 'photo' || captureMode === 'video') && (\n

\n \n

\n )}\n\n

\n {captureMode === 'photo' && (\n \n 📸 Capture Photo\n \n )}\n \n {(captureMode === 'video' || captureMode === 'audio') && (\n \n ⏹️ Stop Recording\n \n )}\n \n \n ❌ Cancel\n \n

\n )}\n\n {/* File Preview */}\n {selectedFile && previewUrl && (\n

\n {selectedFile.type.startsWith('image/') && (\n \n )}\n \n {selectedFile.type.startsWith('video/') && (\n \n )}\n \n {selectedFile.type.startsWith('audio/') && (\n

\n 🎵\n

\n \n

\n )}\n

\n\n

File: {selectedFile.name}

Size: {formatFileSize(selectedFile.size)}

Type: {selectedFile.type}

\n\n

\n \n {isAnalyzing ? (\n

\n Analyzing...\n

\n ) : (\n '🔍 Analyze Media'\n )}\n \n \n \n 🗑️ Clear\n \n

\n )}\n\n {/* Error Display */}\n {error && (\n

\n {error}\n

\n )}\n\n {/* Usage Tips */}\n

💡 Tips for Best Results

• Use good lighting for photos and videos
• Speak clearly for audio recordings
• Keep file sizes under {formatFileSize(MEDIA_CONSTRAINTS.maxFileSize)}
• Supported formats: JPEG, PNG, WebP, MP4, WebM, MP3, WAV

\n );\n}",[],[281,298,300],"18c7f126-3cbb-11f0-a036-9e149126039e","MediaAnalysis.tsx","18c7f1e4-3cbb-11f0-a036-9e149126039e",23,["D",1748543472075],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from \"https://esm.sh/react@18.2.0\";\n\ninterface MediaAnalysisProps {\n data: {\n analysis: {\n description: string;\n tags: string[];\n confidence: number;\n objects?: string[];\n emotions?: string[];\n text?: string;\n };\n file: File;\n previewUrl: string;\n };\n}\n\nexport default function MediaAnalysis({ data }: MediaAnalysisProps) {\n const { analysis, file } = data;\n\n const getConfidenceColor = (confidence: number) => {\n if (confidence >= 0.8) return 'text-green-600';\n if (confidence >= 0.6) return 'text-yellow-600';\n return 'text-red-600';\n };\n\n const getConfidenceLabel = (confidence: number) => {\n if (confidence >= 0.8) return 'High';\n if (confidence >= 0.6) return 'Medium';\n return 'Low';\n };\n\n return (\n

🤖 AI Analysis Results

\n {getConfidenceLabel(analysis.confidence)} Confidence\n

\n {Math.round(analysis.confidence * 100)}%\n

\n\n

📝 Description

{analysis.description}

\n\n {analysis.tags && analysis.tags.length > 0 && (\n

🏷️ Tags

\n {analysis.tags.map((tag, index) => (\n \n {tag}\n \n ))}\n

\n )}\n\n {analysis.objects && analysis.objects.length > 0 && (\n

🎯 Detected Objects/Topics

\n {analysis.objects.map((object, index) => (\n \n {object}\n \n ))}\n

\n )}\n\n {analysis.emotions && analysis.emotions.length > 0 && (\n

😊 Emotions/Mood

\n {analysis.emotions.map((emotion, index) => (\n \n {emotion}\n \n ))}\n

\n )}\n\n {analysis.text && (\n

📄 Extracted Text

{analysis.text}

\n )}\n

\n\n

📊 Analysis Details

\n File Name:\n

{file.name}

\n File Type:\n

{file.type}

\n File Size:\n

{(file.size / 1024 / 1024).toFixed(2)} MB

\n Analysis Time:\n

{new Date().toLocaleTimeString()}

\n\n

💡 What's Next?

• Use the search feature to find similar content
• Check your history to see all analyzed media
• Try analyzing different types of media for comparison
• The analysis is automatically saved to your account

\n );\n}",[],[281,298,308],"ef445c40-3cba-11f0-adf7-9e149126039e","Dashboard.tsx","ef445cfe-3cba-11f0-adf7-9e149126039e",21,["D",1748543402424],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState } from \"https://esm.sh/react@18.2.0\";\nimport MediaCapture from \"./MediaCapture.tsx\";\nimport MediaAnalysis from \"./MediaAnalysis.tsx\";\nimport SearchComponent from \"./SearchComponent.tsx\";\nimport HistoryComponent from \"./HistoryComponent.tsx\";\nimport type { User } from \"../../shared/types.ts\";\n\ninterface DashboardProps {\n user: User;\n onLogout: () => void;\n}\n\ntype TabType = 'capture' | 'search' | 'history';\n\nexport default function Dashboard({ user, onLogout }: DashboardProps) {\n const [activeTab, setActiveTab] = useState('capture');\n const [analysisData, setAnalysisData] = useState(null);\n\n const handleAnalysisComplete = (data: any) => {\n setAnalysisData(data);\n };\n\n const tabs = [\n { id: 'capture', label: 'Capture & Analyze', icon: '📸' },\n { id: 'search', label: 'Search', icon: '🔍' },\n { id: 'history', label: 'History', icon: '📚' }\n ];\n\n return (\n

\n {/* Header */}\n

\n 🤖\n

\n AI Media Recognition\n

\n \n

\n \n Welcome, {user.username}\n \n \n Logout\n \n

\n\n {/* Navigation Tabs */}\n

\n {tabs.map((tab) => (\n setActiveTab(tab.id as TabType)}\n className={`py-4 px-1 border-b-2 font-medium text-sm transition-colors ${\n activeTab === tab.id\n ? 'border-indigo-500 text-indigo-600'\n : 'border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300'\n }`}\n >\n {tab.icon}\n {tab.label}\n \n ))}\n

\n\n {/* Main Content */}\n

\n {/* Primary Content */}\n

\n {activeTab === 'capture' && (\n

\n \n {analysisData && (\n \n )}\n

\n )}\n \n {activeTab === 'search' && (\n \n )}\n \n {activeTab === 'history' && (\n \n )}\n

\n\n {/* Sidebar */}\n

\n Platform Features\n

\n \n

\n 🖼️\n

Image Analysis

\n AI-powered object detection, scene understanding, and text recognition\n

\n \n

\n 🎵\n

Audio Processing

\n Speech-to-text transcription and audio content analysis\n

\n \n

\n 🎬\n

Video Understanding

\n Content analysis and intelligent video categorization\n

\n \n

\n 🔍\n

Smart Search

\n Find similar content using natural language descriptions\n

\n\n {/* Quick Stats */}\n

\n Quick Stats\n

\n \n

\n Account Created\n \n {new Date(user.created_at).toLocaleDateString()}\n \n

\n \n

\n Current Tab\n {activeTab}\n

\n );\n}",[],[281,298,316],"de59bd12-3cba-11f0-8179-9e149126039e","AuthComponent.tsx","de59bdc6-3cba-11f0-8179-9e149126039e",20,["D",1748543374044],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState } from \"https://esm.sh/react@18.2.0\";\nimport { AuthService } from \"../services/AuthService.ts\";\nimport { validateEmail, validatePassword } from \"../../shared/utils.ts\";\nimport type { User } from \"../../shared/types.ts\";\n\ninterface AuthComponentProps {\n onLogin: (user: User, token: string) => void;\n error: string | null;\n setError: (error: string | null) => void;\n}\n\nexport default function AuthComponent({ onLogin, error, setError }: AuthComponentProps) {\n const [isLogin, setIsLogin] = useState(true);\n const [loading, setLoading] = useState(false);\n const [formData, setFormData] = useState({\n email: '',\n username: '',\n password: '',\n confirmPassword: ''\n });\n\n const handleInputChange = (e: React.ChangeEvent) => {\n const { name, value } = e.target;\n setFormData(prev => ({ ...prev, [name]: value }));\n setError(null);\n };\n\n const validateForm = () => {\n if (!formData.email || !formData.password) {\n setError('Email and password are required');\n return false;\n }\n\n if (!validateEmail(formData.email)) {\n setError('Please enter a valid email address');\n return false;\n }\n\n if (!isLogin) {\n if (!formData.username) {\n setError('Username is required');\n return false;\n }\n\n const passwordValidation = validatePassword(formData.password);\n if (!passwordValidation.valid) {\n setError(passwordValidation.message!);\n return false;\n }\n\n if (formData.password !== formData.confirmPassword) {\n setError('Passwords do not match');\n return false;\n }\n }\n\n return true;\n };\n\n const handleSubmit = async (e: React.FormEvent) => {\n e.preventDefault();\n \n if (!validateForm()) return;\n\n setLoading(true);\n setError(null);\n\n try {\n let response;\n \n if (isLogin) {\n response = await AuthService.login(formData.email, formData.password);\n } else {\n response = await AuthService.signup(formData.email, formData.username, formData.password);\n }\n\n if (response.success && response.user && response.token) {\n onLogin(response.user, response.token);\n } else {\n setError(response.message || 'Authentication failed');\n }\n } catch (error) {\n setError('Network error. Please try again.');\n console.error('Auth error:', error);\n } finally {\n setLoading(false);\n }\n };\n\n return (\n

🤖

\n AI Media Recognition Platform\n

\n {isLogin ? 'Sign in to your account' : 'Create your account'}\n

\n\n

\n \n Email address\n \n \n

\n\n {!isLogin && (\n

\n \n Username\n \n \n

\n )}\n\n

\n \n Password\n \n \n

\n\n {!isLogin && (\n

\n \n Confirm Password\n \n \n

\n )}\n

\n\n {error && (\n

\n {error}\n

\n )}\n\n

\n \n {loading ? (\n

\n ) : (\n isLogin ? 'Sign In' : 'Sign Up'\n )}\n \n

\n\n

\n {\n setIsLogin(!isLogin);\n setError(null);\n setFormData({\n email: '',\n username: '',\n password: '',\n confirmPassword: ''\n });\n }}\n className=\"text-indigo-600 hover:text-indigo-500 text-sm font-medium\"\n >\n {isLogin \n ? \"Don't have an account? Sign up\" \n : \"Already have an account? Sign in\"\n }\n \n

\n\n

Secure authentication with encrypted passwords

Your data is protected and never shared

\n );\n}",[],[281,298,324],"cdb7d958-3cba-11f0-b7dd-9e149126039e","App.tsx","cdb7da34-3cba-11f0-b7dd-9e149126039e",19,["D",1748543346137],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\nimport AuthComponent from \"./AuthComponent.tsx\";\nimport Dashboard from \"./Dashboard.tsx\";\nimport { AuthService } from \"../services/AuthService.ts\";\nimport type { User } from \"../../shared/types.ts\";\n\nexport default function App() {\n const [user, setUser] = useState(null);\n const [loading, setLoading] = useState(true);\n const [error, setError] = useState(null);\n\n useEffect(() => {\n checkAuthStatus();\n }, []);\n\n const checkAuthStatus = async () => {\n try {\n const token = localStorage.getItem('auth_token');\n if (token) {\n const response = await AuthService.getCurrentUser();\n if (response.success && response.user) {\n setUser(response.user);\n } else {\n localStorage.removeItem('auth_token');\n }\n }\n } catch (error) {\n console.error('Auth check failed:', error);\n localStorage.removeItem('auth_token');\n } finally {\n setLoading(false);\n }\n };\n\n const handleLogin = (userData: User, token: string) => {\n setUser(userData);\n localStorage.setItem('auth_token', token);\n setError(null);\n };\n\n const handleLogout = async () => {\n try {\n await AuthService.logout();\n } catch (error) {\n console.error('Logout error:', error);\n } finally {\n setUser(null);\n localStorage.removeItem('auth_token');\n }\n };\n\n if (loading) {\n return (\n

\n );\n }\n\n if (!user) {\n return (\n

\n \n

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

\n \n

\n );\n}",[],[281,298,332],"cdb7e4de-3cba-11f0-b7dd-9e149126039e",["D",1748543346137],[183,184,185,186,187,188],[281,298],"c18548fa-3cba-11f0-aa86-9e149126039e",["D",1748543325673],[182,189,190],[281],"b77b18a8-3cba-11f0-8e85-9e149126039e","index.ts","b77b1966-3cba-11f0-8e85-9e149126039e",16,["D",1748543308833],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\nimport { initializeDatabase, cleanupExpiredSessions } from \"./database/migrations.ts\";\nimport authRoutes from \"./routes/auth.ts\";\nimport mediaRoutes from \"./routes/media.ts\";\n\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n throw err;\n});\n\n// Initialize database on startup\nawait initializeDatabase();\n\n// Clean up expired sessions periodically\nsetInterval(cleanupExpiredSessions, 60 * 60 * 1000); // Every hour\n\n// Health check endpoint\napp.get('/health', (c) => {\n return c.json({ \n status: 'healthy', \n timestamp: new Date().toISOString(),\n version: '1.0.0'\n });\n});\n\n// API routes\napp.route('/api/auth', authRoutes);\napp.route('/api/media', mediaRoutes);\n\n// Serve static files\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 main application\napp.get(\"/\", async c => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject initial configuration\n const config = {\n apiUrl: '/api',\n maxFileSize: 50 * 1024 * 1024, // 50MB\n supportedFormats: {\n image: ['image/jpeg', 'image/png', 'image/webp'],\n video: ['video/mp4', 'video/webm'],\n audio: ['audio/mp3', 'audio/wav', 'audio/webm']\n }\n };\n \n const configScript = ``;\n \n html = html.replace(\"\", `${configScript}`);\n return c.html(html);\n } catch (error) {\n console.error('Error serving main page:', error);\n return c.html(`\n \n \n \n AI Media Recognition Platform\n \n \n \n \n

AI Media Recognition Platform

Loading application...

Error: ${error.message}

\n \n \n `);\n }\n});\n\n// Catch-all route for SPA routing\napp.get(\"*\", async c => {\n // For any other route, serve the main app (SPA routing)\n return app.fetch(new Request(new URL(\"/\", c.req.url)));\n});\n\n// This is the entry point for HTTP vals\nexport default app.fetch;",[],[152,348],"8b012448-3cba-11f0-8bcc-9e149126039e","ai-analysis.ts","8b012510-3cba-11f0-8bcc-9e149126039e","25c031c1-741e-47c3-9312-45c5ab04aaf1",13,["D",1748543234212],"import { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { AnalysisResponse } from \"../../shared/types.ts\";\n\nconst openai = new OpenAI();\n\nexport async function analyzeImage(imageData: string, fileName: string): Promise {\n try {\n // Remove data URL prefix if present\n const base64Data = imageData.replace(/^data:image\\/[a-z]+;base64,/, '');\n \n const response = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: `Analyze this image and provide:\n1. A detailed description of what you see\n2. List of objects/subjects in the image\n3. Emotions or mood if people are present\n4. Any text visible in the image\n5. Tags for categorization\n6. Confidence level (0-1)\n\nFormat your response as JSON with the following structure:\n{\n \"description\": \"detailed description\",\n \"objects\": [\"object1\", \"object2\"],\n \"emotions\": [\"emotion1\", \"emotion2\"],\n \"text\": \"any visible text\",\n \"tags\": [\"tag1\", \"tag2\", \"tag3\"],\n \"confidence\": 0.95\n}`\n },\n {\n type: \"image_url\",\n image_url: {\n url: `data:image/jpeg;base64,${base64Data}`\n }\n }\n ]\n }\n ],\n max_tokens: 1000\n });\n\n const content = response.choices[0]?.message?.content;\n if (!content) {\n throw new Error('No response from AI model');\n }\n\n // Parse the JSON response\n const analysis = JSON.parse(content);\n \n return {\n success: true,\n analysis: {\n description: analysis.description,\n tags: analysis.tags || [],\n confidence: analysis.confidence || 0.8,\n objects: analysis.objects || [],\n emotions: analysis.emotions || [],\n text: analysis.text || ''\n }\n };\n } catch (error) {\n console.error('Image analysis error:', error);\n return {\n success: false,\n message: `Failed to analyze image: ${error.message}`\n };\n }\n}\n\nexport async function analyzeAudio(audioData: string, fileName: string): Promise {\n try {\n // For audio analysis, we'll use Whisper for transcription and then analyze the text\n // Note: This is a simplified approach. In production, you might want to use specialized audio analysis APIs\n \n // Convert base64 to blob for Whisper API\n const base64Data = audioData.replace(/^data:audio\\/[^;]+;base64,/, '');\n const audioBuffer = Uint8Array.from(atob(base64Data), c => c.charCodeAt(0));\n const audioBlob = new Blob([audioBuffer], { type: 'audio/wav' });\n \n // Create a File object for the API\n const audioFile = new File([audioBlob], fileName, { type: 'audio/wav' });\n \n const transcription = await openai.audio.transcriptions.create({\n file: audioFile,\n model: \"whisper-1\",\n });\n\n const text = transcription.text;\n \n // Analyze the transcribed text\n const textAnalysis = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"user\",\n content: `Analyze this audio transcription and provide:\n1. A summary of the content\n2. Key topics discussed\n3. Emotional tone\n4. Language detected\n5. Tags for categorization\n6. Confidence level (0-1)\n\nTranscription: \"${text}\"\n\nFormat your response as JSON:\n{\n \"description\": \"summary of audio content\",\n \"topics\": [\"topic1\", \"topic2\"],\n \"emotions\": [\"emotion1\", \"emotion2\"],\n \"language\": \"detected language\",\n \"tags\": [\"tag1\", \"tag2\", \"tag3\"],\n \"confidence\": 0.95\n}`\n }\n ],\n max_tokens: 800\n });\n\n const analysisContent = textAnalysis.choices[0]?.message?.content;\n if (!analysisContent) {\n throw new Error('No response from AI model');\n }\n\n const analysis = JSON.parse(analysisContent);\n \n return {\n success: true,\n analysis: {\n description: analysis.description,\n tags: analysis.tags || [],\n confidence: analysis.confidence || 0.8,\n objects: analysis.topics || [],\n emotions: analysis.emotions || [],\n text: text\n }\n };\n } catch (error) {\n console.error('Audio analysis error:', error);\n return {\n success: false,\n message: `Failed to analyze audio: ${error.message}`\n };\n }\n}\n\nexport async function analyzeVideo(videoData: string, fileName: string): Promise {\n try {\n // For video analysis, we'll extract a frame and analyze it as an image\n // In a production environment, you might want to extract multiple frames or use specialized video analysis\n \n // This is a simplified approach - we'll treat the video thumbnail as an image\n // In practice, you'd need to extract frames from the video\n \n const response = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"user\",\n content: `Analyze this video file and provide:\n1. A description of what the video likely contains based on the filename: \"${fileName}\"\n2. Estimated content type (educational, entertainment, documentary, etc.)\n3. Suggested tags for categorization\n4. Confidence level (0-1)\n\nFormat your response as JSON:\n{\n \"description\": \"estimated video content description\",\n \"contentType\": \"content type\",\n \"tags\": [\"tag1\", \"tag2\", \"tag3\"],\n \"confidence\": 0.7\n}`\n }\n ],\n max_tokens: 500\n });\n\n const content = response.choices[0]?.message?.content;\n if (!content) {\n throw new Error('No response from AI model');\n }\n\n const analysis = JSON.parse(content);\n \n return {\n success: true,\n analysis: {\n description: analysis.description,\n tags: analysis.tags || [],\n confidence: analysis.confidence || 0.6, // Lower confidence for filename-based analysis\n objects: [analysis.contentType] || [],\n emotions: [],\n text: ''\n }\n };\n } catch (error) {\n console.error('Video analysis error:', error);\n return {\n success: false,\n message: `Failed to analyze video: ${error.message}`\n };\n }\n}\n\nexport async function generateSearchSuggestions(query: string): Promise {\n try {\n const response = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"user\",\n content: `Generate 5 related search suggestions for the query: \"${query}\"\n \n Focus on:\n - Similar concepts\n - Related objects or subjects\n - Alternative descriptions\n - Broader or narrower terms\n \n Return as a JSON array of strings:\n [\"suggestion1\", \"suggestion2\", \"suggestion3\", \"suggestion4\", \"suggestion5\"]`\n }\n ],\n max_tokens: 200\n });\n\n const content = response.choices[0]?.message?.content;\n if (!content) {\n return [];\n }\n\n return JSON.parse(content);\n } catch (error) {\n console.error('Search suggestions error:', error);\n return [];\n }\n}",[],[152,364,356],"services","8b014a68-3cba-11f0-8bcc-9e149126039e",["D",1748543234212],[179],[152,364],"ad0f3fc0-3cba-11f0-93f2-9e149126039e","media.ts","ad0f406a-3cba-11f0-93f2-9e149126039e","8ff4979e-279f-4940-9f37-2aa3515ba625",15,["D",1748543291349],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport { requireAuth, getCurrentUser } from \"../auth/utils.ts\";\nimport { \n createMediaAnalysis, \n getUserMediaAnalyses, \n searchMediaAnalyses,\n createSearchHistory,\n deleteMediaAnalysis \n} from \"../database/queries.ts\";\nimport { \n analyzeImage, \n analyzeAudio, \n analyzeVideo,\n generateSearchSuggestions \n} from \"../services/ai-analysis.ts\";\nimport { \n sanitizeFileName, \n extractFileExtension, \n isValidMediaType \n} from \"../../shared/utils.ts\";\nimport { MEDIA_CONSTRAINTS } from \"../../shared/types.ts\";\nimport type { AnalysisRequest, AnalysisResponse, SearchRequest } from \"../../shared/types.ts\";\n\nconst media = new Hono();\n\n// Analyze uploaded media\nmedia.post('/analyze', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n const { mediaType, fileName, fileData }: AnalysisRequest = await c.req.json();\n\n // Validate input\n if (!mediaType || !fileName || !fileData) {\n return c.json({ \n success: false, \n message: 'Media type, file name, and file data are required' \n }, 400);\n }\n\n if (!['image', 'video', 'audio'].includes(mediaType)) {\n return c.json({ \n success: false, \n message: 'Invalid media type' \n }, 400);\n }\n\n // Validate file size (rough estimate from base64)\n const estimatedSize = (fileData.length * 3) / 4;\n if (estimatedSize > MEDIA_CONSTRAINTS.maxFileSize) {\n return c.json({ \n success: false, \n message: `File too large. Maximum size is ${MEDIA_CONSTRAINTS.maxFileSize / (1024 * 1024)}MB` \n }, 400);\n }\n\n // Sanitize filename\n const cleanFileName = sanitizeFileName(fileName);\n \n // Store file in blob storage\n const blobKey = `media_${user.id}_${Date.now()}_${cleanFileName}`;\n await blob.set(blobKey, fileData);\n\n // Analyze based on media type\n let analysisResult: AnalysisResponse;\n \n switch (mediaType) {\n case 'image':\n analysisResult = await analyzeImage(fileData, cleanFileName);\n break;\n case 'audio':\n analysisResult = await analyzeAudio(fileData, cleanFileName);\n break;\n case 'video':\n analysisResult = await analyzeVideo(fileData, cleanFileName);\n break;\n default:\n return c.json({ \n success: false, \n message: 'Unsupported media type' \n }, 400);\n }\n\n if (!analysisResult.success) {\n // Clean up blob if analysis failed\n await blob.delete(blobKey);\n return c.json(analysisResult, 500);\n }\n\n // Save analysis to database\n const mediaAnalysis = await createMediaAnalysis(\n user.id,\n mediaType,\n cleanFileName,\n estimatedSize,\n blobKey,\n analysisResult.analysis!.description,\n analysisResult.analysis!.tags,\n analysisResult.analysis!.confidence\n );\n\n return c.json({\n success: true,\n analysis: analysisResult.analysis,\n mediaId: mediaAnalysis.id,\n message: 'Media analyzed successfully'\n });\n\n } catch (error) {\n console.error('Media analysis error:', error);\n return c.json({ \n success: false, \n message: 'Failed to analyze media' \n }, 500);\n }\n});\n\n// Search media analyses\nmedia.post('/search', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n const { query, mediaType, limit = 20 }: SearchRequest = await c.req.json();\n\n if (!query || query.trim().length === 0) {\n return c.json({ \n success: false, \n message: 'Search query is required' \n }, 400);\n }\n\n // Perform search\n const results = await searchMediaAnalyses(\n user.id, \n query.trim(), \n mediaType, \n Math.min(limit, 50) // Cap at 50 results\n );\n\n // Save search to history\n await createSearchHistory(user.id, query.trim(), mediaType || null, results.length);\n\n // Generate search suggestions\n const suggestions = await generateSearchSuggestions(query);\n\n return c.json({\n success: true,\n results,\n suggestions,\n message: `Found ${results.length} results`\n });\n\n } catch (error) {\n console.error('Search error:', error);\n return c.json({ \n success: false, \n message: 'Search failed' \n }, 500);\n }\n});\n\n// Get user's media history\nmedia.get('/history', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n const limit = parseInt(c.req.query('limit') || '50');\n \n const analyses = await getUserMediaAnalyses(user.id, Math.min(limit, 100));\n \n return c.json({\n success: true,\n analyses,\n message: `Retrieved ${analyses.length} analyses`\n });\n\n } catch (error) {\n console.error('History error:', error);\n return c.json({ \n success: false, \n message: 'Failed to retrieve history' \n }, 500);\n }\n});\n\n// Delete media analysis\nmedia.delete('/:id', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n const id = parseInt(c.req.param('id'));\n\n if (isNaN(id)) {\n return c.json({ \n success: false, \n message: 'Invalid media ID' \n }, 400);\n }\n\n const deleted = await deleteMediaAnalysis(id, user.id);\n \n if (!deleted) {\n return c.json({ \n success: false, \n message: 'Media analysis not found or access denied' \n }, 404);\n }\n\n return c.json({\n success: true,\n message: 'Media analysis deleted successfully'\n });\n\n } catch (error) {\n console.error('Delete error:', error);\n return c.json({ \n success: false, \n message: 'Failed to delete media analysis' \n }, 500);\n }\n});\n\n// Get media file (for viewing)\nmedia.get('/file/:id', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n const id = parseInt(c.req.param('id'));\n\n if (isNaN(id)) {\n return c.json({ \n success: false, \n message: 'Invalid media ID' \n }, 400);\n }\n\n // Get media analysis to verify ownership and get blob key\n const analysis = await getUserMediaAnalyses(user.id, 1000)\n .then(analyses => analyses.find(a => a.id === id));\n\n if (!analysis) {\n return c.json({ \n success: false, \n message: 'Media not found or access denied' \n }, 404);\n }\n\n // Get file from blob storage\n const fileData = await blob.get(analysis.blob_key);\n \n if (!fileData) {\n return c.json({ \n success: false, \n message: 'Media file not found in storage' \n }, 404);\n }\n\n // Determine content type\n const extension = extractFileExtension(analysis.file_name);\n let contentType = 'application/octet-stream';\n \n if (['jpg', 'jpeg'].includes(extension)) contentType = 'image/jpeg';\n else if (extension === 'png') contentType = 'image/png';\n else if (extension === 'webp') contentType = 'image/webp';\n else if (extension === 'mp4') contentType = 'video/mp4';\n else if (extension === 'webm') contentType = 'video/webm';\n else if (extension === 'mp3') contentType = 'audio/mpeg';\n else if (extension === 'wav') contentType = 'audio/wav';\n\n return new Response(fileData, {\n headers: {\n 'Content-Type': contentType,\n 'Content-Disposition': `inline; filename=\"${analysis.file_name}\"`\n }\n });\n\n } catch (error) {\n console.error('File retrieval error:', error);\n return c.json({ \n success: false, \n message: 'Failed to retrieve media file' \n }, 500);\n }\n});\n\nexport default media;",[],[152,378,370],"routes","98a5cb58-3cba-11f0-8962-9e149126039e","auth.ts","98a5cbf8-3cba-11f0-8962-9e149126039e",14,["D",1748543257101],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { \n createUser, \n getUserByEmail, \n getUserByUsername, \n getUserPasswordHash,\n deleteUserSessions \n} from \"../database/queries.ts\";\nimport { \n hashPassword, \n verifyPassword, \n createUserSession, \n invalidateSession,\n requireAuth,\n getCurrentUser,\n getTokenFromRequest\n} from \"../auth/utils.ts\";\nimport { validateEmail, validatePassword } from \"../../shared/utils.ts\";\nimport type { AuthResponse } from \"../../shared/types.ts\";\n\nconst auth = new Hono();\n\n// Register new user\nauth.post('/signup', async (c) => {\n try {\n const { email, username, password } = await c.req.json();\n\n // Validate input\n if (!email || !username || !password) {\n return c.json({ \n success: false, \n message: 'Email, username, and password are required' \n }, 400);\n }\n\n if (!validateEmail(email)) {\n return c.json({ \n success: false, \n message: 'Invalid email format' \n }, 400);\n }\n\n const passwordValidation = validatePassword(password);\n if (!passwordValidation.valid) {\n return c.json({ \n success: false, \n message: passwordValidation.message \n }, 400);\n }\n\n // Check if user already exists\n const existingUserByEmail = await getUserByEmail(email);\n if (existingUserByEmail) {\n return c.json({ \n success: false, \n message: 'User with this email already exists' \n }, 409);\n }\n\n const existingUserByUsername = await getUserByUsername(username);\n if (existingUserByUsername) {\n return c.json({ \n success: false, \n message: 'Username already taken' \n }, 409);\n }\n\n // Create user\n const passwordHash = await hashPassword(password);\n const user = await createUser(email, username, passwordHash);\n const token = await createUserSession(user.id);\n\n const response: AuthResponse = {\n success: true,\n user,\n token,\n message: 'User created successfully'\n };\n\n return c.json(response, 201);\n } catch (error) {\n console.error('Signup error:', error);\n return c.json({ \n success: false, \n message: 'Internal server error' \n }, 500);\n }\n});\n\n// Login user\nauth.post('/login', async (c) => {\n try {\n const { email, password } = await c.req.json();\n\n if (!email || !password) {\n return c.json({ \n success: false, \n message: 'Email and password are required' \n }, 400);\n }\n\n // Get user and password hash\n const user = await getUserByEmail(email);\n if (!user) {\n return c.json({ \n success: false, \n message: 'Invalid email or password' \n }, 401);\n }\n\n const passwordHash = await getUserPasswordHash(email);\n if (!passwordHash) {\n return c.json({ \n success: false, \n message: 'Invalid email or password' \n }, 401);\n }\n\n // Verify password\n const isValidPassword = await verifyPassword(password, passwordHash);\n if (!isValidPassword) {\n return c.json({ \n success: false, \n message: 'Invalid email or password' \n }, 401);\n }\n\n // Create session\n const token = await createUserSession(user.id);\n\n const response: AuthResponse = {\n success: true,\n user,\n token,\n message: 'Login successful'\n };\n\n return c.json(response);\n } catch (error) {\n console.error('Login error:', error);\n return c.json({ \n success: false, \n message: 'Internal server error' \n }, 500);\n }\n});\n\n// Logout user\nauth.post('/logout', requireAuth, async (c) => {\n try {\n const token = getTokenFromRequest(c);\n if (token) {\n await invalidateSession(token);\n }\n\n return c.json({ \n success: true, \n message: 'Logout successful' \n });\n } catch (error) {\n console.error('Logout error:', error);\n return c.json({ \n success: false, \n message: 'Internal server error' \n }, 500);\n }\n});\n\n// Get current user\nauth.get('/me', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n return c.json({ \n success: true, \n user \n });\n } catch (error) {\n console.error('Get user error:', error);\n return c.json({ \n success: false, \n message: 'Internal server error' \n }, 500);\n }\n});\n\n// Logout from all devices\nauth.post('/logout-all', requireAuth, async (c) => {\n try {\n const user = getCurrentUser(c);\n await deleteUserSessions(user.id);\n\n return c.json({ \n success: true, \n message: 'Logged out from all devices' \n });\n } catch (error) {\n console.error('Logout all error:', error);\n return c.json({ \n success: false, \n message: 'Internal server error' \n }, 500);\n }\n});\n\nexport default auth;",[],[152,378,380],"98a5d85a-3cba-11f0-8962-9e149126039e",["D",1748543257101],[176,177],[152,378],"70861c36-3cba-11f0-8367-9e149126039e","queries.ts","70861cf4-3cba-11f0-8367-9e149126039e","7007d617-c866-4d62-b49d-4382369ad7cd",11,["D",1748543189786],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport type { User, MediaAnalysis } from \"../../shared/types.ts\";\n\n// User queries\nexport async function createUser(email: string, username: string, passwordHash: string): Promise {\n const result = await sqlite.execute(\n `INSERT INTO users_v1 (email, username, password_hash) VALUES (?, ?, ?) RETURNING *`,\n [email, username, passwordHash]\n );\n return result[0] as User;\n}\n\nexport async function getUserByEmail(email: string): Promise {\n const result = await sqlite.execute(\n `SELECT id, email, username, created_at FROM users_v1 WHERE email = ?`,\n [email]\n );\n return result[0] as User || null;\n}\n\nexport async function getUserByUsername(username: string): Promise {\n const result = await sqlite.execute(\n `SELECT id, email, username, created_at FROM users_v1 WHERE username = ?`,\n [username]\n );\n return result[0] as User || null;\n}\n\nexport async function getUserById(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT id, email, username, created_at FROM users_v1 WHERE id = ?`,\n [id]\n );\n return result[0] as User || null;\n}\n\nexport async function getUserPasswordHash(email: string): Promise {\n const result = await sqlite.execute(\n `SELECT password_hash FROM users_v1 WHERE email = ?`,\n [email]\n );\n return result[0]?.password_hash || null;\n}\n\n// Session queries\nexport async function createSession(userId: number, tokenHash: string, expiresAt: string): Promise {\n await sqlite.execute(\n `INSERT INTO user_sessions_v1 (user_id, token_hash, expires_at) VALUES (?, ?, ?)`,\n [userId, tokenHash, expiresAt]\n );\n}\n\nexport async function validateSession(tokenHash: string): Promise {\n const result = await sqlite.execute(\n `SELECT user_id FROM user_sessions_v1 WHERE token_hash = ? AND expires_at > datetime('now')`,\n [tokenHash]\n );\n return result[0]?.user_id || null;\n}\n\nexport async function deleteSession(tokenHash: string): Promise {\n await sqlite.execute(\n `DELETE FROM user_sessions_v1 WHERE token_hash = ?`,\n [tokenHash]\n );\n}\n\nexport async function deleteUserSessions(userId: number): Promise {\n await sqlite.execute(\n `DELETE FROM user_sessions_v1 WHERE user_id = ?`,\n [userId]\n );\n}\n\n// Media analysis queries\nexport async function createMediaAnalysis(\n userId: number,\n mediaType: string,\n fileName: string,\n fileSize: number,\n blobKey: string,\n analysisResult: string,\n tags: string[],\n confidenceScore: number\n): Promise {\n const result = await sqlite.execute(\n `INSERT INTO media_analyses_v1 \n (user_id, media_type, file_name, file_size, blob_key, analysis_result, tags, confidence_score) \n VALUES (?, ?, ?, ?, ?, ?, ?, ?) RETURNING *`,\n [userId, mediaType, fileName, fileSize, blobKey, JSON.stringify(tags), analysisResult, confidenceScore]\n );\n \n const analysis = result[0] as any;\n return {\n ...analysis,\n tags: JSON.parse(analysis.tags)\n } as MediaAnalysis;\n}\n\nexport async function getUserMediaAnalyses(userId: number, limit: number = 50): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM media_analyses_v1 WHERE user_id = ? ORDER BY created_at DESC LIMIT ?`,\n [userId, limit]\n );\n \n return result.map((analysis: any) => ({\n ...analysis,\n tags: JSON.parse(analysis.tags)\n })) as MediaAnalysis[];\n}\n\nexport async function getMediaAnalysisById(id: number, userId: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM media_analyses_v1 WHERE id = ? AND user_id = ?`,\n [id, userId]\n );\n \n if (!result[0]) return null;\n \n const analysis = result[0] as any;\n return {\n ...analysis,\n tags: JSON.parse(analysis.tags)\n } as MediaAnalysis;\n}\n\nexport async function searchMediaAnalyses(\n userId: number,\n query: string,\n mediaType?: string,\n limit: number = 20\n): Promise {\n let sql = `\n SELECT * FROM media_analyses_v1 \n WHERE user_id = ? AND (\n analysis_result LIKE ? OR \n tags LIKE ? OR \n file_name LIKE ?\n )\n `;\n \n const params: any[] = [userId, `%${query}%`, `%${query}%`, `%${query}%`];\n \n if (mediaType) {\n sql += ` AND media_type = ?`;\n params.push(mediaType);\n }\n \n sql += ` ORDER BY created_at DESC LIMIT ?`;\n params.push(limit);\n \n const result = await sqlite.execute(sql, params);\n \n return result.map((analysis: any) => ({\n ...analysis,\n tags: JSON.parse(analysis.tags)\n })) as MediaAnalysis[];\n}\n\nexport async function deleteMediaAnalysis(id: number, userId: number): Promise {\n const result = await sqlite.execute(\n `DELETE FROM media_analyses_v1 WHERE id = ? AND user_id = ?`,\n [id, userId]\n );\n return result.changes > 0;\n}\n\n// Search history queries\nexport async function createSearchHistory(\n userId: number,\n query: string,\n mediaType: string | null,\n resultsCount: number\n): Promise {\n await sqlite.execute(\n `INSERT INTO search_history_v1 (user_id, query, media_type, results_count) VALUES (?, ?, ?, ?)`,\n [userId, query, mediaType, resultsCount]\n );\n}\n\nexport async function getUserSearchHistory(userId: number, limit: number = 10): Promise {\n const result = await sqlite.execute(\n `SELECT query, media_type, results_count, created_at \n FROM search_history_v1 \n WHERE user_id = ? \n ORDER BY created_at DESC \n LIMIT ?`,\n [userId, limit]\n );\n return result;\n}",[],[152,400,392],"database","630a8f06-3cba-11f0-bbbe-9e149126039e","migrations.ts","630a8fe2-3cba-11f0-bbbe-9e149126039e",10,["D",1748543167164],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\n// Database schema definitions and migrations\n\nexport async function initializeDatabase() {\n // Users table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS users_v1 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n email TEXT UNIQUE NOT NULL,\n username TEXT UNIQUE NOT NULL,\n password_hash TEXT NOT NULL,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // Media analyses table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS media_analyses_v1 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n media_type TEXT NOT NULL CHECK (media_type IN ('image', 'video', 'audio')),\n file_name TEXT NOT NULL,\n file_size INTEGER NOT NULL,\n blob_key TEXT NOT NULL,\n analysis_result TEXT NOT NULL,\n tags TEXT NOT NULL, -- JSON array of tags\n confidence_score REAL NOT NULL DEFAULT 0.0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n FOREIGN KEY (user_id) REFERENCES users_v1 (id) ON DELETE CASCADE\n )\n `);\n\n // User sessions table for JWT token management\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS user_sessions_v1 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n token_hash TEXT NOT NULL,\n expires_at DATETIME NOT NULL,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n FOREIGN KEY (user_id) REFERENCES users_v1 (id) ON DELETE CASCADE\n )\n `);\n\n // Search history table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS search_history_v1 (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n user_id INTEGER NOT NULL,\n query TEXT NOT NULL,\n media_type TEXT,\n results_count INTEGER NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n FOREIGN KEY (user_id) REFERENCES users_v1 (id) ON DELETE CASCADE\n )\n `);\n\n // Create indexes for better performance\n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_media_analyses_user_id ON media_analyses_v1(user_id)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_media_analyses_type ON media_analyses_v1(media_type)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_media_analyses_created_at ON media_analyses_v1(created_at)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_user_sessions_user_id ON user_sessions_v1(user_id)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_user_sessions_expires_at ON user_sessions_v1(expires_at)\n `);\n\n console.log('Database initialized successfully');\n}\n\nexport async function cleanupExpiredSessions() {\n await sqlite.execute(`\n DELETE FROM user_sessions_v1 \n WHERE expires_at < datetime('now')\n `);\n}",[],[152,400,402],"630ab3dc-3cba-11f0-bbbe-9e149126039e",["D",1748543167164],[173,174],[152,400],["D",1748543204677],[],[152,72,64],["D",1748543204677],[171],[152,72],"630ab242-3cba-11f0-bbbe-9e149126039e",["D",1748543167164],[170,172,175,178,180],[152],["D",1748542306097],{"_11":12,"_122":70},{"_11":12,"_118":-5,"_69":70,"_31":68,"_119":120,"_121":30},{"_39":109,"_13":16,"_33":36,"_110":75,"_111":-5,"_112":-5,"_113":-5,"_114":-5,"_115":-5,"_116":14,"_117":12},"ctx",{"_52":-5,"_141":460,"_461":462,"_463":464,"_465":30,"_466":30,"_467":30,"_468":24,"_469":-5,"_470":-7,"_471":-7,"_472":26,"_473":30,"_474":30,"_475":-5,"_476":30,"_477":30,"_478":30,"_479":-5},"ENV",{"_431":432,"_433":434,"_435":16,"_436":437,"_438":439,"_440":441,"_442":443,"_444":445,"_446":447,"_448":449,"_450":451,"_452":453,"_454":455,"_456":457,"_458":459},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","a3e067d4463eea85f6334f8e583e8516ae58c4f6","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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