\n \n \n

Nelson-GPT

Medical AI Assistant

\n \n \n \n

\n \n \n

\n \n \n \n \n \n \n\n",[],[188,280],"c3c208e4-409b-11f0-a2e8-76b3cceeab13","favicon.svg","c3c209e8-409b-11f0-a2e8-76b3cceeab13",10,["D",1748969819681],"",[],[188,287],"43943952-409c-11f0-8cd4-76b3cceeab13","chatService.ts","43943a06-409c-11f0-8cd4-76b3cceeab13","01ec882f-21d5-449b-b82c-043ed8f3bcd2",18,["D",1748970034127],"interface ChatRequest {\n message: string;\n conversationId?: string;\n userId?: string;\n specialtyFilter?: string;\n streamResponse?: boolean;\n onStreamChunk?: (chunk: any) => void;\n signal?: AbortSignal;\n}\n\ninterface ChatResponse {\n message: any;\n conversationId: string;\n sources?: any[];\n}\n\nclass ChatService {\n private baseUrl: string;\n\n constructor() {\n this.baseUrl = window.location.origin;\n }\n\n async sendMessage(request: ChatRequest): Promise {\n const {\n message,\n conversationId,\n userId,\n specialtyFilter,\n streamResponse = true,\n onStreamChunk,\n signal\n } = request;\n\n const response = await fetch(`${this.baseUrl}/api/chat`, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n message,\n conversationId,\n userId,\n specialtyFilter,\n streamResponse,\n }),\n signal,\n });\n\n if (!response.ok) {\n const errorData = await response.json().catch(() => ({}));\n throw new Error(errorData.error || `HTTP ${response.status}: ${response.statusText}`);\n }\n\n if (streamResponse && onStreamChunk) {\n return this.handleStreamResponse(response, onStreamChunk);\n } else {\n return await response.json();\n }\n }\n\n private async handleStreamResponse(\n response: Response,\n onStreamChunk: (chunk: any) => void\n ): Promise {\n const reader = response.body?.getReader();\n if (!reader) {\n throw new Error('No response body reader available');\n }\n\n const decoder = new TextDecoder();\n let buffer = '';\n let finalResponse: ChatResponse | null = null;\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n \n if (done) break;\n\n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n if (line.startsWith('data: ')) {\n const data = line.slice(6);\n \n if (data === '[DONE]') {\n return finalResponse || { message: null, conversationId: '' };\n }\n\n try {\n const chunk = JSON.parse(data);\n onStreamChunk(chunk);\n\n if (chunk.type === 'message_complete') {\n finalResponse = {\n message: chunk.message,\n conversationId: chunk.conversationId,\n sources: chunk.message.sources,\n };\n }\n } catch (error) {\n console.warn('Failed to parse stream chunk:', data);\n }\n }\n }\n }\n } finally {\n reader.releaseLock();\n }\n\n return finalResponse || { message: null, conversationId: '' };\n }\n\n async getConversation(conversationId: string): Promise {\n const response = await fetch(`${this.baseUrl}/api/chat/conversation/${conversationId}`);\n \n if (!response.ok) {\n if (response.status === 404) {\n return null;\n }\n throw new Error(`Failed to fetch conversation: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.conversation;\n }\n\n async getUserConversations(userId: string, limit: number = 20): Promise {\n const response = await fetch(\n `${this.baseUrl}/api/chat/conversations/${userId}?limit=${limit}`\n );\n \n if (!response.ok) {\n throw new Error(`Failed to fetch conversations: ${response.statusText}`);\n }\n\n const data = await response.json();\n return data.conversations || [];\n }\n\n async deleteConversation(conversationId: string): Promise {\n const response = await fetch(`${this.baseUrl}/api/chat/conversation/${conversationId}`, {\n method: 'DELETE',\n });\n \n if (!response.ok) {\n throw new Error(`Failed to delete conversation: ${response.statusText}`);\n }\n }\n\n // Health check\n async checkHealth(): Promise {\n try {\n const response = await fetch(`${this.baseUrl}/api/health`);\n return response.ok;\n } catch {\n return false;\n }\n }\n}\n\nexport const chatService = new ChatService();",[],[188,303,295],"services","439449f6-409c-11f0-8cd4-76b3cceeab13",["D",1748970034127],[230],[188,303],"d76a859c-409b-11f0-88e8-76b3cceeab13","App.tsx","d76a865a-409b-11f0-88e8-76b3cceeab13","cea4d8ef-a7ff-4bc1-888d-1dc93489f27b",12,["D",1748969852658],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useEffect, useState } from 'react';\nimport { Routes, Route, Navigate } from 'react-router-dom';\nimport { useApp } from './providers/AppProvider.tsx';\nimport Layout from './Layout/Layout.tsx';\nimport ChatInterface from './Chat/ChatInterface.tsx';\nimport Settings from './Settings/Settings.tsx';\nimport Help from './Help/Help.tsx';\nimport Profile from './Profile/Profile.tsx';\nimport ConversationHistory from './Chat/ConversationHistory.tsx';\nimport PWAInstallPrompt from './PWA/PWAInstallPrompt.tsx';\nimport OfflineIndicator from './PWA/OfflineIndicator.tsx';\nimport ErrorBoundary from './UI/ErrorBoundary.tsx';\n\nconst App: React.FC = () => {\n const { state, dispatch } = useApp();\n const [isOnline, setIsOnline] = useState(navigator.onLine);\n\n // Handle online/offline status\n useEffect(() => {\n const handleOnline = () => setIsOnline(true);\n const handleOffline = () => setIsOnline(false);\n\n window.addEventListener('online', handleOnline);\n window.addEventListener('offline', handleOffline);\n\n return () => {\n window.removeEventListener('online', handleOnline);\n window.removeEventListener('offline', handleOffline);\n };\n }, []);\n\n // Initialize app data\n useEffect(() => {\n const initializeApp = async () => {\n try {\n // Load user preferences from localStorage\n const savedPreferences = localStorage.getItem('nelson-gpt-preferences');\n if (savedPreferences) {\n const preferences = JSON.parse(savedPreferences);\n dispatch({ type: 'SET_USER_PREFERENCES', payload: preferences });\n }\n\n // Load conversation history from localStorage\n const savedConversations = localStorage.getItem('nelson-gpt-conversations');\n if (savedConversations) {\n const conversations = JSON.parse(savedConversations);\n dispatch({ type: 'SET_CONVERSATIONS', payload: conversations });\n }\n\n // Check for initial data from server\n if (window.__NELSON_INITIAL_DATA__) {\n dispatch({ \n type: 'SET_APP_INFO', \n payload: window.__NELSON_INITIAL_DATA__ \n });\n }\n\n dispatch({ type: 'SET_LOADING', payload: false });\n } catch (error) {\n console.error('Failed to initialize app:', error);\n dispatch({ type: 'SET_ERROR', payload: 'Failed to initialize application' });\n }\n };\n\n initializeApp();\n }, [dispatch]);\n\n // Save preferences to localStorage when they change\n useEffect(() => {\n if (state.userPreferences) {\n localStorage.setItem('nelson-gpt-preferences', JSON.stringify(state.userPreferences));\n }\n }, [state.userPreferences]);\n\n // Save conversations to localStorage when they change\n useEffect(() => {\n if (state.conversations.length > 0) {\n localStorage.setItem('nelson-gpt-conversations', JSON.stringify(state.conversations));\n }\n }, [state.conversations]);\n\n // Apply theme\n useEffect(() => {\n const theme = state.userPreferences?.ui_theme || 'light';\n if (theme === 'dark') {\n document.documentElement.classList.add('dark');\n } else {\n document.documentElement.classList.remove('dark');\n }\n }, [state.userPreferences?.ui_theme]);\n\n if (state.loading) {\n return (\n

Loading Nelson-GPT...

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

\n {/* Offline Indicator */}\n {!isOnline && }\n \n {/* PWA Install Prompt */}\n \n \n {/* Main App Routes */}\n \n }>\n } />\n } />\n } />\n } />\n } />\n } />\n } />\n \n \n {/* Catch-all route */}\n } />\n \n \n {/* Global Error Display */}\n {state.error && (\n

\n \n {state.error}\n dispatch({ type: 'SET_ERROR', payload: null })}\n className=\"ml-2 text-white hover:text-gray-200\"\n >\n ×\n \n

\n )}\n

\n \n );\n};\n\nexport default App;",[],[188,317,309],"components","ed26189c-409b-11f0-8a8c-76b3cceeab13","AppProvider.tsx","ed261a86-409b-11f0-8a8c-76b3cceeab13","f3211507-4937-4326-b88f-789ac930bfd3",13,["D",1748969889121],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { createContext, useContext, useReducer, ReactNode } from 'react';\n\n// Types\nexport interface ChatMessage {\n id: string;\n role: 'user' | 'assistant' | 'system';\n content: string;\n timestamp: string;\n sources?: any[];\n streaming?: boolean;\n}\n\nexport interface Conversation {\n id: string;\n title: string;\n messages: ChatMessage[];\n specialty_filter?: string;\n created_at: string;\n updated_at: string;\n}\n\nexport interface UserPreferences {\n user_id: string;\n display_name?: string;\n preferred_specialties: string[];\n ui_theme: 'light' | 'dark';\n streaming_enabled: boolean;\n offline_mode: boolean;\n disclaimer_acknowledged: boolean;\n}\n\nexport interface AppState {\n loading: boolean;\n error: string | null;\n currentConversation: Conversation | null;\n conversations: Conversation[];\n userPreferences: UserPreferences | null;\n isStreaming: boolean;\n sidebarOpen: boolean;\n appInfo: any;\n}\n\n// Actions\ntype AppAction =\n | { type: 'SET_LOADING'; payload: boolean }\n | { type: 'SET_ERROR'; payload: string | null }\n | { type: 'SET_CURRENT_CONVERSATION'; payload: Conversation | null }\n | { type: 'ADD_MESSAGE'; payload: { conversationId: string; message: ChatMessage } }\n | { type: 'UPDATE_MESSAGE'; payload: { conversationId: string; messageId: string; content: string } }\n | { type: 'SET_CONVERSATIONS'; payload: Conversation[] }\n | { type: 'ADD_CONVERSATION'; payload: Conversation }\n | { type: 'UPDATE_CONVERSATION'; payload: Conversation }\n | { type: 'DELETE_CONVERSATION'; payload: string }\n | { type: 'SET_USER_PREFERENCES'; payload: UserPreferences }\n | { type: 'SET_STREAMING'; payload: boolean }\n | { type: 'SET_SIDEBAR_OPEN'; payload: boolean }\n | { type: 'SET_APP_INFO'; payload: any };\n\n// Initial state\nconst initialState: AppState = {\n loading: true,\n error: null,\n currentConversation: null,\n conversations: [],\n userPreferences: null,\n isStreaming: false,\n sidebarOpen: false,\n appInfo: null,\n};\n\n// Reducer\nfunction appReducer(state: AppState, action: AppAction): AppState {\n switch (action.type) {\n case 'SET_LOADING':\n return { ...state, loading: action.payload };\n \n case 'SET_ERROR':\n return { ...state, error: action.payload };\n \n case 'SET_CURRENT_CONVERSATION':\n return { ...state, currentConversation: action.payload };\n \n case 'ADD_MESSAGE': {\n const { conversationId, message } = action.payload;\n \n // Update current conversation if it matches\n let updatedCurrentConversation = state.currentConversation;\n if (state.currentConversation?.id === conversationId) {\n updatedCurrentConversation = {\n ...state.currentConversation,\n messages: [...state.currentConversation.messages, message],\n updated_at: new Date().toISOString(),\n };\n }\n \n // Update conversations list\n const updatedConversations = state.conversations.map(conv => {\n if (conv.id === conversationId) {\n return {\n ...conv,\n messages: [...conv.messages, message],\n updated_at: new Date().toISOString(),\n };\n }\n return conv;\n });\n \n return {\n ...state,\n currentConversation: updatedCurrentConversation,\n conversations: updatedConversations,\n };\n }\n \n case 'UPDATE_MESSAGE': {\n const { conversationId, messageId, content } = action.payload;\n \n // Update current conversation if it matches\n let updatedCurrentConversation = state.currentConversation;\n if (state.currentConversation?.id === conversationId) {\n updatedCurrentConversation = {\n ...state.currentConversation,\n messages: state.currentConversation.messages.map(msg =>\n msg.id === messageId ? { ...msg, content, streaming: false } : msg\n ),\n updated_at: new Date().toISOString(),\n };\n }\n \n // Update conversations list\n const updatedConversations = state.conversations.map(conv => {\n if (conv.id === conversationId) {\n return {\n ...conv,\n messages: conv.messages.map(msg =>\n msg.id === messageId ? { ...msg, content, streaming: false } : msg\n ),\n updated_at: new Date().toISOString(),\n };\n }\n return conv;\n });\n \n return {\n ...state,\n currentConversation: updatedCurrentConversation,\n conversations: updatedConversations,\n };\n }\n \n case 'SET_CONVERSATIONS':\n return { ...state, conversations: action.payload };\n \n case 'ADD_CONVERSATION': {\n const newConversation = action.payload;\n const existingIndex = state.conversations.findIndex(conv => conv.id === newConversation.id);\n \n let updatedConversations;\n if (existingIndex >= 0) {\n // Update existing conversation\n updatedConversations = [...state.conversations];\n updatedConversations[existingIndex] = newConversation;\n } else {\n // Add new conversation at the beginning\n updatedConversations = [newConversation, ...state.conversations];\n }\n \n return {\n ...state,\n conversations: updatedConversations,\n currentConversation: newConversation,\n };\n }\n \n case 'UPDATE_CONVERSATION': {\n const updatedConversation = action.payload;\n const updatedConversations = state.conversations.map(conv =>\n conv.id === updatedConversation.id ? updatedConversation : conv\n );\n \n return {\n ...state,\n conversations: updatedConversations,\n currentConversation: state.currentConversation?.id === updatedConversation.id\n ? updatedConversation\n : state.currentConversation,\n };\n }\n \n case 'DELETE_CONVERSATION': {\n const conversationId = action.payload;\n const updatedConversations = state.conversations.filter(conv => conv.id !== conversationId);\n \n return {\n ...state,\n conversations: updatedConversations,\n currentConversation: state.currentConversation?.id === conversationId\n ? null\n : state.currentConversation,\n };\n }\n \n case 'SET_USER_PREFERENCES':\n return { ...state, userPreferences: action.payload };\n \n case 'SET_STREAMING':\n return { ...state, isStreaming: action.payload };\n \n case 'SET_SIDEBAR_OPEN':\n return { ...state, sidebarOpen: action.payload };\n \n case 'SET_APP_INFO':\n return { ...state, appInfo: action.payload };\n \n default:\n return state;\n }\n}\n\n// Context\nconst AppContext = createContext<{\n state: AppState;\n dispatch: React.Dispatch;\n} | null>(null);\n\n// Provider component\nexport const AppProvider: React.FC<{ children: ReactNode }> = ({ children }) => {\n const [state, dispatch] = useReducer(appReducer, initialState);\n\n return (\n \n {children}\n \n );\n};\n\n// Hook to use the app context\nexport const useApp = () => {\n const context = useContext(AppContext);\n if (!context) {\n throw new Error('useApp must be used within an AppProvider');\n }\n return context;\n};\n\n// Utility functions for generating IDs\nexport const generateMessageId = (): string => {\n return 'msg_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n};\n\nexport const generateConversationId = (): string => {\n return 'conv_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n};\n\nexport const generateUserId = (): string => {\n return 'user_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n};",[],[188,317,327,319],"providers","ed26303e-409b-11f0-8a8c-76b3cceeab13",["D",1748969889121],[227],[188,317,327],"1b973e2c-409c-11f0-a73c-76b3cceeab13","Sidebar.tsx","1b973eea-409c-11f0-a73c-76b3cceeab13","16f3de78-c812-498d-aae8-6a2a5ad6258b",16,["D",1748969967040],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'react';\nimport { NavLink, useNavigate } from 'react-router-dom';\nimport { useApp } from '../providers/AppProvider.tsx';\n\nconst Sidebar: React.FC = () => {\n const { state, dispatch } = useApp();\n const navigate = useNavigate();\n\n const handleNewChat = () => {\n dispatch({ type: 'SET_CURRENT_CONVERSATION', payload: null });\n navigate('/chat');\n // Close sidebar on mobile\n dispatch({ type: 'SET_SIDEBAR_OPEN', payload: false });\n };\n\n const handleConversationClick = (conversation: any) => {\n dispatch({ type: 'SET_CURRENT_CONVERSATION', payload: conversation });\n navigate(`/chat/${conversation.id}`);\n // Close sidebar on mobile\n dispatch({ type: 'SET_SIDEBAR_OPEN', payload: false });\n };\n\n const navItems = [\n {\n to: '/chat',\n icon: (\n \n ),\n label: 'Chat',\n },\n {\n to: '/history',\n icon: (\n \n ),\n label: 'History',\n },\n {\n to: '/profile',\n icon: (\n \n ),\n label: 'Profile',\n },\n {\n to: '/help',\n icon: (\n \n ),\n label: 'Help',\n },\n ];\n\n // Get recent conversations (last 10)\n const recentConversations = state.conversations.slice(0, 10);\n\n return (\n <>\n {/* Sidebar */}\n

\n {/* Logo/Brand */}\n

\n \n

Nelson-GPT

Medical AI Assistant

\n\n {/* New Chat Button */}\n

\n \n \n New Conversation\n \n

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

\n {navItems.map((item) => (\n \n `flex items-center px-3 py-2 rounded-lg text-sm font-medium transition-colors duration-200 ${\n isActive\n ? 'bg-blue-100 dark:bg-blue-900 text-blue-700 dark:text-blue-300'\n : 'text-gray-700 dark:text-gray-300 hover:bg-gray-100 dark:hover:bg-gray-700'\n }`\n }\n onClick={() => dispatch({ type: 'SET_SIDEBAR_OPEN', payload: false })}\n >\n {item.icon}\n {item.label}\n \n ))}\n

\n\n {/* Recent Conversations */}\n {recentConversations.length > 0 && (\n

\n Recent Conversations\n

\n {recentConversations.map((conversation) => (\n handleConversationClick(conversation)}\n className={`w-full text-left px-3 py-2 rounded-lg text-sm transition-colors duration-200 ${\n state.currentConversation?.id === conversation.id\n ? 'bg-blue-100 dark:bg-blue-900 text-blue-700 dark:text-blue-300'\n : 'text-gray-600 dark:text-gray-400 hover:bg-gray-100 dark:hover:bg-gray-700'\n }`}\n >\n

\n {conversation.title}\n

\n {new Date(conversation.updated_at).toLocaleDateString()}\n

\n \n ))}\n

\n )}\n\n {/* Footer */}\n

Educational Use Only

\n \n );\n};\n\nexport default Sidebar;",[],[188,317,341,333],"Layout","f4a18bce-409b-11f0-9188-76b3cceeab13","Layout.tsx","f4a18c78-409b-11f0-9188-76b3cceeab13",14,["D",1748969901670],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'react';\nimport { Outlet } from 'react-router-dom';\nimport { useApp } from '../providers/AppProvider.tsx';\nimport Header from './Header.tsx';\nimport Sidebar from './Sidebar.tsx';\n\nconst Layout: React.FC = () => {\n const { state } = useApp();\n\n return (\n

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

\n {/* Header */}\n

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

\n \n

\n \n {/* Sidebar Overlay for Mobile */}\n {state.sidebarOpen && (\n

state.dispatch({ type: 'SET_SIDEBAR_OPEN', payload: false })}\n />\n )}\n

\n );\n};\n\nexport default Layout;",[],[188,317,341,343],"07ec9534-409c-11f0-9134-76b3cceeab13","Header.tsx","07ec95e8-409c-11f0-9134-76b3cceeab13",15,["D",1748969934044],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'react';\nimport { useLocation, useNavigate } from 'react-router-dom';\nimport { useApp } from '../providers/AppProvider.tsx';\n\nconst Header: React.FC = () => {\n const { state, dispatch } = useApp();\n const location = useLocation();\n const navigate = useNavigate();\n\n const getPageTitle = () => {\n switch (location.pathname) {\n case '/chat':\n return state.currentConversation?.title || 'New Conversation';\n case '/history':\n return 'Conversation History';\n case '/settings':\n return 'Settings';\n case '/help':\n return 'Help & Documentation';\n case '/profile':\n return 'Profile';\n default:\n if (location.pathname.startsWith('/chat/')) {\n return state.currentConversation?.title || 'Conversation';\n }\n return 'Nelson-GPT';\n }\n };\n\n const handleNewChat = () => {\n dispatch({ type: 'SET_CURRENT_CONVERSATION', payload: null });\n navigate('/chat');\n };\n\n return (\n

\n {/* Left Section */}\n

\n {/* Mobile Menu Button */}\n dispatch({ type: 'SET_SIDEBAR_OPEN', payload: !state.sidebarOpen })}\n className=\"lg:hidden p-2 rounded-md text-gray-600 dark:text-gray-400 hover:bg-gray-100 dark:hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-blue-500\"\n >\n \n \n\n {/* Page Title */}\n

\n {getPageTitle()}\n

\n\n {/* Center Section - Streaming Indicator */}\n {state.isStreaming && (\n

\n \n \n \n

\n AI is thinking...\n

\n )}\n\n {/* Right Section */}\n

\n {/* New Chat Button */}\n {location.pathname.startsWith('/chat') && (\n \n \n \n )}\n\n {/* Theme Toggle */}\n {\n const currentTheme = state.userPreferences?.ui_theme || 'light';\n const newTheme = currentTheme === 'light' ? 'dark' : 'light';\n const updatedPreferences = {\n ...state.userPreferences,\n ui_theme: newTheme,\n user_id: state.userPreferences?.user_id || 'anonymous'\n };\n dispatch({ type: 'SET_USER_PREFERENCES', payload: updatedPreferences });\n }}\n className=\"p-2 rounded-md text-gray-600 dark:text-gray-400 hover:bg-gray-100 dark:hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-blue-500\"\n title=\"Toggle Theme\"\n >\n {state.userPreferences?.ui_theme === 'dark' ? (\n \n ) : (\n \n )}\n \n\n {/* Settings Button */}\n navigate('/settings')}\n className=\"p-2 rounded-md text-gray-600 dark:text-gray-400 hover:bg-gray-100 dark:hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-blue-500\"\n title=\"Settings\"\n >\n \n \n

\n );\n};\n\nexport default Header;",[],[188,317,341,351],"f4a1938a-409b-11f0-9188-76b3cceeab13",["D",1748969901670],[223,224,225],[188,317,341],"4995a11a-409c-11f0-bd57-76b3cceeab13","MessageList.tsx","4995a1ce-409c-11f0-bd57-76b3cceeab13","9010d341-fd2a-448a-bb10-f5b3197579ca",19,["D",1748970044204],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'react';\nimport MessageBubble from './MessageBubble.tsx';\nimport { ChatMessage } from '../providers/AppProvider.tsx';\n\ninterface MessageListProps {\n messages: ChatMessage[];\n isStreaming: boolean;\n}\n\nconst MessageList: React.FC = ({ messages, isStreaming }) => {\n return (\n

\n {messages.map((message, index) => (\n \n ))}\n

\n );\n};\n\nexport default MessageList;",[],[188,317,371,363],"Chat","382286c8-409c-11f0-a0d4-76b3cceeab13","ChatInterface.tsx","382287ae-409c-11f0-a0d4-76b3cceeab13",17,["D",1748970014927],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useEffect, useRef, useState } from 'react';\nimport { useParams } from 'react-router-dom';\nimport { useApp } from '../providers/AppProvider.tsx';\nimport MessageList from './MessageList.tsx';\nimport MessageInput from './MessageInput.tsx';\nimport TypingIndicator from './TypingIndicator.tsx';\nimport { chatService } from '../../services/chatService.ts';\nimport { generateConversationId, generateMessageId } from '../providers/AppProvider.tsx';\n\nconst ChatInterface: React.FC = () => {\n const { conversationId } = useParams();\n const { state, dispatch } = useApp();\n const [isLoading, setIsLoading] = useState(false);\n const messagesEndRef = useRef(null);\n const abortControllerRef = useRef(null);\n\n // Load conversation if conversationId is provided\n useEffect(() => {\n const loadConversation = async () => {\n if (conversationId && conversationId !== state.currentConversation?.id) {\n setIsLoading(true);\n try {\n // First check local conversations\n const localConversation = state.conversations.find(conv => conv.id === conversationId);\n if (localConversation) {\n dispatch({ type: 'SET_CURRENT_CONVERSATION', payload: localConversation });\n } else {\n // Try to load from server\n const conversation = await chatService.getConversation(conversationId);\n if (conversation) {\n dispatch({ type: 'SET_CURRENT_CONVERSATION', payload: conversation });\n dispatch({ type: 'ADD_CONVERSATION', payload: conversation });\n }\n }\n } catch (error) {\n console.error('Failed to load conversation:', error);\n dispatch({ type: 'SET_ERROR', payload: 'Failed to load conversation' });\n } finally {\n setIsLoading(false);\n }\n }\n };\n\n loadConversation();\n }, [conversationId, state.currentConversation?.id, state.conversations, dispatch]);\n\n // Auto-scroll to bottom when new messages arrive\n useEffect(() => {\n scrollToBottom();\n }, [state.currentConversation?.messages]);\n\n const scrollToBottom = () => {\n messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });\n };\n\n const handleSendMessage = async (content: string, specialtyFilter?: string) => {\n if (!content.trim() || state.isStreaming) return;\n\n // Create user message\n const userMessage = {\n id: generateMessageId(),\n role: 'user' as const,\n content: content.trim(),\n timestamp: new Date().toISOString(),\n };\n\n // Get or create conversation\n let currentConversationId = state.currentConversation?.id;\n if (!currentConversationId) {\n currentConversationId = generateConversationId();\n const newConversation = {\n id: currentConversationId,\n title: generateConversationTitle(content),\n messages: [],\n specialty_filter: specialtyFilter,\n created_at: new Date().toISOString(),\n updated_at: new Date().toISOString(),\n };\n dispatch({ type: 'ADD_CONVERSATION', payload: newConversation });\n }\n\n // Add user message\n dispatch({ \n type: 'ADD_MESSAGE', \n payload: { conversationId: currentConversationId, message: userMessage } \n });\n\n // Start streaming\n dispatch({ type: 'SET_STREAMING', payload: true });\n\n try {\n // Create abort controller for this request\n abortControllerRef.current = new AbortController();\n\n // Create assistant message placeholder\n const assistantMessage = {\n id: generateMessageId(),\n role: 'assistant' as const,\n content: '',\n timestamp: new Date().toISOString(),\n streaming: true,\n };\n\n dispatch({ \n type: 'ADD_MESSAGE', \n payload: { conversationId: currentConversationId, message: assistantMessage } \n });\n\n // Send message to API\n await chatService.sendMessage({\n message: content,\n conversationId: currentConversationId,\n userId: state.userPreferences?.user_id || 'anonymous',\n specialtyFilter,\n streamResponse: state.userPreferences?.streaming_enabled !== false,\n onStreamChunk: (chunk) => {\n if (chunk.type === 'content_delta') {\n // Update message content\n dispatch({\n type: 'UPDATE_MESSAGE',\n payload: {\n conversationId: currentConversationId!,\n messageId: assistantMessage.id,\n content: assistantMessage.content + chunk.delta,\n },\n });\n assistantMessage.content += chunk.delta;\n } else if (chunk.type === 'message_complete') {\n // Finalize message\n dispatch({\n type: 'UPDATE_MESSAGE',\n payload: {\n conversationId: currentConversationId!,\n messageId: assistantMessage.id,\n content: chunk.message.content,\n },\n });\n }\n },\n signal: abortControllerRef.current.signal,\n });\n\n } catch (error: any) {\n if (error.name === 'AbortError') {\n console.log('Request was aborted');\n } else {\n console.error('Failed to send message:', error);\n dispatch({ type: 'SET_ERROR', payload: 'Failed to send message. Please try again.' });\n \n // Add error message\n const errorMessage = {\n id: generateMessageId(),\n role: 'assistant' as const,\n content: 'I apologize, but I encountered an error while processing your request. Please try again.',\n timestamp: new Date().toISOString(),\n };\n \n dispatch({ \n type: 'ADD_MESSAGE', \n payload: { conversationId: currentConversationId!, message: errorMessage } \n });\n }\n } finally {\n dispatch({ type: 'SET_STREAMING', payload: false });\n abortControllerRef.current = null;\n }\n };\n\n const handleStopGeneration = () => {\n if (abortControllerRef.current) {\n abortControllerRef.current.abort();\n dispatch({ type: 'SET_STREAMING', payload: false });\n }\n };\n\n const generateConversationTitle = (firstMessage: string): string => {\n const words = firstMessage.trim().split(' ').slice(0, 6);\n let title = words.join(' ');\n if (firstMessage.length > title.length) {\n title += '...';\n }\n return title || 'Medical Consultation';\n };\n\n if (isLoading) {\n return (\n

Loading conversation...

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

\n {/* Messages Area */}\n

\n {state.currentConversation?.messages.length === 0 || !state.currentConversation ? (\n // Welcome Screen\n

\n \n

\n Welcome to Nelson-GPT\n

\n Your AI-powered medical assistant based on the Nelson Textbook of Pediatrics. \n Ask questions about pediatric medicine, get evidence-based answers, and explore medical knowledge.\n

\n \n {/* Sample Questions */}\n

Clinical Questions

• Fever management in infants
• Asthma treatment protocols
• Growth chart interpretation

Educational Topics

• Developmental milestones
• Vaccination schedules
• Differential diagnoses

\n\n {/* Medical Disclaimer */}\n

\n \n

Medical Disclaimer

\n This tool is for educational purposes only. Always consult current medical literature and follow institutional protocols for patient care.\n

\n ) : (\n // Message List\n \n )}\n \n {/* Typing Indicator */}\n {state.isStreaming && }\n \n {/* Scroll anchor */}\n

\n\n {/* Message Input */}\n \n

\n );\n};\n\nexport default ChatInterface;",[],[188,317,371,373],"3822a842-409c-11f0-a0d4-76b3cceeab13",["D",1748970014927],[220,221],[188,317,371],"d76a9dfc-409b-11f0-88e8-76b3cceeab13",["D",1748969852658],[219,222,226,228],[188,317],["D",1748969814050],[218,229,231,232,233],[188],"587aeee8-409b-11f0-b237-76b3cceeab13","index.ts",4,["D",1748969639694],"http","import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { cors } from \"https://esm.sh/hono@3.11.7/middleware\";\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\nimport { chatRoutes } from \"./routes/chat.ts\";\nimport { embeddingRoutes } from \"./routes/embeddings.ts\";\nimport { runMigrations } from \"./database/migrations.ts\";\n\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n console.error(\"Application error:\", err);\n throw err;\n});\n\n// CORS middleware for API routes\napp.use(\"/api/*\", cors({\n origin: \"*\",\n allowMethods: [\"GET\", \"POST\", \"PUT\", \"DELETE\", \"OPTIONS\"],\n allowHeaders: [\"Content-Type\", \"Authorization\"],\n}));\n\n// Initialize database on startup\nlet dbInitialized = false;\napp.use(\"*\", async (c, next) => {\n if (!dbInitialized) {\n try {\n await runMigrations();\n dbInitialized = true;\n console.log(\"Database initialized successfully\");\n } catch (error) {\n console.error(\"Database initialization failed:\", error);\n // Continue anyway for development\n }\n }\n await next();\n});\n\n// API Routes\napp.route(\"/api/chat\", chatRoutes);\napp.route(\"/api/embeddings\", embeddingRoutes);\n\n// Health check endpoint\napp.get(\"/api/health\", (c) => {\n return c.json({ \n status: \"healthy\", \n timestamp: new Date().toISOString(),\n database: dbInitialized ? \"connected\" : \"disconnected\"\n });\n});\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));\napp.get(\"/public/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Serve PWA files\napp.get(\"/manifest.json\", c => serveFile(\"/public/manifest.json\", import.meta.url));\napp.get(\"/sw.js\", c => serveFile(\"/public/sw.js\", import.meta.url));\n\n// Serve main app with initial data injection\napp.get(\"/\", async (c) => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject initial app state\n const initialData = {\n timestamp: new Date().toISOString(),\n version: \"1.0.0\",\n features: {\n pwa: true,\n offline: true,\n vectorSearch: true,\n streaming: true\n }\n };\n \n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n } catch (error) {\n console.error(\"Error serving main app:\", error);\n return c.text(\"Application temporarily unavailable\", 500);\n }\n});\n\n// Catch-all for SPA routing\napp.get(\"*\", async (c) => {\n try {\n const html = await readFile(\"/frontend/index.html\", import.meta.url);\n return c.html(html);\n } catch (error) {\n return c.text(\"Page not found\", 404);\n }\n});\n\nexport default app;",[],[195,392],"ab71f5ec-409b-11f0-a4fb-76b3cceeab13","embeddings.ts","ab71f6a0-409b-11f0-a4fb-76b3cceeab13","527c4ea7-8a75-4238-8aa0-0525ea168da6",8,["D",1748969778890],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { getCachedEmbedding, cacheEmbedding } from \"../database/queries.ts\";\n\nconst app = new Hono();\n\n// Hugging Face API for embeddings\nconst HF_API_URL = \"https://api-inference.huggingface.co/pipeline/feature-extraction/sentence-transformers/all-MiniLM-L6-v2\";\n\ninterface EmbeddingRequest {\n text: string;\n useCache?: boolean;\n}\n\n// Generate embeddings endpoint\napp.post(\"/generate\", async (c) => {\n try {\n const body: EmbeddingRequest = await c.req.json();\n const { text, useCache = true } = body;\n\n if (!text?.trim()) {\n return c.json({ error: \"Text is required\" }, 400);\n }\n\n const cleanText = text.trim();\n\n // Check cache first if enabled\n if (useCache) {\n const cachedEmbedding = await getCachedEmbedding(cleanText);\n if (cachedEmbedding) {\n return c.json({ \n embedding: cachedEmbedding,\n cached: true,\n dimensions: cachedEmbedding.length\n });\n }\n }\n\n // Generate new embedding\n const embedding = await generateEmbedding(cleanText);\n \n if (!embedding) {\n return c.json({ error: \"Failed to generate embedding\" }, 500);\n }\n\n // Cache the result\n if (useCache) {\n await cacheEmbedding(cleanText, embedding);\n }\n\n return c.json({ \n embedding,\n cached: false,\n dimensions: embedding.length\n });\n\n } catch (error) {\n console.error(\"Embedding generation error:\", error);\n return c.json({ \n error: \"Failed to generate embedding\",\n details: error.message \n }, 500);\n }\n});\n\n// Batch embedding generation\napp.post(\"/batch\", async (c) => {\n try {\n const body: { texts: string[], useCache?: boolean } = await c.req.json();\n const { texts, useCache = true } = body;\n\n if (!Array.isArray(texts) || texts.length === 0) {\n return c.json({ error: \"Texts array is required\" }, 400);\n }\n\n if (texts.length > 50) {\n return c.json({ error: \"Maximum 50 texts per batch\" }, 400);\n }\n\n const results = [];\n \n for (const text of texts) {\n if (!text?.trim()) continue;\n \n const cleanText = text.trim();\n let embedding = null;\n let cached = false;\n\n // Check cache first\n if (useCache) {\n embedding = await getCachedEmbedding(cleanText);\n cached = !!embedding;\n }\n\n // Generate if not cached\n if (!embedding) {\n embedding = await generateEmbedding(cleanText);\n if (embedding && useCache) {\n await cacheEmbedding(cleanText, embedding);\n }\n }\n\n results.push({\n text: cleanText,\n embedding,\n cached,\n success: !!embedding\n });\n }\n\n return c.json({ \n results,\n total: results.length,\n successful: results.filter(r => r.success).length\n });\n\n } catch (error) {\n console.error(\"Batch embedding error:\", error);\n return c.json({ \n error: \"Failed to generate batch embeddings\",\n details: error.message \n }, 500);\n }\n});\n\n// Health check for embedding service\napp.get(\"/health\", async (c) => {\n try {\n // Test with a simple phrase\n const testEmbedding = await generateEmbedding(\"test\");\n \n return c.json({\n status: \"healthy\",\n service: \"embeddings\",\n model: \"sentence-transformers/all-MiniLM-L6-v2\",\n dimensions: testEmbedding?.length || 0,\n available: !!testEmbedding\n });\n } catch (error) {\n return c.json({\n status: \"unhealthy\",\n service: \"embeddings\",\n error: error.message\n }, 500);\n }\n});\n\n// Main embedding generation function\nexport async function generateEmbedding(text: string): Promise {\n try {\n // First try Hugging Face API if key is available\n const hfApiKey = Deno.env.get(\"HUGGING_FACE_API_KEY\");\n \n if (hfApiKey) {\n return await generateHuggingFaceEmbedding(text, hfApiKey);\n }\n\n // Fallback to client-side embedding (would need to be implemented in frontend)\n console.warn(\"No Hugging Face API key found, falling back to client-side embedding\");\n return await generateClientSideEmbedding(text);\n\n } catch (error) {\n console.error(\"Embedding generation failed:\", error);\n return null;\n }\n}\n\nasync function generateHuggingFaceEmbedding(text: string, apiKey: string): Promise {\n try {\n const response = await fetch(HF_API_URL, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n inputs: text,\n options: {\n wait_for_model: true\n }\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n console.error(\"Hugging Face API error:\", response.status, errorText);\n return null;\n }\n\n const embedding = await response.json();\n \n // Handle different response formats\n if (Array.isArray(embedding) && Array.isArray(embedding[0])) {\n return embedding[0]; // Nested array format\n } else if (Array.isArray(embedding)) {\n return embedding; // Direct array format\n }\n \n console.error(\"Unexpected embedding format:\", typeof embedding);\n return null;\n\n } catch (error) {\n console.error(\"Hugging Face embedding error:\", error);\n return null;\n }\n}\n\n// Fallback client-side embedding (simplified)\nasync function generateClientSideEmbedding(text: string): Promise {\n try {\n // This is a very simplified embedding - in production you'd want\n // to use a proper client-side model like TensorFlow.js\n const words = text.toLowerCase().split(/\\s+/);\n const embedding = new Array(384).fill(0); // Match MiniLM dimensions\n \n // Simple hash-based embedding (not semantically meaningful)\n for (let i = 0; i < words.length; i++) {\n const word = words[i];\n for (let j = 0; j < word.length; j++) {\n const charCode = word.charCodeAt(j);\n const index = (charCode + i + j) % 384;\n embedding[index] += Math.sin(charCode * 0.1) * 0.1;\n }\n }\n \n // Normalize\n const norm = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));\n if (norm > 0) {\n for (let i = 0; i < embedding.length; i++) {\n embedding[i] /= norm;\n }\n }\n \n return embedding;\n } catch (error) {\n console.error(\"Client-side embedding error:\", error);\n return null;\n }\n}\n\nexport { app as embeddingRoutes };",[],[195,408,400],"routes","9a2312bc-409b-11f0-b5ed-76b3cceeab13","chat.ts","9a231398-409b-11f0-b5ed-76b3cceeab13",7,["D",1748969749850],"import { Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { streamText } from \"https://esm.sh/hono@3.11.7/streaming\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { \n searchMedicalContent, \n textSearchMedicalContent,\n saveConversation,\n getConversation,\n getUserConversations\n} from \"../database/queries.ts\";\nimport { generateEmbedding } from \"./embeddings.ts\";\n\nconst app = new Hono();\n\ninterface ChatMessage {\n id: string;\n role: 'user' | 'assistant' | 'system';\n content: string;\n timestamp: string;\n sources?: any[];\n streaming?: boolean;\n}\n\ninterface ChatRequest {\n message: string;\n conversationId?: string;\n userId?: string;\n specialtyFilter?: string;\n streamResponse?: boolean;\n}\n\n// Medical disclaimer template\nconst MEDICAL_DISCLAIMER = `\n\n⚠️ **Medical Disclaimer**: This information is for educational purposes only and is based on the Nelson Textbook of Pediatrics. It should not replace professional medical judgment or be used for direct patient care. Always consult current medical literature and follow your institutional protocols.`;\n\n// System prompt for medical AI\nconst SYSTEM_PROMPT = `You are Nelson-GPT, a specialized medical AI assistant based on the Nelson Textbook of Pediatrics. You provide evidence-based information to healthcare professionals and medical students.\n\nGuidelines:\n- Provide accurate, evidence-based medical information\n- Reference specific chapters and page numbers when available\n- Acknowledge limitations and recommend consulting current literature\n- Use clear, professional medical terminology\n- Structure responses with headings, bullet points, and tables when helpful\n- Always include appropriate medical disclaimers\n- If uncertain, recommend consulting additional resources or specialists\n\nYou have access to relevant medical content that will be provided as context for each query.`;\n\n// Chat endpoint with streaming support\napp.post(\"/\", async (c) => {\n try {\n const body: ChatRequest = await c.req.json();\n const { message, conversationId, userId = 'anonymous', specialtyFilter, streamResponse = true } = body;\n\n if (!message?.trim()) {\n return c.json({ error: \"Message is required\" }, 400);\n }\n\n // Load existing conversation if provided\n let conversation = conversationId ? await getConversation(conversationId) : null;\n const messages: ChatMessage[] = conversation?.messages || [];\n\n // Add user message\n const userMessage: ChatMessage = {\n id: generateMessageId(),\n role: 'user',\n content: message.trim(),\n timestamp: new Date().toISOString()\n };\n messages.push(userMessage);\n\n // Generate embedding for semantic search\n let relevantContent: any[] = [];\n try {\n const embedding = await generateEmbedding(message);\n if (embedding) {\n relevantContent = await searchMedicalContent(embedding, 3, specialtyFilter);\n }\n } catch (error) {\n console.warn(\"Embedding search failed, falling back to text search:\", error);\n }\n\n // Fallback to text search if embedding search fails\n if (relevantContent.length === 0) {\n relevantContent = await textSearchMedicalContent(message, 3, specialtyFilter);\n }\n\n // Prepare context from relevant content\n const contextText = relevantContent.length > 0 \n ? relevantContent.map(content => \n `**${content.chapter}** (${content.page_reference})\\n${content.content}`\n ).join('\\n\\n')\n : \"No specific textbook content found for this query.\";\n\n // Prepare messages for OpenAI\n const openaiMessages = [\n { role: \"system\", content: SYSTEM_PROMPT },\n { role: \"user\", content: `Context from Nelson Textbook:\\n${contextText}\\n\\nUser Question: ${message}` }\n ];\n\n // Add conversation history (last 6 messages for context)\n const recentMessages = messages.slice(-7, -1); // Exclude the current user message\n recentMessages.forEach(msg => {\n if (msg.role !== 'system') {\n openaiMessages.push({\n role: msg.role,\n content: msg.content\n });\n }\n });\n\n if (streamResponse) {\n // Streaming response\n return streamText(c, async (stream) => {\n try {\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: openaiMessages,\n stream: true,\n max_tokens: 1500,\n temperature: 0.3, // Lower temperature for medical accuracy\n });\n\n let assistantContent = \"\";\n const assistantMessage: ChatMessage = {\n id: generateMessageId(),\n role: 'assistant',\n content: \"\",\n timestamp: new Date().toISOString(),\n sources: relevantContent.map(c => ({\n chapter: c.chapter,\n page_reference: c.page_reference,\n specialty: c.specialty\n })),\n streaming: true\n };\n\n // Send initial message structure\n await stream.write(`data: ${JSON.stringify({\n type: 'message_start',\n message: assistantMessage\n })}\\n\\n`);\n\n // Stream content chunks\n for await (const chunk of completion) {\n const content = chunk.choices[0]?.delta?.content || '';\n if (content) {\n assistantContent += content;\n await stream.write(`data: ${JSON.stringify({\n type: 'content_delta',\n delta: content\n })}\\n\\n`);\n }\n }\n\n // Add medical disclaimer\n const disclaimer = MEDICAL_DISCLAIMER;\n assistantContent += disclaimer;\n \n await stream.write(`data: ${JSON.stringify({\n type: 'content_delta',\n delta: disclaimer\n })}\\n\\n`);\n\n // Finalize message\n assistantMessage.content = assistantContent;\n assistantMessage.streaming = false;\n messages.push(assistantMessage);\n\n // Save conversation\n const finalConversationId = conversationId || generateConversationId();\n await saveConversation({\n id: finalConversationId,\n user_id: userId,\n title: generateConversationTitle(message),\n messages,\n specialty_filter: specialtyFilter\n });\n\n await stream.write(`data: ${JSON.stringify({\n type: 'message_complete',\n message: assistantMessage,\n conversationId: finalConversationId\n })}\\n\\n`);\n\n await stream.write(`data: [DONE]\\n\\n`);\n\n } catch (error) {\n console.error(\"Streaming error:\", error);\n await stream.write(`data: ${JSON.stringify({\n type: 'error',\n error: 'Failed to generate response'\n })}\\n\\n`);\n }\n });\n } else {\n // Non-streaming response\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: openaiMessages,\n max_tokens: 1500,\n temperature: 0.3,\n });\n\n const assistantContent = completion.choices[0]?.message?.content || \"I apologize, but I couldn't generate a response.\";\n \n const assistantMessage: ChatMessage = {\n id: generateMessageId(),\n role: 'assistant',\n content: assistantContent + MEDICAL_DISCLAIMER,\n timestamp: new Date().toISOString(),\n sources: relevantContent.map(c => ({\n chapter: c.chapter,\n page_reference: c.page_reference,\n specialty: c.specialty\n }))\n };\n\n messages.push(assistantMessage);\n\n // Save conversation\n const finalConversationId = conversationId || generateConversationId();\n await saveConversation({\n id: finalConversationId,\n user_id: userId,\n title: generateConversationTitle(message),\n messages,\n specialty_filter: specialtyFilter\n });\n\n return c.json({\n message: assistantMessage,\n conversationId: finalConversationId,\n sources: relevantContent\n });\n }\n\n } catch (error) {\n console.error(\"Chat error:\", error);\n return c.json({ \n error: \"Failed to process chat request\",\n details: error.message \n }, 500);\n }\n});\n\n// Get conversation history\napp.get(\"/conversations/:userId\", async (c) => {\n try {\n const userId = c.req.param(\"userId\");\n const limit = parseInt(c.req.query(\"limit\") || \"20\");\n \n const conversations = await getUserConversations(userId, limit);\n return c.json({ conversations });\n } catch (error) {\n console.error(\"Error fetching conversations:\", error);\n return c.json({ error: \"Failed to fetch conversations\" }, 500);\n }\n});\n\n// Get specific conversation\napp.get(\"/conversation/:id\", async (c) => {\n try {\n const id = c.req.param(\"id\");\n const conversation = await getConversation(id);\n \n if (!conversation) {\n return c.json({ error: \"Conversation not found\" }, 404);\n }\n \n return c.json({ conversation });\n } catch (error) {\n console.error(\"Error fetching conversation:\", error);\n return c.json({ error: \"Failed to fetch conversation\" }, 500);\n }\n});\n\n// Delete conversation\napp.delete(\"/conversation/:id\", async (c) => {\n try {\n const id = c.req.param(\"id\");\n // For now, just mark as archived\n // In a real implementation, you'd add an archive endpoint\n return c.json({ success: true });\n } catch (error) {\n console.error(\"Error deleting conversation:\", error);\n return c.json({ error: \"Failed to delete conversation\" }, 500);\n }\n});\n\n// Utility functions\nfunction generateMessageId(): string {\n return 'msg_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n}\n\nfunction generateConversationId(): string {\n return 'conv_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n}\n\nfunction generateConversationTitle(firstMessage: string): string {\n // Extract key medical terms or use first few words\n const words = firstMessage.trim().split(' ').slice(0, 6);\n let title = words.join(' ');\n if (firstMessage.length > title.length) {\n title += '...';\n }\n return title || 'Medical Consultation';\n}\n\nexport { app as chatRoutes };",[],[195,408,410],"9a2329be-409b-11f0-b5ed-76b3cceeab13",["D",1748969749850],[214,215],[195,408],"7e021cea-409b-11f0-85a8-76b3cceeab13","queries.ts","7e021dbc-409b-11f0-85a8-76b3cceeab13","b23a0e26-c01c-4fed-b68a-045de5b8adca",6,["D",1748969702623],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { TABLES } from \"./migrations.ts\";\n\nexport interface MedicalContent {\n id: number;\n content: string;\n embedding_vector?: number[];\n chapter: string;\n specialty: string;\n page_reference: string;\n keywords: string[];\n content_type: string;\n confidence_score: number;\n created_at: string;\n}\n\nexport interface Conversation {\n id: string;\n user_id: string;\n title: string;\n messages: any[];\n specialty_filter?: string;\n total_messages: number;\n created_at: string;\n updated_at: string;\n is_archived: boolean;\n}\n\nexport interface UserPreferences {\n user_id: string;\n display_name?: string;\n preferred_specialties: string[];\n ui_theme: 'light' | 'dark';\n streaming_enabled: boolean;\n offline_mode: boolean;\n disclaimer_acknowledged: boolean;\n}\n\n// Vector similarity search using cosine similarity\nexport async function searchMedicalContent(\n queryEmbedding: number[],\n limit: number = 5,\n specialtyFilter?: string,\n chapterFilter?: string\n): Promise {\n try {\n let query = `\n SELECT id, content, embedding_vector, chapter, specialty, \n page_reference, keywords, content_type, confidence_score, created_at\n FROM ${TABLES.MEDICAL_CONTENT}\n WHERE embedding_vector IS NOT NULL\n `;\n \n const params: any[] = [];\n \n if (specialtyFilter) {\n query += ` AND specialty = ?`;\n params.push(specialtyFilter);\n }\n \n if (chapterFilter) {\n query += ` AND chapter = ?`;\n params.push(chapterFilter);\n }\n \n query += ` ORDER BY created_at DESC LIMIT ?`;\n params.push(limit * 3); // Get more results for similarity calculation\n \n const results = await sqlite.execute(query, params);\n \n // Calculate cosine similarity for each result\n const scoredResults = results.map((row: any) => {\n const embedding = row.embedding_vector ? JSON.parse(row.embedding_vector) : null;\n const similarity = embedding ? cosineSimilarity(queryEmbedding, embedding) : 0;\n \n return {\n ...row,\n embedding_vector: embedding,\n keywords: row.keywords ? JSON.parse(row.keywords) : [],\n similarity_score: similarity\n };\n });\n \n // Sort by similarity and return top results\n return scoredResults\n .sort((a, b) => b.similarity_score - a.similarity_score)\n .slice(0, limit);\n \n } catch (error) {\n console.error(\"Error searching medical content:\", error);\n return [];\n }\n}\n\n// Fallback text search when embeddings are not available\nexport async function textSearchMedicalContent(\n query: string,\n limit: number = 5,\n specialtyFilter?: string\n): Promise {\n try {\n const searchTerms = query.toLowerCase().split(' ').filter(term => term.length > 2);\n \n let sqlQuery = `\n SELECT id, content, chapter, specialty, page_reference, \n keywords, content_type, confidence_score, created_at\n FROM ${TABLES.MEDICAL_CONTENT}\n WHERE (\n `;\n \n const params: any[] = [];\n const conditions: string[] = [];\n \n searchTerms.forEach(term => {\n conditions.push(`LOWER(content) LIKE ?`);\n conditions.push(`LOWER(keywords) LIKE ?`);\n params.push(`%${term}%`, `%${term}%`);\n });\n \n sqlQuery += conditions.join(' OR ') + ')';\n \n if (specialtyFilter) {\n sqlQuery += ` AND specialty = ?`;\n params.push(specialtyFilter);\n }\n \n sqlQuery += ` ORDER BY confidence_score DESC LIMIT ?`;\n params.push(limit);\n \n const results = await sqlite.execute(sqlQuery, params);\n \n return results.map((row: any) => ({\n ...row,\n keywords: row.keywords ? JSON.parse(row.keywords) : []\n }));\n \n } catch (error) {\n console.error(\"Error in text search:\", error);\n return [];\n }\n}\n\n// Conversation management\nexport async function saveConversation(conversation: Partial): Promise {\n try {\n const id = conversation.id || generateConversationId();\n \n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.CONVERSATIONS}\n (id, user_id, title, messages, specialty_filter, total_messages, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)\n `, [\n id,\n conversation.user_id || 'anonymous',\n conversation.title || 'New Conversation',\n JSON.stringify(conversation.messages || []),\n conversation.specialty_filter || null,\n conversation.messages?.length || 0\n ]);\n \n return id;\n } catch (error) {\n console.error(\"Error saving conversation:\", error);\n throw error;\n }\n}\n\nexport async function getConversation(id: string): Promise {\n try {\n const results = await sqlite.execute(\n `SELECT * FROM ${TABLES.CONVERSATIONS} WHERE id = ?`,\n [id]\n );\n \n if (results.length === 0) return null;\n \n const row = results[0];\n return {\n ...row,\n messages: JSON.parse(row.messages || '[]')\n };\n } catch (error) {\n console.error(\"Error getting conversation:\", error);\n return null;\n }\n}\n\nexport async function getUserConversations(\n userId: string,\n limit: number = 20\n): Promise {\n try {\n const results = await sqlite.execute(`\n SELECT * FROM ${TABLES.CONVERSATIONS}\n WHERE user_id = ? AND is_archived = FALSE\n ORDER BY updated_at DESC\n LIMIT ?\n `, [userId, limit]);\n \n return results.map((row: any) => ({\n ...row,\n messages: JSON.parse(row.messages || '[]')\n }));\n } catch (error) {\n console.error(\"Error getting user conversations:\", error);\n return [];\n }\n}\n\n// User preferences\nexport async function saveUserPreferences(preferences: UserPreferences): Promise {\n try {\n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.USER_PREFERENCES}\n (user_id, display_name, preferred_specialties, ui_theme, \n streaming_enabled, offline_mode, disclaimer_acknowledged, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)\n `, [\n preferences.user_id,\n preferences.display_name || null,\n JSON.stringify(preferences.preferred_specialties),\n preferences.ui_theme,\n preferences.streaming_enabled,\n preferences.offline_mode,\n preferences.disclaimer_acknowledged\n ]);\n } catch (error) {\n console.error(\"Error saving user preferences:\", error);\n throw error;\n }\n}\n\nexport async function getUserPreferences(userId: string): Promise {\n try {\n const results = await sqlite.execute(\n `SELECT * FROM ${TABLES.USER_PREFERENCES} WHERE user_id = ?`,\n [userId]\n );\n \n if (results.length === 0) return null;\n \n const row = results[0];\n return {\n ...row,\n preferred_specialties: JSON.parse(row.preferred_specialties || '[]')\n };\n } catch (error) {\n console.error(\"Error getting user preferences:\", error);\n return null;\n }\n}\n\n// Embedding cache management\nexport async function cacheEmbedding(\n queryText: string,\n embedding: number[]\n): Promise {\n try {\n const queryHash = await hashString(queryText);\n \n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.EMBEDDINGS_CACHE}\n (query_hash, query_text, embedding_vector, last_accessed, access_count)\n VALUES (?, ?, ?, CURRENT_TIMESTAMP, \n COALESCE((SELECT access_count FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?), 0) + 1)\n `, [queryHash, queryText, JSON.stringify(embedding), queryHash]);\n } catch (error) {\n console.error(\"Error caching embedding:\", error);\n }\n}\n\nexport async function getCachedEmbedding(queryText: string): Promise {\n try {\n const queryHash = await hashString(queryText);\n \n const results = await sqlite.execute(\n `SELECT embedding_vector FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?`,\n [queryHash]\n );\n \n if (results.length === 0) return null;\n \n // Update access count\n await sqlite.execute(\n `UPDATE ${TABLES.EMBEDDINGS_CACHE} \n SET access_count = access_count + 1, last_accessed = CURRENT_TIMESTAMP\n WHERE query_hash = ?`,\n [queryHash]\n );\n \n return JSON.parse(results[0].embedding_vector);\n } catch (error) {\n console.error(\"Error getting cached embedding:\", error);\n return null;\n }\n}\n\n// Utility functions\nfunction cosineSimilarity(a: number[], b: number[]): number {\n if (a.length !== b.length) return 0;\n \n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n \n for (let i = 0; i < a.length; i++) {\n dotProduct += a[i] * b[i];\n normA += a[i] * a[i];\n normB += b[i] * b[i];\n }\n \n if (normA === 0 || normB === 0) return 0;\n \n return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n}\n\nfunction generateConversationId(): string {\n return 'conv_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n}\n\nasync function hashString(str: string): Promise {\n const encoder = new TextEncoder();\n const data = encoder.encode(str);\n const hashBuffer = await crypto.subtle.digest('SHA-256', data);\n const hashArray = Array.from(new Uint8Array(hashBuffer));\n return hashArray.map(b => b.toString(16).padStart(2, '0')).join('');\n}",[],[195,430,422],"database","6805ae98-409b-11f0-989b-76b3cceeab13","migrations.ts","6805af92-409b-11f0-989b-76b3cceeab13",5,["D",1748969665771],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\n// Table names with versioning for schema changes\nconst TABLES = {\n MEDICAL_CONTENT: 'nelson_medical_content_v1',\n CONVERSATIONS: 'nelson_conversations_v1',\n USER_PREFERENCES: 'nelson_user_preferences_v1',\n EMBEDDINGS_CACHE: 'nelson_embeddings_cache_v1'\n};\n\nexport async function runMigrations() {\n console.log(\"Running Nelson-GPT database migrations...\");\n \n try {\n // Medical content table for storing Nelson textbook chunks\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${TABLES.MEDICAL_CONTENT} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n content TEXT NOT NULL,\n embedding_vector TEXT, -- JSON string of embedding array\n chapter VARCHAR(255),\n specialty VARCHAR(100),\n page_reference VARCHAR(50),\n keywords TEXT, -- JSON array of medical keywords\n content_type VARCHAR(50) DEFAULT 'textbook', -- textbook, case_study, reference\n confidence_score REAL DEFAULT 1.0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // Conversations table for chat history\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${TABLES.CONVERSATIONS} (\n id TEXT PRIMARY KEY,\n user_id TEXT,\n title TEXT,\n messages TEXT, -- JSON string of message array\n specialty_filter TEXT,\n total_messages INTEGER DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n is_archived BOOLEAN DEFAULT FALSE\n )\n `);\n\n // User preferences and settings\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${TABLES.USER_PREFERENCES} (\n user_id TEXT PRIMARY KEY,\n display_name TEXT,\n preferred_specialties TEXT, -- JSON array\n ui_theme VARCHAR(20) DEFAULT 'light',\n streaming_enabled BOOLEAN DEFAULT TRUE,\n offline_mode BOOLEAN DEFAULT TRUE,\n disclaimer_acknowledged BOOLEAN DEFAULT FALSE,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // Embeddings cache for performance\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${TABLES.EMBEDDINGS_CACHE} (\n query_hash TEXT PRIMARY KEY,\n query_text TEXT NOT NULL,\n embedding_vector TEXT NOT NULL, -- JSON string\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n access_count INTEGER DEFAULT 1,\n last_accessed DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // Create indexes for performance\n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_medical_content_specialty \n ON ${TABLES.MEDICAL_CONTENT}(specialty)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_medical_content_chapter \n ON ${TABLES.MEDICAL_CONTENT}(chapter)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_conversations_user \n ON ${TABLES.CONVERSATIONS}(user_id, created_at DESC)\n `);\n \n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_embeddings_cache_access \n ON ${TABLES.EMBEDDINGS_CACHE}(last_accessed DESC)\n `);\n\n // Insert sample medical content for development\n await insertSampleContent();\n \n console.log(\"Database migrations completed successfully\");\n return true;\n } catch (error) {\n console.error(\"Migration failed:\", error);\n throw error;\n }\n}\n\nasync function insertSampleContent() {\n const sampleContent = [\n {\n content: \"Fever in children is defined as a rectal temperature ≥38.0°C (100.4°F). The approach to fever management depends on the child's age, appearance, and associated symptoms. In infants younger than 3 months, fever requires immediate medical evaluation due to increased risk of serious bacterial infection.\",\n chapter: \"Fever and Fever Syndromes\",\n specialty: \"General Pediatrics\",\n page_reference: \"Chapter 201\",\n keywords: JSON.stringify([\"fever\", \"temperature\", \"infant\", \"bacterial infection\", \"evaluation\"])\n },\n {\n content: \"Asthma is the most common chronic disease of childhood, affecting approximately 8-10% of children. It is characterized by reversible airway obstruction, airway inflammation, and increased airway responsiveness. The diagnosis is based on clinical history, physical examination, and pulmonary function tests when age-appropriate.\",\n chapter: \"Asthma\",\n specialty: \"Pulmonology\",\n page_reference: \"Chapter 138\",\n keywords: JSON.stringify([\"asthma\", \"airway\", \"chronic\", \"pulmonary function\", \"inflammation\"])\n },\n {\n content: \"Growth charts are essential tools for monitoring child development. Height, weight, and head circumference should be plotted at each visit. Concerning patterns include crossing two major percentile lines downward, height velocity <5 cm/year after age 3, or weight loss without explanation.\",\n chapter: \"Normal Growth and Development\",\n specialty: \"General Pediatrics\",\n page_reference: \"Chapter 13\",\n keywords: JSON.stringify([\"growth\", \"development\", \"percentile\", \"height\", \"weight\", \"monitoring\"])\n }\n ];\n\n for (const content of sampleContent) {\n try {\n // Check if content already exists\n const existing = await sqlite.execute(\n `SELECT id FROM ${TABLES.MEDICAL_CONTENT} WHERE content = ?`,\n [content.content]\n );\n \n if (existing.length === 0) {\n await sqlite.execute(`\n INSERT INTO ${TABLES.MEDICAL_CONTENT} \n (content, chapter, specialty, page_reference, keywords)\n VALUES (?, ?, ?, ?, ?)\n `, [\n content.content,\n content.chapter,\n content.specialty,\n content.page_reference,\n content.keywords\n ]);\n }\n } catch (error) {\n console.warn(\"Failed to insert sample content:\", error);\n }\n }\n}\n\nexport { TABLES };",[],[195,430,432],"6805c342-409b-11f0-989b-76b3cceeab13",["D",1748969665771],[211,212],[195,430],["D",1748969636861],[210,213,216],[195],["D",1748969546453],{"_11":12,"_87":83},{"_11":12,"_81":-5,"_82":83,"_44":45,"_84":85,"_86":38},{"_29":30,"_13":26,"_20":21,"_27":28,"_31":-5,"_32":-5,"_76":-5,"_77":-5,"_78":-5,"_79":14,"_80":12},"ctx",{"_69":-5,"_109":483,"_484":485,"_486":487,"_488":38,"_489":38,"_490":38,"_491":49,"_492":-5,"_493":-7,"_494":-7,"_495":53,"_496":38,"_497":38,"_498":-5,"_499":38,"_500":38,"_501":38,"_502":-5},"ENV",{"_454":455,"_456":457,"_458":26,"_459":460,"_461":462,"_463":464,"_465":466,"_467":468,"_469":470,"_471":472,"_473":474,"_475":476,"_477":478,"_479":480,"_481":482},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","c73f21da490985bff19c279eb0e09db24d688e4f","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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